Case Study in Besitang Watershed, Langkat, North Sumatra, Indonesia

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PARTICIPATORY LAND USE ALLOCATION

  Case Study in Besitang Watershed, Langkat, North Sumatra, Indonesia RAHMAWATY TEODORO R. VILLANUEVA MYRNA G. CARANDANG

  

To my beloved family, husband, Ramzi Sastra, son, Rizky Nabil Andhika, and daughter, Rahmi

Nurul Andhini, for all their support and encouragement over the years and understanding especially

during times when it was so difficult to carry on.

  Rahmawaty

  Preface The study contained in this book was conducted to develop a framework for participatory and

improved land use decision-making in Besitang Watershed, Langkat, North Sumatra, Indonesia.

Specifically, it aimed to: assess land use changes, estimate soil erosion under different land uses,

analyze the actual and potential suitability of the lands for several annual, estate and silvicultural

crops, determine the current and potential land use suitability with stakeholder participation, and

develop a spatial participatory land use allocation based on integrated approach to ensure

sustainability. Socio-economic information and physical data were derived from interviews and field

survey. Collective opinion was derived from the workshop with stakeholders. In this book,

Geographic Information System (GIS) and Analytical Hierarchy Process (AHP) were used for land

use allocation. The Universal Soil Loss Equation (USLE) was used to determine soil erosion in each

land system. The significant contributions of integrated approach using GIS and AHP in land use

decision-making were: as a tool for facilitating efficient land use allocation and sound policy

formulation, as well as in advancing science-based investigations. This approach is efficient and

reliable in land use allocation for watershed management, since it involves the use of physical

components as well as participation of stakeholders to ensure sustainability of land uses.

  Rahmawaty Department of Forestry, Faculty of Agriculture, University of Sumatera Utara (USU),

  Medan, Indonesia Teodoro R. Villanueva

  College of Forestry and Natural Resources, University of the Philippines Los Baños (UPLB) Myrna G. Carandang

  College of Forestry and Natural Resources, University of the Philippines Los Baños (UPLB)

  Acknowledgment The authors acknowledge first and foremost Allah SWT, the Almighty God for fulfilling all the

opportunities and challenges in their life. Profound gratitude to a number of institutions and

individuals for the various roles they played that contributed to the successful completion of this book:

German Academic Exchange Service (DAAD) and Southeast Asian Regional Center for Graduate

Study and Research in Agriculture (SEARCA), for awarding her a Ph.D scholarship program at

UPLB; LAP LAMBERT Academic Publishing GmbH & Co. KG Germany; Dr. Arsenio M. Balisacan,

Dr. Editha C. Cedicol, and all of GSD staff, for their administrative support and services; Prof. dr.

Chaeruddin P. Lubis. DTM&H, Sp.A(K), former Rector of University of Sumatera Utara (USU), for

his permission to given the principal author to pursue her Ph.D program at UPLB and for financial

assistance and support for the study; Dr. Renato L. Lapitan, Dr. Nathaniel C. Bantayan, Dr. Antonio J.

Alcantara, for their professional guidance, personal concerns, and intellectual insights; Prof. Ir.

Zulkifli Nasution, MSc., Ph.D., Prof. Dr. Ir. Abdul Rauf, MP., Ir. Ramzi Sastra, MSc., Bejo Slamet,

S.Hut., MSi., Nurdin Sulistyono, S.Hut., MSi., Riswan, S.Hut., Ahmad Sofyan, SE, MSi, for their

valuable advices, comments, and constructive suggestions; Ludmila Caus as Acquisition Editor; The

entire faculty and staff of the CFNR and SESAM of UPLB, for their professional assistance to the

principal author in her major and cognate courses, respectively; Colleagues of USU, all stakeholders,

respondents for their invaluable assistance during data gathering, and all Indonesian friends at UPLB

for their technical help and moral support; other relatives for their love, prayers, kindness, patience,

and understanding especially during the time when it was so difficult for us to carry on. Their co-

operation has made this book possible.

  CONTENTS Preface iii Acknowledgments iv

1 INTRODUCTION

  30

  23

  25

  4.1. Physical Information

  25

  4.2. Physical Characteristics Of The Study Area

  27

  4.2.1. Rainfall and Erosivity Factor

  27

  4.2.2. Soil Types and Soil Erodibility Factor

  29

  4.2.3. Land System and Decision Zone

  4.2.4. Soil Depth

  21

  33

  4.2.5.The Slopes and the Slope Length Factor and Slope Gradient Factor

  34

  4.2.6. The Cropping management factor and erosion control practice factor

  35

  4.3. Collective Opinion Information

  39

  4.4. Geographic Information System (GIS)

  40

  4.5. Analytical Hierarchy Process (AHP)

  45

  3.7. Extension Workers

  3.6. Preferred and Use of the Land

  1

  8

  1.1. Background of the Study

  1

  1.2. Statement of the Problem

  3

  1.3. Objectives of the Study

  4

  1.4. Importance of the Study

  4

  1.5. Conceptual Framework

  5

  1.6. Operational Definition of Terms

  2 DESCRIPTION OF THE STUDY AREA

  20

  11

  3 SOCIO-ECONOMIC CHARACTERISTICS OF THE STUDY AREA

  15

  3.1.Data Collection for Socio-Economic Information

  15

  3.2. Population of the Besitang Watershed

  16

  3.3. Demographic Characteristics of the Respondents

  17

  3.4. Source of Income of the Respondents in Besitang Watershed

  19

  3.5. Land Holding and Crop Production

4 INTEGRATED APPROACH USING GIS AND AHP

  4.6. Participatory Decision Support System

  51

  5 SOIL EROSION OF DIFFERENT LAND USES

  54

  5.1. Soil Erosion

  54

  5.2. Erosion Rate

  60

  5.3.Soil Erosion Index

  66

  6 LAND USE AND LAND COVER CHANGE

  70

  6.1. Methodology

  70

  6.2. Forest Land Use Change

  70

  6.3. Processes of Land Use Transitions

  78

  7 CLASSIFICATION OF FOREST AND LAND USE

  82

  7.1. Present Land Cover/Land Use In Besitang Watershed

  83

  7.2. The land use and forest classification in Besitang Watershed

  88

  7.3. Land use and land use planning

  92

  7.4. Land Capability Classification

  93

  7.5. Land Suitability Classification

  99

  8 LAND USE ALLOCATION 115

  8.1. Potential Land Use Suitability Classification 116

  8.2. Land Use Allocation in Besitang Watershed 118

  

9 SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS 134

  9.1. Summary 134

  9.2. Conclusions 136

  9.3. Recommendations 138

  REFERENCES 139

  APPENDICES 146 189

  INDEX

  

CHAPTER 1

INTRODUCTION

1.1. Background of the Study

  Indonesia is located in 5,200-km-long chain of some 17,000 islands straddling the equator in

the heart of Southeast Asia. Its 220 million people live in a land area of about 188 million Has

(ITTO, 2005). Land is becoming scarce so that the more fragile upland areas are looked upon as the

last frontier for the expansion of agriculture and other land uses. As a result, numerous problems have

now been threatening the ecological stability of the uplands (Cruz, 1990). The increasing human

population, the more consequent accelerating demands and intensity of human activities on the land

have various degrees of impact on all of the material and natural resources in the world. Human

population pressures have accelerated the increases of land value and the diversity of land use.

  North Sumatra Province has a land area of 71,680 km² or 3.73% of Indonesia ’s total area with

17 regencies. It is one of the provinces with big forest land and big palm fruit plantations, spread in

several regencies. One of them is the Langkat Regency (626,329 ha) which is located close to Medan

(capital of North Sumatra Province). There are six watersheds in Langkat Regency and one of them is

Besitang Watershed which is the second biggest watershed after the Wampu Watershed. According

to the Ministry of Public Works’ Decree Number 39/PRT/1989 on River Allocation, the rivers in the

North Sumatra Province can be grouped into six River Area Units/Satuan Wilayah Sungai (SWS),

they are SWS Wampu-Besitang, SWS Belawan-Belumai-Ular, SWS Bah Bolon, SWS Asahan, SWS

Barumun Kualuh, and SWS Batang Gadis-Batang Toru. As mentioned in the Decree, Besitang

Watershed is located in SWS Wampu-Besitang.

  The conditions of the watersheds in Indonesia are generally damaged to severely damaged

based on the characterization conducted by the Ministry of Forestry, the Republic of Indonesia in

1999. There are 458 watersheds in critical condition in Indonesia, 60 of which are in Category I,

damaged to severely damaged (16 of them are in Java), 222 watersheds are in Category II, from

moderately to severely damaged and 176 watersheds are potentially damaged (Category III).

Unfortunately, the number of severely damaged watersheds is now increasing and their condition has

worsened due to catastrophe, flood and drought. According to the Ministry of Forestry (2005),

Besitang Watershed is one of Priority II watersheds in Indonesia.

  Several rehabilitation efforts have been done, but there has not been any significant result yet,

while catastrophes have increased every year. For example, in December 2006, there was flood in

some watersheds and one of them was in Besitang Watershed. It indicates that the status of

watersheds in damaged condition in some areas have worsened.

  Besitang Watershed borders on Nanggroe Aceh Darussalam (NAD) Province, as a result this

area has become a new place for refugees from NAD. Moreover, Gunung Leuser National Park

(TNGL) as a protected area is a magnet for illegal loggers who do not realize the bad effects of their

action. Many trees in upland stream sub-watershed have been cut down. According to the Ministry of

Forestry (2005), in Besitang Watershed, the soil as categorized as sensitive to erosion so that it should

be taken care further, in order to maintain its watershed ecosystem functions.

  At the moment, there is a trend to make use of the land for the economic interests, which could

increase the income. One of the favorite and high-economic-value plants in this area is oil palm.

Another problem in Besitang Watershed that seems to be a major one is land conversion to oil palm

plantation and fish pond in the mangrove forests by the surrounding community have decreased the

size of remaining areas performing ecosystem services.. According to Ekanayake and Dayawansa

(2003), land as resource cannot be measured by the surface area alone; hence the types of soil which is

critical for productivity, underlying geology, topography, hydrology, and plants and animal population

also has to be considered. These attributes limit the extent of land available for various purposes. The

growing population, industrialization and misuse and overexploitation of land resources have in effect

increased the demand for land.

  According to Anderle et al. (1994), rising population pressure coupled with mounting

competition by different types of land users is a major challenge faced by land use planners and

policy-makers. Rational and sustainable land use is an issue concerning both the government and land

users in preserving the land resources for the benefit of present and future generations. Land use

decisions are based on comprehensive and quantified assessment of potential and development

possibilities of the land resources taking into account the biophysical, environmental and socio-

economic factors as well as the space and time dimensions of sustained land use (Antoine et al., 1997).

  In Indonesia, the spatial management plan related to national land areas cited in the spatial

management plan (RTRWN) is based on Republic of Indonesia Law Number 26 (2007) about Spatial

Management. The spatial management plan related to provincial land areas (RTRWP) was derived

from RTRWN. The spatial management plan related to regional land uses (RTRWK) was made using

RTRWP as a reference. Because Besitang Watershed is included in Langkat Regency administrative

area, the spatial management plan for Besitang was also referred to Langkat RTRWK. However,

RTRWK in the area can be considered as efficient and effective. This is because, community

involvement has not been optimized and there is lack of social awareness about this, so that very few

people understand it. In addition RTRWK is sometimes different with the reality in the field.

  Changes in the use of land occurring at various spatial levels and periods can have either

beneficial or detrimental effects. The latter is the main concern of decision-makers because of its

effect on the population and the environment. The goal of managing land use and its change is to

  

develop the land resources in ways that capitalize on the local potential and suitability, avoid negative

impacts and respond to present and future societal demand within the limits of the carrying capacity of

the natural environment (FAO, 1995). Decision support system has been identified as a critical

component to identify and solve problems, provide information and current scientific knowledge in

watershed management decision making process.

  Based on the Besitang Watershed ’s condition, some thoughts on good steps or actions are

needed to overcome these problems. Rehabilitation of watersheds is also needed so that it can support

and enhance other environment functions. At present, researches on land use and land use planning

have been carried out in the area but these have not fully looked into the driving forces of land use as

influenced by development policies. There is a need for an approach that can involve the participation

of stakeholders so that land use management planning can be effectively and efficiently done in

accordance with Republic of Indonesia Law Number 26 (2007) about Spatial Management. The

research about land use allocation with stakeholder participation and collaboration that is crucial for

successful and sustainable watershed management has never been conducted previously in the area.

For this reason, it is essential that research in this area be conducted.

1.2. Statement of the Problem

  Considering that the status of Langkat Regency is autonomy and there are differences

concerning the status of the region, then the government planned to manage the land for various

activities. It is cited in RTRWK. For these activities, based on Republic of Indonesia Law Number 26

(2007) about Spatial Management, participation of the stakeholders from many sectors should be

considered to achieve the maximum result and to minimize the problems about land use allocation in

this area that was probably occurring.

  Considering that Besitang Watershed constitutes one of Priority II watershed (it means that it is

in a moderately to severely damaged class), the change in watershed condition emerged as result of

deforestation in the TNGL area. Moreover, erosion and land conversion to oil palm areas and the

extension of fish farming areas, the areas need special management to maintain the watersheds

ecosystem. Hence, this study aims to develop an approach of land use allocation for the area based on

integrated approach using Geographic Information System (GIS) and Analytical Hierarchy Process

(AHP). The approach is expected to contribute to the advancement of science and technology in

forestry and environment. Hopefully, the methodology would be useful as an area development

approach to produce goods and services on the basis of sustainability and would contribute to

policymakers in creating guidelines relevant to forestry, environment, and rural development sectors.

  With the foregoing premises, this study sought to dig deeper into the case of the land use

suitability allocation. As mentioned in the previous section, the answers to the following questions

  

have to be found: What is the land use/cover changes in the area? What is the erosion hazard of

different land uses in the watershed? What is the land capability and land suitability in the area? What

is the actual and potential suitability of the lands for several annual crops, estate and silvicultural

plants in the area? What is the current and potential land use suitability in the area? What is the spatial

participatory land use allocation for the area that contributes to sustainability?

  1.3. Objectives of the Study This study generally aimed to develop a framework of participatory and improved land use decision-making in Besitang Watershed, Langkat, North Sumatra, Indonesia.

  Specifically, the study aimed to: 1. assess land use changes in the area; 2. estimate soil erosion under different land uses in the area;

3. analyze the actual and potential suitability of the lands for several annual, estate and silvicultural

crops in the area;

4. determine the current and potential land use suitability in the area with stakeholders; participation;

and

5. develop a spatial participatory land use allocation approach for the area based on integrated

approach to ensure sustainability.

  1.4. Importance of the Study The land use allocation framework would contribute to the development of the area. The

results of the study would inform the stakeholders about the recommended best land use allocation

particularly those of the area and contribute to promoting better understanding of driving forces of

land use change, effects of national policies on agricultural land allocation in Asia, in general and in

Indonesia, in particular. Findings of the study also would facilitate the improvement of the efficiency

in land use of watersheds in Indonesia.

  The methodology would be useful as an area development approach to produce goods and

services on the basis of sustainability and contribute to policymakers in creating guidelines relevant to

forestry, environment, and rural development sectors. The output of the study would also facilitate

policy formulation in a manner that could minimize adverse impacts such as unplanned and

undesirable land use allocation. Furthermore, the study would contribute to the recommendation to

the stakeholders about the best land use allocation. It would also generate the advancement of science

and technology in forestry and environmental resources. Hopefully, this study would provide planners

and decision-makers with a better basis for integrated land use planning, implementation of

management strategies in the process of sustainable rural development.

1.5. Conceptual Framework

  The land is one of the resources that is important to supporting the development in Besitang

Watershed, Langkat, North Sumatra. Consequently, the government should take land use planning in

the area into account. It is realized in the spatial management plan related to regional land areas. As

one of the primary priorities in this area, land should be managed wisely, in the case where sustainable

land management is reached, there are three parameters that are needed, these are: social, economic,

and environment. According to Erskine (1997), sustainability is distinguished based on economics,

ecology, and social perspective. The key to successful economically sound land uses depends on the

efficiency and the wise use of every parcel of land on which the activities are done.

  This study attempts to find out how to achieve the best land use allocation (BLUA) and what

factors influence or affect land use allocation. The concept implies that the best land use allocation is

influenced by integrated components, namely: physical components and public opinion components.

The physical components in this study focused on the physical (environment) factors, which include

land use/cover changes, soil erosion, land classification, land capability and land suitability

classification, and suitable crops in the area. Land use changes were considered as loss (reduction)

and gain (increase) of areas. The loss of area was measured as loss of area from dense area to open

area and both dense and open area to other types of land use, such as: bush, swamp, fish pond, and

paddy field. In contrast, gain in area was measured as an increase in the area due to restoration from

open area to dense area, from bush to open area and from bush to dense area resulting in area

plantation. The public opinion component focused on use of land, preference in land use, expert

perception, and collective opinion (Figure 1).

  According to Bantayan (1996), a spatial decision support system should take into consideration

the three major attributes in land use decision-making, namely: objectives, alternatives, and land units.

Hence, the first stage in the proposed method involves identification of objectives and alternatives. In

this study, the objectives and the alternatives are defined as decision variables. The objectives form

the basis by which alternatives were evaluated. They may contribute to environment and

conservation, education and research, employment, and socio-economic development. The

alternatives, in the context of land use planning, take the form of possible land uses or choice

possibilities which may already exist or may be proposed for the area (Voogd, 1983 cited by

Bantayan, 1996). The key question in land use suitability is how to determine the degree to which the

alternatives contribute to the objectives in each land unit.

  The best land use allocation (BLUA) is expressed in two key processes, namely: physical and

public opinion. The physical process was generated from the physical measures from variables

depending on the approach being incorporated. As mentioned earlier, the physical measures were

derived from land use changes, soil erosion, land classification, land capability classification, and suitable crops in the area. The public opinion process was generated from the form of preference measures of individual or collective opinion. This process requires that individual preferences satisfy a measure of consistency. The results of these two processes were standardized and expressed as priorities. These priorities are related to the degree of suitability of a land unit with regard to the set of alternatives. Then, land allocation achieved from the results of land use suitability. Figure 1 shows the conceptual framework showing the relationship of the components, processes, objectives, and alternatives to realize the best land use allocation in Besitang Watershed, Langkat, North Sumatra, Indonesia. Furthermore, Figure 2 shows the flow diagram activities of the study.

  • Field survey
  • Interview

  • GIS process
  • Workshop

    with

    stakeholders

Figure 1.1. Conceptual framework of the study Objectives:

   Environment and conservation  Education and research  Employment  Socio-economic development

  The best land use allocation Alternatives:  Forestry  Agriculture  Settlement  Industry  Fishery

  Physical components:

   Land use change  Soil erosion  Land classification  Land capability and land suitability  Suitable crops

  Public opinion components:  Use of land  Preference in land use  Expert perception  Collective opinion

  Processes:

  • AHP process

  Public opinion components:

  CP), then multiplied using GIS spatial analysis  RKLSCP divided by T value  Intersect soil erosion map and soil depth map  Used RTRWK and Decrees of Ministry of Forestry Number 44 (2005)  Field survey and laboratory analysis  Evaluated based on FAO guidelines

  Workshop with stakeholder for AHP

  Statistical Package for the Social Sciences (SPSS) and Microsoft Office Excel

   Interview with key informant/stakeholder and used land use policy in the area  Encoded and processed using the

  Participatory land use allocation

  Potential land use suitability classification

  (FAO, 1976) and Criteria of land capability classification in Indonesia  Evaluated based on matching method that reference and criteria was adopted from the Land Suitability for

  Lanvine Formula  Intersect all individual maps (R , K, LS,

   Use of land (questionnaire data)  Preference in land use

  GIS  Counting rainfall erosivity using

   Interview with key informant/stakeholder  Overlaying maps of different periods using GIS (intersect operation)  Counting the decrease or increase in area was by Microsoft Office Excel  Field survey for determine rainfall station  Creating Thiessen polygon in ArcView

   Land use change (past and current land use, questionnaire data)  Soil erosion, soil erosion index (R, K, LS, CP, T value, soil depth data)  Land classification (land use/forest classification, rainfall, soil, and slope data  Land capability and land suitability (soil analysis, land system, slope, soil depth, K, and soil erosion data)  Suitable crops (soil analysis, slope, soil erosion, and rainfall data

  Physical components:

  (questionnaire data, focus group discussion data)

  (questionnaire data)  Collective opinion

  (questionnaire data)  Expert perception

Figure 1.2. Flow diagram activities of the study

1.6. Operational Definition of Terms

  Analytical Hierarchy Process (AHP) is a method that can be used to establish measures in

both the physical and social domains. In using the AHP to model problem, one needs a hierarchic or a

network structure to represent that problem, as well as pairwise comparisons to establish relations

within the structure. In the discrete case these comparisons lead to dominance matrices and in the

continuous case to kernels of Fredholm Operators, from which ratio scales are derived in the form of

principal eigenvectors, or eigenfunctions, as the case may be (Saaty, 1995).

  ArcGIS is an integrated collection of GIS software products for building a complete GIS.

  

ArcGIS enables users to deploy GIS functionality wherever it is needed in desktops, servers, or

custom applications; over the web; or in the field (ESRI, 2007).

  ArcView GIS is a powerful, easy to use tool that brings geographic information to desktop. It

gives our power to visualize, explore, query and analyze data spatially; it is made by Environmental

Systems Research Institute (ESRI), the leading GIS software (ESRI, 2007).

  Attribute data describes the characteristics of the spatial features. These characteristics can be quantitative and/or qualitative in nature. Attribute data is often referred to as tabular data. Current land use classification is a land use based on the spatial management plan related to

regional land areas or regional spatial planning (RTRWK) organized by the local government, in this

case is the regional development planning board of Langkat Regency (BAPPEDA).

  Decision Support System (DSS) is a computerized system for helping make decisions. A

decision is a choice between alternatives based on estimates of the values of those alternatives.

  

Supporting a decision means helping people working alone or in a group gather intelligence, generate

alternatives and make choices.

  Geographic Information System (GIS) is a computer based information system used by

people, which attempts to capture, store, manipulate, analyze and display spatially referenced and

associated tabular attribute data, for solving complex research, planning and management problems. It

is store and analysis spatial data (Godilano, 2001).

  Household is a domestic unit consisting of the members of a family who live together along with non relatives such as servants. It is person or group of people occupying a single dwelling. Integrated approach is an approach that was developed based on integrated information, such

as: physical components (land use/cover change, soil erosion, land classification, land capability, land

suitability, and suitable crops) and public opinion components from the stakeholders (socio-economic,

collective opinion, and land use policy).

  Land is a delineable area of the earth’s terrestrial surface, encompassing all surface climate,

the soil and terrain forms, the surface hydrology (including shallow lakes, rivers, marshes, and

swamps), the near surface sedimentary layers and associated groundwater reserve, the plant and

  

animal population, the human settlement pattern and physical results of past and present human

activity (terracing, water storage or drainage, structures, roads, building) (FAO, 1995).

  Land capability is the inherent capacity of land to perform under a given use. Land capability

classification (LCC) is the description of a landscape unit in terms of its inherent capacity to sustain a

desirable combination of flora and fauna. It is the technical assessment of potential sustainable uses of

landscape units based on inherent characteristics of the land (Villanueva, 2005).

  Land cover represents the natural and artifici al compositions covering the earth’s surface at a

certain location (Avery and Berlin, 1985). It is also defined as attribute of parts of the earth surface

including vegetation, soil, ground water, and topographical features (Manshard, 1998) and also refers

to earth surface condition that reflects the feature land cover and vegetation cover (Ministry of

Forestry, 2006).

  Land suitability is the fitness of a given area for a specific land use (FAO, 1976). The land

may be considered in its present condition or after improvements. Land suitability assessment is a

carried out by matching land use requirements to landscape unit characteristics and measuring the

environmental responses of the landscape to land use management technologies (Villanueva, 2005).

  Land use encompasses several different aspects of man’s relationship to the environment

(Avery and Berlin, 1985). It also refers to the purpose for which land cover is exploited. These uses

can be as varied as agriculture, industry, recreation, or even wildlife conservation (Manshard, 1998).

  Land use allocation is a spatial allocation exercise, the best way to achieve the integrated approach working within the common framework of GIS to provide the solutions. Land use change is a change in the use or management of land by humans, which may lead to

a change in land cover. Land cover and land use change may have an impact on the albedo,

evapotranspiration, sources, and sinks of greenhouse gases, or other properties of the climate system,

and may thus have an impact on climate, locally or globally (Lambin et al., 2003).

  Land use planning is the conscious process of selecting and developing the best course of

action to accomplish the efficient intensive use of the land resources (Hudson, 1981 cited by Oszaer,

1994).

  Multi-Criteria Analysis (MCA) is a decision support approach developed for complex

problems involving tradeoffs between multiple objectives, where both quantitative and qualitative

aspects of the problem can be addressed (Mendosa et al., 1999).

  Participatory is a process in which the people (stakeholders) are directly involved in the study. Potential land use suitability is a land use suitability resulting from integrated approach as a reference in determining the land use allocation in this study.

  Primary data are data gathered by the on going activity or project. These data may be the

result of field measurements (including sketches) from resource inventories and survey or from

interviews (for example: focused group discussions, workshop, and meeting (Bantayan, 2006).

  Priority is a relative position or degree of value in a graded group or the process of positioning

items such as individuals, groups or businesses on an ordinal scale in relation to others. A list

arranged in this way is said to be in priority order.

  Secondary data are data that already exist in some form and only need to be collected,

organized and encoded into a text editor, spreadsheet or database following some pre-determined

format (Bantayan, 2006).

  Spatial data describe the absolute and relative location of geographic features. Spatial data

use location, within a coordinate system, as its reference base. The most common representation of

spatial information is a map on which the location of any point could be given using latitude and

longitude (Bantayan, 2006).

  Stakeholder is a person with a specific stake, experience or interest in the topic addressed. A

stakeholder can be a government official, research scientist, non government organization (NGO)

member, extension officer or practitioner such as a fisher or a farmer.

  Sustainability refers to the property of system that enables it to continue its functions despite

the presence of major perturbation. It is the ability of a system to maintain or improve the productive

capacity of the land, improve soil fertility, minimize soil erosion, uplift the socioeconomic condition

of the people, and preserve their culture despite the increasing population pressure. This could be

evaluated by considering how long the system and/or practice is expected to be implemented, in

relation to the environmental condition (Conway, 1985).

  

CHAPTER 2

DESCRIPTION OF THE STUDY AREA The study was conducted in the Besitang Watershed (Figure 2.1), from March to September

2008. Besitang Watershed has an area of 100,035 ha excluding the islands. In the border of Malaka

Strait in the East, there are islands that have an area of 5,089 ha. Geographically, it is located between

  o o o o

  97 50 ’ 00” to 98 20’ 00” east longitude and 03 45’ 00” to 04 15’ 20” north latitude. It is bordered

by the Province of Nangroe Aceh Darussalam in the North, Sei Lepan Watershed in the South, Malaka

Strait in the East, and the Province of Nangroe Aceh Darussalam in the West (Figure 2.2) . It is

divided by three sub-watersheds, namely: upland stream, middle stream, and lower stream (Ministry of

Forestry, 2005). The area is shown in Table 2.1 and was delineated in Figure 2.2.

  Administratively, Besitang Watershed lies in the Langkat Regency, North Sumatra Province,

Indonesia . It consists of five sub-districts, namely: Besitang, Barandan Barat, Padang Tualang,

Pangkalan Susu, and Sei Lepan (Table 2.2 and Figure 2.3). Each sub-district is divided into several

villages, except in Padang Tualang Sub-district which has no village because of Gunung Leuser

National Park. In Besitang Sub-district, there are 11 villages, namely: Bukit Kubu, Bukit Selamat,

Halaban, Kampung Lama, Pekan Besitang, Salahaji, Sekoci, Serang Jaya, Suka Jaya, Bukit Mas, and

PIR ADB Besitang. In Brandan Barat, there are four villages, namely: Lubuk Kertang, Pangkalan

Batu, Lubuk Kasih, and Sei Tualang. In Pangkalan Susu (Figure 2.4), there are 14 villages, namely:

Alur Cempedak, Beras Basah, Bukit Jengkol, Damar Condong, Limau Mungkur, Pangkalan Siata,

Paya Tampak, Pematang Tengah, Pintu Air, Sei Meran, Sungai Siur, Tanjung Pasir, Perkebunan

Damar Condong, and Perkebunan Perapen. Sei Lepan is one village, namely: Telaga Said (Appendix

Table 1).

Table 2.1. The area of Besitang Sub-watershed SUB-WATERSHED AREA Ha %

  Upland stream 30,815

  30.80 Middle stream 16,722

  16.72 Lower stream 52,497

  52.48 Total 100,035 100.00

Table 2.2. The five sub-districts included in the study area AREA SUB-DISTRICT Ha %

  Besitang 71,213

  71.19 Brandan Barat 1,019

  1.02 Padang Tualang 2,823

  2.82 Pangkalan Susu 24,266

  24.26 Sei Lepan 714

  0.71 Total 100,035 100.00

Figure 2.1. Besitang River in Besitang WatershedFigure 2.2. Map of study area

  Map of Indonesia Besitang Watershed Map

Figure 2.3. Pangkalan Susu Sub-District in Besitang WatershedFigure 2.2. Map of sub-district in Besitang Watershed, Langkat, North Sumatra

  

CHAPTER 3

SOCIO-ECONOMIC CHARACTERISTICS OF THE STUDY AREA

3.1. Data Collection for Socio-Economic Information

  Socio-economic information were gathered based on primary and secondary data. Primary

data were collected from interviews during field survey (Figure 3.1). The study largely relied on the

use of questionnaire at village and household level. Secondary data were collected from the Regional

Statistical Office which are related to the study, such as: Langkat Regency in Figure (2007), Besitang

in Figure (2007), Brandan Barat in Figure (2007), Padang Tualang in Figure (2007), Pangkalan Susu

in Figure (2007), and Sei Lepan in Figure (2007).

  The sample selection was based on random sampling, using the Slovin’s Statistical Formula: N n  (3.1)

  2 1  Ne where, n = sample size (the number of household to be interviewed) N = population size (total number of household) e = desired margin of error/level of precision (10% of margin error)

Figure 3.1. Interview activity with farmer in Besitang Watershed In this study, the level of precision or sampling error (e) was set at ten % because of time and

budget constraints. The household heads or representatives of the families served as the respondents.

The number of household was randomly selected. Based on the data and formula above, the number

of samples (selected sample) was 100 respondents. To determine the sample size from each village in

Besitang Watershed, the following formula was used: nN

  1 n  (3.2)

1 N

  where, n

  1 = sample size determined from each sub-district

  n = sample size for all sub-districts N = total number of household in all sub-districts N

  1 = total number of household in each sub-district

  The distribution of households based on sub-district of Besitang Watershed and the corresponding sample size per sub district as shown in Table 3.1.

Table 3.1. Sample size per sub-district SUB-DISTRICT TOTAL HOUSEHOLD SELECTED SAMPLE

  Besitang 12,979

  46 Brandan Barat 2,985

  10 Padang Tualang Pangkalan Susu 11,592

  41 Sei Lepan 814

  3 Total 28,370 100 The socio-economic data needed to support the analysis in this study consist of population,

farm land ownership, agricultural production, and other related data that were gathered from the area.

The data gathered were encoded and processed in the computer using the Statistical Package for the

Social Sciences (SPSS) Program and Microsoft Office Excel.

3.2. Population of the Besitang Watershed

  The population of Besitang Watershed based on data from the Regional Statistical Office

(2007) is 126,670 people as shown in Table 3.2. All areas in Besitang and Pangkalan Susu Sub-

district are included in Besitang Watershed. Hence, the majority of the population in Besitang

Watershed is found in Besitang and Pangkalan Susu Sub-districts. In contrast, there are only a few

households in Brandan Barat and Sei Lepan Sub-districts. There are no households in Sei Lepan Sub-

  

district in Besitang Watershed. This is because only a few area of Brandan Barat and Sei Lepan Sub-

districts are included in Besitang Watershed. The majority of these areas are included in other

watershed that are located near the Besitang Watershed. Padang Tualang Sub-district area which is

included in Besitang Watershed is a protected area that is Gunung Leuser National Park.

Table 3.2. Population and number of household in the study area NUMBER OF SUB-DISTRICT POPULATION % HOUSEHOLD %

  Besitang 63,505 50.13 12,979

  45.75 Brandan Barat 12,589 9.94 2,985

  10.52 Padang Tualang

  0.00

  0.00 Pangkalan Susu 48,198 38.05 11,592

  40.86 Sei Lepan 2,378 1.88 814

  2.87 Total 126,670 100.00 28,370 100.00

3.3. Demographic Characteristics of the Respondents

  Gender/Sex. Of the total of 100 respondents, 90 % are male and ten % were female (Table 3.3). Age of respondent. The respondents range in age from 21

  • – 70 years. The majority of the respondents are 31-40 years old (38%), followed by 41-50 years old (37%) (Table 3.3).

  Civil status. Of the total of 100 respondents, 97 % are married and two % were single. Only one % is widowed. Time of stay in the area. Majority of respondents (44%) have stayed in the area for more than

  

20 years. About 22 % of the respondents have stayed in the area for 40-49 years and 22 % also for 30-

39 years, followed by 30-39 and 10-19 years of stayed in Besitang Watershed (each 19%). Only ten %

of the respondents have stayed in the area for more than 49 years and only eight % respondents have

stayed in the area (Table 3.3).

  Family size. Most of families (48%) have a family size of 3-4 members, while 28 % have 1-2 members. Other families (24%) have 5-6 members (Table 3.3). Education. Survey findings revealed that 35 % of the respondents finished elementary school,

  

27 % were in senior high school, 22 % were in junior high school, 11 % were in college, and only 5 %

had no formal schooling (Figure 3.2).

  Ethnic groups. There are seven ethnic groups of respondents in Besitang Watershed (Figure

3.3). The ethnic composition shows that majority of the respondents (40%) were Jawa, followed by

Banjar (19%), Melayu (17%), Batak (13%), Aceh (5%), Karo (4%), and Padang (2%).

Table 3.3. Demographic characteristics of the respondents in Besitang Watershed

  1-2

  19 20-29 years

  19

  19 30-39 years

  22

  22 40-49 years

  22

  22 > 49 years

  10

  10 Total 100 100 Number of people in household (people)

  28

  8 10-19 years

  28 3-4

  48

  48 5-6

  24

  24 Total 100 100

  Educational Attainment 5%

  35% 22%

  27% 11%

  No formal schooling Elementary Junior high school Senior high school College/university

  19

  8

  INFORMATION NUMBER OF RESPONDENT % Gender Male

  37

  90

  90 Female

  10

  10 Total 100 100 Age range (years)

  <= 30

  10

  10 31-40

  38

  38 41-50

  37 >=51

  < 10

  15

  15 Total 100 100 Civil status

  Married

  97

  97 Single

  2

  2 Widow/Widower

  1

  1 Total 100 100 Time of stay in the area (years)

Figure 3.2. Education attainment of respondents

  2% Ethnic Groups

  17% 19%

  Melayu Karo Jaw a

  4% Batak

  5% Aceh Banjar Padang

  13% 40%

Figure 3.3. Ethnicity of the respondents

3.4. Source of Income of the Respondents in Besitang Watershed

  The people in Besitang Watershed obtain their income primarily from farming (67%), followed

by other occupation such as: trader and service (14%), government employee (9%), fisherman (6), and

private employee (4%). Majority of the people have worked in their primary occupation for more than

12 years (70%). Majority of the people have monthly income from primary occupation below

2,000,000 Rupiah (77%), as is shown in Table 3.4.

  The majority of the respondents (58%) mentioned that their income was enough for their every

day needs. Only 42 % of the respondents stated that their income were still insufficient to provide for

their basic needs. They indicated their satisfaction because they were deriving their income from

additional sources, such as: trader and services (23 respondents), farmer (17 respondents), and

Fisherman (2 respondents). Twenty eight respondents have worked less than 10 years and 14

respondents have worked more than 10 years in their primary occupation. Majority of the people (38

respondents) have monthly income from secondary occupation below 2,000,000 Rupiah, as is shown

in Table 3.4.

Table 3.4. Primary source of income of the respondents NUMBER OF

  INFORMATION RESPONDENTS % Primary occupation Farmer

  67

  67 Government employee

  9

  9 Fisherman

  6

  6 Private employee

  4

  4 Others (trader/service)

  14

  14 Total 100 100 Time of work in primary occupation (years)

  1

  • – 3

  7

  7

  4

  • – 6

  13

  13 7 – 9

  2

  2 10-12

  8

  8 > 12

  70

  70 Total 100 100 Monthly income from primary occupation (Rupiah)

  < 1,000,000

  41

  41 1,000,000

  • – 1,999,000

  36

  36 2,000,000

  • – 2,999,000

  15

  15 3,000,000 – 3,999,000

  1

  1 4,000,000 – 4,999,000

  2

  2 5,.000,.000

  5

  5 

  Total 100 100 Note: 1 US Dollar equivalent of 10,500 Rupiah (2008)

3.5. Land Holding and Crop Production

Table 3.5. shows that the respondent ’s land holding status ranges from none to land holding of

  

eight Has. The majority of the respondents have land holding that are less than three Has, followed by

those with land holding of three to five Has (42%), and greater than five Has (7%). Only two % of

respondents has no land holding. Majority land status is owner-operator (57%), followed by owner

(22%) and lessee (19%). The majority have occupied the land for more than six years (77%). Only 21

% of the respondents occupied the land for less than or equal to six years. The land productivity

majority of the three to five tons/ha/year (51%), followed by those with less than three tons/ha/year

(23%), and six to eight tons/ha/year (15%). Only nine % of respondents have land productivity of

more than eight tons/ha/year (Table 3.5).

