GIS-Based Multi Criteria Decision Analysis for Promoting Teak Plantation in Bokeo Province, Lao PDR

Master Thesis submitted within the UNIGIS MSc. programme at the Interfaculty Department of Geoinformatics - Z_GIS University of Salzburg, Austria under the provisions of UNIGIS joint study programme with Goa University, India

Anja Nicolay - Grosse Hokamp (GIS_102905)

A thesis submitted in partial fulfilment of the requirements of the award of the degree of Master of Science (Geographical Information Science & Systems) - MSc (GISc)

Advisor (s): Dr. Shahnawaz Interfaculty Department of Geoinformatics - Z_GIS University of Salzburg, Austria

Bangkok, Thailand - May 2014

ACKNOWLEDGEMENT

I would like to thank the following people for their valuable support:

Employees of RECOFTC in Bokeo and Bangkok.

Frank Siegmund, Ministry of Planning and Investment, Department of Planning (MPI/DOP), Vientiane, LAO PDR

Dr Shahnawaz, Interfaculty Department of Geoinformatics - Z_GIS, University of Salzburg, Austria,

Last but not least I would like to thank my family, especially my husband for all the support and patience during my studies.

SCIENCE PLEDGE

By my signature below, I certify that my thesis is entirely the result of my own work. I have cited all sources I have used in my thesis and I have always indicated their origin.

Bangkok, Thailand – 22. May, 2014

Place and Date Signature

ABSTRACT

GIS based multi criteria decision analysis is a process that combines geographic data with value judgement based on decision makers’ preferences. This analysis combines two different scientific methods: (1) the multi-criteria decision analysis, and (2) the GIS-based analysis.

Multi-criteria decision making (MCDM) is a set of tools to structure the process of decision making in order to enable the consideration and evaluation of a high number of often conflicting criteria, alternatives and opinions. In questions of spatial decisions where multiple criteria need to be considered, Multi-Criteria Decision Analysis (MCDA) is often combined with GIS to enable mutual benefits.

In this study GIS-based MCDA was used to find the most suitable locations for teak plantations in the province Bokeo in Lao PDR. Forest resources, mainly timber have played and still play a central role in Lao’s economy and development. The promotion of teak

(Tectona grandis) plantations is seen as one means to generate and increase the income for the local rural population in the nation’s strive to reduce poverty through sustainable resource management. In Bokeo Province teak is an important element in the rural economy, generating between 25 and 55% of the annual household income.

The Analytical Hierarchy Process (AHP) as a technique for organizing and analysing complex decisions based on a hierarchical tree structure with three levels. AHP was used to structure and organize the research problem and to define the necessary criteria used in the

GIS. The decided on criteria were transformed into spatial layers and translated into suitability raster to be used in the weighted overlay analysis.

The technique of pairwise comparison was used to determine inherent weights for each criterion. Four stakeholder groups determined four different sets of weights that were used in the final step of Weighted Overlay Analysis.

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During discussion with stakeholders it was decided on the following list of criteria to be used in the suitability analysis: altitude, slope, aspect, soil type, distance to and influence of transportation infrastructure, distance to sawmills, accessibility of villages, availability of manpower, landuse and poverty of villages. Protected areas and areas close to the hydrologic network were excluded.

Based on the four different set of weights four suitability maps in raster format were created with ArcGIS software. The results show areas possible for teak plantations and their calculated suitability based on the chosen criteria.

The experience of this study shows that the structured method inherent in the MCDA is very suitable for decisions with a multitude of criteria. Additionally the study shows that the method is very suitable in a problem solving environment where different stakeholders have different views about significance of factors and criteria, their measurement and their combination. The structured approach of MCDA helps in clarifying matters and guides the discussion and decision process. Easy replication and the visualisation of the results are two distinct advantages from using a GIS environment in location analysis.