Table 3.5. Secondary source of income of the respondents NUMBER OF

  INFORMATION RESPONDENTS % Secondary occupation Farmer

  17

  17 Fisherman

  2

  2 Others (services and trader)

  23

  23 No secondary occupation

  58

  58 Total 100 100 Time of working secondary occupation (years)

  1-3

  13

  13 4-6

  13

  13 7-9

  2

  2 10-12

  6

  6 > 12

  8

  8 No secondary occupation

  58

  58 Total 100 100 Monthly income from secondary occupation (Rupiah)

  < 1,000,000

  23

  23 1,000,000

  15

  15

  • – 1,999,000 2,000,000
  • – 2,999,000

  2

  2 3,000,000

  • – 3,999,000

  1

  1 4,000,000

  1

  1

  • – 4,999,000 No secondary occupation

  58

  58 Total 100 100 Note: 1 US Dollar equivalent of 10,500 Rupiah (2008)

3.6. Preferred and Use of the Land

Table 3.6. shows that majority of the respondents (68%) mentioned that they prefer to use their land for agriculture, followed by those who preferred plantation crops (16%). Eight % of the respondents

  

preferred to use their land for both agriculture and plantation crops and five % of respondents

preferred to use land for fish pond (Figure 3.4).

Table 3.6. Land holding and production level of the farm of the respondents NUMBER OF

  

INFORMATION RESPONDENTS %

Land ownership Owner

  22

  22 Owner-operator

  57

  57 Lessee

  19

  19 No land

  2

  2 Total 100 100 Period of occupied the land (year)

  < 1

  1

  1 1-2

  10

  10 3-4

  9

  9 5-6

  1

  1 > 6

  77

  77 No land

  2

  2 Total 100 100 Land holding of household (Ha)

  < 3

  49

  49 3-5

  42

  42 6-8

  7

  7 No land

  2

  2 Total 100 100 Production level of the farm of household (ton/ha/year)

  <3

  23

  23 3-5

  51

  51 6-8

  15

  15 9-11

  3

  3 > 11

  6

  6 No land

  2

  2 Total 100 100 Only three % of the respondents preferred to use their land for both agriculture and fish pond.

  

The majority of respondents (72%) use their land as source of primary income, followed by those who

use land as source of secondary income (17%). Eight % of respondents use their land as both

secondary income and investment. Only three % of respondents use their land as investment. In terms

of making decision as to what kind of plant to grow, majority of respondents (58%) mentioned that

both husband and wife are decision-makers, followed by those with only husband (25%), all family

members (10%), and government (6%) as decision maker. Only one % of respondents mentioned the

wife as decision-maker (Table 3.7).

Table 3.7. Preferred use of the landholding of the respondents NUMBER OF CHARACTERISTICS RESPONDENTS % Preferred land use

  Agriculture land

  68

  68 Plantation land

  16

  16 Fish pond land

  5

  5 Agriculture and plantation

  8

  8 Agriculture and fish pond

  3

  3 Total 100 100 Use of land based on preferred land use

  Source of primary income

  72

  72 Source of secondary income

  17

  17 Investment

  3

  3 Secondary income and investment

  8

  8 Total 100 100 Decision maker in deciding kind of crop to grow

  Husband

  25

  25 Wife

  2

  2 Husband and wife

  57

  57 Government

  6

  6 All of family members

  10

  10 Total 100 100

3.7. Extension Workers

  Regarding extension workers in Besitang Watershed, majority of the respondents (93%)

reported the extension workers as attending to their area (Table 3.8). They were one to two persons

who attend one to four times a week to the area. According to respondents, the extension workers are

very helpful in terms of improving their knowledge and introducing new technology especially in

agriculture.

  a b c d

  

Figure 3. 4. Land use in Besitang Watershed: a. oil palm plantation, b. rice field,

  c. rubber plantation, and d. fish pond

Table 3.8. Extension workers in Besitang Watershed NUMBER OF CHARACTERISTICS RESPONDENTS % The extension worker attendance in the area

  Attended

  93

  93 No attended

  7

  7 Total 100 100 Number of extension workers

  1-2

  77

  77 3-4

  16

  16 No extension worker

  7

  7 Total 100 100 Time spent by extension worker in the area

  1-4 times a week

  72

  72 1-4 times a month

  12

  12 1-4 times a year

  9

  9 Never

  7

  7 Total 100 100

CHAPTER 4 INTEGRATED APPROACH USING GIS AND AHP Information for integrated approach were gathered based on primary and secondary data. There are two information needed related to this approach, namely: physical information and

  

collective opinion information. Physical information data were prossessed using Geographical

Information System (GIS) and collective opinion information were prossessed using Analytical

Hierarchy Process (AHP) as a tool.

4.1. Physical Information

  Primary data were collected through a field survey. Ground survey was conducted to gather

soil data. Rainfall data were collected from station points in the watershed area. To gather the soil

data, the area was divided into three sub-watersheds (Table 2.1). Then, the area was divided into 12

decision zones (DZ). The upland stream sub-watershed was divided into four decision zones (DZ 1 to

DZ 4), the middle stream sub-watershed was divided into three decision zones (DZ 5 to DZ 7), and

lower stream sub-watershed was divided into five decision zones (DZ 8 to DZ 12). The decision

zones are sites of more or less common features, that is, homogenous regions. This not only facilitated

the analysis procedure, but also allowed the decision-maker to make well informed assessments

regarding the area (Bantayan, 1996).

  The decision zones were identified using land system. The basic concept of land system is to

divide the landscape into area meaningful for development planning. The land system concept is

based on ecological principles and presumes closely interdependent links between rock types,

hydroclimatology, land form, soils and organism. The same land system is recognized wherever the

same combination of such ecological or environmental factors occurs. A land system therefore, is not

unique to one locality only, but in all areas having the same environmental properties. Furthermore,

because a land system consists always of the same combination of rocks, soil and topography it has the

same potential, and limitation, wherever it occurs.

  In this study, soil samples were taken from the area based on land system/decision zone. The

fifteen soil samples were analyzed to get physical characteristics. They include slope, flood hazard,

soil dept, temperature, texture, cation exchangeable capacity (CEC), saturation, pH (H

  2 O), and Na- exchange.

  Ground survey was conducted to gather rainfall station point from the watershed area to

generalize the stations over the study area by creating Thiessen polygon in ArcView GIS. Of the six

rainfall stations that were obtained as representative points, only four were used as representative

points, namely: Besitang, Pangkalan Susu, Brandan Barat, and Batang Serangan Station. The

  

secondary rainfall data 1996-2006 was retrieved from the Meteorological and Geophysical Agency

Regional I Sampali, Medan, North Sumatra. They were used to derive the rainfall erosivity factor (R).

Erosivity Map (R) is gained from rainfall stations point survey and monthly rainfall data.

Generalizing the stations over the study area was done by creating Thiessen polygons in ArcView

GIS. The rainfall erosivity factor (R) was calculated using Lanvine Formula:

  

1. Langkat administrative map (2003) was obtained from combination earth feature of Indonesia

map/Peta Rupa Bumi Indonesia (RBI) or what is called as basic map with scale 1:50000. This map was released by Langkat District government which also relate with RBI map. This map covered all district boundaries, sub-district, and village map in Langkat.

  

7. Land system map was based on Regional Physical Planning Programme for Transmigration

(RePPProT) survey in 1980 and which is still in use until now.

  

6. Land use maps (1990, 2001, and 2006) and cropping management factor (C) and the erosion

control practice factor (P) were obtained from Landsat image interpretation by BAKOSURTANAL and the Ministry of Forestry. They were gained from a guided satellite image interpretation and a visual interpretation. The geographical land use map coverage was based on Besitang Watershed boundary with the same extent as the previous point.

  

5. Topography and slope maps were obtained from the National Coordinating Agency of Survey and

Mapping (BAKOSURTANAL) (1982) and the slope length factor (L) and slope gradient factor (S) were obtained from the Ministry of Forestry (2005).

  4. Soils type and soil erodibility factor (K) were obtained from the Ministry of Forestry (2005).

  

3. Rainfall (Monthly) data (1996-2006) were retrieved from the Meteorological and Geophysical

Agency Regional I Sampali, Medan.

  

2. Watershed boundary map was obtained from the Ministry of Forestry (2005) (the Wampu Sei Ular

Watershed Management Bureau).

  = Monthly rainfall (cm) Secondary data were gathered from different offices. These are:

  36 .

  m

  = Monthly rainfall erosivity (Rain)

  m

  Rain R  (4.1) where: R

  2 m m

  1 ) ( 21 .

  

8. The land use/forest classification map based on Decrees of Ministry of Forestry Number 44 (2005)

was obtained from the Ministry of Forestry (2006).

  

9. Langkat Regional Spatial Management Planning map (RTRWK) based on the spatial management

planning related to regional land uses (2002-2011) was obtained from the Regional Development Planning Board of Langkat Regency (BAPPEDA).

10. Soil depth in Besitang Watershed was obtained from the Ministry of Forestry (2005).

  Other informationsuch as: explanatory booklet of the land unit and soil map of the Medan

sheet (0619) Sumatra (1990), the field technique planning for Land Rehabilitation and Soil

Conservation in Besitang Watershed (2005), Main Report (Review of Phase I Result, Sumatra) of

RePPProT (1988) Volume I and II, the Spatial Management Plan Related to Regional Land Uses in

Langkat Regency (RTRWK) 2002-2011 and the Regional Langkat Government Regulation Number

03 (2006) about the Langkat Regency Development Planning (2006-2010) were obtained from

BAPPEDA.

4.2. Physical Characteristics of the Study Area

4.2.1. Rainfall and Erosivity Factor (R)

  Rainfall in Besitang Watershed was obtained from four metereological stations spread over the

area, namely: Batang Serangan, Besitang, Brandan Barat, and Pangkalan Susu Station. The 11-year

rainfall data (1996-2006) (Appendix Tables 2, 3, 4, and 5), show rainfall intensities in Besitang

Watershed, Batang Serangan Station have the highest rainfall intensity (27.5 mm/day). Based on

Decrees of Ministry of Agriculture Number 837/Kpts/Um/11/80 and Number 63/Kpts/Um/8/81, it was

classified as Class 3 (moderate), followed by Besitang Station and Pangkalan Susu Station. Brandan

Barat Station has the lowest rainfall intensity of the four stations as shown in Table 4.1.

Table 4.1. Mean rainfall intensity and its score value in Besitang Watershed AREA RAINFALL Ha % METEROLOGICAL

  INTENSITY CLASS SCORE DESCRIPTION STATION NAME (MM/DAY)

  30 Batang Serangan

  27.5

  3 Moderate 8,051.4

  8.01

  20 Besitang

  18.6

  2 Low 58,906.3

  58.89

  20 Brandan Barat

  15.5

  2 Low 2,473.9

  2.47

  20 Pangkalan Susu

  18.2

  2 Low 30,603.4

  30.59 Total 100,035.0 100.00

  In terms of annual rainfall, monthly rainfall, and erosivity in Besitang Watershed, Batang

Serangan Station has the highest annual rainfall (3,484 mm/year), monthly rainfall (29 cm/month), and

rainfall erosivity factor (R) value (3,230 mm/year), followed by Pangkalan Susu Station, and Brandan

Barat Station. Besitang Station has the lowest rainfall among four stations as is shown in Table 4.2.

and Appendix Tables 6, 7, 8, and 9). Thiessen polygon was used in delineating the rainfall erosivity

boundary (Figure 4.1).

Figure 4.1. Map of rainfall and erosivity factor (R) in Besitang Watershed, Langkat, North Sumatra, IndonesiaTable 4.2. Mean annual rainfall, monthly rainfall, and erosivity in Besitang Watershed AREA ANNUAL MONTHLY EROSIVITY METEOROLOGICAL RAINFALL RAINFALL (R) STATION NAME (MM/YEAR) (CM/MONTH) (MM/YEAR) Ha %

  Batang Serangan 3,483.5 29.0 3,230.0 8,051.4

  8.01 Besitang 2,070.0 17.3 1,474.4 58,906.3

  58.89 Brandan Barat 2,090.2 17.4 1,475.1 2,473.9

  2.47 Pangkalan Susu 2,156.9 18.0 1,637.6 30,603.4

  30.59 Total 100,035.0 100.00

  Based on Tables 4.1, 4.2, and Figure 4.1, more than half of the area in Besitang Watershed is

represented by Besitang Meteorological Station (59%), followed by Pangkalan Susu Meteorological

Station (31%). Only 8% of area is represented by the Batang Serangan Meteorological Station and

represented only 3% of area is by the Brandan Barat Meteorological Station.

  4.2.2. Soil Types and Soil Erodibility Factor (K) Soil class according to susceptibility to erosion and score value are presented in Table 4.3.

More than half of the soil type in Besitang Watershed is andosol-podsolik (53%), followed by alluvial-

gley (16%), alluvial (15%), and regosol (13%). Only 3% is podsolik. The largest area is dominated

by soil type Class 4 is susceptible to erosion (56%), followed by soil type Class 1 which is not

susceptible to erosion (31%). Only 13% of soil type Class 5 is very susceptible to erosion.

  There are three soil erodibility values varying from 0.05 (very low) up to 0.30 (moderate).

The soils groups and soil erodibility factor (K) in Besitang Watershed are shown in Table 4.4. and

Figure 4.2. The largest area is dominated by soil erodobility value of 0.3 (72%), which is classified as moderate, followed by soil erodobility value of 0.15 (16%), which is classified as low. Only 13% has

  soil erodibility value of 0.05 (very low).

Figure 4.2. Map of soil types and soil erodibility factor (K) in Besitang Watershed, Langkat, North Sumatra, IndonesiaTable 4.3. Soil types in Besitang Watershed CLASS SOIL TYPE SCORE DESCRIPTION AREA Ha %

  1 Alluvial

  

15 Not susceptible

  15.08 15,084.4

  1 Alluvial, Gley

  15 Not susceptible 15,649.3

  15.64

  4 Podsolik

  60 Susceptible 3,150.6

  3.15

  4 Andosol, Podsolik

  60 Susceptible

  53.23 53,245.7

  5 Regosol

  

75 Very susceptible

  12.90 12,904.6 Total

  100,034.6 100.00

Table 4.4. Soil groups and their soil erodibility factor (K) in Besitang Watershed

  

SOIL GROUP ERODIBILITY CLASS AREA

(K) Ha %

  Moderate Dystropepts, Tropudults, Troportens 0.30 12,904.6

  12.90 Moderate Tropaquepts, Fluvaqurnts, Tropohemists 0.30 2,249.5

  2.25 Very low Hydraquents, Sulfaquents 0.05 12,834.9

  12.83 Moderate Dystropepts, Tropudults 0.30 3,320.6

  3.32 Low Dystropepts, Tropudults, Humitropepts 0.15 15,649.3

  15.64 Moderate Tropudults, Dystropepts 0.30 3,150.6

  3.15 Moderate Tropudults, Dystropepts, Eutropepts 0.30 49,925.1

  49.91 Total 100,034.6 100.00

  4.2..3. Land Systems and Decision Zones Land system is based on Regional Physical Planning Programme for Transmigration

(RePPProT) survey in 1980 and still being used. Basic concept of land system is to divide the

landscape into areas meaningful for development planning. The land system concept is based on

ecological principles and presumes closely interdependent links between rock types,

hydroclimatology, land form, soils and organism. The same land system is recognized wherever the

same combination of such ecological or environmental factors occurs. A land system therefore, is not

unique to one locality only, but considers all areas having the same environmental properties.

Furthermore, because a land system consists always of the same combination of rocks, soil and

topography it has the same potential, and limitation, wherever it occurs. In Besitang Watershed, there

are eight land systems as presented in Table 4.5. and are shown in Figure 4.3.

Table 4.5. shows that the majority land system in Besitang Watershed is Teweh (50%), followed by Maput (16%), Bukit Pandan (13%), Kajapah (13%), Mantalat (3%), Kahayan (2%), and

  

Pendreh (3.%). Only 0.02% is Air Hitam Kanan. The land system is used as a basis to determine

decision zone (DZ) in this study. Decision Zone 1 is located in Bukit Pandan and Air Hitam Kanan

  

Land Systems, Decision Zone 2 is located in Pendreh Land System, Decision Zones 3, 5, and 10 are

located in Maput Land System, Decision Zones 4, 6, and 11 are located in Teweh Land System,

Decision Zones 7 and 8 are located in Mantalat Land System, Decision Zone 9 is located in Kahayan

Land System, and Decision Zone 12 is located in Kajapah Land System (Table 4.6. and Figure 4.4).

Based on the explanation above, look that the decision zones areas were different each others and have

broad scale. Hence, for detailed information and assessment (planning), need to rearrange the area of

decision zone by stakeholder participation considering the interaction among land uses (ecological

interaction).

  Shale, mudstone, conglomerate, sandstone 15,649.3

  Dystropepts, Tropudults

  Moist primary lowland forest, heath forest, bush, alang-alang, shifting cultivation

  Linear sedimentary ridge system with steep diplopes

  Shale, sandstone, mudstone 3,320.6

  3.32 Maput Hilly System

  Dystropepts, Tropudults, Humitropepts

  Moist primary lowland forest, logged forest, submontane forest, bush, alang-alang, shifting cultivation, settlements

  Asymmetric non- orientated sedimentary ridges

  15.64 Pendreh Plateau and Mountain System

  Inter-tidal mudflats under hlophytes Alluvium, recent estuarine marine 12,834.9

  Tropudults, Dystropepts

  Moist primary lowland forest, submontane forest, heath forest, montane forest, bush, shifting cultivation

  Asymmetric broadly dissected ridges Siltstone, sandstone, mudstone, shale, conglomerate 3,150.6

  3.15 Teweh Plain System

  Tropudults, Dystropepts, Eutropepts

  Moist primary lowland forest, heath forest, bush, shifting cultivation, settlements

  Hillocky plain over mixed sedimentary rocks Shale, mudstone, conglomerate, sandstone, siltstone 49,925.1

  49.91 Total

  12.83 Mantalat Hilly System

  Tidal forest, undifferentiated, mangrove nipah, halophytes fishponds (prawns)

Table 4.5. Land systems in Besitang Watershed LAND SYSTEM NAME LAND SYSTEM TYPE SOIL GROUP

  0.02 Bukit Pandan

  VEGETATION TYPE LAND CHARACTERISTIC ROCK TYPE AREA Ha %

  Air Hitam Kanan

  Hilly System

  Dystropepts, Tropudults, Troportens

  Moist primary lowland forest, submontane forest, montane forest, bush, alang-alang, shifting cultivation

  Very steep-sided ridges over tuffaceous sediments

  Siltstone, sandstone, mudstone, tuffite, fine grained tephra

  20.5

  Plateau and Mountain System

  2.25 Kajapah Tidal Flat Hydraquents, Sulfaquents

  Dystropepts, Tropudults, Troportens

  Moist primary lowland, submontane and montane forest, bush, shifting cultivation

  Precipitous orientated metamorphic mountain ridges

  Phyllite, quartzite, schist, shale, sandstone 12,884.1

  12.88 Kahayan Alluvial Plain

  Tropaquepts, Fluvaqurnts, Tropohemists

  Swamp forest, bush, swamp grassland, rainfed wetland rice, rubber and coconut estates, settlements

  Coalescent estuarine/river line plain Alluvium, recent estuarine marine, riverine, peat 2,249.5

  100,034.6 100.00 Source: Regional Physical Planning Programme for Transmigration (RePPProT) survey in 1980.

Figure 4.3. Map of land systems in Besitang Watershed, Langkat, North Sumatra, IndonesiaTable 4.6. Decision zones and land systems in different sub-watersheds of Besitang Watershed SUB-

  AREA WATERSHED LAND SYSTEM DZ Ha % Upland stream Air Hitam Kanan and Bukit Pandan

  1 12,904.6

  12.90 Pendreh 2 3,150.6

  3.15 Maput 3 3,490.5

  3.49 Teweh 4 11,269.6

  11.27 Middle stream Maput 5 7,402.2

  7.40 Teweh 6 8,441.4

  8.44 Mantalat 7 878.5

  0.88 Lower stream Mantalat 8 2,442.1

  2.44 Kahayan 9 2,249.5

  2.25 Maput 10 4,756.6

  4.75 Teweh 11 30,214.1

  30.20 Kajapah 12 12,834.9

  12.83 Total 100,034.6 100.00

  Note: DZ = Decision zone

Figure 4.4. Map of decision zones in Besitang Watershed, Langkat, North Sumatra, Indonesia

  4.2.4. Soil Depth Soil depth in Besitang Watershed as shown in Table 4.7., ranges from moderately deep class to

deep class. The majority of the areas in Besitang Watershed has soil depth of more than 90 cm (87%)

followed by 60-90 cm of soil deep (13%). Soil depth in Besitang Watershed is depicted in Figure 4.5.

Table 4.7. Soil depth in Besitang Watershed AREA SOIL DEPTH SOIL DEPTH (CM) CLASS Ha %

  • < 30 Very shallow
  • - 30 - 60 Shallow

  13.30 60 - 90 Moderately deep 13,308.7 86.70 > 90 Deep 86,726.3

  100.00 Total 100,035.0

Figure 4.5. Map of soil depth in Besitang Watershed, Langkat, North Sumatra, Indonesia

4.2.5. The Slope Length Factor (L) and Slope Gradient Factor (S)

  Topography and slope maps were obtained from the National Coordinating Agency of Survey

and Mapping (BAKOSURTANAL) (1982) and the slope length factor (L) and slope gradient factor

(S) were obtained from the Ministry of Forestry (2006) which are shown in Table 4.8. and depicted in

Figure 4.6. The largest area is flat with 0

  • – 8% slope (73%). Only 27% of the area has a slope of greater than 8%.

  The extent of slope classes and LS factor values in the computation of erosion rate with USLE

formula of the watershed is listed in Table 4.8 and depicted in Figure 4.6. Slope or LS factor values

used are based on the classification of the Ministry of Forestry (1993) through experimentation. It

seems that the distribution of slope class value is an important factor affecting computed erosion rate.

The higher the LS values, the higher the erosion rate, other factors being equal.

Table 4.8. Class of slope and LS values in Besitang Watershed CLASS SLOPE SCORE DESCRIPTION LS AREA (%)

  VALUES Ha %

  1

  20 Flat 0.4 73,274.2

  73.25

  • – 8

  2

  8

  40 Moderate 1.4 6,542.6

  6.54

  • – 15

  3

  15

  60 Steep 3.1 4,751.5

  4.75

  • – 25

  4

  25

  80 Steeper 6.8 5,021.1

  5.02

  • – 40 5 > 40 100 Steepest

  9.5 10,445.7

  10.44 Total 100,035.0 100.00

Figure 4.6. Map of slopes and LS values in Besitang Watershed, Langkat, North Sumatra, Indonesia

4.2.6. The Cropping management factor (C) and the erosion control practice factor (P)

  The cropping management factor represents the ratio of soil loss from a specific cropping or

cover condition to the soil loss from a tilled, continuous fallow condition for the same soil and slope

and for the same rainfall. This factor includes the interrelated effects of cover crop sequence,

productivity level, growing season length, cultural practices, residue management, and rainfall

distribution (Kirkby and Morgan, 1980 cited by Ozsaer, 1994).

  The cropping management factor of the USLE represents an integration of several factors that

affect erosion including vegetation cover, plant litter, soil surface, and land management. Imbedded in

the term is a reflection of how intercepted reformed raindrops on the plant canopy affect splash

erosion. The binding effect of plant roots in preventing erosion and how the properties of soil change

as it lie idle are considered (Brooks et al., 1991). Furthermore, the C factor represents how crops and

management affect raindrops and flow of water or runoff to reduce soil erosion. The C factor value is

actually how much that particular crop and management situation will reduce erosion. In Indonesia,

the management cropping and C factor value are shown in Table 4.9.

  The erosion control practice factor values for the three major mechanical practices as

recommended by Wischmeier and Smith (1978) are shown in Table 4.10. The soil conservation

  

practices and their corresponding P values used by the Indonesian Soil Research Institute are presented

in describing sediment yield as shown in Table 4.11. In Indonesia, the cropping management factor

and conservation practice factor and their corresponding CP values are shown in Table 4.12.

  0.5 Selective cutting

  0.5

  18 Sifting cultivation

  0.4

  19 Land with management 1.000

  20 Empty land without management 0.950 21 primary forest High ground cover

  0.001 low ground cover 0.005

  22 Forest production Unselective cutting

  0.2

  0.1 Medium density

  23 Primary bush/scrub 0.010

  24 Coarse grass permanent 0.020

  25 Coarse grass with burn 0.700

  26 Paraserianthes falcataria with bush 0.012

  27 Paraserianthes falcataria without bush and litter 1.000

  29 Trees without bush 0.320

  30 Rice field

  0.2 Low density

  17 Mix garden High density

Table 4.9. Management cropping and C value NO. TYPE OF PLANT/LAND MANAGEMENT C VALUE

  7 Dry land Paddy (Oriza sativa) 0.560

  1 Grass (Brachiria sp.) 0.290

  2 Monggo bean (Phaseolus radiatus)

  0.35

  3 Wheat (Triticum aestivum) 0.242

  4 Cassava (Manihot esculenta) 0.363

  5 Soy bean (Glycine maximum) 0.399

  6 Vetiver root (Vetivera ziazinodes) 0.434

  8 Wetland Paddy (Oriza sativa) 0.010

  15 Planting system without rotated 0.398 16 cropping system with rotated + mulch 0.357

  9 Corn (Zea mays) 0.637

  10 Ginger (Zingiber officinale), Chili (Capsicum annuum) 0.900

  11 Potato that plant follow the slopes

  1.00

  12 Potato that plant follow the contour 0.350

  13 Planting system with rotated + mulch 0.079

  14 Planting system without rotated + mulch 0.347

  0.01 Sources: Abdurrachman et al. (1984) cited by the Watershed Management Bureau (2005); Arsyad (2006)

Table 4.10. Erosion control practice factor value LAND SLOPE CONTOURING CONTOUR STRIP TERRACING %AGE CROPPING AND

IRRIGATED FURROWS

  1 - 2

  0.60

  0.30

  0.12

  3

  0.50

  0.25

  0.10

  • – 8 9 – 12

  0.60

  0.30

  0.12

  13

  0.70

  0.35

  0.14

  • – 16

  17

  0.80

  0.40

  0.16

  • – 20 21 - 25

  0.90

  0.45

  0.18 Source: Kirkby and Morgan (1980) cited by Ozsaer (1994)

Table 4.11. Soil conservation practice and P value TYPE OF SOIL CONSERVATION PRACTICE P-VALUE

  Bench terrace High standard

  0.04 Medium standard

  0.15 Low standard

  0.35 Bench terrace: corn-cassava/soy been 0.056 Bench terrace: sorghum 0.024 Traditional terrace

  0.40 Gulud terrace: paddy-corn 0.013 Gulud terrace: cassava 0.063 Gulud terrace: corn-peanut with mulch 0.006

  Gulud terrace: soy been 0.105 Contour cropping

  Slope gradient 0

  0.50

  • – 8% Slope gradient 9

  0.75

  • – 20% Slope gradient > 20%

  0.90 Applied mulch 6 ton/ha/year 0.30 3 ton/ha/year 0.50 1 ton/ha/year

  0.80 Plantation cropping High ground cover

  0.10 Medium ground cover

  0.50 Grassland High standard

  0.04 Low standard

  0.40 Sources: Hammer (1980) cited by Ozsaer (1994); Abdurrachman et al. (1984) cited by the Watershed Management Bureau (2005); Arsyad (2006)

Table 4.12. Estimation of CP factor values of different land uses NO. MANAGEMENT CROPPING AND CONSERVATION CP VALUES

  5.36 Plantation

  Bush

  

0.25

0.40 5,029.3

  5.03 Dry land agriculture

  

0.53

0.15 22,377.7

  22.37 Primary forest

  

0.01

0.15 1,516.8

  1.52 Secondary forest

  

0.01

0.60 36,571.5

  36.56 Mangrove forest

  

0.17

0.40 5,362.8

  

0.15

0.15 17,117.7

  0.14 Sources: Abdurrachman et al. (1984); Ambar and Syafrudin (1979) cited by the Watershed Management Bureau (2005) Crop management factor (C) and soil conservation practices factor (P) in Besitang Watershed

are also presented in Table 4.13., based on the cropping pattern and existing land use, C and P values

of the watershed. They were obtained from Ministry of Forestry. Figure 4.7. shows the existing or

present land use of the watershed which serves as basis of values of crop management (C) and soil

conservation practices (P) factors.

  17.11 Wetland agriculture (rice field)

  

0.01

0.15 5,410.1

  5.41 Swamp

  

0.09

0.40 372.5

  0.37 Unvegetated

  

0.95

0.90 461.7

  0.46 Fish pond

  

0.01

0.15 4,559.0

  4.56 Water

  

0.00

  0.00 1,255.7

Table 4.13. CP factor of different land uses and their extent in Besitang Watershed TYPES OF LANDUSE C FACTOR P VALUE AREA Ha %

  0.04 Contour cropping

  1 Forest Primary

  0.01 Medium ground cover with coarse grass

  0.01 Without ground cover and litter

  0.05 Without ground cover

  0.50

  2 Bush/Scrub Primary

  0.01 Bush with grass

  0.20

  3 Garden Talon garden (kebun-talun)

  0.01 Pekarangan garden (kebun-pekarangan)

  0.07

  4 Plantation High ground cover

  0.02 Coarse grass with burn once a year

  0.14 Bench terrace

  0.06 Vetiver root (Vetivera ziazinodes)

  0.65

  5 Agriculture cropping Corm (umbi-umbian)

  0.51 Bulk (biji-bijian)

  0.51 Legume (kacang-kacangan)

  0.36 Mixture (campuran)

  0.43 Irrigated paddy (padi irigasi)

  0.02

  6 Cultivation 1 year plant-1 year no plant 0.28 1 year plant-2 year no plant

  0.19

  7 Agriculture with conservation Mulch

  1.26 Total 100,034.6 100.00

Figure 4.7. Map of CP values of different land uses in Besitang Watershed, Langkat, North Sumatra,

   Indonesia

4.3. Collective Opinion Information

  Information for collective opinion were gathered from primary data (interviews during field

survey and during the workshop) (Figure 4.8). The workshop was conducted to determine land use

suitability allocation based on public opinion from stakeholders using AHP. The study largely relied

on the use of questionnaire and key informant/stakeholder interviews. Key informants/stakeholders

provided qualitative in-depth information about physical and socio-economic conditions of the study

area. Key informants/stakeholders were local officers, extension workers, teachers, researchers, and

people who know well the study sites at provincial, regional, sub-regional, and village level.

  Secondary data were collected from offices which are related to the study. The following data

and thematic layers were needed to support during the workshop, namely: socio-economic data,

Langkat administrative map, watershed boundary map, land use (change) map, soil erosion map, land

capability and land suitability classification map, land use classification map based on Decrees of

Ministry of Agriculture Number 837/Kpts/Um/11/80 and Number 63/Kpts/Um/8/81 and RTRWK,

forest function map based on Decrees of Ministry of Forestry Number 44 (2005), land system map,

and decision zone map.

  b a d c f E

Figure 4.8. Interviews and workshop with stakeholders for collective opinion Information: a. Head of Natural Resources

  and conservation Bureau b. Provincial Forestry Offices c. Head the Regional Development Planning Board of Langkat Regency (BAPPEDA). d. Plantation manager e,f. workshop at University of Sumatera Utara (USU).

4.4. Geographic Information System (GIS)

4.4.1. GIS Definition

  Many people offer various definitions of GIS. According to Burrough (1986), a geographic

information system (GIS) is a powerful set of tools for collecting, storing, retrieving, transforming and

displaying spatial data from the real world for a particular set of purpose. Mulder (1984), on the other

  

hand, defined the GIS as a geo-database system, which contains both feature (spectral, spatial,

temporal) and thematic data, with emphasis on information extraction rather than on data storage. In

the strictest sense, a GIS is a computer system capable of assembling, storing, manipulating, and

displaying geographically referenced information, that is, data identified according to their locations.

Practitioners also regard the total GIS as including operating personnel and the data that go into the

system 2007). A GIS is a computer-based tool for mapping and analyzing things that exist

and events that happen on earth. GIS technology integrates common database operations such as

query and statistical analysis with the unique visualization and geographic analysis benefits offered by

maps 2007). A GIS also can be defined as an integrated system of computer hardware,

software, and trained personnel linking topographic, demographic, utility, facility, image and other

resource data that are geographically referenced (Short, 2007).

  According to Bettinger and Wing (2004), a GIS can be defined by how it is used (a land

information system, a natural resource management information system), by what it contains (spatially

distinct features, activities, or events defined as points, lines, polygons, or raster grid cells), by its

capabilities (a powerful set of tools for collecting, storing, retrieving, transforming, and displaying

spatial data), or by its role in an organization (a map productions systems, a spatial analysis system, a

system for assisting in making decisions regarding basic geographic questions; where is it? What is it?

Why is it there?). Godilano (2001) has described GIS as a spatial decision support system; a system

for input, storage, analysis and output of geographic data; or geographically referenced information. It

is an information system that allows for digital input, storage, query, manipulation, analysis, display,

and output of spatial data and associated database. Technological emphasis in GIS, therefore, is on the

linkage of attributes to geographic entities, or the definition and memorization of spatial relationships

and efficient spatial query, manipulation, and analysis. A GIS can be considered as a tool for the

integration and analysis of geographically-referenced data (Maguire, 1991).

  According to Jaya (2002), geography analysis includes spatial analysis and tabular analysis.

Furthermore, Bantayan (2006) described GIS as decision tools for managing geographic data in a

computer environment. The development of GIS coincided with the development of the computer.

  

However, the technology that is called GIS derives its functionalities from existing and mature

sciences and technologies.

4.4.2. GIS Components

  Geographic information system consists of four important components these need to be balanced if the system is to function satisfactorily (Burrough, 1986):

1. Data

  Perhaps the most important component of a GIS is the data. Data may be defined as a body of

facts or figures gathered systematically. In their raw form, data are mere numbers representing

  

measurements from field surveys and inventories (Bantayan, 2006). Geographic data and related

tabular data can be collected in-house, compiled to custom specifications and requirements, or

occasionally purchased from a commercial data provider. A GIS can integrate spatial data with other

existing data resources, often stored in a corporate data base management system (DBMS). The

integration of spatial data (often proprietary to the GIS software), and tabular data stored in a DBMS is

a key functionality afforded by GIS.

  According to Godilano (2001are the core of any GIS. Data alone, however, are not

enough; data integrity and currentness should be an important component in database development

and management (garbage in, garbage out). There are two primary types of data that are used in GIS:

(1) a geodatabase is a database that is in some way referenced to locations on the earth. Geodatabases

are grouped into two different types: vector and raster. Coupled with these data is usually data known

as attribute data. (2) attribute data are generally defined as additional information, which can then be

tied to spatial data. Documentation of GIS datasets is known a

  2. Hardware Hardware is the computer system on which a GIS operates. Today, GIS software runs on a

wide range of hardware types, from centralized computer servers to desktop computers used in stand-

alone or networked configurations. According to Godilano (2001) Hardware comprises the equipment

needed to support the many activities of GIS ranging from data collection to data analysis. The central

piece of equipment is the work station, which runs the GIS software and is the attachment point for

ancillary equipment. Data collection efforts can also require the use of a digitizer or scanner for

conversion of hard copy data to digital data and a GPS data logger to collect data in the field. The use

of handheld field technology is also becoming an important data collection tool in GIS. With the

advent of web-enabled GIS, web servers have also become an important piece of equipment for GIS.

  3. Software GIS software provides the functions and tools needed to store, analyze, and display geographic

information. A review of the key GIS software sub-systems is provided above. According to Godilano

  

(2001), different software packages are important for GIS. Central to this is the GIS application

package. Such software is essential for creating, editing and analyzing spatial and attributes data;

therefore these packages contain a myriad of GIS functions inherent to them. Extensions or add-ons

are software that extend the capabilities of the GIS software package. For exampleis an

GIS seeks to build software applications that meet a

specific purpose and thus are limited in their spatial analysis capabilities. Utilities are stand-alone

programs that perform a specific function. For example, a file format utility that converts from one

  

type of GIS file to another. There is asoftware that helps serve data through Internet

browsers.

4. People

  GIS technology is of limited value without the people who manage the system and develop

plans for applying it to real world problems. GIS users, range from technical specialists who design

and maintain the system to those who use it to help them perform their everyday work. The

identification of GIS specialists versus end users is often critical to the proper implementation of GIS

technology. There are three factors to the people component: education, career path, and networking.