Key Words: MCDA, GIS, Lao PDR, Analytical Hierarchy Process, Pairwise Comparison,

Location Analysis, Weighted Overlay Analysis

CONTENTS

Acknowledgement ...... i Science Pledge ...... ii Abstract...... iii Contents ...... v List of tables ...... vii List of figures ...... viii List of maps ...... ix List of abbreviations ...... x 1 Introduction ...... 1 1.1 Background ...... 1 1.2 Area of focus ...... 4 1.3 Aims and objectives ...... 9 1.4 Literature review ...... 10 2 Methodology ...... 14 2.1 Methodology ...... 14 2.2 Data ...... 21 2.3 Software ...... 22 3 Processes and results ...... 22 3.1 MCDA procedure ...... 22 3.1.1 Altitude ...... 23 3.1.2 Slope ...... 24 3.1.3 Aspect ...... 27 3.1.4 Soil ...... 30 3.1.5 Transportation infrastructure ...... 32 3.1.6 Distance to sawmills ...... 35 3.1.7 Accessibility ...... 37 3.1.8 Availability of manpower ...... 38 3.1.9 Protected areas ...... 41 3.1.10 Sustainability ...... 42 3.1.11 Land use ...... 42 3.1.12 Poverty reduction ...... 45 3.2 Pairwise comparison ...... 46 3.3 Modelbuilder (steps within ArcGIS) ...... 48 3.3.1 Reference systems ...... 48

3.3.2 Resolution ...... 48 3.3.3 GIS Data Preparation ...... 49 3.4 Results of the location analysis ...... 59 3.5 Validation of Weighted Overlay Analysis...... 65 4 Discussion ...... 66 4.1 Interpretation of results ...... 66 4.2 Discussion on suitability of methods used ...... 70 4.2.1 Multi-criteria decision analysis ...... 70 4.2.2 GIS...... 72 4.3 Recommendations ...... 73 5 Conclusions ...... 75 5.1 Summary ...... 75 5.2 Outlook ...... 76 References ...... 77 Annex ...... 81

LIST OF TABLES

Table 1: Steps and activities ...... 16

Table 2: Meaning and description of scales used in pairwise comparison ...... 20

Table 3: Suitability of aspect ...... 28

Table 4: Soil suitability ...... 32

Table 5: Length and suitability of different transportation routes ...... 34

Table 6: Availability of manpower ...... 40

Table 7: Area sizes of landuse types ...... 44

Table 8: Pairwise comparison matrix ...... 47

Table 9: Weights resulting from pairwise comparison...... 47

Table 10: Analysis result summary ...... 64

Table 11: Analysis criteria ...... 81

Table 12: Data sources ...... 83

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LIST OF FIGURES

Figure 1: Hierarchy tree ...... 17

Figure 2: Influence of radiation and precipitation on aspect ...... 29

Figure 3: Major soils in km^2 ...... 31

Figure 4: Histogram of population density 2009, Bokeo Province, Lao PDR ...... 40

Figure 5: Steps in ArcMap to create elevation, slope and aspect raster ...... 51

Figure 6: Steps in ArcMap to create soil raster ...... 52

Figure 7: Steps in ArcMap to create raster for infrastructure ...... 53

Figure 8: Steps in ArcMap to create raster of distance to processing facilities ...... 54

Figure 9: Steps in ArcMap to create raster: manpower, accessibility and poverty ...... 55