The right education is the key, such as: taking the rigas a GIS

developer if he does not have any programming skills. Finally, continuous networking with other GIS

professionals is essential for the exchange of ideas as well as a support community (Godilano, 2001).

4.4.3. GIS Processing The main purpose of a GIS is to manage spatial information for decision making (Berry, 1988).

  

GIS involves some steps, namely: input, manipulation, management, analysis, and visualization

(ESRI, 2007). The principle of GIS processing involves three main processes, that is: data input, data

manipulation, and data output (Aronoff, 1989). The following is a brief description of the basic

process of GIS:

  1. Data input or data entry . Data input covers all aspects of transformation of data captured in

the form of existing maps, field observation, and sensors into compatible digital form (Burrough,

1986).

  2. Data storage and retrieval. Data storage and data management concerns the way in which

the data about position, linkages (topology), and attributes of geographical element (point, line, and

areas representing objects on the earth surface) are structured and organized, both with respect to the

way they must be handled in the computer and how they are perceived by the users of the system

(Burrough, 1986). Data management for GIS includes those functions that need to be stored and

retrieved from the database (Aronoff, 1989).

  3. Data manipulation and analysis. According to Godilano (1991), data manipulation and

analysis involves the creation of composite variable through processing activities directed both on

spatial and non spatial attributes of system entities. Data manipulation operations typically needed by

users and found in many GIS include: a) reclassification and aggregation of attribute data, b)

geometrical operations such as restoration, translation and scaling of coordinates to specific map

projection, rectification, registration and removal of distortion, c) centered line allocation, d)

  

conversion of data structure, e) spatial analysis of such properties as connectively and neighborhood

statistics, f) measurement of distance and direction, g) statistical analysis.

  According to Aronoff (1989), data output has three types, 4. Data output or data display.

namely: hardcopy, softcopy, and electronic. Hardcopy outputs are permanent means of display. The

information is printed on paper, mylar, photographic film or similar materials. Maps and tables are

commonly the output in this type of data, softcopy outputs are used to allow operator interaction and

to preview data before final output because of its small size and loss in quality when the screen

  ’s

image is photographed or electronically captured. The result of analysis may be presented in the form

of maps, tables, and figures (graphs and charts) in a variety of ways suitable to the users (Burrough,

1986).

4.4.4. Applications of GIS

  The GIS has been used in many fields and industries, such as: agriculture, military, marketing,

petroleum industry, transportation, environment, and forestry. The application of GIS in natural

resources management is becoming popular. Geographic Information System is a powerful

management tool for resource managers and planners. Its applications are limited only by the quality,

quantity and coverage of data that are fed into the system. Some of the standard GIS applications can

be enumerated as follows: land use changes, land site planning, farm location, crop harvest

projections, forest inventory, mapping monitoring and management, soil inventories, watershed

delimitations and management, endangered species/habitat tracking, erosion and sedimentation

selection, road planning, environmental impact assessment, and population distribution study. It has

been used in classifying the watershed, land capability classification and land use suitability

assessment. Cruz (1990) for instance, successfully used GIS tools for land capability classification

and land use suitability assessment in the Ibulao Watershed in the Philippines. On the other hand,

Oszaer (1994) used GIS to classify the existing land uses, evaluate land capability, and assess land use

suitability in Waeriupa Watershed, Kairatu, Seram, Maluku, Indonesia.

  Breiby (2006) using GIS and the Revised Universal Soil Loss Equation (RUSLE) to assess soil

erosion risk within a Zumbro River Sub-watershed in Southeastern Minnesota. Geographic

Information Systems, coupled with the use of an empirical model to assess risk, can identify and

assess soil erosion potential and estimate the value of soil loss. This study demonstrates that GIS is a

valuable tool in assessing soil erosion modeling and in assisting the estimation of erosion loss at the

sub-watershed scale.

  Rajan and Shibasaki (2001) used a GIS based integrated land use/cover change model to study

agricultural and urban land use change. They have developed a national scale, integrated, dynamic

  

time series simulation model. In the process of development of this model, a new spatial model for

population growth and changes has also been developing.

4.5. Analytical Hierarchy Process (AHP)

4.5.1. An Overview of AHP

  The AHP introduced by Thomas Saaty, is a theory of measurement that provides the ability to

incorporate both qualitative and quantitative factors in the decision making process. It is a

multicriteria decision making (MCDM) method to solve a complex problem by decomposing the

problem into a structural hierarchy (Saaty, 1980; Saaty and Vargas, 2001) and provides a hierarchical

structure by reducing multiple variable decisions into a series of pair comparisons and develops

subjective priorities based upon user judgment (Weerakoon, 2002).

  The AHP facilitates decision making by organizing perceptions, feelings, judgments, and

memories into a multi-level hierarchic structure that exhibits the forces that influence a decision. In

the most common case, the forces are factors that are arranged from the more general to the more

specific. Showing on a hierarchical structure of the AHP provides to examine the interactions of goals,

criteria, sub-criteria, and alternatives on the entire system. For this purpose, absolute measurement

and relative measurement approaches is used in the application of AHP. Absolute comparisons are

generally used when desired to rank independent alternatives according to standards developed by the

experience of experts (Saaty, 1990). However, relative comparisons require priorities to be

established with respect to hierarchical goals by making sets of pairwise comparisons in a systematic

manner.

  

According to Saaty (1986), in the AHP method, there are three basic principles, these are:

1. Decomposition, which is applied to structure a complex problem into a hierarchy of clusters.

  

2. Comparative judgment, which is applied to construct pairwise comparisons of all elements in a

cluster with respect to the parent of the cluster.

  

3. Synthesis of priorities, which is applied to produce global priorities throughout the hierarchy by

considering the local priorities of elements in a cluster and the priority of the parent element.

Commonly, there are two AHP models used in practice, namely: relative measurement model and

rating measurement model. The relative measurement model is used to prioritize a limited number of

alternatives by comparing directly one to another. Meanwhile, the rating measurement model

(absolute or scoring model) is used to gauge the alternatives against and established scale and not

against each other.

  One of the most relevant parts of the AHP is related with to give a structure to the problem to

be solved through the hierarchy. In this phase, the decision group involved should divide the problem

on his fundamental components (Saaty, 1980). A normal hierarchy is composed by: one global

  

objective (goal), sub-objective (strategic criteria), more specific sub-criteria (technical criteria) and at

the last level can find the alternatives.

  The next step in the process involves calculation of the column vector. It is derived by

multiplying the matrix of objectives by the relative weights, for example objectives weightings. In

equation form: k k k

  VM  (4.2) * i where,

  k th

  V = column vector for k decision-maker

  k th

  M = matrix of objectives for k decision-maker k

  th th

   = relative weight of i objective for k decision-maker i

Table 4.14. The fundamental scale of AHP

VERBAL JUDGMENT

  IMPORTANCE (IF A IS…AS/THAN B) (THEN THE VALUE TO BE ASSIGNED IS…) Equal importance

  1 Equal importance or indifference Moderate importance

  3 Experience and judgment slightly favor one activity over another Essential or strong

  5 Experience and judgment importance strongly favor one activity over another

  Very strong importance

  7 An activity is favored very strongly over another; its dominance is demonstrated in practice

  Extreme importance

  9 The evidence favoring one activity over another is of the highest possible order of affirmation

  Intermediate preferences 2, 4, 6, 8 When compromise is between two adjacent needed judgments

  Source: Saaty and Vargas (2001)

With the column vector of weights, the maximum or principal eigenvalue (donated by

 ) is max

computed. The closer the principal eigenvalue is to n, the more consistent are the subjective

assessments (Saaty, 1980). It is derived by taking the average of the sum of the ratios of the column

vector and relative weights. The equation as follows:

  q k v k

   k 1 

   i

    (4.3) max q where, k = order of matrix 1 to q which is equivalent to the number of decision-maker

  Central to AHP is a measure of consistency in human judgments. The deviation from

consistency may be represented by an index of consistency (CI). This value is the difference between

the maximum or principal eigenvalue and the number of objectives (n) divided by n-1. The equation

form as follows:

   n  max CI  (4.4) n

  1  where, CI = consistency index

   = principal eigenvalue max n = number of objectives To get an idea of the consistency of judgment, CI is compared with a random consistency

index (RI) of values as shown in Table 4.15. A consistency ratio of ten % or less is considered

acceptable. The equation of consistency ratio (CR) as follows:

  CI CR  (4.5) RI where, CI = consistency index RI = random index

Malczewski (1999) reported that when CR is less than 0.1, there is a reasonable level of consistency in

the pair-wise comparisons. If CR more than or equal 0.1, the values of the ratio are inconsistent. In

the latter case, the original value in the pair wise comparison matrix should be revised.

Table 4.15. Consistency random index (RI) values ORDER OF MATRIX (N) AVERAGE RANDOM INDEX (RI)

  1

  0.00

  2

  0.00

  3

  0.58

  4

  0.90

  5

  1.12

  6

  1.24

  7

  1.32

  8

  1.41

  9

  1.45

  10

  1.49

  11

  1.51

  12

  1.48 Source: Saaty (1980) In the process related to derivation of the priorities of the alternatives, with respect to each

objective at level 3 of the hierarchy, the relative weights of the alternatives based on each objective

was be calculated in the same manner. The final ranking of the alternatives (denotes by ωj) was be

calculated by performing a matrix multiplication of the relative weights of the alternatives per

objective (denoted by ωij) and the relative weights of the objectives (denoted by ωi). It was calculated

using the equation: (4.6)

  ωj = Mij * ωi where, ωj = final weight of alternative j Mij = matrix of alternative relative weights per objective ωi = objective weightings in addition, Mij takes the form:

   ...   

  11 1 p Mij =   (4.7)

   ...   n 1 np    where,

  11

  ω = the relative weight of alternatives 1 (j to p) for objective 1 (i to n)

  3. Aggregating All Priority Vectors The final step is to aggregate the priority (weight) vectors of each level obtained in the second

step, to produce overall weights. This can be done by means of a sequence multiplication of the

weight vectors at each level of the hierarchy. The overall weights represent rating of alternatives with

respect to the overall goal. The overall score Ri of the i-th alternative is the total sum of its ratings at

each level which is computed as follows (Malczewski, 1999):

  Ri   r (4.8) k ik

   k where,

  k

  ω = vector of priorities associated with the k-th element of the hierarchy r ik = vector of priorities derived from comparing alternatives on each criterion There are several ways to aggregate the preference pattern of individual decision-makers. By

using operations of fuzzy set theory, the dominant alternative can be determined (Bantayan, 1996).

According to Tanaka (1996), there are three fundamental operations of fuzzy sets, namely: (1)

Complement of fuzzy set A is a fuzzy set that is defined by the membership function:

   x  1   x , for all x   A   є X; (2) Union of two fuzzy sets A and B is a fuzzy set that is defined by

  A the membership function:    x  max      x ,  x

  ABA B  , for all x є X; (3) Intersection of two fuzzy

sets A and B is a fuzzy set that is defined by the membership function:    x  min       x ,  x  ,

  AB A B for all x є X.

4.5.2. The Application of AHP

  Schmoldt et al. (2001) state that because natural resource management often entails making

choices among alternative management regimes, decision support tools are proposed as instruments

for making rational, carefully reasoned, and justifiable decisions. The AHP which was developed only

in the late 1970’s, has become one of the most widely used techniques as shown by the extensive

literature published in journals and books, most of which are in areas outside natural resources.

Applications of AHP in forestry, agriculture, and natural resources are still surprisingly limited.

  One of the areas where the AHP has received wide application is land use suitability analysis.

Banai-Kashani (1989); Xiang and Whitley (1994) offer excellent reviews describing the potential of

the AHP for general site suitability and land capability analyses. Huchinson and Toledano (1993)

describe the use of the AHP in conjunction with GIS for designing land use plans considering multiple

objectives and participatory approaches to planning and decision making. As land use becomes more

constrained and the land allocated to various activities continues to shrink, suitability analyses take on

added importance (Schmoldt et al., 2001).

  Furthermore, Schmoldt et al. (2001) mentioned that considering the complexity of most

management issues and compliance regulations, the AHP can extend to a wide array of managerial and

planning tasks, for example: management and planning for a large watershed may include issues

related to water quality and quantity, forest management, wildlife management, and recreation. Input

is required from subject matter experts in each of these disciplines in order to establish priorities and

make informed decisions regarding spatial and temporal distributions of resources. Because

watersheds generally involve the flow of materials between public and private lands, additional input

  

is often needed on social, legal, and political aspects of resource condition and value. In addition to its

breadth of application, the AHP is relatively easy to apply, to understand, and to interpret.

4.6. Participatory Decision Support System

  Decision support system typically combines expert based procedural rules with the information

processing capabilities of computers, to derive quantitative modeling programs used to optimize

among multiple objectives (Varma et al., 2000; Kangas et al., 2000). According to Steiguer et al.

(2003); Loomis (1993), such decision support tools, as multi-criteria decision models (MCDMs),

range from highly complex mathematical models using linear programming or spatial modeling, to

relatively simple applications of multi-criteria analysis (MCA).

  Multi-criteria analysis (MCA) as defined by Mendosa et al. (1999) is a decision support

approach developed for complex problems involving trade offs between multiple objectives, where

both quantitative and qualitative aspects of the problem can be addressed. Multi-criteria analysis

(MCA) can be applied to the evaluation of alternative management scenarios across a range of criteria.

The resulting matrix allows systematic comparison across scenarios and across criteria. Scenarios can

be ranked, for example, according to the overall pattern of evaluations or rated based on numerical

weighting applied to the criteria.

  Nelson (2003) stated that systems based on spatial data and underlying GIS based modeling

can address the complexities of spatial and temporal variation. However, the traditional use of

MCDMs by experts has been criticized for being too technocratic when used in a public decision

making context (Cohen, 1997; Kangas et al., 2001; McCool and Stankey, 2001; Mendoza and Prabhu,

2005). Such programs may build in the subjective weightings of the model builder (Martin et al.,

2000; Costanza and Ruth, 1998) which may not be apparent to users or managers. According to

Gregory (2002); Steiguer et al. (2003), complex models can seem like a black box to the public,

sometimes fostering distrust in the decision making process.

  Various approaches to participatory decision support already exist. Sheppard (2005) has

classified these into three participatory decision support methods. The first class is a system which

uses fairly complex mathematical or statistical formulas to represent certain public values. Decision

scientists have develop methods for complex trade-off analysis across sets of values, incorporating

public preferences and priorities through techniques such as means ends networks and swing

weighting (Keeney and McDaniels, 1999; Gregory, 2002). Furthermore, Haider et al. (1998) have

described the use of choice experiments to determine recreationists’ preferences for different forest

management scenarios, Kangas et al. (2001) reviewed several studies applying MCDM tools adapted

to derive value functions describing stakeholders’ preferences and priorities. Kangas (1994) used

AHP in participatory forest planning, deriving stake holder weightings for management objectives.

  The second class of simpler systems, including MCA methods and other related forms of

integrated assessment, have begun to incorporate stake holder input into the core evaluation process in

recent years (Cohen, 1997; Brown et al., 2001; Mendoza and Prabhu, 2005). According to Brown et

al. (2001); Sheppard and Meitner (2005), the participatory or public MCA approach provides a

structured collaborative process for combining expert evaluations and stakeholder input across

multidisciplinary criteria or management objectives. Here, stakeholders in focus groups prioritize

criteria by means of weightings, which can be used to aggregate scores for various management

scenarios. The criteria may be developed by technical experts based on initial consultation with

stakeholders, who can also be involved in selecting or confirming the scenarios to be evaluated.

Garuti and Sandoval (2005), on the other hand, show a very powerful methodology for decision

making process, specifically in the shift work area, where the huge numbers of variables and

knowledge to be structured, integrated and synthesized, forces the need for a system analysis process,

which can deal with such complexity.

  The third class of systems can be seen emerging as planning and analysis tools, based on

integrated GIS, visualization, and related modeling technologies, to meet the needs of both public

communication and spatial quantification. The simpler participatory version of MCA appears to

deliver systematic criteria-based information (Martin et al., 2000) by transparently demonstrating the

effect of different stakeholder priorities on the analytical outcome (Mendosa and Prabhu, 2005).

Public MCA appears to provide an accountable trustworthy process for stakeholders (O’riordan and

Ward, 1997), which is especially important where there is a history of mistrust in how forest resource

decisions have been made (Daniels and Cheng, 2004). Bantayan (1996), on the other hand

demonstrated the participatory decision support systems which introduces a new procedure physico-

subjective modeling that involved a careful consideration of three major attributes of land use

suitability, namely: objectives, alternatives, and land units. Furthermore, Bantayan and Bishop (1998)

describe the application of multi-criteria decision analysis (MCDA) to decision problems concerning

land use planning.

  

CHAPTER 5

SOIL EROSION OF DIFFERENT LAND USES

5.1. Soil Erosion

  According to Brooks et al. (1991), soil erosion is the process of the dislodgment and transport

of soil particles from the surface by water and wind. The Universal Soil Loss Equation (USLE) is an

erosion model designed to predict the long-term average soil loss from specific soils with specific site

characteristics. This model computes soil loss for a given site as the product of five factors whose

values for a specific site can be expressed numerically. Erosion variables reflected by these factors

vary about their means from storm to storm, but these fluctuations tend to average out over an

extended period of time. Calculated soil loss values are substantially less accurate when predicting

specific events than predicting long-term averages (Wischmeier and Smith 1978).

  

Soil loss prediction using USLE equations (Wischemeier and Smith, 1978) is as follows:

A = R x K x LS x C x P (5.1) where, A = the soil loss per unit area (tons/ha/year) R = the rainfall erosivity factor K = the soil erodibility factor LS = the slope length and slope gradient factor C = the cropping management factor P = the erosion control practice factor.

  The Rainfall Erosivity Factor (R) Erosivity refers to the vertical power of raindrops to dislodge soil particles from the ground

(splash erosion) and move them horizontally over ground while in suspension (surface runoff). As the

intensity of rain increases, so does the kinetic energy as more soil particles are detached and

transported. This direct relationship between rainfall intensity and kinetic energy is defined as rainfall

erosivity (Wischmeier, 1959 cited by Oszaer, 1994). The R factor is a measure of the erosivity of

rainfall events and is defined as the product of two rainstorm characteristics that is, kinetic energy and

the maximum 30 minute intensity or EI

  30 . A regression equation describing the kinetic energy of a

  

rainstorm or portion of a rainfall event was developed and given as (Kirkby and Morgan, 1980 cited

by Oszaer, 1994): E = 1.213 + 0.890 log

10 I (5.2)

  where,

2 E = the kinetic energy, kg/m

  I = rainfall intensity, mm/hour

The rainfall erosivity factor, R, is obtained by dividing the EI product by 173.6. The computation of

the rainfall erosivity factor R for a storm is defined by: n

  1 . 213  . 890 log Ij   IjTj

  I  10 

  30   j

  1  

  R  (5.3)  173 . 6  where,

  R = the rainfall erosivity index Ij = the rainfall intensity for a specific storm increment, in mm/hour Tj = the time period of the specific storm increment, in hour

30 I = the maximum 30 minute rainfall intensity for the storm, in mm/hr

  j = the specific storm increment, and n = the number of storm increment

  30 Barus and Suwardjo (1977) as cited by Oszaer (1994) reported that the EI value is closely

  related to the amount of erosion produced at several places in Java. The EI

  30 value can be obtained

  from recording rain gauge charts, with the following equation (Arsyad, 2006): E = 210.3

  • – 89 (log I)
    • 2

  EI

  30 = E (I 30 * 10 ) (5.4)

  where, E = kinetic energy for one rainfall event in metric ton per Ha, I = rainfall intensity in cm per hour

  I 30 = maximum 30 minute rainfall intensity for the storm in cm per hour.

  Since the distribution of rain gauge in the study area is very limited, Lenvain (1975) cited by

  30 Oszaer (1994) obtained the relationship between EI and annual rainfall (Ra) as:

  1.98 EI 30 = 2.34 Ra (5.5)

  Rainfall erosivity factor (EI

  30 ) is estimated by the applying the following equation (Bols, 1978

  cited by Oszaer, 1994):

  1.211 -0.47 0.526

  EI

  30 = 6.119 (0.1 RAIN) (DAYS) (0.1 MAXP) (5.6)

  where: EI

  30 = monthly rainfall erosivity factor

  RAIN = average monthly rainfall in mm DAYS = average number of rainy days per month MAXP = maximum rainfall within the period of 24 hours for the month under consideration in cm.

  Since the rainfall within the period of 24 hours for the month and average number of rainy

days per month are very limited, the rainfall erosivity factor (R) is calculated using Lenvain Formula:

1 .

  36 R  2 . 21 ( Rain ) (5.7) m m

  Where:

  m

  R = Monthly rainfall Erosivity (Rain) m = Monthly rainfall (cm)

Annual rainfall erosivity factor can be counted by adding the monthly rainfall erosivity factor for one

year (12 months).

  Soemarno (1990) as cited by Oszaer (1994) stated that Bols’ equation can be used in estimating EI

  30 and it is able to provide a valid predicted soil erosion rate in the Konto Watershed in East Java.

  The Soil Erodibility Factor (K) The soil erodibility factor, K, in the USLE is a quantitative description of the inherent

erodibility of a particular soil. This factor reflects the fact that different soils erode at different rates

when the other factors that affect erosion are the same (Kirkby and Morgan, 1980 cited by Oszaer,

1994). Furthermore, David (1987) as cited by Oszaer (1994) simplified the equation of Wischmeier

and Mannering (1969) for estimating the soil erodibility factor on the basis of particle size distribution,

organic matter content and pH. It was tested in Magat and Pantabangan Watershed in the Philippines.

  Such simplified equation is as follows: K = [(0.043) (H) + 0.621/OM + 0.0082S

  • – 0.0062 C] Si (5.8) where, K = soil erodibility factor H = pH of soil OM = organic matter content in % S = % sand C = clay ratio = % clay/(% sand + % silt)

  Si = % silt/100

However, if more complete soil parameters are available, K factor can be determined using the

following equation (Arsyad, 1989):

  1.14 -4

  K = 0.027M 10 (12-OM) + 0.0325 (A-2) + 0.025 (B-3) (5.9)

  • M = (Si) (100-C)
where, K = soil erodibility factor in ton/ha OM = organic matter content in % A = soil structure code (Table 5.1) B = soil permeability code (Table 5.2) Si = silt fraction in % C = clay fraction in %.

Table 5.1. Soil structure code CLASS OF SOIL DIAMETER SIZE CODE STRUCTURE

  Very fine granular < 1 mm

  1 Fine granular 1 to 2 mm

  2 Medium to coarse granular 2 to 10 mm

  3 Blocky, platy, or massive

  4 Source: Arsyad (2006)

Table 5.2. Permeability of soil profile code PERMEABILITY PERMEABILITY RATE CODE CLASS (CM/HA)

  Very slow < 0.5

  6 Slow 0.8 to 2.0

  5 Slow to moderate 2.0 to 6.3

  4 Moderate 6.3 to 12.7

  3 Moderate to rapid 12.7 to 25.4

  2 Rapid > 25.4

  1 Source: Arsyad (2006) The Slope Length Factor (L) and Slope Gradient Factor (S)

  Kirkby and Morgan (1980) cited by Oszaer (1994) pointed out that the effect of slope length

and gradient are represented in the USLE as L and S, respectively. However, L and S are often

evaluated as a single topographic factor, LS. Slope length is defined as the distance from the point of

origin of overland flow to the point where the slope decreases sufficiently for deposition to occur or to

the point where runoff enters a defined channel. The channel may be part of a drainage network or a

constructed channel Slope gradient is the field or segment slope, usually expressed as a %age. The

development of the USLE is based on a standard plot length of 22.13 meters. Therefore, the slope-

length factor is defined as:

  m   x L  (5.10)

    ( 22 . 13 )   where, L = slope length factor x = slope length, meter m = an exponent Recommendations by Wischmeier and Smith (1978) for the exponent m are: m = 0.5, if slope  5 % m = 0.4, if slope < 5% and > 3 % m = 0.3, if slope  3% and  1 % m = 0.2, if slope < 1%

Slope length factors such as that developed by Williams and Berndt (1972), as cited by Cruz (1990) is

computed as: .

5 At

  L  (5.11) Lc where,

2 At = area of a cell (km )

  Lc= length of a cell (km)

Wischmeier and Smith (1978) also determined that soil loss is correlated with parabolic description of

the effect of slope steepness or gradient. Normalizing this equation to a standard plot slope of nine %

resulted in a description of the slope gradient factor:

  2 . 43  . 30 s  . 04 s S  (5.12)

  6 . 613 where, S = the slope gradient factor s = the gradient (%) In addition, Arnoldus (1986) develop an equation for the LS factor as:

  L

  2 LS  1 . 38  . 965 s  . 138 s  

  100 .

  6 1 .

4 L S

      LS  (5.13)

      22 .

  1

  9     where, LS = slope length and slope steepness factor L = average slope length in meter S = slope steepness in % The equation above is applied when slopes are greater than twenty %.

  Average slope length for each slope range is computed using Eyle’s method that is adopted by

Ambar and Achmad (1979) as cited by Oszaer (1994) in mapping soil erosion for Jatiluhur Watershed-

West Java, Indonesia as follows:

1 L  D (5.14)

  2 D  1 . 35 d  .

26 S  2 .

  8 l d

  A where, L = average slope length, in meter D = drainage density l = total river length (km)

2 A = total watershed area (km ) S = average slope steepness, in %.

  The Cropping Management Factor (C) and the Erosion Control Practice Factor (P) As mentioned earlier in Chapter 4, the cropping management factor of the USLE represents in

integration of several factors that affect erosion including vegetation cover, plant litter, soil surface,

and land management. The C factor represents how crops and management affect raindrops and flow

of water or runoff to reduce soil erosion. The C factor value is actually a measure of how much that

particular crop and management situation will reduce erosion. In Indonesia, the management cropping

and C factor value are shown in Table 4.9. Furthermore, the erosion control practice factor values for

the three major mechanical practices as recommended by Wischmeier and Smith (1978) are shown in

Table 4.10. The soil conservation practices and their corresponding P values used by the Indonesian Soil Research Institute are presented in describing sediment yield as shown in Table 4.11. In

  

Indonesia, the cropping management factor and conservation practice factor and their corresponding

CP values are shown in Table 4.12.

5.2. Erosion Rate

  The universal soil loss equation (USLE) was used to estimate erosion rate in the area. Soil loss prediction using USLE (Wischemeier and Smith, 1978) follows the equation: A = R x K x LS x CP (5.15) where,

  A = the soil loss per unit area (tons/ha/year) R = the rainfall erosivity factor K = the soil erodibility factor LS = the slope length and slope gradient factor CP = the cropping management factor (C) and the erosion control practice factor (P) The RKLSCP/USLE map results from overlay (intersect) of all individual maps (R map, K

map, LS map, CP map), then multiplied by RKLSCP factors (Figure 5.1). The GIS spatial analysis

was used to overlay to all thematic maps. The soil erosion map shows the distribution of soil erosion

potential for the different land uses. The original set of land uses was aggregated to form major land

uses for this area. Soil erosion map is done by reclassification of RKLSCP based on Table 5.3.

  Soil erosion rate (USLE) Rain fall Soil erodibility (K) Topographic Cropping management factor (C)

erosivity (R) map map factor and erosion control practice factor

  (LS) map (P) values map Overlaying maps

  R map Overlaying map

K map

LS map

  CP map

Figure 5.1. Soil erosion analysis using GISTable 5.3. Distribution of soil erosion rate

  Quantify and classify

  CLASS DESCRIPTION SOIL EROSION (TON/HA/YEAR)

  I <15 Very low

  II 15-60 Low

  III 60-180 Medium

  IV 180-480 High

  V >480 Very high

  Source: Ministry of Forestry (2005) According to Ministry of Forestry (2005), soil erosion classification resulted from overlay between estimate soil erosion map and soil depth map (Table 5.4).

Table 5.4. Soil erosion classification based on estimate soil erosion and soil depth SOIL DEPTH CLASS OF EROSION (TON/HA/YEAR) (CM)

  I II

  III

  IV V (<15) (15-60) (60-180) (180-480) (>480) Deep Very Low Low Medium High Very high (>90)

  I II

  III

  IV Low Medium High Very high Very high Medium (60-90)

  I II

  III

  IV IV Medium High Very high Very high Very high Shallow (30-60)

  II III

  IV IV

  IV Very shallow High Very high Very high Very high Very high (<30)

  III

  IV IV

  IV IV Source: Ministry of Forestry (2005)

  In Besitang Watershed, the universal soil loss equation (USLE) was used to estimate erosion

rate and predict soil loss. (Wischemeier and Smith, 1978). Rainfall erosivity factor (R) values in

Besitang Watershed were 3,230, 1,474, 1,475, and 1,638 in the respective stations as shown in Table

4.1. and Figure 4.1. The figures show that the values of rainfall erosivity were quite different between

the lower stream area (Pangkalan Susu) and the upland stream area (Batang Serangan). It greatly

influences erosion rate within upland stream to be much higher than lower stream area. This

observation is in agreement with Murtilaksono (1995).

  Soil erodibility factor (K) values are presented in Table 4.4 and Figure 4.2. There are three soil

erodibility values, namely 0.05 (very low), 0.15 (low), and 0.30 (moderate). The largest area is

predominated by soil erodobility value of 0.3 (72%), which is classified as moderate, followed by soil

erodobility value of 0.15 (16%), which is classified as low. Only 13% has soil erodibility value of

0.05 (very low).

  As mention earlier in Chapter 4, the slopes and the slope length factor (L) and slope gradient

factor (S) values are shown in Table 4.8. and depicted in Figure 4.6. It seems that the distribution of

slope class value is an important factor affecting computed erosion rate. The higher the LS value, the

  

higher the erosion rate, other factors being equal. According to Wischmeier and Smith (1976), runoff

from cropland generally increases with increased slope gradient; otherwise the relationship is also

influenced by such factors as type of crop, surface roughness and profile saturation. Under natural

conditions, runoff from row crops is linearly and directly proportional to % slope.

  Crop management factor (C) and soil conservation practices factor (P) in Besitang Watershed

are presented in Table 4.13. and Figure 4.7. They show the existing or present land use of the

watershed which served as basis of values of crop management (C) and soil conservation practices (P)

factors.

  The result of overlaying of the erosion factors (rainfall erosivity, soil erodibility, slope length

factor (L) and slope gradient factor (S), crop management and soil conservation practices as described

above show that distribution of estimate erosion rate in Besitang Watershed as shown in Figure 5.2.

and listed in Table 5.5 . (the classification of erosion rate is presented in Table 5.6). The most

extensive area is erosion rate Class I (72,223 ha or 72%), and the smallest area is occupied by erosion

rate Class V (12 ha or 0.01%). These figures indicate that the mean soil erosion rate which is 23 falls

in Class 2 (15

  • – 60 ton/ha/year). Based on the map, when the slope class is greater than Class 2,

    erosion rate tends to be higher, except the forest area though the other erosion factors are similar. This

    study is in agreement with Murtilaksono (1995).

Table 5.5. Distribution of estimate soil erosion in Besitang Watershed CLASS DESCRIPTION ESTIMATE EROSION AREA (TON/HA/YEAR) Ha %

  I Very low <15 72,222.9

  72.20 II Low 15-60 25,978.2

  25.97 III Medium 60-180 1,511.4

  1.51 IV High 180-480 310.0

  0.31 V Very high >480

  12.3

  0.01 Total 100,034.8 100.00

Figure 5.2 Map of estimate soil erosion in Besitang Watershed, Langkat, North Sumatra, Indonesia

  In fact, large area of watershed is accounted for under erosion rate Class I (72%), less than 15

ton/ha/year (72,223 ha or 72%) and is less than tolerable soil loss (16 ton/ha/year). This small erosion

rate comes from land use of rice field, fish pond, swamp, and forest area. This study is in agreement

with that of Oszaer (1994) which mentioned that the lowest mean soil erosion rate came from forest

area.

  The result of overlaying of the erosion factors (rainfall erosivity, soil erodibility, slope length

factor (L) and slope gradient factor (S), crop management and soil conservation practices) as described

above also show that the lowest and the highest erosion rate in Besitang Watershed are 1,172

ton/ha/year. The mean soil erosion of the watershed is 23 ton/ha/year. The erosion rates indicate that

the crop management and soil conservation practices are urgently needed to be carried out as soon as

possible. High mean of soil erosion is common in unvegetated areas (Table 5.6). The problem is

focused on this bare area.. Quite high erosion rate (69 ton/ha/year) is also observed under bush,

followed by dry land agriculture (60 ton/ha/year) and plantation (15 ton/ha/year). The least mean soil

erosion is in primary forest and secondary forest (6 ton/ha/year and 9 ton/ha/year, respectively),

followed by mangrove forest (5 ton/ha/year), swamp (4 ton/ha/year), wetland agriculture (rice field)

(0.6 ton/ha/year), and fish pond (0.1 ton/ha/year). The lowest mean soil erosion rate is in land cover of

water (0 ton/ha/year). Moreover, distribution of land per decision zone under estimate soil erosion in

Besitang Watershed is as shown in Table 5.7.

Table 5.6. Extent of estimate soil erosion under different land uses in Besitang Watershed

MEAN SOIL TOTAL PRESENT ESTIMATE EROSION (TON/HA/YEAR) TOTAL EROSION SOIL LOSS LAND

  AREA Very low Low Medium High Very RATE (TON/YEAR) USE/LAND (HA) (<15) (15-60) (60-180) (180-480) high (TON/HA/ COVER

  (>480) YEAR ) 47.02 5,029.3

- Bush 1,252.5 3,207.4 522.3

69.3 348,656.0

  • Dry land agriculture 12,481.4 8,914.8 718.5 262.97 22,377.7

  59.7 1,336,804.0

  • - Primary forest 1,516.8 - - - 1,516.8

    6.4 9,723.4
  • Secondary forest 23,400.8 13,170.7 36,571.5

  9.2 335,667.9

  • Mangrove forest 5,361.6

  1.3 5,362.8 4.8 25,449.4 Plantation 16,611.5 410.7 95.5 17,117.7 15.4 264,187.4 - Wetland agriculture - - - - (rice field) 5,410.1

  5,410.1 0.6 3,023.2

  • Swamp 372.5

  372.5 4.2 1,550.1 Unvegetated 1.0 273.3 175.1 - 12.3 461.7 185.2 85,521.5

  • Fish pond 4,559.0

  4,559.0 0.1 651.9 Water

  • 1,255.7 - - - 1,255.7

  0.0

  0.0 Total 72,222.9 25,978.2 1,511.4 310.0 12.3 100,034.8 2,294,798.0 Mean

  22.9 Furthermore, according to Ministry of Forestry (2005), soil erosion classification map was

done by overlay between estimate soil erosion map and soil depth map (Table 5.4). The result from

overlay based on Table 5.4., as shown in Table 5.8. and Figure 5.3. The most extensive area is that

with erosion very low (61,412 ha or 61%), and the smallest area is occupied by erosion very high (13

ha or 0.01%).

Table 5.7. Extent of land use per decision zone under estimate soil erosion in Besitang Watershed ESTIMATE EROSION (TON/HA/YEAR) DZ PRESENT LAND

  Very TOTAL USE/LAND COVER Very low Low Medium High high AREA (<15) (15-60) (60-180) (180-480) (>480) (HA)

  1 Primary forest, secondary

  • forest 3,180.9 9,723.7 12,904.6
  • 2 Secondary forest, bush 691.1 2,454.3

  4.5 0.7 3,150.6

  3 Secondary forest, bush 3,093.1 396.2

  1.1

  0.1 3,490.5

  • 4 Secondary forest, bush, plantation, unvegetated 8,942.4 2,080.3 222.9

  19.3 4.8 11,269.6

  5 Secondary forest, bush, plantation, unvegetated, dry land agriculture, wetland agriculture, water

  7,335.3

  42.2

  24.4 0.3 7,402.2 -

  6 Secondary forest, bush, 7,606.1 518.4 274.4

  34.9 7.5 8441.4 plantation, unvegetated, dry land agriculture, wetland agriculture, water

  7 Secondary forest, plantation, dry land

  • agriculture 634.1 121.4

  79 44.1 878.5

8 Plantation, dry land

  • agriculture, fish pond 1,152.3 936.3 224 129.5 2,442.1

  9 Wetland agriculture, fish pond, bush, plantation, mangrove forest

1,975.2 274.3 2,249.5 - - -

  10 Wetland agriculture, fish pond, bush, plantation, mangrove forest, dry land agriculture

  

4,233.4 358.4 163.9

1.0 4,756.6 -

  11 Wetland agriculture, fish pond, bush, plantation, mangrove forest, dry land agriculture, unvegetated, swamp, water

  • 20,908.9 8,708.2 516.7

  80.2 30,214.1

  12 Wetland agriculture, fish pond, bush, plantation, mangrove forest, dry land agriculture, unvegetated, swamp, water 12,469.9 364.5

  0.5 12,834.9 - - Total 72,222.8 25,978.2 1,511.4 310.0 12.3 100,034.6

  Note: DZ = Decision zone

Table 5.8. Extent of soil erosion in Besitang Watershed AREA SOIL EROSION DESCRIPTION Ha %

  Very low 61,412.1

  61.39 I Low 34,297.1

  34.29 II Medium 3,998.7

  4.00 III High 314.1

  0.31 IV Very high

  13.0

  0.01 Total 100,034.8 100.00

Figure 5.3. Map of soil erosion classification in Besitang Watershed, Langkat, North Sumatra, Indonesia

5.3. Soil Erosion Index

  Soil erosion index is the maximum rate of soil loss that is permissible at each unit of land. It

was determined as the ratio of soil loss rate to the soil loss tolerable limit of each cell. Erosion index

(EI) was estimated using the following model:

  RK ( LS ) CP (5.16)

  EIT where, EI = erosion index R = the rainfall erosivity factor K = the soil erodibility factor LS = the slope length and slope gradient factor C = the cropping management factor P = the erosion control practice factor T = soil loss tolerable limit (Table 5.9.)

  In Indonesia, soil loss tolerable limit is shown in Table 5.9. The EI indicates the ratio of soil

erosion rate to soil loss tolerance. Erosion Index equal to one indicates that soil erosion rate from the

  

area is moderate, less than one indicates that soil erosion rate is several times smaller than soil loss

tolerance, and EI greater than one indicates that soil loss is several times greater than the soil loss

tolerance limit. Based on this criterion, the area having EI less than or equal to one has suitable land

uses, while EI greater than one has unsuitable land uses.

  1.4

Table 5.10. and depicted in Figure 5.4). Based on erosion index map (Figure 5.4), a total area of 79,712 ha or 80% falls under EI less than or equal to one. About 20,323 ha or 20% falls under EI

  Source: Arsyad (2006)

Erosion index map for Besitang Watershed was done based on Table 5.9. and the result as shown in

  2.5 Note : mm depth x soil volume weight x 10 = ton/ha/year. The volume weight is about 0.8 to 1.6 g/cc, how ever, the soil volume weight with high clay content is about 1.0 to 1.2 g/cc.

  8. Depth of soil with permeable sublayer on weathered substratum

  2.0

  7. Depth of soil with moderate permeability sub layer on weathered substratum

  1.6

  6. Depth of soil with slow permeability sublayer on weathered substratum

  5. Depth of soil with impermeable sublayer on weathered substratum

Table 5.9. Soil loss tolerable limit (T) value in Indonesia NO. SOIL NATURE AND STRATUM T VALUE (MM/YEAR)

  1.2

  4. Soil with moderate depth on weathered materials

  0.8

  3. Shallow soil on weathered material

  0.4

  2. Very shallow soil on weathered materials unconsolidated

  0.0

  1. Very shallow soil on the rock

  

more than one. Based on the criterion, the area having EI less than or equal to one has suitable land

uses, while EI greater than one has unsuitable land uses. This implies that large areas within the

Besitang Watershed are under suitable land uses.

Table 5.10. Erosion index under existing land use in Besitang Watershed LAND USE AREA (HA) MEAN EROSION

  INDEX Bush 5,029.3

  4.14 Dry land agriculture 22,377.7

  3.56 Primary forest 1,516.8

  0.38 Secondary forest 36,571.5

  0.56 Mangrove forest 5,362.8

  0.30 Plantation 17,117.7

  0.92 Wetland agriculture (rice field) 5,410.1

  0.04 Swamp 372.5

  0.25 Unvegetated 461.7

  11.08 Fish pond 4,559.0

  0.01 Water 1,255.7

  0.00 Total 100,034.8 Mean

  1.37 Figure 5.4. Map of soil erosion index in Besitang Watershed, Langkat, North Sumatra, Indonesia Table 5.10., shows the mean EI for the various land uses. It shows that high mean erosion

index is common in unvegetated area (11.1), followed by bush (4.1), dry land agriculture (3.6). They

are classified as EI greater than one. The rest of the watershed is classified as EI less than or equal to

one (forest, plantation, field rice, swamp, and fish pond). It can be seen in Table 5.10 and Figure 5.4.

that EI is influenced by vegetative cover and slope class. Unvegetated area has the higher EI value

which is indicative of highest soil loss is in bad protective cover.

  

CHAPTER 6

LAND USE AND LAND COVER CHANGE

  6.1. Methodology Land use and land cover change in the area was determined by overlaying maps of different

periods, using ArcView GIS and ArcGIS (multi-thematic intersect operation). The data gathered were

encoded in the computer using a spreadsheet program (Microsoft Office Excel) and output tables were

exported as data based file (dbf) table. The decrease or increase in area was computed by Microsoft

Office Excel. Then, land use change map was determined by map from different years of acquisition

by using geoprocessing analysis with two thematic layers overlay resulting from different year

interpretation. XTool extension in ArcView can be used to analyze this (Figure 6.1). Microsoft Excel

software supported the conversion of the tabulated data into a dbf IV format. The ArcView was used

in converting the tabulated data into shape files and the merging of the different themes through the

geoprocessing wizard.

  6.2. Forest Land Use Change The existing digital GIS files of land use 2006 maps as is shown in Figure 6.1 from Ministry of

Forestry and the existing digital GIS files of land use 1990 and land use 2001 maps from

  

BAKOSURTANAL were used (Figures 6.2 and 6.3). The distribution of land use area across three-

time periods (1990-2001-2006) is shown in Figure 6.4. The change in land cover/land use was

determined by overlaying land use maps of 1990-2001, 2001-2006, and 1990-2006 using ArcView

GIS and ArcGIS (Table 6.1). The results of overlaid maps were delineated in Figures 6.5, 6.6 and 6.7.

Changes in land use in each decision zone in Besitang Watershed are shown in Appendix Table 10.

Table 6.1 shows that land use change in Besitang Watershed for the past 11-year period (1990-2001) and indicates that mangrove forest and primary forest areas have a high rate of forest disturbance in

  

the forest area. It accounted for -26% or -2,976 ha drop for mangrove forest and -34% or -15,239

ha for primary forest, while there was a steady rise between 1990 and 2001, +100% or +11,911

ha secondary forest (Figure 6.8). For the other kinds of land uses, such as in plantation, there is

increase of +170% or +6,512 ha and +100% or +429 ha in unvegetated, in dry land agriculture, there is

marked increased of +11% or 3,799 ha. In the rice field, a reduction of -59% or -1,285 ha was

obtained. A decline of -159 ha for swamp and -3,869 ha for bush or -100% in both swamp and bush

are also noted (Figure 6.9).

  Existing digital GIS file (land use map)

Land use map-1990 Land use map-2001 Land use map-2006

  Land use map Land use map

  2001 1990

  Land use map Land use map

  2006

  2001

  Overlaying maps Overlaying maps Overlaying maps

  

Land use change map Land use change map

1990 and 2001 2001 and 2006

Figure 6.1. Land use change analysis using GIS

  

Identify and quantify

changes

Land use change map

Figure 6.2. Map of land use 2001 in Besitang Watershed, Langkat, North Sumatra, IndonesiaFigure 6.3. Map of land use 1990 in Besitang Watershed, Langkat, North Sumatra, Indonesia

  Hectare

  45000

  Types of land use:

  40000 Bush

  35000 Dry land agriculture Primary forest

  30000 Secondary forest

  25000 Mangrove forest

  20000 Plantation

  15000 Rice field

  10000 Swamp Unvegetated

  5000 Fish pond

  Year

  Water 1990 2001 2006

Figure 6.4. Distribution of land use areaFigure 6.6. Map of land use changes in 2001-2006 in Besitang Watershed, Langkat, North Sumatra, IndonesiaFigure 6.7. Map of land use changes in 1990-2006 in Besitang Watershed, Langkat, North Sumatra, Indonesia

  

with decreased by -40% or -14,977 ha (Figure 6.9), while rice field increase of +508% or +4,520 ha,

bush increase of +100% or +5,029 ha, swamp increase of +100% or +373 ha, and fish pond increase of

  • -100% or 4,559 ha. Moreover, a rise of -66% or 6,779 ha in plantation and +8% or 33 ha in the

    unvegetated areas were noted (Table 6.1).

  Between 1990-2006 (past 16-year period), the same as with past five-year period (2001-2006),

a high rate of forest disturbance occurred in the primary forest. It decreased by -97% or -42,833 ha,

followed by the mangrove forest -53% or -5,963 ha, while there was a steady rise between 1990 and

2006, 100% or 36,572 ha secondary forest (Figure 6.8). For the other kinds of land uses, dry land

agriculture also declined by -33 or -11,178 ha, while plantation increased by +347% or 13,291 ha,

rice field increase by +149% or +3,235 ha, and swamp increased of +134% or 214 ha (Figure 6.9).

Unvegetated and fish pond increased by +100% or +462 ha and +4,559 ha, respectively. Moreover, a

rise of 30% or 1,160 ha in bush was noted (Table 6.1).

  Overall, the rate of forest disturbance in the period of 1990-2001 is as follows: primary forest

declined by -3% or -1,385 ha annually and mangrove forest by -2% or -270 ha annually. During 2001-

2006 period, forest degradation was down to -19% or -5,519 ha annually in primary forest and

  • –7% or
  • –597 ha in the mangrove forest. During 1990-2006 periods, the rate of forest disturbance was as

    follows: primary forest decline
  • –6% or –2,677 ha annually and mangrove forest by -3% or –373

    ha annually. However, there was an increase in the secondary forest of +9% or +1,083 ha annually,

    +41% or +4,932 ha annually, and + 6% or 2,286 ha annually for three-time periods respectively.

6.3. Processes of Land Use Transitions

  The processes of land use changes across three time periods (1990-2001-2006) are shown in

Table 6.1. Transition matrix of land use change for 1990-2001, 2001-2006, and 1990-2006 is shown in Tables 38, 39, 40, respectively. In 1990, the condition of forest was still very good, because in this

  

time there was no secondary forest. It means that the forest was not yet disturbed. Between 1990-2001

(past 11-year period), about 11,076 ha or 25% of the total primary forest were converted to secondary

forest, followed by conversion to dry land agriculture of 2,675 ha or 6%, converted to plantation, 887

ha or 2%, and converted to unvegetated, 396 ha or 0.9%. Mangrove forest was converted to dry land

agriculture by 3,139 ha or 28% of total mangrove forest, followed by conversion to plantation of 453

ha or 0.04% and converted to unvegetated 24 ha or 0.2%. For the other kinds of land uses, the high

rate of land conversion was from conversion of dry land agriculture area into plantation by 4,848 ha or

15% of total dry land agriculture, followed by conversion of bush to plantation by 1,787 ha or 46% of

total area of bush, conversion of plantation to dry land agriculture by 1,545 or 40% of total plantation,

and conversion of rice field to dry land agriculture by 1,124 ha or 52% of total rice field (Table 6.2).

  During the period 2001-2006, the high rate of forest disturbance came from the conversion of

26,423 ha into secondary forest (91%) of total primary forest, followed by conversion of 1,171 ha

primary forest into bush (4%). Mangrove forest area was transformed into fish pond by 2,149 ha or

26% of total mangrove forest, followed by conversion of 706 ha or 9% into dry land agriculture,

conversion of 397 ha or

Table 6.2. Transition matrix of land use change for 1990-2001

  

LAND USE AREA IN 2001

LAND

TOTAL USE

  1990 1990 DLA PF SF MF P WA S U FP W B

  • B 1,877.2 205.1 1,786.9 3,869.2
  • DLA 26,941.1 -

  9.1 - - - 629.5 1,107.4 4,847.8 20.7 33,555.7

  • PF 2,675.1 29,110.9 11,076.7 198.6 886.6 396.0

  5.5 44,349.4

  • SF
  • -
  • MF 3,139.1 6,757.0 453.1

  23.8 952.8 11,325.7

  • P 1,544.8

  30.0 2,251.6 3,826.4 WA - 1,124.3 - - 158.7 2.1 2,174.9 - - - 889.9 -

  • 46.6 112.3 159.0
  • S - -
  • >U >FP
  • W

  6.1 98.2 670.2 774.5

  Total area in - 2001 37,354.2 29,110.9 11,911.3 8,349.9 10,338.4 889.9 - 428.9 1651.2 100,034.6 - Land use - - change - - (ha) 3,869.2 +3,798.5 15,238.5 +11,911.3 2,975.8 +6,512.1 1,285.0 -159.0 +428.9 0.0 +876.7 Land use change -100 +11.32 -34.36 +100 -26.27 +170.19 -59.08 -100 +100 +113.2 (%)

  Note:: B=Bush, DLA=Dry land agriculture, PF= Primary forest, SF=Secondary forest, MF= Mangrove forest, P= Plantation, WA= Wetland agriculture (rice field) , S=Swamp, U=Unvegetated, FP= Fish pond, W= Water

Table 6.3. Transition matrix of land use change for 2001-2006

  

LAND USE AREA IN 2006

LAND USE AREA TOTAL

  

IN DLA PF SF MF P WA S U FP W AREA

B 2001

  IN 2001

  • <
  • - - 20,418.

  1

  • DLA 1,878.3 77.4 833.5 7,871.4 3,940.6

  96.0 9.3 2,063.6 165.9 37,354.2

  • PF 1,171.2 1,516.8 26,422.9 - - 29,110.9 - - - - SF 999.7 219.5 - 9,382.6 - - - 933.4 302.5

  51.2 22.3 11,911.3 MF 397.0 - - 706.2 4,036.9 82.1 220.2 263.1 293.9 2,149.2 201.2 8,349.9

  • P 393.0 965.9 311.3

  38.5 8,226.2 240.9

  96.0

  41.0 25.6 10,338.4

  • WA 138.1

  687.9 - - -

  64.0 889.9

  • S
  • - -

  U

  18.3 12.8 377.1

  16.2

  0.6

  3.8 0.2 428.9

  • FP
  • - - -

  W

  33.7 55.2 437.7

  4.4

  18.1

  13.5 10.7 237.4 840.5 1,651.2

  Total

area in 22,377. 17,117. 100,034.

2006 5,029.3 7 1,516.8 36,571.4 5,362.8 7 5,410.1 372.5 461.7 4,559.0 1,255.7

  6 Land use - +6,779. - change - 14,976. (ha) +5,029.3 5 27,594.1 +24,660.2 2,987.0 3 +4,520.3 +372.5 +32.8 +4,559 -395.5 Land use change (%) +100 -40.09 -94.79 +207.03 -35.77 +65.57 +507.96 +100 +7.64 +100 -23.95

  Note:: B=Bush, DLA=Dry land agriculture, PF= Primary forest, SF=Secondary forest, MF= Mangrove forest, P= Plantation, WA= Wetland agriculture (rice field) , S=Swamp, U=Unvegetated, FP= Fish pond, W= Water

Table 6.4. Transition matrix of land use change for 1990-2006 LAND USE AREA IN 2006 LAND USE AREA

  TOTAL B

  

IN DLA PF SF MF P WA S U FP W AREA IN

1990 1990

  • B 119.5 804.7 172.1 - - 2,193.5 486.3

  67.9 25.2 3,869.2 - 133. DLA 1,077.7 - 18,497.6 0.1 760.7 9,913.5 2,521.1

  64.5 76.4 510.9 3 33,555.7 PF 2,687.3 1,809.0 1,516.8 36,399.2 109.0 1,143.3 414.5 - 55.4 190.6 24.5 44,349.4

  • SF
  • >- 308. 3,369.
  • MF 775.8 1,213.4 4,398.1 402.4 150.0 262.1

  6 3 11,325.7

  97.7 30.7 3,275.4 209.1 212.9 - - - P 0.6 3,826.4

  0.3

  19.0 30.2 1,627.3 234.9 2,174.9 - - - - - WA 263.2 S - - - -

  • 159.0 159.0 -
  • - - - U -
  • - - - - - - - - - - - - FP 625.

  8.2

  21.9 - - W 76.1 - -

  0.6

  1.8

  40.1

  8 774.4 Total area in 372. 4,559. 1,25 2006 5,029.3 22,377.7 1,516.8 36,571.4 5,362.8 17,117.7 5,410.1 5 461.7 5.7 100,034.6 Land use change +1,160. +36,571. 213. +48 +4,55 - - (ha) 1 11,178.0 42,832.7

  

5 -5,962.8 +13,291.3 +3,235.2

5 +461.7

  9.0

  1.3 Land use change +13 +62. (%) +29.98 -33.31 -96.58 +100 -52.65 +347.36 +148.75 4.34 +10 +100

  14 Note:: B=Bush, DLA=Dry land agriculture, PF= Primary forest, SF=Secondary forest, MF= Mangrove forest, P= Plantation, WA= Wetland

  agriculture (rice field) , S=Swamp, U=Unvegetated, FP= Fish pond, W= Water

  

5% into bush, conversion of 294 ha or 4% into unvegetated, conversion of 263 ha or 4% into swamp,

conversion of 220 ha or 3% into rice field, and conversion of 82 ha or 1% into plantation. For the

other kinds of land uses, the high rate of land conversion was from conversion of dry land agriculture

area. It was transformed into plantation by 7,871 ha or 21% of total dry land agriculture, followed by

conversion into rice field by 3,941 ha or 11%, conversion into fish pond by 2,064 ha or 6%, and

conversion into bush by 1,878 ha or 5% (Table 6.3).

  During a 16-year period (1990-2006), the high rate of forest disturbance came from conversion

of 36,399 ha primary forest into secondary forest (82% of total primary forest), followed by

conversion of 3,370 ha mangrove forest into fish pond (30%), conversion of 2,687 ha or 6% primary

  

forest into bush, conversion of 1,809 ha or 4% primary forest into dry land agriculture, conversion of

1,213 ha or 11% mangrove forest into dry land agriculture, and conversion of 402 ha or 4% into

plantation. For the other kinds of land uses, the high rate of land conversion was from conversion of

dry land agriculture area which was transformed into plantation by 9,914 ha or 30% of total dry land

agriculture, followed by conversion into rice field by 2,521 ha or 8%, conversion into bush by 1,078

ha or 3%, and conversion into fish pond by 511 ha or 2%. Land conversion also occurred from bush

to plantation by 2,194 ha or 57% of total bush, conversion of bush into dry land agriculture by 805 ha

or 21 %, conversion of rice field into bush by 263 ha or 12%, conversion of rice field into fish pond by

235 ha or 11%, and conversion of swamp to plantation by 159 ha or 100% of total swamp. Swamp

remained its original area of 159 ha (Table 6.4).

CHAPTER 7 CLASSIFICATION OF FOREST AND LAND USE In this study, current land use and forest classification map was gathered from secondary data,

  

namely: The Regional Spatial Management Planning (RTRWK) map of Langkat Regency based on

the spatial management plan related to regional land uses (RTRWK) (2002-2011) and the forest

function based on Decrees of Ministry of Forestry Number 44 (2005) that was obtained from the

Ministry of Forestry (2006). Geodatabased in this map consists of spatial management plan related to

regional land uses, such as: protection area, buffer area (limited production), and (normal

production/cultivation area or convertible). Protection forest, nature reserve, and national park

includes protection areas, while cultivation areas consist of spatial management plan related to

regional land uses for wet land agriculture activities, dry land agriculture, plantation, settlement,

fishery, industrial area, trading area, public building area, and production forest area.

  Land use guideline for Indonesia is based on criteria and method assigned to the protection and

production of forest, which are included in Decrees of Ministry of Agriculture Number

837/Kpts/Um/11/80 and Number 63/Kpts/Um/8/81. The factors that determine the land use are field

slope, soil type based on erosion susceptibility, and average rainfall. Land use suitability classification

in this study was defined according to three criteria, namely: slope, soil, and rainfall intensity, each of

which is ranked on a scale of 1 to 5 and used to calculate a site index. Differential weighting is used

to place greatest emphasis on slope and least on rainfall intensity with each class in the scale weighted

by 20 for slope, 15 for soil, and 10 for rainfall intensity. Each factor of land unit is classified on score

value as shown in Tables 7.1., 7.2., and 7.3.

Table 7.1. Class of field slopes and their score value CLASS SLOPE (%) SCORE DESCRIPTION

  1

  20 Flat

  • – 8

  2 8 – 15

  40 Moderate

  3 15 – 25

  60 Steep

  4 25 – 45

  80 Steeper 5 &gt; 45 100 Steepest

Table 7.2. Soil class according to susceptibility to erosion and their score value CLASS SOIL TYPES SCORE DESCRIPTION

  1 Aluvial, gley, planosol, hydromorf

  15 Unsusceptible grey, lateritic

  2 Latosol

  30 Moderate

  3 Brown forest, non calcic brown,

  45 Medium mediteran

  4 Andosol, grumosol, podsolic

  60 Susceptible

  5 Regosol, litosol, organosol, renzina

  75 Very susceptible

  • – 20.7
  • – 27.7

  50 Very high The land use suitability classification is determined by adding all score values, and considers other conditions. Land use suitability in an area based on decree is categorized as follows:

  A watershed is a topographically delineated area that is drained by a stream system of the total

land area to some point on a stream or river. A watershed is a hydrological unit that has been

described and used as a physical-biological unit and also, on many occasions, as a socio-economic-

political unit for planning and management of natural resources. Catchment is often used as a

synonym for watershed. There is no definite size for a watershed; it may be as large as several

  In this study, land use classification map was done through overlay (intersect) all individual

maps (slope map, soil map, and rainfall map). It was done based on Indonesia land use guideline. The

GIS spatial analysis was used to overlay to all thematic maps. The land use classification map is done

by reclassification of total score value. Land use suitability classification map that was done was

needed to compare the land use suitability classification based on secondary data.

  4. The Ministry of Agriculture (formerly having responsibility for forestry affairs) so determines.

The classification as it stands has the advantages of simplicity and of being orientated towards

preventing soil erosion.

  3. The area comprises a 200 m radius around a spring

  2. The soil class is five and the slope is greater than 15%

  1. The slope is greater than 45%

  124, assigned for annual crop land on private land, traditional land and state land. Land is classified as protection forest irrespective of its site index if any of the following conditions is met:

  4. Normal production or convertible (annual crop area): total score value 

  

3. Normal production or convertible (perennial crop area): total score value  124

  2. Buffer area (limited production): total score value = 125

  1. Protection area: total score value  175

  40 High 5 &gt; 34.8

  27.7

  4

  30 Moderate

  20.7

  3

  20 Low

  13.6

  2

  10 Very low

  1 &lt; 13.6

Table 7.3. Average rainfall intensity and their score value CLASS RAINFALL (MM/DAY) SCORE DESCRIPTION

  • – 34.8
  • – 175

7.1. PRESENT LAND COVER AND LAND USE IN BESITANG WATERSHED

  

thousand square kilometers or as small as only a few square kilometers. A watershed is differentiated

from a river basin in that a river basin, with its trunk stream flowing to the sea, may encompass

hundreds of watersheds and many other types of land formations (Sheng, 1990). Brooks et al. (1991)

define a watershed as a topographically delineated area that is drained by a stream system. It is a

hydrologic response, a physical-biological, and a socioeconomic-political unit for management

planning and implementation purposes. As a physical system, watershed has an inventory of soil,

geologic materials, and stored water. Inputs to the system are precipitation and solar radiation, and

outflows are composed of water and solution of soil, rock and organic matter in various forms.

  Watersheds are the most appropriate spatial units on the terrestrial landscape for managing

natural resources. They contain and define the geophysical and ecological processes related to surface

water and its movement to a common point. Human modification of these units, their soils and

vegetation has a direct impact upon the delivery of water, sediments, and nutrients into these river

drainage systems. Watersheds therefore integrate the interrelations between key natural resources and

human activity within a natural geographical and biophysical unit. It is because of these attributes that

water resources can best be managed on the watershed scale and that the concept of watershed

management has emerged (FAO, 2006).

  According to Siligato et al. (2008), the focus of watershed management is to maintain the

ecological health within a watershed by controlling the quantity and quality of water. It is a

participatory and multidisciplinary subject requiring the collaboration of all stakeholders and which is

based on geology, ecology, environmental economics and social sciences. It is the process of guiding

and organizing the use of a watershed’s forests, land and other resources to sustainably provide people

with desired goods and services and without adversely affecting ecology. This recognizes the

interrelationships among land use, soil and water, the linkages between upland and down stream areas,

and the numerous types of stakeholders. Thus, stakeholder participation and collaboration is crucial

for successful and sustainable watershed management.

  According to Sheng (1990), watershed management is the process of formulating, organizing

and guiding, and carrying out a course of action involving the manipulation of resources in a

watershed to provide goods and services without adversely affecting the soil and water base. Usually,

watershed management must consider the social, economic and institutional factors operating within

and outside the watershed area (Brooks et al. 1991; Sheng, 1990). Since watershed management

involves decision making about the use of resources for many purposes, a multi-disciplinary approach

is essential. Work should include government institutions from various disciplines, and should also

involve people from different parts of society. On the other hand, involvement of too many elements

in planning and decision-making can lead to inefficiency and unsatisfactory end results. Participation

  

should be limited to representatives from key government institutions and the local communities

which will be directly affected (Sheng, 1990).

  There are 11 kinds of land cover/land use in Besitang Watershed (Table 7.4). The Besitang

Watershed (Figure 7.1.) is dominated by 43,451 ha (43%) of forest. The following land cover/land use

type of 22,378 ha (22%) and 17,118 ha (17%) are denoted to dry land agriculture and plantation crops,

respectively. The other land cover/land use type in Besitang Watershed are wetland agriculture (rice

field) 5,410 ha (5%), bush 5,029 ha (5%), fish pond 4,559 ha (5%), unvegetated area 462 ha (0.5%),

and swamp 373 ha (0.4%). The water body (river) also included in this area is 1,256 ha (1%).

Besitang Watershed is an ideal watershed based on Republic of Indonesia Law Number 26 (2007)

about Spatial Management (Chapter 17) that a minimum 30% of watershed area is covered by forest

area.

Table 7.4. Land uses and their extent in Besitang Watershed AREA TYPES OF LANDUSE Ha %

  Bush 5,029.3

  5.03 Dry land agriculture 22,377.7

  22.37 Primary forest 1,516.8

  1.52 Secondary forest 36,571.5

  36.56 Mangrove forest 5,362.8

  5.36 Plantation 17,117.7

  17.11 Wetland agriculture (rice field) 5,410.1

  5.41 Swamp 372.5

  0.37 Unvegetated 461.7

  0.46 Fish pond 4,559.0

  4.56 Water 1,255.7

  1.26 Total 100,034.6 100.00 Present land use under decision zone and distribution area in Besitang Watershed is listed in

Table 7.5. In terms of utilization types the existing land cover of the watershed could be named as follows:

  

a. Bush refers to entire former forest that is visible and has grown back (succession), but not optimal,

or land with tree coverage rarely or land with low vegetation dominance.

  

b. Dry land agriculture refers to entire agricultural activities that are visible in the dry land such as:

non irrigated/dry land paddy, field, and farm.

  

c. Primary forest refers to entire lowland forests, plateaus, and mountains that do not appear as

former logging area.

  

d. Secondary forest refers to entire lowland forests, plateaus, and mountains that appear as former

logging area.

  

e. Mangrove forest refers to entire mangroves, nipah, and nibung that is located in areas surrounding the coast. f. Plantation refers to entire plantations that have been planted.

  

g. Wetland agriculture (rice field) refers to entire wet land agricultural activity characterized by

pattern embankment.

  

h. Swamp refers to the entire swamp land that is not timbered or unvegetated or former mangrove

forest.

i. Unvegetated refers to entire open land without vegetation or after land clearing.

  

j. Fish pond refers to the entire area of land fishery activities (fish and prawn) or salting that is

characterized by the pattern embankment and is usually stagnant, suffused and is located around the coast. k. Water refers to entire territorial water including sea water and river.

Figure 7.1. Map of present land use in Besitang Watershed, Langkat, North Sumatra, IndonesiaTable 7.5. Extent of present land use under the different decision zones in Besitang Watershed

  DZ PRESENT LAND USE/LAND COVER AREA Ha %

  1 Primary forest 1,516.8

  1.52 Secondary forest 11,387.8

  11.38 Sub-total 12,904.6

  12.90

  2 Bush

  27.1

  0.03 Secondary forest 3,123.5

  3.12 Sub-total 3,150.6

  3.15

3 Bush

  0.01 Secondary forest 3,476.8

  0.74 Plantation 129.2

  2.54 Plantation 2,700.6

  2.70 Rice field 503.3

  0.50 Secondary forest 2,229.3

  2.23 Unvegetated 113.3

  0.11 Water

  87.0

  0.09 Sub-total 8,441.4

  8.44

  7 Dry land agriculture 740.8

  0.13 Secondary forest

  6 Bush 267.4

  8.5

  0.01 Sub-total 878.5

  0.88

  8 Dry land agriculture 2,052.3

  2.05 Fish pond

  18.5

  0.02 Plantation 371.4

  0.37 Sub-total 2,442.1

  2.44 Note: DZ = Decision zone

  0.27 Dry land agriculture 2,540.5

  7.40

  3.48 Sub-total 3,490.5

  5 Bush 480.7

  3.49

  4 Bush 1,701.7

  1.70 Plantation

  3.2

  0.00 Secondary forest 9,542.2

  9.54 Unvegetated

  22.5

  0.02 Sub-total 1,1269.6

  11.27

  0.48 Dry land agriculture

  0.02 Sub-total 7,402.2

  5.8

  0.01 Plantation

  88.0

  0.09 Rice field

  3.0

  13.6

  6.80 Unvegetated

  0.2

  0.00 Water

  21.4

  0.00 Secondary forest 6,803.2

Table 7.5. Continued DZ PRESENT LAND USE/LAND COVER AREA Ha %

9 Bush 274.3

  0.81 Dry land agriculture 1,348.0

  64.1

  0.06 Unvegetated

  57.7

  0.06 Water

  65.4

  0.07 Sub-total 30,214.1

  30.20

  12 Bush 808.3

  3.37 Mangrove forest 4,472.1

  1.35 Fish pond 3,368.4

  11.12 Rice field 2,693.7

  4.47 Plantation 921.6

  0.92 Rice field 258.2

  0.26 Swamp 308.4

  0.31 Unvegetated 268.0

  0.27 Water 1,081.9

  1.08 Sub-total 12,834.9

  12.83 Total 100,034.6 100.00 Note: DZ = Decision zone

  The land use and forest classification in Besitang Watershed based on Decree of Ministry of

Agriculture (Tables 7.1, 7.2, and 7.3.) and the regional spatial management plan related to regional

land uses (RTRWK) (2002-2011) of Langkat Regency, and the forest classification based on Decrees

of Ministry of Forestry Number 44 (2005) (SK 44) that was obtained from the Ministry of Forestry

(2006) is shown in Table 7.6 and delineated in Figure 7.2. Distribution of land per decision zone

under different land classifications in the area is shown in Table 7.7 and classification of land under

the different land systems, decision zones, land covers, and land classifications in the area is shown in

Appendix Table 11.

  2.69 Swamp

  0.86 Plantation 11,123.7

  0.27 Fish pond 410.8

  2.51 Fish pond

  0.41 Mangrove forest

  12.2

  0.01 Plantation 135.2

  0.14 Rice field 1,417.0

  1.42 Sub-total 2,249.5

  2.25

  10 Bush

  20.3

  0.02 Dry land agriculture 2,507.0

  32.6

  0.73 Mangrove forest 861.8

  0.03 Mangrove forest

  16.7

  0.02 Plantation 1,645.0

  1.64 Rice field 535.0

  0.53 Sub-total 4,756.6

  4.75

  11 Bush 1,435.8

  1.44 Dry land agriculture 13,183.3

  13.18 Fish pond 728.7

7.2. The land use and forest classification in Besitang Watershed

Table 7.6. Distribution of land use and forest based on Decree of Ministry of Agriculture, RTRWK, and SK 44 LAND USE/FOREST CLASSIFICATION* LAND USE/FOREST FUNCTION** AREA Ha %

  Protection area Nature reserve forest 40,790.0

  40.78 Protection forest 1,865.0

  1.86 Sub-total 42,655.0

  42.64 Buffer area/limited production area Limited production forest 17,142.1

  17.14 Sub-total 17,142.1

  17.14 Normal production/cultivation/convertible (for perennial crop and annual crop) Production forest 13,775.2

  13.77 Dry land agriculture 1,684.9

  1.68 Plantation 14,173.8

  14.17 Wetland agriculture 10,604.0

  10.60 Sub-total 40,237.6

  40.22 Total 100,034.6 100.00

  Note: * Based on existing land use policy in Indonesia (Decrees of Ministry of Agriculture Number 837/Kpts/Um/11/80 and Number 63/Kpts/Um/8/81)

  • Based on existing land use policy in Indonesia (RTRWK Langkat Regency, and Decrees of Ministry of Forestry Number 44 (2005))

Figure 7.2. Map of present land use (SK 44 and RTRWK) in Besitang Watershed, Langkat, North Sumatra, Indonesia

  • 12,904.6
  • 3,150.6
  • 3,490.5
  • 11,269.6

  10 Wetland agriculture, fish pond, bush, plantation, mangrove forest, dry land agriculture Limited production forest, production forest, plantation, dry land agriculture, wetland agriculture

  2.44

  9 Wetland agriculture, fish pond, bush, plantation, mangrove forest

  Protection forest, limited production forest, production forest, plantation, dry land agriculture, wetland agriculture

  

2.8

  86.5 2,160.2 2,249.5

  2.25

  4.75

  7 Secondary forest, plantation, dry land agriculture limited production

forest - 878.5 -

878.5

  11 Wetland agriculture, fish pond, bush, plantation, mangrove forest, dry land agriculture, unvegetated, swamp, water Protection forest, limited production forest, production forest, plantation, dry land agriculture, wetland agriculture

  98.3 7,530.3 22,585.5 30,214.1

  30.20

  12 Wetland agriculture, fish pond, bush, plantation, mangrove forest, dry land agriculture, unvegetated, swamp, water Protection forest, limited production forest, production forest, plantation, wetland agriculture

  1,765.9 1,860.2 9,208.8 12,834.9

  12.83 Total 42,655.0 17,142.1 40,237.6 100,034.6 100

  0.88 8 plantation, dry land agriculture, fish pond limited production forest, production forest, plantation

  8.44

  

Note: * Based on existing land use policy in Indonesia (RTRWK Langkat, and Decrees of Ministry of Forestry Number 44 (2005))

  2,975.5 5,465.9 - 8,441.4

Table 7.7 . Allocation of existing land uses in each decision zone in the area DZ PRESENT LAND USE/ LAND COVER LAND USE/ FOREST FUNCTION* LAND USE/FOREST CLASSIFICATION ** TOTAL Protection area (Ha) Buffer area (Ha) Normal production (Ha) Ha %

  1 Primary forest, secondary forest Nature reserve forest

  12,904.6

  12.90

  2 Secondary forest, bush Nature reserve forest 3,150.6

  3.15

  3 Secondary forest, bush Nature reserve forest 3,490.5

  3.49

  4 Secondary forest, bush, plantation, unvegetated Nature reserve forest 11,269.6

  11.27

  5 Secondary forest, bush, plantation, unvegetated, dry land agriculture, wetland agriculture, water Nature reserve forest, limited production forest

  6,997.1 405.2 - 7,402.2

  7.40

  6 Secondary forest, bush, plantation, unvegetated, dry land agriculture, wetland agriculture, water Nature reserve forest, limited production forest

  • -

    908.7 1,533.4 2,442.1
  • -

    6.9 4,749.7 4,756.6

  • Based on existing land use policy in Indonesia (Decrees of Ministry of Agriculture Number 837/Kpts/Um/11/80 and Number 63/Kpts/Um/8/81)

  Based on RTRWK, function area in Besitang Watershed is explained as follows:

 Protection area has the main function of sustaining the biodiversity ecosystem services, cultural

heritage, and geologic monument among others for sustainable development.

  

 Buffer area is the area between the Leuser Ecosystem (KEL) and the Gunung Leuser National Park

(TNGL) that functions as a buffer area.

 Normal production or cultivated area is the area that is allocated for sustainable product for

community needs. The area consists of cultivation for agricultural and non agricultural purposes.

  According to the Ministry of Forestry (2006), forest area is a specific territory of forest

ecosystem determined and or decided by the government as a permanent forest. Such decision is

important to maintain the size of forest area and to ensure its legitimate and boundary demarcation of

permanent forest. The establishment of forest area is also intended to maintain and secure the

existence and integrity of forest area for local income generation and life support system at local,

regional and national level.

  Indonesian forest area is determined by the Minister for Forestry in the format of Ministerial

Decree on the Designation of Provincial Forest Area and Inland Water, Coastal and Marine

Ecosystem. The designation of forest area is formulated based on integrated and harmonized of

Provincial Spatial Planning and Forest Land Use by Consensus (TGHK). The designation of forest

area in some cases also covers inland water, coastal and marine ecosystem that may become part of

Sanctuary Reserve Area (KSA) and Nature Conservation Area (KPA).

  In accordance with the Act on Forestry Number 41 (1999), forest area is categorized as conservation forest, protection forest and production forest, which is defined as follows:

 Conservation forest is a forest area having specific characteristic established for the purposes of

conservation of animal and plant species and their ecosystem.

   Protection forest is a forest area designated to serve life support system, maintain hydrological system, prevention of flood, erosion control, seawater intrusion, and maintain soil fertility.