Figure 10: Steps in ArcMap to create the mask ...... 56

Figure 11: Weighted Overlay: final step in ArcMap ...... 59

Figure 12: Comparison of result 2 (left) and result 1 (right) ...... 68

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LIST OF MAPS

Map 1: Overview of Bokeo Province, Lao PDR ...... 5 Map 2: Physiography in Bokeo Province, Lao PDR ...... 6 Map 3: Mean annual temperature in Bokeo Province, Lao PDR ...... 7 Map 4: Mean annual precipitation in Bokeo Province, Lao PDR ...... 7 Map 5: Suitability of altitudes in Bokeo Province, Lao PDR ...... 24 Map 6: Slopes in Bokeo Province, Lao PDR ...... 25 Map 7: Suitability of slopes in Bokeo Province, Lao PDR ...... 26 Map 8: Aspect in Bokeo Province, Lao PDR ...... 27 Map 9: Suitability of aspect in Bokeo Province, Lao PDR ...... 29 Map 10: Major soils in Bokeo Province, Lao PDR ...... 30 Map 11: Transportation network in Bokeo Province, Lao PDR ...... 33 Map 12: Location of sawmills in Bokeo Province, Lao PDR ...... 35 Map 13: Distance to sawmills in Bokeo Province, Lao PDR ...... 36 Map 14: Accessibility of village areas in Bokeo Province, Lao PDR ...... 38 Map 15: Population density in Bokeo Province, Lao PDR ...... 39 Map 16: Suitability based on population density in Bokeo Province, Lao PDR ...... 41 Map 17: Protected areas and protected forests in Bokeo Province, Lao PDR ...... 42 Map 18: Land use in Bokeo Province, Lao PDR, 2008 ...... 43 Map 19: Villages classified as poor in Bokeo Province, Lao PDR ...... 45 Map 20: Mask showing suitable areas for teak plantations in Bokeo Province, Lao PDR .... 57 Map 21: Analysis result (1) using weights determined by the ministry of Planning and Investment ...... 60 Map 22: Analysis result (2) using weights determined by RECOFTC Bokeo ...... 61 Map 23: Analysis result (3) using weights determined by RECOFTC Bangkok ...... 62 Map 24: Analysis result (4) using weights determined by the international expert ...... 63 Map 25: Validation of Weighted Overlay Analysis ...... 65 Map 26: Influence of the transportation infrastructure on analysis results ...... 67

LIST OF ABBREVIATIONS

AHP Analytical Hierarchy Process

DEM Digital Elevation Model

DSS Decision Support System

GIS Geographical Information Science

MADM Multi-attribute decision making

MAF Ministry of Agriculture and Forestry, Lao PDR

MCDA Multi Criteria Decision Analysis

MoNRE Ministry of Natural Resources and Environment, Lao PDR

NGD National Geographic Department Lao PDR

PDPI Provincial Department of Planning and Investment, Bokeo, Lao PDR

RECOFTC Regional Community Forest Training Center for Asia and the Pacific

UNDP United Nations Development Program

UTM Universal Transverse Mercator

1 INTRODUCTION

1.1 BACKGROUND

Location is one of the central concepts in any Geographic information system. The location determines a place or position where something is or happens. The science of GIS is concerned with the location of features, their spatial relationships and correlations. (Longley et al. 2011)

One of the central analysis topics in GIS is location analysis. It uses the common attribute of position to combine different values in order to compute a specific value at every single location. In its definition local analysis is linked with raster data and the computation of values in an output raster based on a function of the input values. (Environmental Systems

Research Institute, Inc. 1995)

Finding the most suitable location is a location analysis in which carefully chosen favourable attributes are selected, combined and analysed to determine the most suitable location for a specific investment or action. In this context the location analysis is a decision support system.

Inherent in location analysis is the consideration of a multitude of different criteria. GIS has the capability of managing and analyse a high volume of diverse data. (Baban 2004)

Multi-criteria decision making (MCDM) is a set of tools to structure the process of decision making in order to enable the consideration and evaluation of a high number of often conflicting criteria, alternatives and opinions. As mentioned by Pomerol & Adam decision is always “a matter of compromise”. No straightforward decision exists. Instead there are always a number of more or less contradictory objectives involved. (Pomerol & Adam 2004)

Herbert Simon was the pioneer in the study of decision making. His work promoted the decision making process from a simple matter of choice into the field of Information

Systems. Today it is widely agreed that decision making is a scientific process with information and the consideration of possible alternatives at its centre.

One frequently used class of methods in the science of decision making are the multi-criteria decision making (MCDM) methods. They involve the evaluation of a set of alternatives on the basis of conflicting and often incommensurate criteria. Conventionally MCDM techniques have been aspatial and assumed a homogenous environment. (Malczewski 1999; Longley et al. 2011)

Another inherent characteristic of decision making is the multicriteria aspect. This is either based on the fact that several objectives are present or that more than one individual is involved in the decision making process. (Pomerol & Adam 2004)

Belton (2002) speaks of a multi-criteria framework in which there is no ‘right answer’ but only a consideration of different choices. The decision is based on criteria as a means or standard of judgement. Every decision requires the balancing of multiple criteria. Multi- criteria decision analysis (MCDA) is therefore an aid to decision making. (Belton 2002)

Malczewski (2006) defines criterion as a “standard of judgement or rule on the basis of which alternative decisions can be evaluated and ordered according to their desirability”.