   Production forest is a forest area designated mainly to promote sustainable forest production. Production forest is classified as permanent production forest, limited production forest, and convertible production forest.

  Conservation forest is divided into:

 Sanctuary reserve area consisting of strict nature reserve and wildlife sanctuary; a sanctuary

reserve shall be a specific terrestrial or aquatic area having specific criteria for preserving

biodiversity of plants and animals as well as ecosystem, which also serves as life support system.

  

 Nature conservation area consists of national park (TN), grand forest park (THR) and nature recreation park (TWA); a nature conservation area shall be a specific terrestrial or aquatic area whose main functions are to serve as life support system and preserve diversity of plant and animal

species, as well as to provide a sustainable utilization of living resources and their ecosystems.

 Game hunting park (TB) is a forest area devoted to game hunting recreation.

7.3. Land Use and Land Use Planning

  Land comprises the physical environment, including climate, relief, soils, hydrology and

vegetation, to the extent that these influence the potential for land use. It includes the results of past

and present human activity (for example: reclamation from the sea and vegetation clearance) and also

adverse results (for example: soil salinization). Purely economic and social characteristics, however,

are not included in the concept of land as these form part of the economic and social context. Land is

thus a wider concept than soil or terrain. Variation in soils, or soils and landforms, is often the main

cause of differences between land mapping units within a local area. It is for this reason that soil

surveys are sometimes the main basis for definition of land mapping units. However, the fitness of

soils for land use cannot be assessed in isolation from other aspects of the environment, and hence it is

land which is employed as the basis for suitability evaluation. (FAO, 1976).

  Suitability evaluation involves relating land mapping units to specified types of land use. The

types of use considered are limited to those which appear to be relevant under general physical,

economic and social conditions prevailing in an area. These kinds of land use serve as the subject of

land evaluation. They may consist of major kinds of land use or land utilization types (FAO, 1976).

  Land use planning as defined by Hudson (1981) and cited by Oszaer (1994) is the conscious

process of selecting and developing the best course of action to accomplish the efficient intensive use

of the land resources. According to Troeh et al. (1980), land use planning has three objectives, that is:

to protect current land use, to guide future development, and to reduce present and future conflicts.

The function of land use planning is to guide decisions on land use in such a way that the resources of

the environment are put to the most beneficial use for man, while at the same time conserving those

resources for the future (FAO, 1976).

  Land use planning based on a scheme of land capability classification is an effort to make

man’s uses of the land as compatible as possible with the environment, thus mitigating on site and off

site effects of uses. Basic to concepts of wise land use is classifying land for uses to control soil

erosion (Oszaer, 1994).

  A part of the important process of land use planning is land evaluation. Land evaluation is

identifying possible changes in land use or management, such as meeting national and local needs, and

estimating the consequence of alternative changes. The basic feature of land evaluation is the

comparison of the requirement of the land use with the resources offered by the land. Fundamental to

  

the evaluation procedures is the fact that different kinds of uses have different requirements (FAO,

1984).

7.4. Land Capability Classification

  The term "land capability" is used in a number of land classification systems notably that of the

Soil Conservation Service of the U.S. Department of Agriculture (Klingebiel and Montgomery, 1961

cited by FAO, 1976). In the USDA system, soil mapping units are grouped primarily on the basis of

their capability to produce common cultivated crops and pasture plants without deterioration of the

soil over a long period of time. Capability is viewed by some as the inherent capacity of land to

perform at a given level for a general use, and suitability as a statement of the adaptability of a given

area for a specific kind of land use (FAO, 1976).

  Land capability is defined as the inherent capacity of land to perform under a given use; thus,

land capability classification (LCC) is the description of a landscape unit in terms of its inherent

capacity to sustain a desirable combination of flora and fauna. It is the first approximation in the

process of subdividing a land use planning unit into land use response units. Important factors in

analyzing land capabilities are slope, elevation, climate, topographic, landform, vegetation, geology,

soils and fauna (Oszaer, 1994).

  According to Wooldridge (1990), LCC is the technical assessment of potential sustainable uses

of landscape units based on inherent characteristics of the land. The current LCC includes eight

classes of land designated by Roman Numerals I thru VIII. The first four classes are arable land

suitable for cropland in which the limitations on their use and necessity of conservation measures and

careful management increase from I thru IV. The criteria for placing a given area in a particular class

involve the landscape location, slope of the field, depth, texture, and reaction of the soil. The

remaining four classes, V thru VIII, are not to be used for cropland, but may have uses for pasture,

range, woodland, grazing, wildlife, recreation, and esthetic purposes (Helms, 1992).

  Furthermore, Helms (1992) indicated that the Universal Soil Loss Equation (USLE) brought

systematic quantification to farm planning for soil conservation. The six factors, namely: rainfall

erosiveness (R), soil erodibility (K), slope length (L), slope steepness (S), cropping and management

practices (C), and supporting conservation practices (P) provide a prediction of expected soil loss, and

indicate a set of alternative conservation measures to reduce soil loss. As in the case of LCC, the

system was developed mainly for the purpose of planning conservation measures, but with the

possibility of measuring the influence of the various factors.

  Land capability classification (LCC) was evaluated based on FAO guidelines (FAO, 1976).

Land capability classification was done is based on criteria in Table 7.8. According to Morgan (1986)

cited by Oszaer (1994), the objective of the classification is to regionalize an area of land into units

  

with similar kinds and degrees of limitations. The basic unit is the capability unit. This consists of a

group of soil types of sufficiently similar conditions in terms of profile form, slope and degree of

erosion as to make them suitable for similar crops and warrant the use of similar conservation

measures. Furthermore, the capability units are combined into sub-classes according to the nature of

the limiting factor and these, in turn, are grouped into classes based on the degree of limitation. The

criteria and description of Land Capability Classes (FAO, 1976; Arsyad, 2006) are described below:

Table 7.8. Criteria of land capability classification in Indonesia MAIN CRITERIA LAND Soil CAPABILITY

  Gravel/ Slope Erosion Soil erodibility Flood depth Texture Drainage CLASS (%) hazard factor (K) hazard rock (cm) I &lt; 3 &gt; 9 Fine, Well Very low Very low (0- None None moderately drained 0.1) - low (F0) fine, (0.11-0.2) medium

  II 3-8 90-50 Fine, Moderatel Low Medium None Low (F1) moderately y well (0.21-0.32) fine, drained medium 8-15 50-25 Fine, Moderatel Medium Moderately high Low Medium

  III moderately y poorly (0.33-0.43)- (1 month per fine, drained High (0.44- year &gt;24 medium, 0.55) hours) moderately

  (F2) coarse

  IV 15-30 &lt; 25 Fine, Poorly Moderately Very high Mediu Moderately moderately drained high (0.56-0.64) m high fine,

  (2-5 month/ medium, year &gt;24 moderately hours) (F3) coarse

  V &lt; 3 (1) (1) Very (2) (1) High High poorly (  6 month/ drained year &gt;24 hours) (F4)

  

VI 30-45 (1) Fine, (2) High (1) (1) (2)

moderately fine, medium, moderately coarse

  

VII 45-65 (1) Fine, (2) Very high (1) (1) (2)

moderately fine, medium, moderately coarse

  

VIII &gt; 65 (1) Coarse Excessivel (1) (1) Very (1)

y drained high Notes: (1) can have no particular characteristic; (2) is not in effect

  Source: Arsyad (2006)

  Class I : Soils that have few limitations for very intensive cultivation Class II : Soils that have moderate limitations that reduce the choice of adapted plants or that require moderate conservation practices. Class III : Soils that have severe limitations that reduce the choice of plants.

  Require intensive conservation practices or both. Class IV : Soils that have severe limitations that reduce the choice of plants.

  Require very intensive management or both. Class V : Soils that are not likely to deteriorate but have other limitations, that are impractical to remove, that limit their use primarily to pasture grasses, range grasses, woodlands, wildlife or aesthetics. Class VI : Soils that have severe limitations that make them generally unsuited to cultivation and limit their use primarily to pasture grasses, range grasses, woodlands, wildlife or aesthetics. Class VII : Soils that have very severe limitations that make them unsuited to cultivation and that restrict their use primarily to pasture grasses, range grasses, woodlands, wildlife or aesthetics. Class VIII : Soils and landforms that have limitations that preclude their use for commercial plants and restrict their use to wildlife, aesthetic, recreation, and/or watersheds.

  

According to Arsyad (2006), the schematic relationship between land capability class and type of land

use suitability is as shown in Figure 7.3. In land capability classification map, land that is Class I is

colored green, Class II is yellow, Class III is red, Class IV is blue, Class V is dark green, Class VI is

orange, Class VII is brown, and no color for Class VIII or white.

  INTENSITY AND TYPE OF LAND USE INCREASE LAND Protection Limit Limit Modera Intensive Limited Moderate Intensive Very CAPABILITY forest/ ed ed te pasture cultivation cultivation cultivation intensive CLASS Nature produ pastu pasture grasses cultivation reserve ction re grasses forest grass es

  I Limitations increase,

  II alternative type of

  III land use

  IV suitability decrease

  V VI

  VII

  VII

  I Source: Arsyad (2006)

Figure 7.3. Schematic relationship between land capability class and type of land use

  The adopted reference in LCC is FAO guidelines (FAO, 1976) and criteria in LCC in Table

  

7.8. The land characteristics are slope, erosion hazard, soil erodibility factor, oxygen availability

(drainage), texture, soil depth, and flood hazard (Appendix Tables 12 and 13).

Capability classification of land under the different land systems, decision zones, and land covers in

the watershed is listed in Table 7.9 and depicted in Figure 7.4. Table 7.9 shows that land capability in

Besitang Watershed ranges from Class II to Class VI. The majority of land in Besitang Watershed

under the land capability Class III (58,986 ha or 59%) which means soils that have severe limitations

that reduce the choice of plants require intensive conservation practices or both, which are spread in

three sub-watersheds, namely: upland stream, middle stream, and lower stream. followed by land

capability Class VI (12,905 ha or 13%) which means soils that have severe limitations that make them

generally unsuited to cultivation and limit their use primarily to pasture grasses, range grasses,

woodlands, and wildlife or aesthetics. It is located in upland stream sub-watershed. Class V (12,835

ha or 13%) which means soils that are not likely to deteriorate but have other limitations, impractical

to remove, that limit their use primarily to pasture grasses, range grasses, woodlands, and wildlife or

aesthetics. It is located in lower stream sub-watershed, and Class II (12,159 ha or 12%) which means

soils that have moderate limitations that reduce the choice of adapted plants or that require moderate

  

conservation practices, which are spread in two sub-watersheds (middle stream and lower stream).

Only 3,151 ha or 3% is land capability Class IV which means soils that have severe limitations that

reduce the choice of plants require very intensive management or both which are spread in upland

stream sub-watershed.

  In Besitang Watershed, majority land capability (Class III) is found under Maput Land System

(spread in DZ 3) with vegetation type moist primary lowland forest, logged forest, sub-montane forest,

bush, alang-alang, shifting cultivation, and settlements. Teweh spread in DZ 4 and DZ 6 with

vegetation type moist primary lowland forest, heath forest, bush, shifting cultivation, and settlements.

Mantalat spread in DZ 7 and DZ 8 with vegetation type moist primary lowland forest, heath forest,

bush, alang-alang, and shifting cultivation. Kahayan spread in DZ 9 with vegetation type swamp

forest, bush, swamp grassland, rainfed wetland rice, rubber coconut estates, and settlements. Teweh

spread in DZ 11 with vegetation type moist primary lowland forest, heath forest, bush, shifting

cultivation, and settlements. Land capability Class IV was found under DZ 1 which is located in Air

Hitam Kanan Land System with vegetation type moist primary lowland forest, sub-montane forest,

montane forest, bush, alang-alang, shifting cultivation and Bukit Pandan Land System with vegetation

type moist primary lowland, sub-montane and montane forest, bush, shifting cultivation and land

cover in this class was forest. Land capability Class V is found under DZ 12 that is located in Kajapah

Land System with vegetation type tidal forest, undifferentiated, mangrove, nipah, halophytes

fishponds (prawns). Land capability Class II is found under Decision Zones 5 and 10 which is located

in Maput Land System with vegetation type moist primary lowland forest, logged forest, sub-montane

forest, bush, alang-alang, shifting cultivation, and settlements. Land capability Class IV is found

under DZ 2 which is located in Pendreh Land System with vegetation type moist primary lowland

forest, sub-montane forest, heath forest, montane forest, bush, and shifting cultivation. There are

several land covers in each class as is shown in Table 7.9.

Table 7.9. Land capability classification (LCC) and suitability land use under the different decision zones (DZ), land covers, and land classifications in the area LCC SUITABLE LAND USE DZ PRESENT LAND USE/ LAND COVER LAND USE/FOREST CLASSIFICATION (SK 44 AND RTRWK ) LAND USE/FOREST CLASSIFICATION (DECREE OF MINISTRY OF AGRICULTURE ) AREA Ha %

  4.75 III Protection forest/nature reserve, limited production forest, limited, moderately, and intensive pasture grasses, limited and moderately cultivation.

  9 Wetland agriculture, fish pond, bush, plantation, mangrove forest

  Protection forest, limited production forest, production forest, plantation, dry land agriculture, wetland agriculture

  Protection area, buffer area, normal production

  2,249.5

  2.25 II Protection forest/nature reserve, limited production forest, limited, moderately, and intensive pasture grasses, limited, moderate, and intensive cultivation.

  10 Wetland agriculture, fish pond, bush, plantation, mangrove forest, dry land agriculture,

  Limited production forest, production forest, plantation, dry land agriculture, wetland agriculture

  Buffer area, normal production 4,756.6

  11 Wetland agriculture, fish pond, bush, plantation, mangrove forest, dry land agriculture, unvegetated, swamp, water

  2,442.1

  Protection forest, limited production forest, production forest, plantation, dry land agriculture, wetland agriculture

  Protection area, buffer area, normal production

  30,214.1

  30.20 V Protection forest/nature reserve, limited production forest, limited, moderately, and intensive pasture grasses.

  12 Wetland agriculture, fish pond, bush, plantation, mangrove forest, dry land agriculture, unvegetated, swamp, water

  Protection forest, limited production forest, production forest, plantation, wetland agriculture

  Protection area, buffer area, normal production

  12,834.9

  12.83 Total

  2.44 III Protection forest/nature reserve, limited production forest, limited, moderately, and intensive pasture grasses, limited and moderately cultivation.

  8 plantation, dry land agriculture, fish pond limited production forest, production forest, plantation buffer area, normal production

  VI Protection forest/nature reserve, limited production forest, limited and moderately pasture grasses.

  11.27 II Protection forest/nature reserve, limited production forest, limited, moderately, and intensive pasture grasses, limited, moderate, and intensive cultivation.

  1 Primary forest, secondary forest Nature reserve forest Protection area

  12,904.6

  12.90 IV Protection forest/nature reserve, limited production forest, limited, moderately, and intensive pasture grasses, limited cultivation.

  2 Secondary forest, bush Nature reserve forest Protection area 3,150.6

  3.15 III Protection forest/nature reserve, limited production forest, limited, moderately, and intensive pasture grasses, limited and moderately cultivation.

  3 Secondary forest, bush Nature reserve forest Protection area 3,490.5

  3.49 III Protection forest/nature reserve, limited production forest, limited, moderately, and intensive pasture grasses, limited and moderately cultivation.

  4 Secondary forest, bush, plantation, unvegetated Nature reserve forest Protection area 11,269.6

  5 Secondary forest, bush, plantation, unvegetated, dry land agriculture, wetland agriculture, water

  0.88 III Protection forest/nature reserve, limited production forest, limited, moderately, and intensive pasture grasses, limited and moderately cultivation.

  Nature reserve forest, limited production forest

  Protection area, buffer area 7,402.2

  7.40 III Protection forest/nature reserve, limited production forest, limited, moderately, and intensive pasture grasses, limited and moderately cultivation.

  6 Secondary forest, bush, plantation, unvegetated, dry land agriculture, wetland agriculture, water

  Nature reserve forest, limited production forest

  Protection area, buffer area 8,441.4

  8.44 III Protection forest/nature reserve, limited production forest, limited, moderately, and intensive pasture grasses, limited and moderately cultivation.

  7 Secondary forest, plantation, dry land agriculture limited production forest buffer area

  878.5

  100,034.6 100

Figure 7.4. Map of land capability classification in Besitang Watershed, Langkat, North Sumatra, Indonesia

7.5. Land Suitability Classification (LSC)

  Land suitability is the fitness of a given type of land for a defined use. The land may be

considered in its present condition or after improvements. The process of land suitability classification

is the appraisal and grouping of specific areas of land in terms of their suitability for defined uses

(FAO, 1976). Land use suitability is a critical factor in land use suitability assessment. This is so

because any land use that is not sustainable reduces the productive capacity of the landscape and is

environmentally destructive (Carpenter, 1981). Furthermore, land suitability assessment is an

important activity to land use allocation. Gregorio (1990) cited by Oszaer (1994) revealed that land

suitability assessment is carried out by matching land use requirements to landscape unit

characteristics and measuring the environmental responses of the landscape to land use management

technologies. However, it is important to note that a given landscape unit may not be suitable for a

given land use management technology.

  Land use suitability assessment is a rating of response of landscape unit to sustain uses. Land

suitability refers to the fitness of a given area for a specific land use (FAO, 1976). According to Duc

(2006), land use suitability is the ability of a given type of land to support a defined use. The process

of land suitability analysis involves evaluation and grouping of specific areas of land in terms of their

suitability for a defined use. The principles of sustainable development make land use suitability

  

analysis increasingly complex due to consideration of different requirements/criteria. It includes

consideration not only of inherent capacity of a land unit to support a specific land use for a long

period of time without deterioration, but also the socio-economic and environmental costs.

Description for land suitability classification is structured according to four levels of decreasing

generalization, namely, order, class, subclass and unit (FAO, 1976).

  The process of land use suitability classification is the appraisal and grouping of specific areas

of land in terms of their suitability for defined uses. The structure of suitability classification is shown

in Table 7. 10.

Table 7.10. Structure of the land suitability classification LAND LAND LAND LAND SUITABILITY SUITABILITY SUITABILITY SUITABILITY ORDERS CLASSES SUB-CLASSES UNITS

  Reflecting the kinds Reflecting degrees Reflecting kinds of Reflecting minor of suitability of suitability within limitation or main differences in orders kind of required improvement management within measures required sub-classes within classes

  S= Suitable S1= Highly suitable for example: for example: S2= Moderately S2n, S3me possible S2e-1, S2e-2, S2n-1

suitable codes of limitations:

S3= Marginally m= Moisture suitable availability o= Oxygen availability n= Nitrogen availability c= Climate hazards e= Resistance to erosion w= Work ability N= Not Suitable N1= Currently not for example: suitable N1m, N1me N2= Permanently not suitable

  Source: FAO (1976) Based on the scale of measurement of the suitability there are two types of classifications

(FAO, 1976), namely: qualitative and quantitative. In qualitative classification, the classes are

evaluated based on physical production potential of land, commonly employed in reconnaissance

studies. It is used to evaluate environmental, social and economic criteria. In quantitative

  

classification, the classes are defined in common numerical terms; where comparison between the

objectives is possible. Here considerable amount of economic criteria are used.

  In Indonesia, the classification system based on the American Soil Conservation System was

adapted to Indonesia conditions. The main objective of this system is to combine the most intensive

and productive forms of land use together with the appropriate conservation method necessary to

avoid erosion. It recommends using tree crops on land too steep for arable farming of food crops. The

two main considerations which determine erosion susceptibility are the slope gradient and the depth of

the soil, which are interrelated. The least intensive type of land use, that would be protection forest,

should be assigned to land most susceptible to erosion. On the other hand, the most intensive type of

land use, which is farming of annual crops, should be assigned to land, least susceptible to erosion.

Between these extremes soil conservation measures should be applied in accordance to the erosion

susceptibility of the land (Oszaer, 1994).

  Gregorio (1990) cited by Oszaer (1994) revealed that land suitability assessment is carried out

by matching land use requirements to landscape unit characteristics and measuring the environmental

responses of the landscape to land use management technologies. Land suitability classification (LSC)

was evaluated based on matching method that reference and criteria was adopted from the Land

Suitability for Agricultural Plants by the Centre for Soil and Agroclimate Research, Bogor (2003).

The process of land suitability classification is the appraisal and grouping of specific areas of land in

terms of their suitability for defined uses. The methodology adopted the land utilization type’s

concept which allows two orders of suitability, namely: suitability (S) and not suitability (N) and the

three classes of the order suitability, namely: S1, S2, and S3, and two classes under not suitability,

namely N1 and N2. The land suitability ratings have been defined by FAO (1976) for international

use as follows: Class S

  1 : Highly suitable, land having no significant limitations to sustain application of a given

  use or only minor limitations that will not significantly raise inputs above and acceptable level. Class S

  2 : Moderately suitable, land having limitations that in aggregate are moderately severe

  for sustained application of given use. The limitation will reduce productivity or benefits and increase required inputs to the extent that the overall advantages to be gained from the use, although still attractive, will be appreciably inferior to that expected on class S1 land.

3 Class S : Marginal suitable, lands having limitations, which are severe for sustained application

  of a given use and will so reduce productivity or benefits or increase required inputs that this expenditure will be only marginally justified. Class N

  1 : Currently not suitable, lands having limitations which may be surmountable in time but which cannot be corrected with existing knowledge at currently acceptable costs.

  The limitations are so severe as to preclude successful sustained use of the land type in a given manner. Class N

  2 : Permanently not suitable, lands having limitations which appear too severe as to preclude any possibility of successful sustained use of the land in a given manner.

  This study used one class under not suitable, which is N (not suitable). The criteria of land

suitability classification for each crop were different (The Centre for Soil and Agroclimate Research,

2003) (Appendix Tables 12 to 24). For the purpose of this study, the actual and potential land

suitability in the area were determined for several annual crops, such as: paddy, corn, and soybean and

for several estate and silvicultural plants, such as: oil palm, rubber, cacao, coffee, coconut, durian,

rambutan, citrus, and mango.

  The adopted reference and criteria in LSC is the land suitability for agricultural plants (the

Centre for Soil and Agroclimate Research, Bogor, 2003). The land quality and characteristics are

temperature, water availability (annual rainfall), oxygen availability (drainage), root zone medium

(texture, soil depth), nutrients retention (cation exchange capacity, base saturation, pH, C-organic),

sodicity (alkalinity), terrain (slope, soil erosion), and flood hazard (inundation) (Appendix Tables 14

to 26).

Land suitability classification to several annual crops in Besitang Watershed is listed in Table 7.11 and

delineated in Figures 7.5 to 7.8. LSC to several estate and silvicultural plants in Besitang Watershed is

listed in Table 7.12 and delineated in Figures 7.9 to 7.17. In DZ 1 with Bukit Pandan Land System,

both annual crops (upland rice, irrigated paddy rice, corn, and soybean) and several estate and

silvicultural plants (oil palm, rubber, cacao, coffee, coconut, durian, rambutan, citrus, and mango) are

not suitable (N). In general, slope steepness is the main limitation of land suitability classification in

Decision Zone 1. In DZ 12 with Kajapah Land System, in general both annual crops (upland rice,

irrigated paddy rice, corn, and soybean) and several estate and silvicultural plants (oil palm, rubber,

cacao, coffee, coconut, durian, rambutan, citrus, and mango) also are not suitable (N). Oxygen

availability and flood hazard are the dominant limiting factors.

  Actual and potential land suitability classes of annual crops such as upland rice, irrigated

paddy rice, corn, and soybean are different from each other. Similarly with several annual crops in

Besitang Watershed, actual and potential land suitability classes of several estate and silvicultural

plants (oil palm, rubber, cacao, coffee, coconut, durian, rambutan, citrus, and mango) also are different

from each other.

Table 7.11. Suitability of land under the different decision zones to several annual crops in the area

CROPS LAND SUITABIL

  2

  S3.wa S3.wa.r c.fh

  .eh S3.wa S3.wa S3.wa. nr. fh

  S3.wa .nr S3. wa. nr.rc .fh

  S3.wa . nr.fh S3.wa. nr.fh

  S3.wa .nr.fh S3.wa. nr S3.wa. nr.rc.fh

  N.oa.fh

  Potential

  N.eh S3.wa S3.wa S3.wa S3.wa.f h S3.wa S3. wa.r c.fh

  S3.wa .fh

  S3.wa.f h S3.wa .fh

  N.oa.fh

  S3.rc.fh

  Soybean Actual N.eh S3.eh S2.eh S2.eh S3.nr.f

  h S3.nr S3.r c. nr.fh

  N.fh S3.nr.f h S3.nr.

  Fh S3.nr N.fh

  N.oa.fh

  Potential N.eh S1 S1 S1 S3.fh S2.tc S3.r

  c.fh N.fh S3.fh S3.fh S2.tc

  N.fh N.oa.fh

  Note: S1 = highly suitable, S2 = moderately suitable, S3 = marginal suitable, N = not suitable tc = temperature, wa = water availability, rc = root zone medium, nr = nutrient retention, eh = erosion hazard, fh = flood hazard

  Corn Actual N.eh S3.wa

  S1 S1 S1 S2.rc S3.rc

  3

  c.nr S3.nr. fh

  4

  5

  6

  7

  8

  9

  10

  11

  12 Upland rice Actual N.eh S2.eh S2.eh S2.eh S3.nr S3.nr S3.r

  S3.nr S3.nr S3.nr S3.rc.n r.fh

  1

  N.fh N.oa.fh

  Potential N.eh S1 S1 S1 S2.fh S1 S3.r

  c S3.fh S2.fh S2.fh S1

  S3.rc.f h N.fh N.oa.fh

  Irrigated paddy rice Actual N.eh N.eh N.eh N.eh S3.nr.e

  h S3.nr. eh N.eh

  S3.r c.nr

  S3.nr S3.nr S3.nr S3.nr.e h S3.rc.n r

  S3.rc.nr.fh

  Potential N.eh N.eh N.eh N.eh S2.rc S2.rc

  N.eh S3.r c

Figure 7.5. Map of suitability of land to upland rice in Besitang WatershedFigure 7.6. Map of suitability of land to irrigated paddy rice in Besitang WatershedFigure 7.7. Map of suitability of land to corn in Besitang WatershedFigure 7.8. Map of suitability of land to soybean in Besitang WatershedTable 7.12. Suitability of land under the different decision zones to several estate and silvicultural plants in the area

PLANTS LSC DECISION ZONE

  2

  Potential N.eh S2.rc S2.r

  S3.n r.fh

  S3.n r S3.nr S3.rc. nr

  c.eh S2. rc. eh

  Rambutan Actual N.eh S3.eh S2.r

  N.fh S2.wa , S1 N.fh N.fh N.fh N.fh S1, N.fh N.oa. fh

  c S2. rc

  N.fh S3.nr N.fh N.fh N.fh N.fh S3.nr N.fh N.oa. fh

  Potential N.eh S2.rc S2.r

  c.eh S2. rc. eh

  Durian Actual N.eh S3.eh S2.r

  N.fh N.oa. fh

  S3.f h S1 S3.fh N.fh S3.fh S3.fh S1

  c S2. rc

  Potential N.eh S2.rc S2.r

  S3.fh N.fh S3.fh S3.fh S2.nr.eh N.fh N.oa. fh

  S3.nr S3.nr S3.nr S3.rc.nr.fh N.oa. fh

  c S2. rc

  c.eh S2. rc. eh

  a.eh S3. wa S3. wa

  Note: S1 = highly suitable, S2 = moderately suitable, S3 = marginal suitable, N = not suitable

tc = temperature, wa = water availability, rc = root zone medium, nr = nutrient retention, eh = erosion hazard, fh = flood

hazard

  N.fh S3.wa N.fh N.fh N.fh N.fh S3.wa N.fh N.oa. fh

  a S3. wa S3. wa

  Potential N.eh S3.w

  N.fh N.fh N.fh N.fh S3.wa.nr N.fh N.oa. fh

  N.fh S3.wa .nr

  Mango Actual N.eh S3.w

  S2.f h S1 S3.rc S3.f h

  N.fh S1 N.fh N.fh N.fh N.fh S1 N.fh N.oa. fh

  c S2. rc

  Potential N.eh S2.rc S2.r

  N.fh S3.nr N.fh N.fh N.fh N.fh S3.nr N.fh N.oa. fh

  c.eh S2. rc. eh

  Citrus Actual N.eh S3.eh S2.r

  S2.fh S2.fh S1 S3.rc.fh N.oa. fh

  S3.f h S2.nr. eh

  Coconut Actual N.eh S3.eh S2.r

  3

  12 Potential N.eh S3.rc S2.r

  Potential N.eh S2.w

  N.fh S3.oa.fh S3.fh S2.wa.nr.eh S2.tc.wa.nr.eh N.fh N.oa. fh

  S3.f h S2.wa . nr.eh S3.rc. fh

  S2.fh S2.fh S1, S3.rc.fh N.oa. fh Rubber Actual N.eh S3.eh S2. wa. rc.eh S2. wa. rc. eh

  S2.f h S1 S3.rc S3.f h

  c S2. rc

  11

  S2. wa. rc

  10

  9

  8

  7

  

6

  5

  4

  a.rc S2. wa.r c

  S3.f h S2.wa S3.rc. fh

  1

  Coffee Actual N.eh S3.eh S2.e

  S3.f h S2.tc. wa S3.rc. fh

  N.eh S2.rc S2.r c S2. rc

  Potential

  N.fh S3.oa.fh S3.fh S3.nr S2.tc.nr.eh N.fh N.oa. fh

  S3.n r.fh S3.nr S3.rc. fh

  h.rc S2. eh. rc

  N.fh S3.fh S3.fh S1 N.fh N.oa. fh

  N.fh S3.fh S3.fh S2.wa S2.tc.wa N.fh N.oa. fh

  S3.f h S1 S3.rc. fh

  c S2. rc

  Potential N.eh S2.rc S2.r

  N.fh S3.nr.fh S3.nr.fh S3.nr N.fh N.oa. fh

  S3.n r.fh S3.nr S3.rc. nr.fh

  h.rc S2. eh. rc

  Cacao Actual N.eh S3.eh S2.e

  N.fh S3.fh S3.fh S2.tc N.fh N.oa. fh

Figure 7.10. Map of suitability of land to rubber in Besitang WatershedFigure 7.12. Map of suitability of land to coffee in Besitang WatershedFigure 7.13. Map of suitability of land to coconut in Besitang WatershedFigure 7.15. Map of suitability of land to rambutan in Besitang WatershedFigure 7.14. Map of suitability of land to durian in Besitang WatershedFigure 7.17. Map of suitability of land to mango in Besitang WatershedFigure 7.16. Map of suitability of land to citrus in Besitang Watershed Actual land suitability of upland rice under the different decision zones is N.eh (DZ 1), N.fh

and N.oa.fh (DZ 12), S3.rc.nr.fh (DZ 11), S3rc.nr (DZ 7), S3.nr.fh (DZ 8), S3.nr (DZ 5, 6, 9, 10, and

11), and moderately suitable (S2.eh) with slope limitation (DZ 2, 3, 4). The limiting factors in upland

rice could practically be managed by the farmers themselves. Nutrient availability, particularly

nitrogen and phosphorus could be supplied with higher doses of fertilizers (urea and superphosphate).

Bench terraces were constructed in order to counter slope limitation. Farmers even construct bench

terraces on extremely steep slope with shallow soil depth, though the cultivated land becomes narrow,

sometimes only a meter in width of ten meters in length. The most difficult constrain to counter is

root zone medium and flood hazard because they are natural limitations. The rating could be upgraded

by some inputs to correct the limitation. For upland rice, the rating could be improved by applying

fertilization (organic and inorganic) such as by urea and superphosphate fertilizers, while limitation

factor slope and soil erosion can be solved by soil and water conservation practice, such as terracing

which should be applied on steep slope areas. Hence, potential LSC for upland rice in DZ 11 could

become S3.rc.fh. S3.rc.nr (DZ 7), in DZ 8 could become S3.fh, in DZ 5, 9, 10, and 11 could become

S2.fh, in DZ 2, 3, 4, and 6 could become S1.

  Actual land suitability of irrigated paddy rice under the different decision zones is classified as

not suitable (N.eh) for DZ 1, 2, 3, 4 and 6, with slope limitation, S3.nr.eh for DZ 5, 6, and 11, S3 rc.nr

for DZ 7 and 11, S3.rc.nr.fh for DZ 12, and S3.nr for DZ 8, 9 and 10. The limiting factors in upland

rice could practically be managed by the farmers themselves. For irrigated paddy rice, the rating could

be improved by fertilization (organic and inorganic) such as by urea and superphosphate fertilizers,

while limitation factor slope and soil erosion can be solved by soil and water conservation practice,

such as terracing which should be applied on steep slope areas. The most difficult constrain to counter

is root zone medium and flood hazard because they are natural limitations. Hence, potential LSC for

irrigated paddy rice in DZ 5, 6, 11 could become S2.rc, in DZ 7 and 11 could become S3.rc, in DZ 12

could become S3.rc.fh, and in DZ 8, 9, and 10 could become S1.

  Actual land suitability of corn under the different decision zones is not suitable with slope

limitation (N.eh) in DZ 1, not suitable with oxygen availability (drainage) and flood hazard limitations

in DZ 12. In general, actual land suitability for corn is marginally suitable (S3) with water availability

(wa) limitations, followed by nutrient retention (nr), flood hazard (fh), root zone medium (rc), and

slope (eh). The rating could be upgraded by some inputs to correct the limitation. As with rice, the

rating could be improved by fertilization and limiting factor of slope connected by terracing. Hence,

potential LSC for corn in DZ 2, 3, 4, 6, and 11 could become S3.wa, in DZ 5, 8, 9, and 10 could

become S3.wa.fh, and in DZ 7 and 11 could become S3.wa.rc.fh.

  Actual land suitability of soybean under the different decision zones is classified as not suitable

with slope limitation (N.eh) in DZ 1, not suitable with flood hazard limiting factor (DZ 8 and 11), and

  

not suitable with oxygen availability (drainage) and flood hazard limitations in DZ 12. Majority of

actual land suitability for corn is marginally suitable (S3) (DZ 2, 5, 6, 7, 9, 10, and 11) with slope ,

nutrient retention (nr), flood hazard (fh), and root zone medium (rc) limiting factors, followed by

moderately suitable (S2) (DZ 3 and 4) with slope as limiting factor. Rating can be improved also by

fertilization and terracing. Hence, potential LSC for soybean in DZ 5, 9, and 10 could become S3.fh,

in DZ 7 could become S3.rc.fh, in DZ 6 and 11 could become S2.tc and in DZ 2, 3, and 4 could

become highly suitable (S1).

  Actual land suitability class of rubber under the different decision zones is not suitable with

slope limitation (N.eh) in DZ 1, not suitable with flood hazard limiting factor (DZ 8 and DZ 11), and

not suitable with oxygen availability (drainage) and flood hazard limitations in DZ 12. In general,

actual land suitability for rubber is marginally suitable (S3) and moderately suitable (S2). There are

several limiting factors, namely: water availability (wa) limitations, oxygen availability, temperature,

nutrient retention (nr), flood hazard (fh), root zone medium (rc), and slope. In DZ 2 actual land

suitability is S3.eh, DZ 3 and 4 actual land suitability was S2.wa.rc.eh, DZ 5 and 10 land suitability

was S3.fh, DZ 6 and 11 land suitability was S2.wa.nr.eh, DZ 7 land suitability was S3.rc.fh, DZ 9

land suitability was S3.oa.fh, and DZ 11 land suitability was S2.tc.wa.nr.eh. The rating can be

improved by fertilization, terracing, and ditch/channel drainage. Hence, potential LSC for rubber in

DZ 2, 3, and 4, could become S2.wa.rc, in DZ 5, 9, and 10 could become S3.fh, and in DZ 6 and 11

could become S2.wa, in DZ 7, could become S3.rc.fh, in DZ 11, and could become S2.wa.tc.

  Actual land suitability class of cacao under the different decision zones is N, S3, and S2.

Majority of actual land suitability class of cacao under the different decision zones is marginally

suitable (S3) (DZ 2, 5, 6, 7, 9, 10, and 11.). Only in DZ 3 and 4 that moderately suitability (S2) and

only in DZ 1, 8, and 12 that not suitable. There are several limiting factors for cacao in Besitang

Watershed, namely: slope (eh), nutrient retention (nr), flood hazard (fh), and root zone medium (rc).

  

The rating can be improved by fertilization and terracing. Hence, potential LSC for cacao could

become S1 (DZ 6 and 11), S2 (DZ 2, 3, and 4), and S3 (DZ 5, 7, 9, and 10).

  Similar to cacao, actual land suitability class of coffee under the different decision zones is N,

S3, and S2. Majority of actual land suitability class of coffee under the different decision zones is

marginally suitable (S3) (DZ 2, 5, 6, 7, 9, 10, and 11). Only in DZ 3 and 4 that moderate suitability

(S2) and only in DZ 1, 8, and 12 that is not suitable. There are several limiting factors for coffee in

Besitang Watershed, namely: slope (eh), oxygen availability (oa), temperature (tc), nutrient retention

(nr), flood hazard (fh), and root zone medium (rc). The decision zone that is located in upland stream

sub-watershed, generally is limited by slope, in the middle stream is nutrient retention, and the lower

stream is nutrient retention and flood hazard. The rating can be improved by fertilization, terracing,

  

and ditch/channel drainage. Hence, potential LSC for coffee could become S2 (DZ 2, 3, 4, 6, and 11)

and S3 (DZ 5, 7, 9, and 10).