Criteria are therefore factors that are important and influence the outcome of the decision.

In MCDA the term ‘criteria’ includes both ‘objectives’ and ‘attributes’ resulting in two different methods: multi-objective decision analysis (MODA) and a multi-attribute decision analysis

(MADA). It is important to distinguish between ‘objective’ and ‘attribute’. Hwang and Yoon

(1981) define objectives as a desired direction of change while attributes are defined as synonym to performance parameters, characteristics, or properties. An attribute or a set of attributes enables the evaluation of an objective. These definitions explain the difference between MODA and MADA. (Hwang & Yoon 1981)

Simon (in Malczewski 2006) structures the method of MCDA in three main stages: intelligence, design and choice. This places an emphasis on the identification and evaluation of relevant components, the organization, structuring and understanding the different components and on the communication of participants and stakeholders. The result is a deeper understanding of the nature of the decision. The decision itself is therefore a result of the process but not the main activity. (Malczewski 2006)

In questions of spatial decisions where multiple criteria need to be considered, MCDA is often combined with GIS to enable mutual benefits. Cowen describes GIS as “a decision support system involving the integration of spatially referenced data in a problem solving environment (Cowen in (Malczewski 2006)). Malczewski (2006) shows in his literature review that the application of MCDA combined with GIS is a frequently and increasingly applied method. Additionally the myriad of application domains confirm the success of this methodology. Urban planning, site selection for economic or environmental decisions, land use resource planning, and hazard and risk maps are only a few domains mentioned.

(Malczewski 2006; Effat & Hegazy 2013; Al-Hanbali 2011, p.-; Nyeko 2012; Onunkwo-

Akunne et al. 2012; Peng et al. 2012; van Westen & Damen 2013)

Huang et al. also confirm in their study that GIS-based MCDA was successfully used in an increasing number of environmental applications and problems solving analyses during the considered decade up to 2009. (Huang et al. 2011) Recent successful examples are described by Uribe et al. (2014), Arianoutsou et al. (2011) or Nas et al. (2009) and many others. (Nas et al. 2009; Arianoutsou et al. 2011; Uribe et al. 2014)

Additionally MCDM allows for weighting the criteria according to their importance. (Uribe et al. 2014)

1.2 AREA OF FOCUS

Lao People’s Democratic Republic (PDR) is a landlocked country in South-East Asia, the local heart of the Indo-Chinese Peninsula. The country is situated between 13°50’ and

22°30’ N and 100°4’ and 107°49’ E. The total area is 236,800 km 2. Bordering countries are

China and Myanmar in the North, Vietnam in the East, Cambodia in the South and Thailand in the West. (Lao PDR 2010)

Within the country four major geographic regions can be found: Upper Mekong, Upper

Annamite, Central Plain, Lower Mekong Basin. The Mekong River runs for a length of 1,898 km through the country, draining 80% of Lao PDR’s land area. Approximately 80% of the country is classified as mountainous or hilly. More than 30% of the country’s area has slopes steeper than 30%. (PAD Partnership 2003)

The nation is divided into seventeen provinces. Vientiane situated in the municipal province of Vientiane is the capital. In 2005 the census counted a population of 5.8 million people. In

2012 the population was estimated at 6.5 million people. On average the population density is 24 persons per km 2 which is the lowest density within South-East Asia.