  Actual land suitability class of coconut under the different decision zones is N, S3, and S2.

Majority of actual land suitability class of coconut under is marginally suitable (S3) that was located in

DZ 2, 5, 7, 9, and 10, followed by moderately suitable (S2) that was located in DZ 3, 4, 6, and 11 and

not suitable that was located in DZ 1, 8, 11, and 12. There are several limiting factors for coconut in

Besitang Watershed, namely: slope (eh), nutrient retention (nr), flood hazard (fh), and root zone

medium (rc). The rating can be improved by fertilization and terracing steep areas. Hence, potential

LSC for coconut could become S1 (DZ 6 and 11), S2 (DZ 2, 3, and 4), and S3 (DZ 5, 7, 9, and 10)

similar with potential LSC for cacao.

  Actual land suitability class of durian and citrus are the same under the different decision zones

is that N, S3, and S2. Most of the actual land suitability class of durian and citrus under the different

decision zones is not suitable (N), with limiting factors such as slope, flood hazard, and drainage,

followed by marginally suitable (S3) and moderately suitable (S2) with limiting factors such as: slope,

nutrient retention (nr), and root zone medium (rc). The decision zones that is located in upland stream

sub-watershed, generally is limited by slope, in the middle stream and the lower stream are flood

hazard. Hence, potential LSC for durian and citrus could become S1 (DZ 6 and 11) and S2 (DZ 2, 3,

and 4).

  Actual land suitability class of rambutan under the different decision zones is N, S3, and S2.

Majority of actual land suitability class of rambutan under the different decision zones is marginally

suitable (S3) with limiting factors, namely: slope, nutrient retention (nr), flood hazard (fh), and root

zone medium (rc). Only in DZ 3 and 4 that moderately suitability (S2) and only in DZ 1 and 12 that

not suitable. Rating as in the other cited crops can be improved by fertilization and terracing. Hence,

potential LSC for rambutan could become S1 (DZ 6 and 11), S2 (DZ 2, 3, 4, 5, 9, and 10), and S3 (DZ

7, 8, and 11).

  Actual land suitability class of mango under the different decision zones is N and S3.

Generally, the actual land suitability class under the different decision zones is not suitable for mango

with limiting factors such as slope, flood hazard, and drainage, followed by marginal suitable (S3)

with limiting factors such as: slope, nutrient retention (nr), and water availability (wa). The rating

could be improved by applying fertilizer, while limiting factor slope can be solved by soil and water

conservation measures. The most difficult constraint to counter is water availability because they are

natural limitations. Hence, potential LSC for mango could become marginal suitable (S3) with

limiting factor water availability (wa).

CHAPTER 8 LAND USE ALLOCATION According to Bantayan (2006), land use allocation is a spatial allocation exercise, the best way

  

to have the physical and subjective models working together within the common framework of GIS to

provide the solutions. The classification procedure puts the alternatives in classes signifying the

degree of suitability of a particular alternative to the land units. The land use suitability classification

may have the following categories: high suitability (S1), moderate suitability (S2), low suitability (S3),

and very low suitability (S4). These were expressed as relative weight of alternatives by decision zone.

Based on these priorities, the degrees of preference for the major land use groups can be expressed as

degrees of land use suitability for each decision zone.

  There are three kinds of land use allocation (Bantayan, 2006), namely:

  1. Allocation for Single Use The process of allocation depends on whether or not the results of the land use suitability

classification are regarded in absolute terms. When this is the case, a process of exclusion is

implemented where only the alternative that receives the highest suitability is chosen for allocation

and the rest are excluded in regard to the land units in question. This approach follows the single use

concept of the land use allocation.

  2. Allocation for Multiple-Use An alternative approach is to recognise the gradation in the suitability classification. Such an

approach follows the multiple-use concept for land use allocation. The objective of multiple-use

management is to manage the natural resources in a manner that maximises the benefits which can be

derived. Land use allocation for multiple-use can be accomplished by concurrent and continuous use

of several natural resource products obtainable on a particular land unit requiring the production of

several goods and services from the same area.

  The allocation follows the assumption that land units classified as suitable for a particular land

use at the lower end of the scale are also capable of being utilised for all the other land uses. As the

limitations increase, land use options decrease with regard to the land units in question. These

limitations may come in the form of slope angle, climate, flood and erosion risk, and soil properties

(Davidson, 1980 cited by Bantayan, 2006). Multiple-use may either be resource-oriented or area

oriented. Resource-oriented multiple use management is characterised by the production of one or

more products from a natural resource. Area-oriented multiple-use refers to the production of a mix of

products and amenities from a given land area (Brooks et al., 1991).

3. Allocation for Multiple-Use using Graduated Method

  Another approach to land use allocation is to use a graduated method. This approach also

follows the multiple-use concept management. Initially, the overall ranking of the alternatives is

established from analysis of land use suitability. The degrees of suitability form the basis for

determining the best area for allocating the alternatives. On the basis of satisfaction of certain

requirements, the best areas are first allocated to the alternative that receives the highest overall

ranking. Allocation proceeds to the next highest ranked alternative until all the alternatives are

allocated. In general, there are four requirements that may be used to allocate the alternatives to the

land units, namely: a. Land condition . The condition of the land is an important indicator of its susceptibility to

degradation. Thus, identification of critical and non critical areas is necessary to prescribe the most

appropriate alternative. For instance, critical areas should be protected and set aside for rehabilitation.

  b. Area. Certain alternatives require a minimum and maximum area for optimum performance.

  

For certain species of wildlife, for example, a minimum area is required for survival. This involved

maintaining a certain amount of vegetation of a specific area.

  c. Contiguity . This is another important rule for allocation. In order for certain species to

survive, connectivity of habitat must be ensured. This should allow the movement of the species from

one corridor to another.

  d. Proximity. It is especially important along rivers and streams. The soils around these areas

are most susceptible to erosion. Buffers should be established to avoid soil erosion, sedimentation and

other forms of degradation. Distance to roads is also important especially for transporting goods and

services.

8.1. Potential Land Use Suitability Classification

  The potential land use suitability resulted from integrated approach. The physical processes

was developed based on GIS as a tool and the collective opinion was used based on AHP as a tool and

used Expert Choice as software. All data resulted from the physical approach (land cover/use change,

soil erosion, land capability and suitability classification, suitable crops, and current land use

classification) were be used to support and develop an integrated approach. Hence, we call this

integrated approach (Figure 8.1).

  The AHP utilizes the system approach to decision making. The recommended procedure to solve a problem using the AHP method based on the AHP principles is as follow:

  1. Structuring the Problem into a Hierarchy In this study, generating the integrated measure (the first step in the AHP) was to define the

hierarchy, each representing a level in the system. Three identified levels of hierarchy in the area are

shown in Figure 8.1.

  2. Comparing the Hierarchy Elements on a Pairwise Base Based on the hierarchy levels, then the questionnaire was done and the respondents were filled during the workshop (Appendix C). Preference is denoted by a vector of weights following and AHP scale of relative importance ranging from 1 to 9 (Table 8.1). The pairwise comparison matrix (PCM) is a reciprocal matrix in which elements under its main diagonal are inverses of the upper elements. If there are n elements to be compared, which form an n x n PCM, then there was n (n-1)/2 pairwise comparisons in a PCM. In this study there were four elements for objectives and five elements for alternatives to be compared, then there were be six pairwise comparisons for objective and ten pairwise comparisons for alternatives in a PCM.

  The best land use allocation Level 1 Goal

  Environment Education Employment Socio-eco Level 2 Objectives and conservation (EC) and research (ER) (EP) develop

  (SED Soil erosion Land use/forest classification

  VH H M L

  VL PA LPA NP

  L

  Level 3 Alternatives Forestry Agriculture Settlement Industry Fish

  Note: VH=very high, H=high, M=moderate, L=low, VL=very low = GIS processing PA= protected area, LPA=limited production area, NP=normal production = AHP processing

Figure 8.1. Hierarchical levels for decision-making showing how the physical components are integrated in AHP model In this study, in order to make informed and valid assessments, the area was divided into zone

with relatively homogenous characteristics. The analysis was preceded by conducting a pairwise

comparison of the objectives. Each decision-maker was asked independently to establish the

relationship among the objectives using the ratio scale of measurement. For each decision-maker,

therefore, a square objectives matrix was generated, then, the matrices were normalized. This was

processed in Expert Choice Program. Central to AHP is a measure of consistency in human

judgments (Table 4.15). According to Malczewski (1999) if consistency ratio (CR) is less than 0.1,

there is a reasonable level of consistency in the pair-wise comparisons. If CR is more than or equal

0.1, the values of the ratio are inconsistent. In the latter case, the original value in the pair wise

comparison matrix should be revised. Consistency measurement was be done by using Expert Choice

Program.

  4. Aggregating All Priority Vectors The final step was to aggregate the priority (weight) vectors of each level obtained in the second step, to produce overall weights. This was processed in Expert Choice Program.

  The land use allocation was derived from the results of the potential land use suitability based

on integrated approach. In this study, land use allocation was adopted from Bantayan (2006). The

classification procedure puts the alternatives in classes signifying the degree of suitability of a

particular alternative to the land units. The land use suitability classification in this study was

categorized as follows: very high suitability (S1), high suitability (S2), moderate (S3), low suitability

(S4), and very low suitability (S5). These were expressed as relative weight of alternatives by

decision zone. Based on these priorities, the degrees of preference for the major land use groups can

be expressed as degrees of land use suitability for each decision zone.

8.2. Land Use Allocation in Besitang Watershed

  In this study, land use allocation used two kinds of land use allocation that was adopted from

Bantayan (2006), namely: allocation for single use and allocation for multiple-use. Land use

allocation for single use, the process of allocation depends on whether or not the results of the land use

suitability classification are regarded in absolute terms. A process of exclusion is implemented where

only the alternative that receives the highest suitability is chosen for allocation and the rest are

excluded in regard to the land units in question. Land use allocation for multiple-use, an alternative

approach is to recognise the gradation in the suitability classification. It can be accomplished by

concurrent and continuous use of several natural resource products obtainable on a particular land unit

requiring the production of several goods and services from the same area.

  Information for integrated approach was gathered based on primary and secondary data.

Primary data were collected from interviews during field survey and during the workshop. Secondary

data were collected from offices which are related to the study. Data and thematic layers as mention

earlier needed as supporting data and as guide for stakeholders to fill the questionnaire during the

workshop. A workshop with the stakeholders was conducted for AHP on July 31, 2008 in University

of Sumatera Utara, Medan, North Sumatra, Indonesia (Figure 8.2). The participants are the

stakeholders from provincial level, regional level, sub-regional level, and village level. The

participants came from different occupations and institutions . Of the 37 stakeholders that were invited

as respondent experts, only 26 came, only 19 were able to complete inputting of data, only 16 were

able to consistently input data (Figure 8.3). A profile of the respondent-experts is shown in Appendix

Tables 27 and 28. Based on Appendix Table 28, eight respondents came from official

government, one is a lecturer, one is a researcher, one is a graduate student, one is from Non

Government Organization (NGO), one is from agricultural extension, one is a farmer, and two are elite

figures in Besitang Watershed.

Figure 8.2. Workshop with stakeholders at University of Sumatera UtaraFigure 8.3. Stakeholders were filling the questionnaire during the workshop

  

Before conducting the workshop, interview with some stakeholders that were selected from

100 respondents during gathered data for socio-economic was done to determine several choices for objectives and alternatives. Then, the stakeholders were invited in the workshop as experts in the area. The group agreed with four of objectives and five of alternatives for potential land use allocation in

Besitang Watershed, during the workshop. The objectives were: 1) Environment and conservation,

2) Education and research, 3) Employment, 4) Socio-economic development and the alternatives were: 1) Forestry, 2) Agriculture, 3) Settlement, 4) Industry, and 5) Fishery. Supporting data such as: socio- economic, land use changes, soil erosion, land classification, land capability classification, land suitability classification, suitable crops in the area, and secondary data (Langkat administrative map, watershed boundary map, land system map, and soil depth) were shown to respondents during the workshop.

  

Based on interviews with stakeholders and discussion during the workshop, there are some

reasons why they choose the objectives and alternatives. The reason for choosing the environment and conservation is because in this area there are protected areas. As mentioned earlier, it has the main function of sustaining the biodiversity ecosystem services, cultural heritage, and geologic monument

  

among others for sustainable development. Besides, there are buffer areas between the Leuser

Ecosystem (KEL) and the Gunung Leuser National Park (TNGL) and Besitang Watershed as one of

the Priority II watersheds in Indonesia (it means that it is in a moderately to severely damaged class).

Hence, the areas need special management to maintain the watersheds ecosystem. In relation to land

use changes assessment, this area has the large portion of forest change from 1990 to 2006. Decrease

of forest area will affect the environment in this area. As we know that forest is a system that has

some functions, such as: nutrient cycles, water or hydrologic cycles, succession and forest

regeneration, wildlife-habitat interactions, and photosynthesis. If any one of these systems breaks

down, the forest will not function as it should. According to Perschel et al. (2007), forest has an

important role in mitigating climate change by naturally taking carbon out of the atmosphere. Forest

preservation maintains carbon storage and forest management that increases carbon sequestration can

augment forests’ natural carbon storage capacity in working forest. In addition, based on estimate soil

erosion and analysis land capability and suitability in this area, conservation needed to reduce soil

erosion to ensure sustainability in this area.

  This area has very good potential for research and educated activity because in this area there

is nature reserve forest. The area was also a potential to employment especially in agriculture because

in this area there are big areas for agriculture and plantation. Also, majority of the people work as

farmers’. Socio-economic development is important to achieve human welfare in this area.

  In terms of potential land use suitability in Besitang Watershed, the area was classified by five alternative land uses, namely:

  1. Forestry. 2 . Agriculture.

  3. Settlement 4. Industry.

  5 . Fishery.

  The reason for choosing forestry as one of the alternatives for potential land use allocation in

this area is because of Republic of Indonesia Law Number 26 (2007) about Spatial Management

(Chapter 17) that minimum 30% of watershed area must be covered by forest area. The reason for

choosing agriculture as one of the alternatives for potential land use allocation in this area is because

there are big areas for agriculture and plantation. Also, majority of the people work as farmers. The

reason for choosing the settlement as one of the alternatives for potential land use allocation in this

area is because in Besitang Watershed there are 30 villages and they live in their watershed for a long

time. Moreover, as mentioned earlier, in this area the main agricultural activities include the

management of natural resources with the order of the area as a function of the rural settlement,

services, government services, social services and economic activities. The reason for choosing the

  

industry as one of the alternatives for potential land use allocation in this area is because of very high

accessibility. This area connects two provinces (NAD and North Sumatra) and there are road accesses

from the land even from the sea. The reason for choosing the fishery as one of the alternatives for

potential land use allocation in this area is because of the location of Besitang Watershed especially in

low stream sub-watershed bordered by sea and river.

  In this study, the workshop was conducted only one day because of time and budget

constraints. Some stakeholders brought their laptop during the workshop (Figure 45), so they could

fill the AHP questionnaire in Expert Choice Program and the consistency could directly determine.

The stakeholders who did not bring the laptop filled the questionnaire manually. Hence, test of

consistency was conducted after the workshop was finished. In the end, sixteen sets of data were

generated from stakeholders in the workshop.

  The 16 sets of data were generated from the workshop processed using Expert Choice Program

to determine priority of objective for each stakeholder and the result as is shown in Appendix Table

  

29. Furthermore, mixed aggregation of objective used geometric average as is shown in Table 8.1. It

is derived to represent group judgment. Based on Table 8.1., the priority objective for all decision

zones using mixed aggregation was determined (Table 8.2). These values are used to establish the

priority of alternative for all decision zones as is shown in Tables 8.3., 8.4., and 8.5. The values show

the priorities in view of environment and conservation as being majority in this area, followed by

education and research, employment, and social economic development.

Table 8.1. Mixed aggregation of objective using geometric average for all decision zones in integrative approach

DECISION ZONE OBJECTIVE

  1

  2

  3

  4

  5

  6

  7

  8

  9

  10

  11

  12 Environment and

conservation 0.558 0.527 0.552 0.541 0.558 0.433 0.331 0.310 0.234 0.226 0.141 0.353

Education

and research 0.247 0.256 0.256 0.254 0.263 0.239 0.201 0.189 0.205 0.234 0.208 0.222

Employment 0.082 0.088 0.081 0.084 0.082 0.131 0.180 0.176 0.195 0.177 0.224 0.131

Socio- economic

development 0.058 0.060 0.061 0.061 0.057 0.088 0.116 0.134 0.163 0.153 0.194 0.108

  Total

  0.95

  0.93

  0.95

  0.94

  0.96

  0.89

  0.83

  0.81

  0.80

  0.79

  0.77

  0.81

Table 8.2. Priority of objective for all decision zones using mixed aggregation OBJECTIVE DECISION ZONE

  1.00

  0.04

  0.04

  0.04

  0.00

  0.01

  0.01

  0.01

  0.04

  0.04

  1.00 Overall Inconsistency

  1.00

  1.00

  1.00

  1.00

  0.05

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  Total

  Fishery 0.084 0.084 0.090 0.093 0.098 0.107 0.329 0.329 0.303 0.207 0.325 0.172

  Industry 0.083 0.083 0.090 0.092 0.098 0.107 0.064 0.066 0.077 0.249 0.243 0.172

  Settlement 0.084 0.084 0.090 0.093 0.098 0.107 0.072 0.074 0.097 0.274 0.236 0.172

  Agriculture 0.084 0.084 0.090 0.093 0.098 0.107 0.456 0.449 0.446 0.224 0.153 0.177

  0.665 0.665 0.640 0.630 0.607 0.573 0.080 0.081 0.078 0.046 0.042 0.306

  Forestry

  2. Abdul Rauf

  0.03

  0.04

  0.02

  1.00

  0.03

  0.02

  0.06

  0.02

  0.02

  0.04

  0.08

  0.08

  0.08

  0.08

  0.08

  1.00 Overall Inconsistency

  1.00

  1.00

  1.00

  3. Agung Siswoyo

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  Total

  Fishery 0.091 0.090 0.091 0.090 0.089 0.163 0.109 0.109 0.305 0.109 0.226 0.231

  Industry 0.044 0.046 0.046 0.046 0.046 0.075 0.107 0.107 0.093 0.107 0.163 0.081

  Settlement 0.061 0.061 0.063 0.061 0.061 0.088 0.107 0.107 0.093 0.107 0.115 0.081

  Agriculture 0.152 0.150 0.151 0.158 0.158 0.217 0.257 0.257 0.158 0.257 0.244 0.141

  0.652 0.653 0.649 0.647 0.646 0.457 0.419 0.419 0.350 0.419 0.253 0.465

  Forestry

  0.08

  0.02

  1

  1.00

  6

  5

  4

  3

  2

  1

  1.00 Table 8.3. Priority of alternative for each stakeholder using Expert Choice Program in integrated approach NAME POTENTIAL LAND USE DECISION ZONE

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  8

  1.00

  1.00

  Total

  12 Environment and

conservation 0.590 0.566 0.581 0.575 0.581 0.486 0.399 0.383 0.293 0.286 0.184 0.434

Education

and research 0.262 0.275 0.270 0.271 0.274 0.268 0.243 0.233 0.257 0.296 0.271 0.272

Employment 0.087 0.095 0.085 0.089 0.086 0.147 0.217 0.218 0.245 0.225 0.292 0.161

Socio- economic

development 0.061 0.065 0.064 0.065 0.059 0.099 0.140 0.165 0.205 0.193 0.253 0.132

  11

  10

  9

  8

  7

  6

  5

  4

  3

  2

  7

  9

  0.09

  1.00

  0.08

  0.09

  0.08

  0.09

  0.09

  0.08

  0.09

  0.09

  1.00 Overall Inconsistency

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  10

  1.00

  1.00

  1.00

  1.00

  Total

  Fishery 0.070 0.063 0.066 0.063 0.061 0.066 0.082 0.173 0.162 0.239 0.239 0.182

  Industry 0.061 0.060 0.060 0.060 0.060 0.060 0.070 0.137 0.129 0.270 0.270 0.140

  Settlement 0.081 0.083 0.083 0.083 0.083 0.083 0.120 0.171 0.192 0.239 0.239 0.174

  Agriculture 0.155 0.158 0.155 0.158 0.163 0.155 0.173 0.306 0.341 0.226 0.226 0.308

  0.632 0.636 0.636 0.636 0.636 0.636 0.555 0.213 0.177 0.027 0.027 0.196

  Forestry

  1. US Ardi

  12

  11

  0.04

Table 8.3. Continued NAME POTENTIAL LAND USE DECISION ZONE

  0.06

  0.03

  0.03

  0.03

  0.03

  0.03

  0.03

  0.06

  0.06

  0.06

  0.06

  1.00

  0.06

  8.Heru Rozaldi Forestry

  0.676 0.647 0.601 0.602 0.579 0.523 0.025 0.025 0.185 0.390 0.646 0.678

  Agriculture 0.107 0.144 0.177 0.175 0.187 0.224 0.093 0.093 0.110 0.180 0.146 0.106

  Settlement 0.077 0.090 0.106 0.107 0.116 0.131 0.178 0.178 0.181 0.122 0.089 0.081

  Industry 0.069 0.067 0.070 0.070 0.070 0.073 0.264 0.264 0.239 0.141 0.067 0.069

  Fishery 0.070 0.052 0.046 0.046 0.048 0.048 0.440 0.440 0.285 0.168 0.052 0.066

  Total

  1.00

  1.00

  1.00 Overall Inconsistency

  1.00

  1.00

  Settlement 0.113 0.113 0.113 0.113 0.113 0.135 0.137 0.137 0.177 0.177 0.177 0.177

  0.07

  0.09

  0.09

  0.07

  0.07

  0.09

  7.Hardi Guchi Forestry

  0.549 0.549 0.549 0.549 0.549 0.455 0.265 0.265 0.128 0.128 0.128 0.128

  Agriculture 0.113 0.113 0.113 0.113 0.113 0.141 0.402 0.402 0.297 0.297 0.297 0.297

  Industry 0.113 0.113 0.113 0.113 0.113 0.135 0.091 0.091 0.111 0.111 0.111 0.111

  1.00

  Fishery 0.113 0.113 0.113 0.113 0.113 0.135 0.105 0.105 0.287 0.287 0.287 0.287

  Total

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  0.08

  1.00 Overall Inconsistency

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  0.04

  1.00

  0.04

  0.04

  0.04

  0.04

  0.02

  0.06

  0.06

  0.05

  0.05

  0.02

  1.00

  Total

  1.00

  0.08

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00 Overall Inconsistency

  0.02

  0.04

  0.07

0.07 .0.08

  0.09

  Fishery 0.104 0.104 0.104 0.104 0.104 0.086 0.114 0.114 0.648 0.648 0.086 0.641

  0.09

  0.09

  0.09

  0.04

  0.02

  9.Irawati Azhar Forestry

  0.597 0.597 0.597 0.597 0.597 0.469 0.388 0.388 0.087 0.087 0.470 0.088

  Agriculture 0.127 0.127 0.127 0.127 0.127 0.271 0.250 0.250 0.083 0.083 0.271 0.085

  Settlement 0.089 0.089 0.089 0.089 0.089 0.088 0.134 0.134 0.096 0.096 0.088 0.098

  Industry 0.083 0.083 0.083 0.083 0.083 0.086 0.114 0.114 0.086 0.086 0.086 0.087

  0.07

  0.06

  1

  0.03

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00 Overall Inconsistency

  0.03

  0.03

  0.03

  0.03

  1.00

  0.02

  0.04

  0.04

  0.00

  0.00

  0.00

  0.02

  5. Amon Zamora

  Forestry

  0.692 0.692 0.692 0.692 0.692 0.498 0.498 0.498 0.097 0.097 0.106 0.106

  1.00

  1.00

  Settlement 0.077 0.077 0.077 0.077 0.077 0.096 0.096 0.096 0.280 0.293

  11

  2

  3

  4

  5

  6

  7

  8

  9

  10

  12

  1.00

  4. Ahmad Syofyan

  Forestry

  0.692 0.692 0.692 0.692 0.692 0.583 0.389 0.389 0.111 0.200 0.270 0.498

  Agriculture 0.077 0.077 0.077 0.077 0.077 0.110 0.200 0.200 0.222 0.200 0.182 0.126

  Settlement 0.077 0.077 0.077 0.077 0.077 0.102 0.143 0.143 0.222 0.200 0.182 0.124

  Industry 0.077 0.077 0.077 0.077 0.077 0.103 0.131 0.131 0.222 0.200 0.182 0.127

  Fishery 0.077 0.077 0.077 0.077 0.077 0.102 0.138 0.138 0.222 0.200 0.182 0.126

  Total

  1.00

  1.00

  Agriculture 0.077 0.077 0.077 0.077 0.077 0.204 0.204 0.204 0.373 0.358 0.275 0.275

  0.355 0.355

  0.08

  1.00

  6. Elvi Syahrani Forestry

  0.600 0.567 0.625 0.565 0.549 0.582 0.537 0.572 0.572 0.366 0.366 0.523

  Agriculture 0.177 0.176 0.173 0.188 0.182 0.204 0.228 0.230 0.227 0.368 0.368 0.251

  Settlement 0.091 0.095 0.078 0.099 0.104 0.088 0.099 0.085 0.085 0.141 0.141 0.108

  Industry 0.058 0.086 0.052 0.079 0.082 0.074 0.092 0.053 0.051 0.066 0.066 0.055

  Fishery 0.074 0.075 0.072 0.068 0.082 0.052 0.045 0.060 0.065 0.059 0.059 0.063

  Total

  1.00

  1.00

  1.00

  0.05

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00 Overall Inconsistency

  0.09

  0.06

  0.05

  0.07

  Industry 0.077 0.077 0.077 0.077 0.077 0.040 0.040 0.040 0.047 0.048 0.055 0.055

  1.00

  Fishery 0.077 0.077 0.077 0.077 0.077 0.162 0.162 0.162 0.203 0.203 0.209 0.209

  Total

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  0.07

  1.00

  1.00 Overall Inconsistency

  0.04

  0.04

  0.04

  0.04

  0.04

  0.05

  0.05

  0.05

  0.05

Table 8.3. Continued NAME POTENTIAL LAND USE DECISION ZONE

  0.09 14.

  0.08

  0.08

  0.08

  0.08

  0.07

  0.07

  0.07

  0.07

  0.07

  Reonald Syahrial

  0.08

  Forestry

  0.644 0.644 0.644 0.644 0.644 0.463 0.463 0.077 0.077 0.077 0.077 0.077

  Agriculture 0.129 0.129 0.129 0.129 0.129 0.242 0.242 0.229 0.229 0.229 0.229 0.229

  Settlement 0.076 0.076 0.076 0.076 0.076 0.128 0.128 0.214 0.214 0.214 0.214 0.214

  Industry 0.076 0.076 0.076 0.076 0.076 0.105 0.105 0.230 0.230 0.230 0.230 0.230

  Fishery 0.076 0.076 0.076 0.076 0.076 0.063 0.063 0.251 0.251 0.251 0.251 0.251

  Total

  1.00

  1.00

  1.00

  0.08

  1.00 Overall Inconsistency

  1.00

  Fishery 0.111 0.111 0.112 0.112 0.112 0.112 0.117 0.117 0.117 0.117 0.117 0.170

  0.01

  0.05

  0.05

  0.08

  13. Puji Hartono

  Forestry

  0.568 0.568 0.568 0.568 0.568 0.568 0.519 0.519 0.519 0.519 0.519 0.526

  Agriculture 0.172 0.172 0.170 0.170 0.170 0.170 0.182 0.182 0.182 0.182 0.182 0.160

  Settlement 0.051 0.051 0.051 0.051 0.051 0.051 0.065 0.065 0.065 0.065 0.065 0.058

  Industry 0.098 0.098 0.099 0.099 0.099 0.099 0.121 0.121 0.121 0.121 0.121 0.087

  Total

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  0.06

  1.00 Overall Inconsistency

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  0.06

  1.00

  0.06

  0.06

  0.06

  0.06

  0.06

  0.07

  0.07

  0.05

  0.07

  0.07

  1.00

  Total

  1.00

  0.06

  1.00

  1.00

  1.00

  1.00

  1.00 Overall Inconsistency

  0.02

  0.02

  0.02

  0.02

  0.02

  0.06

  Fishery 0.085 0.085 0.085 0.085 0.085 0.074 0.081 0.081 0.103 0.081 0.154 0.125

  0.05

  0.05

  0.05

  0.05

  0.05 15.

  Suharso Forestry

  0.660 0.660 0.660 0.660 0.660 0.526 0.547 0.547 0.454 0.547 0.098 0.566

  Agriculture 0.086 0.086 0.086 0.086 0.086 0.206 0.185 0.185 0.237 0.185 0.304 0.163

  Settlement 0.084 0.084 0.084 0.084 0.084 0.114 0.109 0.109 0.111 0.109 0.273 0.083

  Industry 0.085 0.085 0.085 0.085 0.085 0.080 0.078 0.078 0.095 0.078 0.171 0.064

  0.06

  0.05

  1

  0.10

  1.00

  1.00

  1.00

  1.00 Overall Inconsistency

  0.08

  0.06

  0.06

  0.06

  0.06

  0.10

  1.00

  0.09

  0.08

  0.06

  0.06

  0.08 11.

  Muchtar Asrul

  Forestry

  0.578 0.578 0.579 0.578 0.579 0.578 0.551 0.183 0.185 0.027 0.027 0.193

  Agriculture 0.178 0.178 0.128 0.178 0.128 0.178 0.176 0.323 0.313 0.270 0.270 0.288

  Settlement 0.114 0.114 0.113 0.114 0.113 0.114 0.118 0.185 0.164 0.239 0.239 0.177

  1.00

  1.00

  Fishery 0.069 0.069 0.069 0.069 0.069 0.069 0.085 0.165 0.190 0.239 0.239 0.204

  12 10. Mhd.

  2

  3

  4

  5

  6

  7

  8

  9

  10

  11

  Suheri Forestry

  1.00

  0.420 0.355 0.317 0.305 0.305 0.344 0.343 0.520 0.589 0.301 0.303 0.549

  Agriculture 0.187 0.198 0.181 0.187 0.187 0.288 0.287 0.213 0.197 0.188 0.185 0.262

  Settlement 0.165 0.153 0.206 0.182 0.182 0.130 0.134 0.115 0.096 0.180 0.181 0.084

  Industry 0.117 0.153 0.154 0.159 0.159 0.112 0.112 0.085 0.068 0.164 0.164 0.060

  Fishery 0.111 0.141 0.143 0.168 0.168 0.126 0.124 0.067 0.050 0.167 0.168 0.047

  Total

  1.00

  1.00

  1.00

  1.00

  Industry 0.061 0.061 0.061 0.061 0.061 0.061 0.064 0.145 0.148 0.226 0.226 0.137

  Total

  0.07

  1.00

  Agriculture 0.124 0.124 0.124 0.124 0.124 0.200 0.191 0.191 0.224 0.209 0.242 0.110

  Settlement 0.055 0.055 0.055 0.055 0.055 0.117 0.116 0.116 0.218 0.163 0.242 0.076

  Industry 0.055 0.055 0.055 0.055 0.055 0.079 0.106 0.106 0.145 0.158 0.229 0.077

  Fishery 0.087 0.087 0.087 0.087 0.087 0.079 0.106 0.106 0.219 0.162 0.229 0.306

  Total

  1.00

  1.00

  1.00

  1.00

  1.00

  Forestry

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00 Overall Inconsistency

  0.07

  0.07

  0.07

  0.07

  0.679 0.679 0.679 0.679 0.679 0.524 0.481 0.481 0.195 0.309 0.057 0.431

  12. Tatag BN

  1.00

  1.00 Overall Inconsistency

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  0.09

  0.08

  0.09

  0.09

  0.09

  0.09

  0.09

  0.09

  0.08

  0.07

  0.02

  0.02

  0.06

Table 8.3. Continued DECISION ZONE NAME POTENTIAL

  1

  2

  3

  4

  5

  6

  7

  8

  9

  10

  11

  12 LAND USE

  16.Syafaruddin Nst. 0.545 0.545 0.545 0.639 0.577 0.545 0.650 0.649 0.584 0.582 0.635 0.529 Forestry

  0.126 0.126 0.126 0.158 0.119 0.126 0.132 0.133 0.136 0.182 0.155 0.190 Agriculture

  0.101 0.101 0.101 0.046 0.092 0.101 0.074 0.074 0.095 0.071 0.045 0.087 Settlement

  0.101 0.101 0.101 0.047 0.092 0.101 0.048 0.048 0.077 0.042 0.040 0.057 Industry

  0.126 0.126 0.126 0.110 0.119 0.126 0.096 0.096 0.107 0.122 0.124 0.137 Fishery

  Total

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00

  1.00 Overall Inconsistency

  0.05

  0.05

  0.05

  0.08

  0.05

  0.05

  0.07

  0.08

  0.06

  0.08

  0.09

  0.07 Table 8.4. Mixed aggregation of alternative using geometric average for all decision zones in integrative approach DECISION ZONE POTENTIAL LAND USE

  1

  2

  3

  4

  

5

  6

  7

  8

  9

  10

  11

  12 Forestry 0.210 0.171 0.157

  0.611 0.601 0.596 0.596 0.589 0.515 0.344 0.279 0.295

Agriculture 0.124 0.127 0.125 0.131 0.127 0.183 0.213 0.225 0.216 0.216 0.225 0.184

Settlement 0.084 0.085 0.086 0.083 0.088 0.102 0.111 0.118 0.136 0.153 0.158 0.119

Industry 0.076 0.079 0.077 0.076 0.080 0.084 0.091 0.100 0.108 0.124 0.131 0.091

Fishery 0.087 0.087 0.086 0.086 0.088 0.092 0.115 0.135 0.183 0.175 0.163 0.165

Total

  0.98

  0.98

  0.97

  0.97

  

0.97

  0.98

  0.87

  0.86

  0.85

  0.84

  0.83

  0.85 Based on the results, the first objective in DZ 1 to 9 and DZ 12 was environment and

conservation, followed by education and research, employment, and socio-economic development. It

means that to achieve the best land use allocation in Besitang Watershed (DZ 1 to 9 and DZ 12), the

first priority of objective should be environment and conservation. For DZ 10, the first priority

objective to achieve the best land use was education and research, followed by environment and

conservation, employment, and socio-economic development. For DZ 11, the first priority objective

to achieve the best land use was employment, followed by education and research, socio-economic

development, and environment and conservation.

  The priority of alternatives for each stakeholder using Expert Choice Program in integrated

approach is shown in Table 8.2. Furthermore, mixed aggregation of alternative used geometric

average as is shown in Table 8.3. Based on Table 8.3, the priority of alternatives for all decision zones

using mixed aggregation was determined (Table 8.4).

  Land allocation in this area is based on Table 8.6 using the single use concept that was adopted

from Bantayan (2006). The decision zone that receives the highest suitability shall be allocated to the

land systems in that particular decision zone. Hence, based on the potential land use suitability

classification as is shown in Table 8.6, the majority of land in Besitang Watershed is allocated to

forestry (63%) potential land use (DZ 1 to 8 and DZ 12) followed by agriculture (37%) potential land

use (DZ 9, 10, and 11). Another concept of land use allocation is based on allocation for multiple-use.

As explained earlier, an alternative approach is to recognise the gradation in the suitability

classification. Such an approach follows the multiple-use concept for land use allocation.

  According to Bantayan (2006), the objective of multiple-use management is to manage the

natural resources in a manner that maximises the benefits which can be derived. Land use allocation

for multiple-uses can be accomplished by concurrent and continuous use of several natural resource

products obtainable on a particular land unit requiring the production of several goods and services

from the same area. As shown in Table 8.6 and Figure 8.5, Besitang Watershed has very high

potential suitability for forestry (63%) and agriculture (37%) land uses.

  

All stages in the process of developing participatory land use allocation are shown in the flowchart

as illustrated in Figure 8.6 and called framework of participatory land use allocation. Based on the

flowcart, there are some stages in the participatory land use allocation approach, namely:

  1. Define Data Needs All data in this study were gathered from primary and secondary data, both in digital shape

(spatial and non spatial data) and non-digital file. Spatial data are information that show the

georeferenced point of component of the land or area. Spatial data comprised both a measurement or a

description of an attribute or characteristic and the spatial location of where such data is applied.

There are two forms of representing spatial data in the computer which are as follows (Burrough,

1986; Bantayan, 2006) that is vector data and raster data. Vector data represents spatial data by three

main geographical entities that are points, line and polygon. Raster data represents spatial data in the

form of a set of cell located by coordinates; each cell is independently addressed with the value of an

attribute. Examples of spatial data are soil map, geology map, and topography map.