Lao PDR belongs to the group of least developed countries. Together with Cambodia, Lao

PDR is ranked 138 by UNDP in the Human Development Index, placing the country in the

last ranks of medium human development. (Malik & United Nations Development

Programme 2013) With 33.9 % of the population living below the poverty line of 1.25$ per

day (Ravaillon et al. 2008), poverty reduction is one of the main development challenges

within the country. (UNDP 2013; Lao PDR 2010)

The Lao PDR is rich in forests and forest resources. Forest resources, mainly timber have

played and still play a central role in Lao’s economy. In 1998 for example forest products

accounted for 42% of the country’s foreign earning. (Lao PDR 2010) Next to timber water for

hydropower is the principal national resource. (Keonakhone 2006)

Bokeo Province is with an area of 6968 km 2 the smallest province of Lao PDR. It is situated

at the north-western border of the nation, between N 19° 47’ and N 20° 50’ latitude and E

100° 5’ and 101° 15’ longitude. The province is subdivided into five districts: Houayxay,

Tonpheung, Meung, Pha-oudom and Paktha with the capital Houayxay. The main economic

activity is mining and maize production. (IMF 2008)

MAP 1: OVERVIEW OF BOKEO PROVINCE, LAO PDR

Bokeo province is situated in the physiographic unit of the northern Highlands. Apart from the Mekong floodplains along the western border, the entire province has a rugged mountainous topography with an elevation between 500 and 2000m. Map 2 shows the general physiography of the region including the main rivers to give an impression of the area.

MAP 2: PHYSIOGRAPHY IN BOKEO PROVINCE, LAO PDR

Bokeo province has a dry subtropical climate with less than 2000mm annual precipitation.

Map 3 displays the mean annual temperature in Bokeo ranging from 18.2° to 25.1° Celsius.

Coldest months are December and January with lowest mean temperatures of 13° Celsius, hottest month is May with a maximum mean temperature above 28° Celsius. (Hijmans et al.

1965)

Map 4 shows the mean annual precipitation in Bokeo ranging from 1398 to 1686mm annually. The climate is strongly influenced by the annual south west monsoon cycle with a distinct wet season from April to October when up to 90% of the precipitation occurs.

Precipitation in these months is up to 400mm. The dry season from November to March has months with no rainfall at all. (PAD Partnership 2003; Hijmans et al. 1965)

MAP 3: MEAN ANNUAL TEMPERATURE IN BOKEO PROVINCE, LAO PDR

MAP 4: MEAN ANNUAL PRECIPITATION IN BOKEO PROVINCE, LAO PDR

In 2009 the population in Bokeo province was 156,173 people resulting in an average population density of 22.4 people per km 2. The rural population depend strongly on forests and forest products for their livelihood. The impacts of forest loss and degradation and the resulting reduced availability of forest products and environmental services like soil and water protection affect these poor rural communities most severely. (Senyavong 2010;

Roder et al. 1995)

Teak (Tectona grandis) plantations are one means to generate income for the local population in the nations strive to reduce poverty through sustainable resource management. Farmer-owned teak plantations were introduced by the French colonial regime as early as 1950 and expanded rapidly since 1998. The north of Laos with Luang Prabang as the commercial centre is the focus area of this development. In this area teak is an important element in the rural economy, generating between 25 and 55% of the annual household income. (Mohns & Laity 2010)

Teak has many advantages that make the plant ideal for plantations in Bokeo province. Teak is an indigenous plant in the northern Highlands of Lao PDR (Fogdestam & Galnander

2003). The northern highlands have a moist to dry sub-tropical climate with annual rainfalls between 1500-2000mm. Precipitation mainly occurs during the monsoon months April to

October and leads to a seasonal climate with a distinct dry season (PAD Partnership 2003).

This corresponds with the ideal conditions for the production of high quality wood as described by Kaosa-ard (Kaosa-ard in Regional Seminar on Teak et al. 1998).

Additionally teak is easy to manage, grows quickly and is fire tolerant. The timber is valued for its durability, strength, resistance to fungus and termites, little risk of splitting and warping during drying and its carving capability. This is reflected in the high market price that can be achieved by qualitative high teak.

1.3 AIMS AND OBJECTIVES

In the province Bokeo many communities rely on forests and forest products for their daily needs (Roder et al. 1995). The development aim of the government is to get away from the unsustainable slash and burn practices and to enable communities to use forests sustainably and efficiently to increase and stabilize their livelihoods (Mohns & Laity 2010).

To achieve this sustainable forest based production of teak timber and accompanying non- timber goods must be enabled and improved in such a way that smallholder can compete in markets locally as well as on the global level. (Hansen et al. 2005; Newby et al. 2010) Site suitability is seen as a basic question that has not yet been considered scientifically

(Manivong & Sophathilath 2007) in Bokeo province.