Non-spatial data or attribute data is information in table form which show the characteristics or

properties of spatial data. Examples of non-spatial data are soil data base and climatic database.

  2. List of Data There are two kinds of data integrated in this study, namely: physical data and public opinion data. The physical data focused on modeling soil erosion using USLE and

Figure 8.5. Map of land use allocation based on integrated approach in Besitang Watershed

  Start Define data List of data Determine measure of operations

  Processes data Determine objectives and alternatives Input data Create an integrated approach

  

Execute Modify approach

An integrated approach result No Acceptable? Test of consistency

  Yes Best land use allocation

  End Prioritizati

Figure 8.6. Flowchart of land use allocation framework using integrated approach

  

land and forest classification based on Decrees of Ministry of Agriculture Number 837/Kpts/Um/11/80

and Number 63/Kpts/Um/8/81, RTRWK, and Decrees of Ministry of Forestry Number 44 (2005), as

mentioned earlier.

  According to Bantayan (2006), there are basically three types of erosion models, namely:

empirical, conceptual, and physically-based models. The USLE is an empirically-derived equation

that estimates soil loss on the basis of four groups of physical factors. The public opinion data from

  

stakeholders as decision-maker can be described as integrating theory of the concepts of multi-criteria

decision making models (AHP). This approach allows the flexibility of treating quantitative and

qualitative information simultaneously. In addition, the viewpoints of interested individuals and

groups could be aggregated. The following data and thematic layers were needed: socio-economic

data, Langkat administrative map, watershed boundary map, land use (change) map, soil erosion map,

land classification map, land capability and suitability classification map, suitable crops maps, land

system map, and decision zone map.

  3. Determine Measure of Operations and Process Data as Needed The GIS, SPSS, and Microsoft Office Excel were used for data processing. The physical

information were processed using GIS and Microsoft Office Excel. Socio-economic information were

processed using SPSS, and Microsoft Office Excel.

  4. Determine Objectives and Alternatives Before creating an integrated approach, AHP is required to determine objectives and

alternatives. In this study, there are four objectives and five alternatives. The stakeholders were

directly involved during determined objectives and alternatives for AHP.

  5. Input Data, Create, Execute, and Result of the Integrated Approach The result from GIS, SPSS, and Microsoft Office Excel processes were integrated in the

collective opinion used the concepts of multi-criteria decision making models using AHP. All

information were integrated to satisfy the approach. The stakeholders were directly involved during

this study by conducting workshop to fill up the AHP questionnaire. To determine the “eigenvector”

in AHP, the stakeholders were invited (the scientists and the stakeholders who have an understanding

and experience on the subject). All data were processed with computer and the AHP assisted

stakeholder with the possible solution options regarding spatial allocation questions and is called

participatory land use allocation.

  6. Test of Consistency and Modify Integrated Approach As mentioned earlier, consistency value is allowed if it less than or equal 0.1. If the

consistency not yet acceptable, the approach is modified by returning to the respondent expert until the

consistency value less than or equal 0.1 is reached. Furthermore, approach result from AHP process

was stored in GIS file and the final result of this study called spatial participatory land use allocation.

  Based on the result of this study, the most significant contributions of integrated approach using GIS and AHP in facilitating land use decision-making, are:

1. As a Tool in Policy Making

  It means that integrated approach can help policy makers to formulate good land use policy.

To formulate good land use policy, needed are data, because decision-making without data is

something like a book without story. The data are important because they can provide information to

be dealt with by policy makers at a particular level to formulate good land use policy, and to assess the

sustainability of current and past policy. The GIS and AHP in this case are tools that had great help

because they allowed a more explicit, objective analysis that leads to more rapid examination of

alternatives. In other words, they speed up the decision-making process by the policy makers.

2. As a Tool for Better Land Management It means that integrated approach enable land managers to device better management plans.

  

The integrated approach using GIS and AHP is one of those great ideas whose time has come. The

new generation of GIS that integrated satellite images with map data means that this technology can be

successfully used for remotely monitoring land use cover. An integrated approach enables land

managers to device better management plans and to assess the current and past management practices

on the land. Any government agency and/or entity with the direct jurisdiction over the area, must

decide how to manage that land. Each may have different objectives for the land, but to make rational

decisions, all must have information.

  The GIS on the other hand is an appropriate tool for the storage and retrieval of information. It

provides analysts and planners, specialized users, and the general population a standard user-friendly

graphic interface with which to work. Land managers will be benefited too as it can communicate

with other applications, such as the widely used Global Positioning Systems (GPS) and other server to

perform sophisticated analysis. According to Weese (2002), GIS also provides much functionality

that makes it such a widely used tool. For instance, it provides functions for precisely aligning map

features so that maps of all sorts of different projections, datum, and scales can be used together. Each

individual map can also have their characteristics manipulated by categorizing features and using

mathematical operations. Proximity and distance to other features can be analyzed and quantified,

providing new information. For example, elevation and distance can be calculated together to

determine local aspect and slope of the landscape. Perhaps the most useful tool of all in GIS, however,

is its ability to form overlay operations between layers. Maps with nominal categories (water, corn

field, forest, settlement area) can be combined using Boolean logic to find the intersection and union

between features on different layers. Maps with numerical values (such as: elevation or slope) can

also be combined using mathematical operations. For example, a GIS might be used to find a good

site for a power plant by recoding map layers for soils, slope, and proximity to cooling water and

markets into suitability scores. These suitability maps could then be combined mathematically to

create a map indicating the relative suitability for building a power plant throughout the entire region.

  Likewise, AHP can be used for very complex decisions. It can be used for wide variety of

applications, for instance: strategic planning, resource allocation, source selection, business/public

policy, and program selection. An AHP method is superior because it can deal with inconsistent

judgments and provides a measure of the inconsistency of the despondences, the number of criteria

made many of the other weighting methods infeasible, it allows for many criteria to be simplified to

individual comparison choices, and the time constrains required each participant to take the test at the

same time. Furthermore, AHP could be administered as an individual test, it has one of the strongest

theoretical foundations, and the ability to easily incorporate the normalized weights into a GIS ranking

approach. The availability of AHP software made calculations easy and provided many display tools

to quickly view results. Group and individual comparisons could be made to identify trends and

potential trade offs.

3. As a Starting Point for Scientific Investigations It means that integrated approach can serve as a starting point for other scientific researchers.

  

Many researches about GIS and AHP are on trends in ecosystems area, the subjects of many

ecological and biological researches nowadays.

  Based on explanation above, the integrated approach using GIS and AHP provides efficient,

acceptable, and realistic results of land use analysis, since it involves the physical components as well

as participation of stakeholders to ensure sustainability.

  

CHAPTER 9

SUMMARY, CONCLUSIONS, AND RECOMMENDATIONS

9.1. Summary

  This study was conducted to develop a framework for participatory and improved land use

decision-making in Besitang Watershed, Langkat, North Sumatra, Indonesia. Specifically, it aimed to:

1) assess land use changes in the area; 2) estimate soil erosion under different land uses; 3) analyze the

actual and potential suitability of the lands for several annual, estate and silvicultural crops; 4)

determine the current and potential land use suitability with stakeholder participation ; and, 5) develop

a spatial participatory land use allocation based on integrated approach to ensure sustainability.

  Socio-economic information were gathered based on primary and secondary data. Primary

data were collected from interviews during field survey. The study largely relied on the use of

questionnaires at village and household level. Secondary data were collected from the Regional

Statistical Office which is related to the study. Data were gathered from 100 household respondents

that were randomly selected. Structured questionnaire was utilized in conducting in-depth and face-to-

face interviews. Information for integrated approach were gathered based on primary and secondary

data. Data collection for primary data was done through an interview during field survey and during

the workshop. The study largely relied on the use of questionnaire and key informant/stakeholder.

Key informants/stakeholders provided qualitative in-depth information about physical and socio-

economic conditions of the study area. Geographic Information System and AHP were used in the

analysis and processing of the data to generate integrated approach for land use allocation. The USLE

was used to determine soil erosion in each land system.

  The results show that there were land use changes in Besitang Watershed across three time

periods (1990-2001-2006). Between 1990-2001 (past 11-year period), about 11,076 ha or 25% of the

total primary forest were converted to secondary forest, to dry land agriculture by 2,675 ha or 6%, to

plantation area by 887 ha or 2%, and to unvegetated area by 396 ha or 0.9%. Mangrove forest areas

were converted to dry land agriculture by 3,139 ha or 28% of total mangrove forest, to plantation area

by 453 ha or 0.04%, and to unvegetated area by 24 ha or 0.2%. For the other kinds of land uses, the

high rate of land conversion was from conversion of dry land agriculture area into plantation area by

4,848 ha or 15% of total dry land agriculture, conversion of bush to plantation by 1,787 ha or 46% of

total area of bush, converted plantation to dry land agriculture by 1,545 ha or 40% of total plantation,

and conversion of rice field to dry land agriculture by 1,124 ha or 52% of total rice field.

  During the period 2001-2006, the high rate of forest disturbance was from conversion of

26,423 ha into secondary forest (91%) of total primary forest, followed by conversion of 1,171 ha

primary forest into bush (4%). Mangrove forest area was transformed into fish pond by 2,149 ha or

  

26% of total mangrove forest, conversion of 706 ha or 9% into dry land agriculture, conversion of 397

ha or 5% into bush, conversion of 294 ha or 4% into unvegetated area, conversion of 263 ha or 4%

into swamp area, conversion of 220 ha or 3% into rice field, and conversion of 82 ha or 1% into

plantation area. For the other kinds of land uses, the high rate of land conversion was from conversion

of dry land agriculture area into plantation area by 7,871 ha or 21% of total dry land agriculture,

followed by conversion into rice field by 3,941 ha or 11%, conversion into fish pond by 2,064 ha or

6%, and conversion into bush by 1,878 ha or 5%.

  During 16-year period (1990-2006), the high rate of forest disturbance was from conversion of

36,399 ha primary forest into secondary forest (82%) of total primary forest, followed by conversion

of 3,370 ha mangrove forest into fish pond (30%), conversion of 2,687 ha or 6% primary forest into

bush, conversion of 1,809 ha or 4% primary forest into dry land agriculture, conversion of 1,213 ha or

11% mangrove forest area into dry land agriculture, and conversion of 402 ha or 4% of total

mangrove forest into plantation. For the other kinds of land uses, the high rate of land conversion was

from conversion of dry land agriculture area. It was transformed into plantation by 9,914 ha (30% of

total dry land agriculture), followed by rice field (2,521 ha or 8%), bush (1,078 ha or 3%), and fish

pond (511 ha or 2%). Land conversion also occurred from bush to plantation by 2,194 ha or 57% of

total bush, conversion of bush into dry land agriculture by 805 ha or 21%, conversion of rice field into

bush by 263 ha or 12%, conversion of rice field into fish pond by 235 ha or 11% and conversion of

swamp to plantation area by 159 ha or 100% of total swamp. Swamp remained at its original area of

159 ha.

  Erosion rate in Besitang Watershed ranged from zero to 1,172 ton/ha/year. The mean soil

erosion of the watershed is 23 ton/ha/year. The high erosion rates indicate that appropriate crop

management and soil conservation practices are urgently needed to be carried out as soon as possible.

High mean of soil erosion is common in unvegetated areas. The problem is prevalent on this area

which is bare. Based on erosion index map, a total area of 79,712 ha or 80% falls under EI less than

or equal than one. About 20,323 ha or 20% falls under EI more than one. Based on the criterion, the

area having EI less than or equal to one has suitable land uses, while EI greater than one has unsuitable

land uses. This implies that large areas within the Besitang Watershed are under suitable land uses.

  Based on RTRWK and SK 44 (2005), the functional areas in Besitang Watershed are the

protected area, buffer area, and normal production area. Protected area is divided by nature reserve

forest with area 40,790 ha (41%) and protection forest by 1,865 ha (2%). Buffer area consists of

limited production forest with an area of 17,142 ha (17%). Normal production area is divided into

production forest with area of 13,775 ha (14%), dry land agriculture 1,685 (2%), plantation 14,174

(14%), and wetland agriculture by 10,604 (11%).

  Land capability in Besitang Watershed ranges from Class II to Class VI. The majority of lands

in Besitang Watershed under the land capability Class III (58,986 ha or 59%) which means soils that

have severe limitations that reduce the choice of plants, and require intensive conservation practices or

both. They are found in three sub-watersheds, namely: upland stream, middle stream, and lower

stream. Land suitability to several annual crops (upland rice, irrigated paddy rice, corn, and soybean)

and several estate and silvicultural plants (oil palm, rubber, cacao, coffee, coconut, durian, rambutan,

citrus, and mango) in Besitang Watershed are classified as not suitable (N) in DZ 1. In general, slope

steepness is the main limitation of LSC in DZ 1. In DZ 12, in general both annual crops and several

estate and silvicultural plants also are not suitable (N), due to drainage and flood hazard.

  As alternative for forestry in potential land use suitability based on integrated approach,

Besitang Watershed has very high suitability (62,814 ha) except for DZ 9 (2,250 ha) (high), DZ 10

(4,757 ha) (moderate), and DZ 11 (30,214 ha) (low). Majority for agriculture is high suitability (63%)

and for DZ 9, 10, and 11 is very high suitability (37,220 ha). Settlement is moderate suitability

(76,873 ha) in DZ 1 to 6 and DZ 11 and low suitability (23,162 ha) in DZ 7 to 10 and DZ 12.

Fishery is high suitability (34,970 ha) in DZ 10 and 11, moderate suitability (18,405 ha) in DZ 7, 8, 9,

and 12 and low suitability (46,659 ha) in DZ 1 to 6. In this area, industry indicated with the very low

suitability in all decision zones (100,035 ha).

  Land allocation in Besitang Watershed using the single use concept indicate that the majority

of land allocation in Besitang Watershed is allocated to the forestry potential land use (DZ 1 to 8 and

DZ 12) followed by agriculture potential land use (DZ 9, 10, and 11). Based on allocation for

multiple-use, Besitang Watershed has very high potential suitability for forestry (63%) and agriculture

(37%) land uses.

9.2. Conclusions

  The most significant contributions of integrated approach using GIS and AHP in facilitating

land use decision-making were: 1) as a tool in policy making; 2) as a tool for better land management;

and, 3) as a starting point for scientific investigations. The integrated approach using GIS and AHP

provides efficient, acceptable, and realistic results of land use analysis, since it involves the physical

components as well as participation of stakeholders to ensure sustainability.

  

In relation to the specific objectives of the study, the following conclusions were drawn:

  

1. The large portion of land use changes from 1990 to 2006 was from conversion of 36,399 ha primary forest into secondary forest (82%), followed by conversion of 3,370 ha mangrove forest into fish pond (30%), conversion of 2,687 ha or 6% primary forest into bush, conversion of 1,809 ha or 4% primary forest into dry land agriculture, conversion of 1,213 ha or 11% mangrove forest into dry land agriculture, and conversion of 402 ha or 4% of total mangrove forest into plantation area. Dry land agriculture was transformed into plantation area by 9,914 ha or 30% of total dry land agriculture, rice field by 2,521 ha or 8%, bush by 1,078 ha or 3%, and fish pond by 511 ha or 2%. Bush was transformed into plantation area by 2,194 ha or 57% of total bush, into dry land agriculture by 805 ha or 21%. Conversion of rice field into bush by 263 ha or 12%, and into fish pond by 235 ha or 11%. Conversion of swamp into plantation area by 159 ha or 100% of total swamp (original area).

  

2. The highest erosion rate in Besitang Watershed is 1,172 ton/ha/year. The mean soil erosion of the

watershed is 23 ton/ha/year (Class 2 or 15

  • – 60 ton/ha/year). The erosion rates indicate that the crop management and soil conservation practices are urgently needed to be carried out as soon as possible. High mean soil erosion rate is common in unvegetated area. The problem is prevalent on this area which is bare. Total area of 79,712 ha or 80% falls under EI less than or equal than one. About 20,323 ha or 20% falls under EI more than one. This implies that large areas within the Besitang Watershed are under suitable land uses.

  

3. Land capability in Besitang Watershed ranges from Class II to Class VI. The majority of land in

Besitang Watershed is under the land capability Class III (58,986 ha or 59%) which means soils have severe limitations that reduce the choice of plants and require intensive conservation practices or both. These are spread in three sub-watersheds, namely: upland stream, middle stream, and lower stream. Land suitability classification to several annual crops (upland rice, irrigated paddy rice, corn, and soybean) and several estate and silvicultural plants (oil palm, rubber, cacao, coffee, coconut, durian, rambutan, citrus, and mango) are classified as not suitable (N) in DZ 1. In general, slope steepness is the main limitation of land suitability classification in decision zone 1. In DZ 12, in general both annual crops and several estate and silvicultural plants also were not suitable (N). Drainage and flood hazard are the dominant limiting factors.

  

4. As alternative for forestry, potential land use suitability classification in Besitang Watershed based

on integrated approach indicated that most areas have very high suitability. Majority of agriculture area has high to very high suitability. Settlement area has low to moderate suitability. Fishery area has low to high suitability. In this area, industry has very low suitability in all decision zones.

5. Land allocation in Besitang Watershed using the single use concept indicated that the majority of land is allocated to the forestry potential land use followed by agriculture potential land use.

  Based on allocation for multiple-use, Besitang Watershed has high potential suitability for forestry and agriculture land use.

9.3. Recommendations

  Based on the aforementioned findings and conclusions of this study, several recommendations are proposed:

  

1. To apply the procedure and reexamine current land use allocation in the Besitang Watershed,

under the coordination and control of the Regional Development Planning Board of Langkat Regency (BAPPEDA). An intersectoral management of Besitang Watershed should be implemented to achieve sustainability of the Besitang Watershed. The involvement of all parties or stakeholders is necessary to ensure its sustainability.

  

2. Re-evaluation of the decision zones currently being used through participatory process to

determine the minimum size of a decision zone.

  Finally, a number of salient points are given to improve the datasets used and for future studies as well. These are as follows:

  

1. The methodology used in this study was constrained to use existing digital files to analyze physical

components. For more accurate results, analysis of physical components should be done directly from an interpretation of aerial photographs, satellite imagery, and use of remote sensing and GIS.

  

2. The methodology used in this study was limited to analyze land use allocation for single use and

for multiple-use. Alternatively, land use allocation for multiple-use using graduated method should be persued.

  

3. For further research in this area, it is important to analyze alternative land use allocation using

integrated approach together with economic analysis.

  

4. Workshop with stakeholders for land use allocation should be conducted more than one day to

make this study more participatory.

  

5. Field validation of the framework should be done to determine its accuracy and reliability for

watershed development.

  

REFERENCES

Antoine, J., G. Fischer., and M. Makowski. 1997. Multiple Criteria Land Uses Analysis. Applied

Mathematics and Computation, 83(2-3):195-215.

Anderle, C., M. Fedrizzi., S. Giove., and R. Full´Er. 1994. Fuzzy Multiple Objective Programming

Techniques in Modeling Forest Planning. Proceedings of EUFIT’94 Conference, September

  20-23, 1994, Aachen, Germany, Verlag der Augustinus Buchhandlung, Aachen, 1994 1500-

Arnoldus, H.M.J. 1986. Predicting Soil Losses due to Sheet and Rill Erosion. In Guidelines for

Watershed Management. FAO Conservation. Guide No.1. Rome.

Aronoff, S. 1989. Geographic Information Systems and Rural Development. In the Proceedings of

Franco-Thai Workshop on Remote Sensing. Khon-kaen, Thailand: pp. 162-166.

Arsyad, S. 2006. Soil and Water Conservation (Konservasi Tanah dan Air). Penerbit IPB (IPB press),

Bogor. 396p.

  th

  

Avery, T.E. and G.L. Berlin. 1985. Interpretation of Aerial Photograph. 4 Edition. McMillan

Publishing Company. New York.

Banai-Kashani, R. 1989. A New Method for Site Suitability Analysis: The Analytic Hierarchy Process.

  Environmental Management 13: 685-693.

Bantayan, N.C. 2006. GIS in The Philippines Principles and Applications in Forestry and Natural

Resources. PARRFI and AKECU. Los Banos. 173p.

  

________________. 1996. Participatory Decision Support Systems: The Case of the Makiling Forest

Reserve, Philippines. Ph.D. Dissertation. Department of Geomatics, the University of Melbourne. 196p.

Bantayan, N.C. and I.D. Bishop. 1998. Linking Objective and Subjective Modelling for Land Use

Decision Making. Landscape and Urban Planning 43: 35-48.

  

Berry, J.K. 1988. Map as Data. Computer Assisted Map Analysis Management. Mapping and

Analysis for Geographic Information System. Colorado State University. Colorado: 120p.

Bettinger, P. and M.G. Wing. 2004. Geographic Information Systems Applications in Forestry and

Natural Resources Management. Mc Graw Hill. New York. 229p.

Breiby, T. 2006. Assessment of Soil Erosion Risk within a Subwatershed using GIS and RUSLE with

a Comparative Analysis of the Use of STATSGO and SSURGO Soil Databases. Volume 8,

  Papers in Resource Analysis. 22pp. Saint Mary’s University of Minnesota Central Services Press. Winona, MN.

Brooks, K.N., P.F. Ffolliott., H.M.Gregersen., and J.L. Thames. 1991. Hydrology and Management

of Watersheds. Iowa State University Press/Ames.

  

Brown, K., W.N. Adger., E. Tompkins., P. Bacon., D. Shim., and K. Young. 2001. Trade-off

Analysis for Marine Protected Area Management. Ecology Economy, 37:417-434.

Burrough, P. A. 1986. Principles of Geographic Information Systems for Land Resources

Assessment. Oxford, Clarendon Press. 193p.

Carpenter, R.A. 1981. Editor Assessing Tropical Forest Lands: Their Suitability for Sustainable

Uses. Natural Resources and the Environmental Series, Vol.3, 337 pp.

Cohen, S.J. 1997. Scientist-Stakeholder Collaboration in Integrated Assessment of Climate Change:

Lessons from a Case Study of Northwest Canada. Environment Model Assessment 2: 281-

  293. Conway, G. R. 1985. Agroecosystem Analysis. Agricultural Administration, 20:31-55.

Costanza, R. and M. Ruth. 1998. Using Dynamic Modeling to Scope Environmental Problems and

Build consensus. Environmental Management 22: 183-195.

  

Cruz, R.V.O. 1990. Land Use Suitability Assessment and Land Capability Classification in Ibulao

Watershed, Philippines. Ph.D. Dissertation. University of Arizona, USA.

Duc, T. T. 2006. Using GIS and AHP Technique for Land Use Suitability Analysis. International

Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied

  

Sciences. Department of Geomatics Polytechnic University of Hochiminh city, Vietnam.

Ekanayake, G. K. and N.D.K. Dayawansa. 2003. Land Suitability Identification for a Production

Forest through GIS Techniques.

Erskine, J.M. 1997. Sustainability Measures for Natural Resources. In: Proceedings of an

International Conference on People and Participation in Sustainable Development. Nepal.

ESRI. 2007. What is GIS?

  

Food and Agriculture Organization (FAO). 1976. A Framework for Land Evaluation. FAO Soils

bulletin 32, Rome, Italy.

______________________________________________. 1995. Planning for Sustainable Use of Land

Resources. FAO Land and Water Bulletin 2. Rome: Food and Agriculture Organization of United Nations.

______________________________________________. 2006. The New Generation of Watershed

Management Programmes and Projects. Food and Agriculture Organization of the United

  

Nation, Forestry Paper 150. R

Garuti, C. and M. Sandoval. 2005. The AHP, A Multicriteria Decision Making Methodology for

Shift Work Prioritization.

  

Godilano, E.C. 1991. Application of Remote Sensing and GIS for Ecosystem Planning and

Management. IRRI-ARFN, Los Banos, Philippines.

_______________. 2001. Understanding GIS. Republic of the Philippines. Department of

Agriculture. Bureau of Agricultural Researc

Gregory, R. 2002. Incorporating Value Trade-offs into Community-Based Environmental Risk

Decisions. Environment Value, 11: 461-488

Haider, W., D.A. Anderson., T.C. Daniel., J.J. Louviere., B. Orland., and M. William. 1998.

  Combining Calibrated Digital Imagery and Discrete Choice Experiments: An Application to Remote Tourism in Northern Ontario. Proceedings from Shaping Tomorrow’s North: The Role of Tourism and Recreation. P 257-278.

Helms, D. 1992. The Development of the Land Capability Classification. NRCS (Natural Resources

Conservation Services) History Articles Readings in the History of the Soil Conservation

  Service, Washington, DC: Soil Conservation Service, 1992, pp. 60-73.

Hucthinson, C. F. and J. Toledano. 1993. Guidelines for Demonstrating Geographical Information

Systems Based on Participatory Development. Int. J. Geographical Information Systems 7: 453-461.

  

ITTO, 2005. Status of Tropical Forest Management 2005.

Jaya, I.N.S. 2002. Geographical Information System application for Forestry. Practical work Using

Arc / Info and ArcView (Aplikasi Sistem Informasi Geografis untuk Kehutanan. Penuntun Prakatis Menggunakan Arc/Info dan ArcView) . Faculty of Forestry. Bogor Agricultural University. Bogor

Kangas, J. 1994. An Approach to Public Participation in Strategic Forest Management Planning.

  Forest Ecology Management, 70:75-88.

Kangas, J., R. Store., P. Leskinen., and L. Mehtatalo. 2000. Improving the Quality of Landscape

Ecological Forest Planning by Utilizing Advanced Decision-Support Tools. Forest Ecology

  Management. 132: 157-171.

Kangas, J., A. Kangas., P. Leskinen., and J. Pykalainen. 2001. MCDM Methods in Strategic Planning

of Forestry on State-owned Lands in Finland. Journal Multi-Criteria Decision Analysis,

  10:257-271.

Keeney, R.L. and T.L. Mcdaniels. 1999. Identifying and Structuring Values to Guide Integrated

Resource Planning at BC Gas. Operation Research, 47: 651-662.

  Lambin, E.F., H.J. Geist. and E. Lepers. 2003. Dynamics of Land Use and Land Cover Change in Tropical Regions. Annual Review Environment Resources, 28:205

  • –41. http://www.geo.ucl.ac.be/LUCC/lucc.html Loomis, J.B. 1993. Integrated Public Lands Management. Columbia University Press, New York.

  

Maguire, D.I. 1991. An Overview and Definitions of GIS. In Geographical Information systems edited

by D.J. Maguire, M.F. Goodchild and D.W. Rhind (London: Longman), 9-20.

Malczewski, J. 1999. GIS and Multi-criteria Decision Analysis. John Wiley and Sons, Inc, New

York. 408pp.

Manshard, W. 1998. The Biophysical Basis of Eco-restructuring: An Overview of Current Relations

between Human Economic Activities and the Global System In Eco-restructuring: Implications for Sustainable Development. UNU. 417p.

Martin, W.E., H.W. Bender., and D.J. Shields. 2000. Stake Holder Objectives for Public Lands:

Rankings of Forest Management Alternatives. Journal Environment Management, 58:21-32.

Mccool, S.F. and G. Stankey. 2001. Representing the Future: A Framework for Evaluating the Utility

of Indicators in the Search for Sustainable Forest Management. IUFRO Research Series No. 7.

  Edited by R.J. Raison, A.G. Brown, and D.W. Flinn. CABI Publishing, Wallingford, UK. Pp. 99-105.

  

Mendoza, G.A. and R. Prabhu. 2005. Combining Participatory Modeling and Multi-Criteria Analysis

for Community Based Forest Management. Forest Ecology Management, 207: 145-156.

Mendoza, G.A., P. Macoun., R. Prabhu., D. Sukadri., H. Purnomo., and H. Hartanto. 1999.

  Guidelines for Applying Multi-criteria Analysis to the Assessment of Criteria and Indicators.

C and I Tool no. 9. Center for International Forestry Research (CIFOR), Bogor, Indonesia.

  

Ministry of Agriculture. 2003. Land Suitability for Agricultural Plants (Petunjuk Teknis Evaluasi

Lahan untuk Komoditas Pertanian ). The Centre for Soil and Agroclimate Research (Balai Penelitian Tanah, Pusat Penelitian dan Pengembangan Tanah dan Agroklimat-Badan Penelitian dan Pengembangan Pertanian , Departemen Pertanian), Bogor. 153p.

  

Ministry of Forestry. 2005. Field Technique Planning for Land Rehabilitation and Soil Conservation

in Besitang Watershed (Rencana Teknik Lapangan Rehabilitasi Lahan dan Konservasi Tanah DAS Besitang ). Directorate General of Land Rehabilitation and Social Forestry, Wampu Sei Ular Watershed Management Bureau (Direktorat Jenderal Rehabilitasi Lahan dan Perhutanan Sosial, Balai Pengelolaan DAS Wampu Sei Ular ), Medan. 126p.

  

_______________________. 2005. Minister of Forestry Decree No. 44 (SK 44) in 2005 about Forest

Areas in North Sumatra by 3.742.120 Ha (Surat Keputusan Menteri Kehutanan RI No. 44 Tahun 2005 tentang Penunjukan Kawasan Hutan di Sumatera Utara Seluas Kurang Lebih 3.742.120 hektar ), Jakarta.

  Mulder, N. J. 1984. Data Bases, Geo Information Systems. Enschede, ITC: 108

Murtilaksono, K. 1995. Land Use Development Strategies for the Rehabilitation of Gajahmungkur

Reservoir Subwatershed. Ph.D. Dissertation. University of the Philippines Los Banos.

Nelson, J. 2003. Forest Level Models and Challenges for Their Successful Application. Canadian

Journal Forest Resources, 33: 422-429.

O’riordan, T. and R. Ward. 1997. Building Trust in Shoreline Management: Creating Participatory

Consultation in Shoreline Management Plans, Land Use Policy, 14: 257-276.

  

Oszaer, R. 1994. Land Use Optimization in Waeriuapa Watershed, Kairatu, Seram, Maluku,

Indonesia Using Geographical Information System and Linear Programming. Ph.D.

  Dissertation. University of the Philippines Los Banos.

Perschel, R.T., A.M. Evans., and M.J. Summers. 2007. Climate Change, Carbon, and the Forest of

the

  Northeast.

  

Rajan, K.S. and R. Shibasaki. 2001. A GIS Based Integrated Land Use/Cover Change Model to

Study Agricultural and Urban Land Use Changed.

Regional Development Planning Board of Langkat Regency (BAPPEDA). 2002. Langkat Spatial

Management Planning Related to Regional Land Uses) (RTRWK) 2002-2011 (Peraturan Daerah Kabupaten Langkat No. 15 Tahun 2003 tentang Rencana Tata Ruang Wilayah Kabupaten Langkat ), Stabat.

  

Regional Physical Planning Programme for Transmigration (REPPPROT). 1980. Main Report

(Review of Phase I Result, Sumatra) of Regional Physical Planning Programme for Transmigration (RePProt) Volume I and II, Land Resources Department ODNRI Overseas Development Administration Foreign and Commonwealth Office, London, England and Direktorat Bina Program Direktorat Jendral Penyiapan Pemukiman Departement Transmigrasi , Jakarta, Indonesia.

  

Saaty, T. L., 1980. The Analytical Hierarchy Process: Planning Setting Priorities, Resource Allocation.

  McGraw Hill, New York. 287pp.

____________. 1986. Decision Making for Leaders: The Analytical Hierarchy Process for Decisions

in a Complex World. University of Pittsburgh Press, Pittsburgh. 291pp.

  

____________. 1988. Multi-criteria Decision Making: The Analytic Hierarchy Process, RWS

Publications, Pittsburgh.

____________. 1995. Decision Making for Leaders. Vol II. AHP Series. RWS Publications,

Pittsburgh.

____________. 1990. Multi-criteria Decision Making: The Analytic Hierarchy Process. RWS

Publications, Pittsburgh.

Saaty, T.L. and L.G. Vargas. 2001. Models, Methods, Concept and Applications of the AHP.

  International Series in Operations Research and Management Science, 34. Kluwer Academic Publishers, Boston.

Schmold, D.L., J. Kangas., G.A. Mendoza., and M. Pesonen. 2001. The Analytic Hierarchy Process

in Natural Resource and Environmental Decision Making. Kluwer Academic Publishers.

  Netherlands. p. 1-

Sheng. T.C. 1990. Watershed management field manual. Watershed survey and planning. FAO

Conservation Guide 13/6. Food and Agriculture Organization of the United Nations. Rome.

  

Sheppard, S.R.J. 2005. Participatory Decision Support for Sustainable Forest Management: A

Framework for Planning with Local Communities at the Landscape Level in Canada.

  Canadian Journal of Forest Research: 35: 7 p. 1515.

Sheppard, S.R.J. and M.J. Meitner 2005. Using Multi-criteria Analysis and Visualization for

Sustainable Forest Management Planning with Stakeholder Groups. Forest Ecology

  Management, 207:171-187.

Short, N.M. 2007. GIS-The GIS Approach to Decision Making.

Siligato, S., C. Feldkötter, and C. Van Tuyll. 2008. Integrated Watershed Management as a Tool for

Ecologically Sound Water Resources Management and Sustainable Economic and Social Development.

Statistic of Langkat Regency (BPS). 2007. Langkat in Figure (Langkat dalam Angka). Cooperation

by Statistic of Langkat Regency with the Regional Development Planning Board of Langkat Regency, Stabat.

  

________________________________________. 2007. Besitang in Figure (Besitang dalam Angka).

  Cooperation by Statistic of Langkat Regency with the Regional Development Planning Board of Langkat Regency, Stabat.

________________________________________. 2007. Brandan Barat in Figure (Brandan Barat

dalam Angka ). Cooperation by Statistic of Langkat Regency with the Regional Development Planning Board of Langkat Regency, Stabat.

  

________________________________________. 2007. Pangkalan Susu in Figure (Pangkalan Susu

dalam Angka ). Cooperation by Statistic of Langkat Regency with the Regional Development Planning Board of Langkat Regency, Stabat.

  

________________________________________. 2007. Sei Lepan in Figure (Sei Lepan dalam

Angka ). Cooperation by Statistic of Langkat Regency with the Regional Development Planning Board of Langkat Regency, Stabat.

  

Steiguer, J.E., L. Liberti., A. Schuler., and B. Hansen. 2003. Multicriteria Decision Models for

Forestry and Natural Resource Management: an annotated bibliography. USDA For. Serv.

  Gen. Tech. Rep. NE-307.

Tanaka, K. 1999. An Introduction to Fuzzy Logic for Practical Applications. Springer-Verlac, New

York, 138pp.

  

Troeh, F.R., J.A. Hobbs., and R.L. Donahue. 1980. Soil and Water Conservation. Prentice Hall,

Englewood Cliffs, New Jersey. USGS. 2007. Geographic Information Syste

Varma, V.K., I. Ferguson., and I. Wild. 2000. Decision Support System for the Sustainable Forest

  Management. Forest Ecology Management, 128: 49-55.

Villanueva, T.R. 2005. Upland Ecosystem Management. Second Edition. University of the

Philippines Open University, Los Banos, Laguna. 200pp.

  

Weerakoon, K.G.P.K. 2002. Integration of GIS Based Suitability Analysis and Multi Criteria

Evaluation for Urban Land Use Planning; Contribution from the Analytic Hierarchy Process, Proceedings of the 2002 Asian Conference on Remote Sensing, Kathmandu, Nepal.

Weese, A. 2002. The Integration of Remote Sensing Data into Geographic Information System.

  George Mason University.

Wischmeier, W.H. and D. Smith. 1978. Predicting Rainfall Erosion Losses. US Dept. Agr.

Superseded Agr. Handbook No. 282.

Wischmeier, W.H. and J.V. Mannering. 1969. Relation of Soil Properties to Its Erodibility. Soil Sci.

  Soc. Amer.Proc. Vol. 33:131-137.

Wooldridge, D.D. 1990. Manual on Land Use Survey and Capability Classification for Upland

Watershed. ASEAS-US Watershed Project. College, Laguna, Philippines.

  

Xiang, W. and D.L. Whitley. 1994. Weighting Land Suitability Factors by the PLUS Method.

  Environment and Planning B: Planning and Design 21: 273-304.

  

Appendix Table 1. Population and number of household per village in Besitang Watershed

DISTRICT SUB-DISTRICT

  Langkat Brandan Barat Lubuk Kertang 3,053 669 Pangkalan Batu 4,522 1,030 Lubuk Kasih 3,003 793 Sei Tualang 2,011 493 Sub-total 12,589 2,985

  Langkat Pangkalan Susu Alur Cempedak 3,816 847 Beras Basah 9,579 2,205 Bukit Jengkol 6,469 1,962 Damar Condong 706 724 Limau Mungkur 1,678 342 Pangkalan Siata 5,079 1,149 Paya Tampak 2,767 587 Pematang Tengah 2,358 500 Pintu Air 1,675 522 Sei Meran 1,711 396 Sungai Siur 4,666 960 Tanjung Pasir 3,761 937 Perkebunan Damar Condong 2,804 154 Perkebunan Perapen 1,129 307 Sub-total 48,198 11,592

  Langkat Besitang Bukit Kubu 6,625 1,456 Bukit Selamat 6,959 1,422 Halaban 8,885 1,876 Kampung Lama 5,586 956 Pekan Besitang 9,670 2,158 Salahaji 3,480 770 Sekoci 3,281 931 Serang Jaya 1,943 310 Suka Jaya 2,340 636 Bukit Mas 10,486 1,357 PIR ADB Besitang 4,250 1,107 Sub-total 63,505 12,979

  Langkat Sei Lepan Telaga Said 2,378 814 Langkat Padang Tualang Gunung Leuser National Park Total 126,670 28,370

  

Appendix Table 2. Monthly rainfall (mm) and number of rainy days (1996-2006) in Kantor

Camat Batang Serangan Station in Besitang Watershed

MONTH YEAR

  TOTAL

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

  R 1996

  32 92 132 153 529 184 132 145 382 454 370 396

  3,001

  D

  1

  5

  5

  6

  18

  9

  7

  9

  13

  12

  13 13 111 R 1997 125 293 403 320 207 428 340 157 394 330 2,997 D

  5

  12

  17

  10

  9

  11

  10

  6

  12 12 104 R 1998 185

  50 79 141 189 337 344 501 315 369

  2,510

  D

  9

  3

  3

  5

  6

  10

  10

  16

  9

  16

  87 R

  1999 217 159 205 138 231 255 243 289 400 375 528 210 3,250 D

  8

  7

  6

  7

  14

  8

  10

  15

  22

  21

  20 9 147 R

  2000

  75

  52 99 223 164 223 228 262 189 414 873 162

  2,964

  D

  6

  4

  3

  15

  13

  14

  11

  9

  10

  11

  12 5 113 R

  2001

  19 20 147 219 240 150 215 480 492 416 196 271 2,865 D

  1

  2

  8

  12

  12

  7

  16

  16

  21

  18

  11 22 146 R 2002 201 163

  18 329 542 179 123 171 414 446 495 219 3,300 D

  9

  8

  7

  10

  17

  8

  8

  9

  18

  22

  20 12 148 R 2003 132 139 144

  83 49 141 185 180 81 336 1,470 D

  10

  11

  6

  4

  3

  5

  10

  12

  6

  14

  81 R

  2004 154 3 131 44 225 477 348 291 305 296 390 235 2,899 D

  7

  2

  8

  7

  14

  17

  18

  10

  19

  16

  17 16 151 R 2005 305

  56 115 177 327 268 214 126 236 348 450 440 3,062 D

  13

  5

  7

  7

  15

  14

  12

  11

  17

  19

  16 21 157 R 2006 102 136 117 410 626 2438 1459 3260 258 530 180 484 10,000 D

  9

  12

  6

  20

  7

  13

  17

  16

  10

  20

  10 7 147

  R

Total 1,290 731 1,300 2,125 3,572 4,766 3,218 5,930 3,545 4,117 4,272 3,452 38,318

D

  63

  48 68 107 138 110 111 121 160 173 146 147 1,392 R Average 117.27 66.45 118.18 193.18 324.73 433.27 292.55 539.09 322.27 374.27 388.36 313.82 3,483.45 Source: The Meteorological and Geophysical Agency Regional I Sampali, Medan.

  Note : R=Rainfall (mm), D=Rainy days (days) Annual rainfall average(mm/year) : 3,483.45 Monthly rainfall (cm/month) : 29.03 Monthly rainfall (mm/month) : 290.29 Rainfall intensity (mm/day) : 27.53

  

Appendix Table 3. Monthly rainfall (mm) and number of rainy days (1996-2006) in BPP

Besitang Station in Besitang Watershed

MONTH

YEAR

  TOTAL

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

  R 1996 257 252 136 218 222 214 88 236 296 326 221

  2,466

  D

  14

  12

  7

  10

  17

  16

  9

  16

  19

  19 15 154 R 1997 111

  30 100 109

  55

  43 50 194 332 1,024 D

  9

  3

  6

  6

  4

  3

  4

  12

  22

  69 R

  1998 144 19 198 392 305 154 385 513 453 398

  2,961

  D

  10

  2

  9

  21

  16

  10

  21

  24

  21 18 152 R 1999 310 186 143 233 244

  85

  29

  56

  63

  94 85 315 1,843 D

  20

  11

  9

  14

  17

  6

  3

  9

  7

  11

  10 17 134 R

  2000 45 126 160 184 239

  72 62 117 212 148 227 402

  1,994

  D

  7

  10

  9

  15

  13

  6

  6

  10

  18

  15 14 123 R 2001 194 116 224 160 241 130

  99

  90 90 641 361 513 2,859 D

  14

  7

  10

  8

  11

  8

  6

  7

  7

  27

  12 17 134 R

  2002

  59

  49

  71 68 123 93 161 104 206 394 111 457 1,896 D

  7

  2

  9

  8

  8

  8

  9

  7

  15

  8

  81 R

  2003 20 272 114 312 867 134 1,719 D

  1

  8

  5

  10

  16

  14

  54 R

  2004 -

  • - - - D - - - - - - - - - - - - -

  R 2005 152

  60

  70 70 115 147 195 180 171 215 200 315 1,890 D

  13

  1

  3

  2

  5

  15

  7

  11

  8

  15

  19 11 110 R

  2006

  90 49 110 153 195 412 113 156 180 306 176 108 2,048 D

  6

  3

  4

  5

  8

  14

  9

  10

  10

  15

  13 6 103

  R

Total 1,362 868 1,033 1,215 1,904 1,588 1,102 1,207 1,619 3,474 2,267 3,061 20,700

D

  100

  49

  59 69 100

  97

  69

  85 81 157 134 114 1,114 R Average 136.20 86.80 103.30 121.50 190.40 158.80 110.20 120.70 161.90 347.40 226.70 306.10 2,070.00 Source: The Meteorological and Geophysical Agency Regional I Sampali, Medan.

  Note : R=Rainfall (mm), D=Rainy days (days) Annual rainfall average(mm/year) : 2,070.00 Monthly rainfall (cm/month) : 17.25 Monthly rainfall (mm/month) : 172.50 Rainfall intensity (mm/day) : 18.58

  

Appendix Table 4. Monthly rainfall (mm) and number of rainy days (1996-2006) in BPP

Brandan Barat Station in Besitang Watershed

MONTH YEAR

  TOTAL Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

  R 1996

  43

  62 50 198 225 163 129 248 189 252 262 188

  2,009

  D

  6

  6

  5

  7

  11

  6

  6

  18

  13

  14

  14 14 120 R

  1997

  13

  95 67 102

  58

  7

  9 75 273 360 218 90 1,367 D

  3

  7

  6

  7

  5

  6

  7

  10

  10

  19

  18 18 116 R 1998 133

  47

  37 69 214 321 318 402 456 207 315 435

  2,954

  D

  2

  2

  2

  3

  9

  14

  18

  24

  22

  20

  27 27 170 R

  1999

  71 63 206 247 135 65 110 179 71 343 127 1,617 D

  15

  9

  9

  9

  16

  7

  10

  11

  4

  22

  14 14 140 R

  2000

  24

  15 49 128

  66 51 111 107 473 407 179 802

  2,412

  D

  13

  6

  9

  15

  9

  8

  7

  5

  22

  24

  10 10 138 R 2001 165 310 274 127 214

  8

  56 40 228 549 540 456 2,967 D

  16

  7

  17

  13

  16

  4

  6

  8

  14

  24

  22 22 169 R

  2002

  83 37 126 247 88 110 7 257 152 154 202 1,463 D

  8

  5

  8

  7

  7

  13

  5

  9

  12

  12

  12

  98 R

  2003 148 45 115 268 90 147 151 346 353 70 180 1,913 D

  17

  5

  7

  12

  11

  11

  9

  19

  26

  11 11 139 R 2004 138

  63 217 120 183 289 279 68 1,357 D

  10

  10

  3

  8

  16

  19

  9

  9

  84 R

  2005 261

  83 49 124 168 207 220 223 120 253 242 359 2,309 D

  16

  2

  6

  8

  8

  19

  19

  19

  7

  20

  21 25 170 R 2006 109

  94 327 234 168 388 114 133 297 298 161 301 2,624 D

  7

  8

  9

  15

  13

  14

  10

  10

  11

  15

  11 18 141

  R

Total 1,188 877 1,313 1,470 1,883 1,388 1,324 1,565 2,893 3,463 2,547 3,081 22,992

D

  113

  62 71 100 106 96 107 119 147 215 169 180 1,485 R Average 108.00 79.73 119.36 133.64 171.18 126.18 120.36 142.27 263.00 314.82 231.55 280.09 2,090.18 Source: The Meteorological and Geophysical Agency Regional I Sampali, Medan.

  Note : R=Rainfall (mm), D=Rainy days (days) Annual rainfall average(mm/year) : 2,090.18 Monthly rainfall (cm/month) : 17.42 Monthly rainfall (mm/month) : 174.18 Rainfall intensity (mm/day) : 15.48

  

Appendix Table 5. Monthly rainfall (mm) and number of rainy days (1996-2006) in Kantor

Camat Pangkalan Susu Station in Besitang Watershed YEAR MONTH TOTAL Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

  96

  7

  8

  7

  10

  14

  9

  10

  2004 R

  9

  55 80 127

  9 95 119 281 1890 101 2,757 D

  7

  4

  8

  2

  8

  8

  6

  9

  7

  24

  22 24 171 2002

  R

  83 37 126 247 88 110 7 257 156 154 202 1,467 D

  8

  5

  8

  7

  8

  13

  5

  19

  12

  12 10 106 2003

  R 246

  50 87 260 57 102 256 262 310 143 250 2,023 D

  7

  6

  8

  13 17 120

  8

  13

  14

  10

  10

  11

  7

  Total R 1,030 835 1,080 1,322 1,884 1,304 1,273 1,592 2,774 3,326 4,222 3,084 23,726 D

  5

  97

  54

  72 88 109

  83 95 105 138 175 155 135 1,306 Average R

  93.64

  75.91 98.18 120.18 171.27 118.55 115.73 144.73 252.18 302.36 383.82 280.36 2,156.91 Source: The Meteorological and Geophysical Agency Regional I Sampali, Medan.

  Note : R=Rainfall (mm), D=Rainy days (days)

Annual rainfall average(mm/year) : 2,156.91 Monthly rainfall (cm/month) : 17.97

Monthly rainfall (mm/month) : 179.74 Rainfall intensity (mm/day) :

  9

  3

  6

  7

  57

  2005 R

  148

  44 41 114 197 156 214 145 149 193 253 292 1,946 D

  9

  2

  2

  7

  49 69 192 115 168 388 114 133 297 268 141 268 2,202 D

  10

  10

  7

  6

  10

  20 12 102 2006

  R

  14

  6

  1996 R

  10

  7

  6

  7

  5

  6

  7

  10

  19

  9 75 273 360 218 90 1,367 D

  18 13 111 1998

  R 133

  47

  37 69 214 321 318 402 456 207 315 435 2,954 D

  9

  2

  2

  3

  7

  9

  6

  43

  62 50 198 225 163 129 248 189 252 262 188 2,009 D

  6

  6

  5

  7

  11

  6

  58

  18

  13

  14

  14 14 120 1997

  R

  13

  95 67 102

  3

  14

  4

  22 24 118 2001

  6

  9

  15

  9

  8

  7

  5

  R 165 310 274 127 219

  66 51 111 107 473 407 179 802 2,412 D

  8

  56 40 228 549 540 456 2,972 D

  16

  7

  17

  13

  16

  13

  15 49 128

  18

  9

  24

  22

  20

  27 29 179 1999

  R

  71 63 206 247 135 65 110 179 71 343 127 1,617 D

  15

  9

  24

  9

  16

  7

  10

  11

  4

  22 14 126 2000

  R

  18.17

  

Appendix Table 6. Rainfall erosivity (R) based on Lanvine Formula in Kantor Camat Batang

Serangan Station (1996-2006) in Besitang Watershed

MONTH

YEAR

  TOTAL Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec R 10. 2,320

  1996 75 45.20

  73.85 90.28 487.86 116.02

  73.85 83.92 313.33 396.27 300.02 329.05 .39

  0.0

  2,316

  1997

  0.00 68.58 218.44 336.98 246.26 136.18 365.73 267.43 93.50 326.79 256.79 .67 116

  19.7

  1,903

  1998 .88

  2

  36.74

  0.00 80.78 120.33 0.00 264.22 271.71 453.08 241.04 298.92 .43 145 95.1 134.3

  2,440

  1999 .20

  2

  9 78.46 158.09 180.84 169.36 214.39 333.58 305.54 486.60 138.87 .45 34.

  20.8

  2,380

  2000

  24 49.94 150.69 99.22 150.69 155.30 187.62 120.33 349.55 964.21

  97.57

  .17

  5.2

  2,185

  2001

  9

  5.67 85.50 147.03 166.53 87.88 143.39 427.45 442.04 351.85 126.43 196.44 .49 130

  98.3

  2,607

  2002 .84

  9 4.92 255.73 504.23 111.76 67.09 105.02 349.55 386.80 445.71 147.03 .07

  0.0

  906.1

  2003

  0.00

  73.85

  79.23

  83.13

  39.30

  19.19 80.78 116.88 112.61 38.01 263.16

  4 91. 2,186

  2004

  08

  0.43

  73.09 16.58 152.53 423.82 276.02 216.41 230.70 221.49 322.29 161.82 .25 230

  23.0

  2,293

  2005 .70

  1 61.23 110.06 253.62 193.49 142.48 69.33 162.76 276.02 391.53 379.74 .96 52. 76.9 3897.2 1938.6 5785.8 13,98 2006

  01

  1 62.68 344.97 613.39

  5

  9 6 183.74 489.11 112.61 432.30

  9.51 816 385. 724.7 1,491.4 2,936.3 5,567.6 3,121.5 7,800.7 3,435.8 3,755.2 2,701.6 35,52 Total .99

  27

  7

  5

  5

  3

  5 3 2,792.05

  2

  4

  8

  9.54 74.

  35.0 3,229 Average

  27

  2 65.89 135.59 266.94 506.15 283.78 709.16 253.82 312.35 341.39 245.61 .96 Appendix Table 7. Rainfall erosivity (R) based on Lanvine Formula in BPP Besitang Station (1996-2006) in Besitang Watershed MONTH YEAR TOTAL Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec R

  182.7 177.9 146.1 162.7 252.5 148.8 1996

  7

  5

  76.91 1 149.77 142.48

  42.55

  6 0.00 221.49

  6 6 1,704.22

  50.6 124.6 258.9

  1997

  58.35

  9.85

  3

  56.92

  22.45

  16.07

  19.72

  0.00

  0.00

  0.00 8 617.58 230.7 395.0 331.3

  1998

  83.13

  0.00

  5.29 0.00 128.19 324.53 91.08 316.68 467.90

  8 1 2,373.89 235.8 117.7 82.3 159.9

  241.0 1999

  5

  4

  5 5 170.31

  40.59

  9.40

  23.01

  27.01

  46.54

  40.59 4 1,194.39 95.9 116.0

  154.3 335.8 2000

  17.09

  69.33

  4 2 165.58

  32.39

  26.43 62.68 140.67

  86.29

  8 5 1,302.64 124.6 151. 290.1 467.9

  2001

  8

  61.95

  61 95.94 167.47

  72.34

  49.94

  43.87 43.87 633.46 4 2,203.16

  31.7 399.8

  2002

  24.70

  19.19

  8

  29.96

  67.09

  45.87

  96.76 53.40 135.29 326.79

  58.35 3 1,289.01 2003

  0.00

  0.00

  0.00 5.67 197.43

  0.00

  0.00 60.50 237.93 955.21

  75.38 0.00 1,532.12

  • - - - - - - - - - - - -

  2004 31.1 125.5 112.6 129.9 241.0

  2005

  89.47

  25.27

  7

  31.17

  61.23

  85.50

  6 1 105.02 143.39

  6 4 1,181.38

  57.6 109.2

  2006

  43.87

  19.19

  4 90.28 125.56 347.26

  59.78 92.69 112.61 231.73

  2 56.21 1,346.02

  859.9 500.4 583. 732.0 1,255.0 1,107.0 660.8 702.6 1,119.0 3,112.7 1,630 2,480. 14,744.4 Total

  2

  7

  31

  4

  8

  2

  3

  7 9 .33

  95

  58.3 163.0 248.0 Average

  85.99

  50.05

  3 73.20 125.51 110.70

  66.08 70.26 111.91 311.28

  3 9 1,474.44

  Appendix Table 8. Rainfall erosivity (R) based on Lanvine Formula in BPP Brandan Barat Station (1996-2006) in Besitang Watershed YEAR

MONTH

TOTAL

  70.08 147.03

  7.27

  3.84

  19.19

  70.83

  28.77

  20.26

  58.35 55.51 418.99 341.54 111.76 859.1

  5

  1,99

  5.45

  2001 100.04 235.8

  5 199.4

  1.63

  3.45

  23.01 14.56 155.30 513.11 501.70 398.6

  4

  2,36

  0.36

  2002

  39.30

  0.00

  13.10 69.33 173.17

  42.55

  57.64 1.36 182.77

  92.69

  91.08 131.7

  2000

  1,01

  69

  13.10 30.57 142.48 247.3

  3.16

  47.22

  29.37

  52.01

  24.14

  1.36

  1.91 34.24 198.41 289.04 146.11

  43.87 870.

  84

  1998

  74.62

  18.13

  1 244.1 7 335.85 398.64 136.18 241.04

  0.00

  373.8

  8

  2,25

  5.96

  1999

  31.78

  27.01 135.2

  9 173.1

  7

  76.15

  28.18 57.64 111.76 31.78 270.64

  70.08

  3 894.

  2003 172.21

  2.40

  9 1,067.7

  30.57 122.9

  4 61.23 102.52 320.0

  4

  60.50 74.62 222.51 193.49

  80.78 193.4

  9

  1,48

  1.87 Total 572.36 462.7

  2 637.2

  3 759.7 1,206.9

  8 875.9

  9 769.2

  2 2,067.5

  2006

  8 2,580.6

  7 4,448.0

  8 2,565.

  01 18,0

  13.3

  2 Average

  52.03

  42.07

  57.93 69.06 109.73

  79.64

  69.94 97.07 187.96 234.61 404.37 233.1

  8 1,63

  19.19

  2.05

  19.72

  0.00

  0.00 41.89 185.68

  23.57 52.01 181.80 187.62 235.85

  82.35 176.0

  3

  1,35

  8.74

  2004

  22.45

  37.38

  70.08

  1.91

  47.22

  0.00

  1,23

  0.00 64.14 206.36 2756.64

  51.32

  3,25

  7.50

  2005

  86.29

  16.58

  15.06 60.50 127.31

  92.69 142.4

  8

  83.92 87.08 123.81 178.91 217.4

  3

  1997

  1,29

  R Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

  58.35 55.51 418.99 341.54 111.76 859.15

  28.18 57.64 111.76 31.78 270.64

  70.08

  0.00

  1,01

  3.45

  2000

  7.27

  3.84

  19.19

  70.83

  28.77

  20.26

  1,99

  7

  5.45

  2001 100.04 235.8

  5 199.4

  70.08 142.48

  1.63

  23.01 14.56 155.30 513.11 501.70 398.64

  2,35

  5.82

  2002

  39.30

  0.00

  13.10 69.33 173.17

  76.15

  9 173.1

  57.64 1.36 182.77

  24.14

  1996

  16.07

  26.43

  19.72 128.1 9 152.53

  98.39 71.58 174.12 120.33 177.95 187.62 119.47

  1,29

  2.40

  1997

  3.16

  47.22

  29.37

  52.01

  1.36

  27.01 135.2

  1.91 34.24 198.41 289.04 146.11

  43.87 870.

  84

  1998

  74.62

  18.13

  13.10 30.57 142.48 247.3

  1 244.1 7 335.85 398.64 136.18 241.04 373.88

  2,25

  5.96

  1999

  31.78

  42.55

  89.47 91.08 131.73 891.

  7

  25.5

  1,84

  5.03 Total 680.53 488.4

  1 847.1

  7 884.1

  1 1,203.1

  2 939.7

  7 808.2

  4 1,041.3

  7 2,182.6

  4 2,716.1

  9 1,850.1 2,583.8

  5 16,2

  1 Average

  4

  61.87

  44.40

  77.02 80.37 109.37

  85.43

  73.48 94.67 198.42 246.93 168.19 234.90 1,47

  5.05 Appendix Table 9. Rainfall erosivity (R) based on Lanvine Formula in Kantor Camat Pangkalan Susu Station (1996-2006) in Besitang Watershed YEAR

MONTH

TOTAL

   R Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

  1996

  16.07

  26.43

  19.72 128.1 9 152.53

  98.39 71.58 174.12 120.33 177.95 187.62 119.4

  60.50 74.62 222.51 223.53 96.76 226.59

  320.0

  47

  64.88

  2003

  86.29

  17.09

  0.00 61.23 193.49

  43.87

  85.50 88.67 273.86 281.43 31.17 112.61

  1,27

  5.20

  2004

  78.46

  27.01 145.2

  0.00

  0.00

  2 160.8 9 102.52

  0.00 0.00 115.17 214.39 204.37

  29.96 879.

  44

  2005 186.65

  39.30

  19.19 67.83 102.52 136.1

  8 147.9 4 150.69

  64.88 178.91 168.42 287.95

  1,55

  0.45

  2006

  56.92

  46.54 253.6

  7.57

  

Appendix Table 10. Land use changes across three-time periods under different decision zones

in Besitang Watershed

DZ LAND USE CHANGE

IN 1990-2001 AREA DZ LAND USE CHANGE

  12.80

  0.00

  0.00 primary forest to primary forest 9557.96

  3.17

  0.05 primary forest to plantation

  47.21

  0.05 plantation to secondary forest

  47.21

  1.70 primary forest to plantation

  4 primary forest to bush 1701.72

  0.02

  1.16 primary forest to secondary forest 9542.21

  4 dry land agriculture to secondary forest

  0.00

  0.02

  4 primary forest to dry land agriculture

  3.49

  3490.45

  3.49

  3490.45

  3.49

  9.55 primary forest to bush 1159.28

  9.54 primary forest to secondary forest 1585.02

  93.52

  0.02 unvegetated to bush

  1 primary forest to primary forest 12806.19

  11.27

  0.00 Sub-total 11269.63 11.27 11269.63 11.27 11269.63

  0.48

  0.07 unvegetated to unvegetated

  68.35

  0.01 unvegetated to secondary forest

  10.59

  22.05

  1.58 primary forest to secondary forest 8398.69

  1.03 secondary forest to unvegetated

  0.00 secondary forest to secondary forest 1027.95

  3.17

  0.53 secondary forest to plantation

  0.08 secondary forest to bush 531.86

  79.42

  0.02 primary forest to unvegetated

  22.53

  8.40 primary forest to unvegetated

  0.09 Sub-total 3490.45

  0.00 secondary forest to secondary forest

  1 primary forest to primary forest 1516.79

  0.00 unvegetated to secondary forest

  2 primary forest to bush

  3.00

  2 primary forest to secondary forest 3000.13

  3.00

  2 primary forest to primary forest 3000.13

  12.90

  0.10 Sub-total 12904.57 12.90 12904.57 12.90 12904.57

  98.38

  0.00

  0.03 primary forest to secondary forest 150.51

  0.10 secondary forest to secondary forest

  98.38

  11.38 primary forest to unvegetated

  11.29 primary forest to secondary forest 11387.78

  0.00 primary forest to secondary forest 11289.41

  0.00

  1.52 primary forest to secondary forest

  1 primary forest to primary forest 1516.79

  1.52

  27.11

  0.15 secondary forest to bush

  1.72

  3 primary forest to bush

  IN 1990-2006 AREA Ha % Ha % Ha %

  3.38 primary forest to secondary forest 3476.84

  0.10 primary forest to secondary forest 3383.32

  95.24

  0.01 primary forest to secondary forest

  13.61

  3 primary forest to bush

  0.01

  11.89

  3.39

  27.11

  3 primary forest to primary forest 3395.21

  3.15

  3150.64

  3.15

  3150.64

  3.15

  0.12 Sub-total 3150.64

  3.12 secondary forest to secondary forest 123.40

  0.03 primary forest to secondary forest 3123.53

  3.48 secondary forest to bush

  Appendix Table 10. Continued DZ LAND USE CHANGE IN 1990-2001 AREA DZ LAND USE CHANGE

IN 2001-2006 AREA DZ LAND USE CHANGE

  5 bush to dry land agriculture

  6 bush to dry land agriculture 768.73

  0.66 bush to secondary forest 180.33

  2.33 bush to dry land agriculture 662.43

  0.05 bush to plantation 949.13 0.95 dry land agriculture to dry land agriculture 2333.95

  50.90

  0.08 6 bush to bush

  77.48

  6 dry land agriculture to bush

  0.77

  7.40

  1.03 bush to plantation 921.12

  0.06 Sub-total 7402.24 7.40 7402.24 7.40 7402.24

  61.91

  0.00 unvegetated to secondary forest

  2.98

  0.02 unvegetated to bush

  19.66

  0.00 secondary forest to water

  0.20

  6.52 secondary forest to unvegetated

  0.18 dry land agriculture to plantation 1033.65

  0.92 dry land agriculture to dry land agriculture 2283.31

  2.96

  67.85

  1.52

  0.14 dry land agriculture to dry land agriculture 1520.61

  0.62 0.00 plantation to bush 139.73

  0.02 plantation to plantation

  22.69

  82.57 0.08 bush to water

  0.10 dry land agriculture to water

  99.22

  0.07 plantation to dry land agriculture

  0.00 bush to unvegetated

  2.28 dry land agriculture to rice field 187.01

  0.30

  0.23 dry land agriculture to unvegetated

  0.16 dry land agriculture to secondary forest 226.45

  0.07 bush to secondary forest 160.55

  71.20

  0.49 dry land agriculture to secondary forest

  0.01 dry land agriculture to plantation 486.04

  12.64

  0.19 bush to rice field

  0.00 secondary forest to secondary forest 6526.47

  0.04 secondary forest to rice field

  0.72

  11.53

  0.00 primary forest to dry land agriculture

  1.12

  0.05 dry land agriculture to secondary forest

  47.16

  0.47 primary forest to plantation

  0.05 primary forest to bush 466.77

  50.51

  0.12 dry land agriculture to plantation

  0.01 primary forest to dry land agriculture 117.57

  0.00 bush to secondary forest

  0.01 primary forest to primary forest 188.37

  2.54

  0.02 dry land agriculture to dry land agriculture

  24.75

  0.01 bush to secondary forest

  13.95

  0.06 5 bush to bush

  62.41

  5 dry land agriculture to bush

  0.00

  5.81

  0.19 dry land agriculture to water

  IN 1990-2006 AREA Ha % Ha % Ha %

  0.03 primary forest to secondary forest 6791.66

  0.00 secondary forest to plantation

  3.28

  0.02 secondary forest to dry land agriculture

  21.38

  0.39 primary forest to water

  0.00 secondary forest to bush 393.49

  0.20

  0.19 primary forest to unvegetated

  6.79 primary forest to secondary forest 188.37

  25.32

  1.71

  0.06 plantation to secondary forest

  64.89

  0.00 primary forest to unvegetated

  2.96

  0.02 primary forest to rice field

  21.83

  0.09 primary forest to secondary forest 6958.78 6.96 plantation to bush

  87.99

  0.00 primary forest to plantation

  37.49

  Appendix Table 10. Continued DZ LAND USE CHANGE IN 1990-2001 AREA DZ LAND USE CHANGE

IN 2001-2006 AREA DZ LAND USE CHANGE

  primary forest to dry land agriculture 634.90

  0.11 dry land agriculture to secondary forest

  0.01 primary forest to dry land agriculture 627.92

  5.05

  0.59 dry land agriculture to secondary forest

  0.00 primary forest to dry land agriculture 592.46

  1.97

  0.02 dry land agriculture to plantation

  22.31

  0.00 dry land agriculture to plantation

  1.40

  7 dry land agriculture to dry land agriculture 112.89

  0.17 secondary forest to dry land agriculture

  0.68

  7 dry land agriculture to dry land agriculture 678.56

  0.11

  7 dry land agriculture to dry land agriculture 113.46

  8.44

  8.44 8441.41 8.44 8441.41

  0.00 Sub-total 8441.41

  0.09

  0.15 unvegetated to unvegetated

  0.63 primary forest to secondary forest 171.18

  62.25

  4.71

  1.73

  0.02

  18.46

  0.02 dry land agriculture to fish pond

  18.46

  0.26 dry land agriculture to fish pond

  1.65 dry land agriculture to plantation 263.64

  8 dry land agriculture to dry land agriculture 1648.54

  1.97

  8 dry land agriculture to dry land agriculture 1968.74

  8 dry land agriculture to dry land agriculture 1735.59

  0.06 primary forest to plantation 127.20

  0.88

  0.88 878.49 0.88 878.49

  0.00 Sub-total 878.49

  0.00

  3.47

  0.01 secondary forest to secondary forest

  8.52

  0.11 primary forest to secondary forest

  0.13 secondary forest to plantation 106.85

  0.00 unvegetated to secondary forest 148.49

  0.00 unvegetated to bush

  0.63 plantation to dry land agriculture

  0.10

  63.38

  0.08 dry land agriculture to water

  83.96

  0.15 plantation to unvegetated

  0.02 primary forest to unvegetated 153.29

  15.45

  0.24 dry land agriculture to unvegetated

  2.03 plantation to secondary forest 238.78

  0.00 primary forest to secondary forest 2033.91

  0.02 dry land agriculture to secondary forest

  1.80

  16.67

  0.16 plantation to rice field

  0.08 primary forest to primary forest 163.03

  79.07

  1.33 dry land agriculture to rice field

  0.46 plantation to plantation 1326.79

  1.32 primary forest to plantation 462.44

  0.09 dry land agriculture to plantation 1317.20

  90.51

  0.06 plantation to water

  0.00 plantation to dry land agriculture

  IN 1990-2006 AREA Ha % Ha % Ha % 6.

  0.30 primary forest to rice field 411.55

  0.00 primary forest to water

  2.66

  0.03 secondary forest to water

  30.03

  0.03 primary forest to unvegetated

  28.98

  2.07 secondary forest to unvegetated

  1.61 primary forest to secondary forest 2068.62

  0.41 secondary forest to secondary forest 1607.78

  0.38 secondary forest to rice field 299.57

  20.52

  0.34 primary forest to plantation 382.94

  0.34 secondary forest to plantation 340.15

  0.12 primary forest to dry land agriculture 336.93

  0.22 secondary forest to dry land agriculture 116.03

  0.05 primary forest to bush 216.54

  45.53

  0.08 secondary forest to bush

  79.32

  0.16 plantation to plantation

  0.02 primary forest to secondary forest 163.03

  0.96

  Appendix Table 10. Continued DZ LAND USE CHANGE IN 1990-2001 AREA DZ LAND USE CHANGE

IN 2001-2006 AREA DZ LAND USE CHANGE

  8 primary forest to dry land agriculture 364.87

  0.51

  8.81

  0.00 mangrove forest to plantation

  0.10

  0.01 plantation to rice field

  5.20

  0.01 rice field to mangrove forest

  5.80

  0.00 mangrove forest to mangrove forest

  0.00 plantation to plantation

  0.89 rice field to bush 138.06

  1.43

  0.08 plantation to mangrove forest

  80.38

  0.00 mangrove forest to fish pond

  2.45

  0.24 mangrove forest to rice field

  0.01 plantation to dry land agriculture 245.03

  10.57

  0.02 mangrove forest to bush

  0.01 rice field to rice field 888.90

  0.14 mangrove forest to rice field

  0.01 mangrove forest to fish pond

  80.21

  2.25

  2.25 2249.51 2.25 2249.51

  0.20 Sub-total 2249.51

  1.22 rice filed to fish pond 199.70

  0.02 rice field to rice field 1218.21

  22.29

  0.01 rice field to plantation

  6.39

  0.08 rice field to bush 263.21 0.26 rice field to mangrove forest

  0.04 plantation to rice field

  7.73

  36.46

  0.13 plantation to plantation

  0.69 plantation to fish pond 129.27

  0.00 rice field to rice field 686.89

  0.52

  0.06 plantation to bush

  63.95

  0.82 rice field to fish pond

  0.01 rice filed to dry land agriculture 815.70

  17.04

  11.09

  0.36 dry land agriculture to plantation 113.26

  39.13

  0.14

  9 bush to dry land agriculture 137.94

  2.44

  2.44 2442.10 2.44 2442.10

  0.01 Sub-total 2442.10

  11.34

  0.03 secondary forest to plantation

  34.22

  0.04 secondary forest to dry land agriculture

  0.25 primary forest to plantation

  0.14

  0.05 plantation to plantation 246.77

  45.55

  0.40 primary forest to secondary forest

  0.05 primary forest to dry land agriculture 403.74

  49.32

  0.03 plantation to dry land agriculture

  32.44

  0.33 primary forest to plantation

  0.11 dry land agriculture to plantation 332.24

  9 dry land agriculture to bush 136.24

  9 bush to plantation

  IN 1990-2006 AREA Ha % Ha % Ha %

  1.46

  34.50

  0.73 dry land agriculture to rice field

  0.10 dry land agriculture to rice field 727.61

  0.01 mangrove forest to dry land agriculture 102.20

  5.45

  0.13 dry land agriculture to plantation

  0.00 dry land agriculture to plantation 134.66

  1.77

  0.00 dry land agriculture to mangrove forest

  0.01 dry land agriculture to fish pond

  62.16

  12.19

  0.04 dry land agriculture to mangrove forest

  39.65

  0.08 dry land agriculture to dry land agriculture

  76.39

  0.33 bush to rice field

  0.00 dry land agriculture to fish pond 329.82

  0.61

  0.06 bush to plantation

  0.03 mangrove forest to mangrove forest

  Appendix Table 10. Continued DZ LAND USE CHANGE IN 1990-2001 AREA DZ LAND USE CHANGE

IN 2001-2006 AREA DZ LAND USE CHANGE

  10 bush to dry land agriculture 190.00

  0.11 plantation to plantation

  0.75

  11 bush to dry land agriculture 748.77

  4.75

  4.75 4756.60 4.75 4756.60

  0.16 Sub-total 4756.60

  0.04 swamp to plantation 158.96

  37.21

  0.01 rice field to rice field

  11.19

  0.02 swamp to plantation 112.34

  1.33 11 bush to bush

  20.28

  0.01 plantation to bush

  12.15

  0.05 plantation to rice field

  46.62

  0.00 swamp to dry land agriculture

  1.73

  0.90 mangrove forest to rice field

  0.04 plantation to plantation 904.07

  11 dry land agriculture to bush 1329.38

  9.41

  0.00 rice filed to dry land agriculture

  0.04

  12.46

  0.04 dry land agriculture to dry land agriculture 12468.12

  36.73

  0.01 dry land agriculture to swamp

  9.13

  1.06 dry land agriculture to unvegetated

  2.40 dry land agriculture to bush 1057.25

  0.40 dry land agriculture to rice field 2397.30

  0.00 dry land agriculture to secondary forest 401.65

  5.65 bush to water

  0.01 bush to plantation 211.79

  3.83 dry land agriculture to plantation 5650.03

  0.28 dry land agriculture to plantation 3836.06

  0.40 bush to rice field 282.09

  0.92 dry land agriculture to mangrove forest 396.37

  0.53 dry land agriculture to mangrove forest 924.50

  0.35 bush to plantation 534.83

  18.92 dry land agriculture to fish pond 350.12

  0.13 dry land agriculture to dry land agriculture 18926.80

  12.01 bush to dry land agriculture 134.19

  0.21 dry land agriculture to dry land agriculture 12015.27

  36.78

  0.05

  0.19

  0.02 dry land agriculture to dry land agriculture 2500.26

  0.74 dry land agriculture to mangrove forest

  0.25 dry land agriculture to plantation 740.89

  0.03 dry land agriculture to plantation 253.83

  25.35

  0.02 dry land agriculture to fish pond

  16.53

  0.01 dry land agriculture to mangrove forest

  9.04

  2.50 dry land agriculture to mangrove forest

  24.80

  0.02 mangrove forest to dry land agriculture

  3.52 dry land agriculture to fish pond

  0.05 dry land agriculture to dry land agriculture 3521.43

  53.52

  2.51 bush to rice field

  0.54 dry land agriculture to dry land agriculture 2507.02

  0.68 bush to plantation 538.87

  10 bush to plantation 675.34

  0.02

  15.31

  10 dry land agriculture to bush

  16.65

  10.33

  IN 1990-2006 AREA Ha % Ha % Ha %

  6.77

  4.97

  0.43 0.00 plantation to bush

  0.01 rice field to mangrove forest

  7.26

  0.01 mangrove forest to fish pond

  6.97

  0.02 mangrove forest to rice field

  16.16

  0.01 plantation to plantation

  0.00 mangrove forest to dry land agriculture

  0.01 dry land agriculture to rice field 515.91

  0.18

  0.02 mangrove forest to mangrove forest

  15.31

  0.44 plantation to dry land agriculture

  0.01 dry land agriculture to rice field 442.56

  7.81

  0.01 mangrove forest to fish pond

  5.49

  0.80 mangrove forest to mangrove forest

  0.52 dry land agriculture to plantation 799.48

  0.00 mangrove forest to mangrove forest

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