The aim of this study is to prove the suitability of the method of using a GIS-based

Multicriteria Decision Analysis to find the most suitable locations for teak plantations. The results of the study will additionally contribute to the development of the study area and will form the basis of decisions concerning the promotion of locations for teak plantations by the provincial government in Bokeo.

The main objective of this study is to identify areas most suitable for promoting teak plantations using a geographic information system. In order to achieve this, the following minor objectives were defined:

Identify, define and evaluate ecologic criteria for teak plantations based on

physiography and climatic conditions. Possible factors include:

o Slope,

o Aspect,

o Altitude, and

o Soil.

Identify, define and evaluate criteria related to the pursued policy of economic

development and poverty reduction

Identify, define and evaluate economic criteria like

o Transportation infrastructure

o Distances, and

o Accessibility

Identify, define and evaluate socio-economic criteria like

o Existing land use, and

o Manpower based on population.

Identify, define and evaluate criteria based on the National Planning Framework like

o Protected areas.

Transform and transfer the identified criteria into spatial parameters and attributes to

integrate them into the GIS environment.

1.4 LITERATURE REVIEW

Herbert Simon is regarded as the pioneer in decision making processes. Since the 1940’s he is seen as the most influential and important contemporary author in terms of organisational theory. Simon changed the viewpoint in organisations and management from the hierarchy in management to the concept of information flow within organisations and the manager as decision maker and the decision making and the resulting action as central part.

Hwang and Yoon define 1980 the term Multi-attribute decision making (MADM) as “making decisions in the presence of multiple, usually conflicting, criteria” (Hwang & Yoon 1981). In their book, they focus on a summary of MADM methods. They also describe decision making as a central concept present in common problems in everyday life. This includes spatial problems like the communal water resources development plan they mention as example.

Pomerol and Adam examined Simons legacy in 2004 and illustrated his impact on research carried out in the decision making area. In their text they cite Crozier, who describes Simon as the “father of the sciences of decisions” and Simon’s work as revolutionary for social

10 sciences and the topic of decision making (Crozier in (Pomerol & Adam 2004)). Pomerol and

Adam link Simon’s work to the current topic of Decision Support Systems (DSS) in which decisions and the resulting actions can-not be separated (Pomerol & Adam 2004).

In 2002 Belton summarized the current most common schools of thoughts within Multicriteria

Decision Analysis (MCDA) and in that created a comprehensive summary of the topics and aspects of decision making (Belton 2002). This summary aims at providing background and information to enable decision maker, students and researchers to use MCDA in an informed manner.

Fundamental concepts of the approach in MCDA are also described and taught in social sciences. Additionally supporting tools exists. An example is the workbook of Mabin &

Beattie that forms a practical guide to employ MCDA practices with the help of supporting software (Mabin & Beattie 2006).

For approximately 25 years the integration of GIS and MCDA started with increasing interest over the years since many spatial decisions involve a large number of different and often conflicting criteria. Joining GIS with the MCDA tools and procedures proved to be a beneficial combination for tackling decision problems. The synergetic effect of combining both techniques is described in several research studies and led to advancement in theoretical and applied research on combining GIS and MCDA (Malczewski 1999).

The success of combining MCDA with GIS is also explained by the fact, that MCDA provides a structured framework that helps in handling and understanding complex problems, and the relationships between different criteria. The same principal benefit to “facilitate decision makers’ learning about and understanding the problem faced” as stated by Belton (2002) is regarded as the main aim of MCDA.

Malczewski also reasons that the increase of research in GIS-MCDA is based on the increased recognition and perception of the decision analysis and support element within

GIS, the availability of low-cost and user friendly MCDA software and an integration of

MCDA techniques in GIS software products (Malczewski 2006).

Within MCDA Malczewski differentiates between two main schemes: multi-attribute decision analysis (MADA) and multi-objective decision analysis (MODA). MADA unite problems with a focus on attributes. They have per definition a limited number of alternatives and this is therefore considered as a selection process. MODA are described as a continuous design process where the best solution can be found within a range of possible solutions

(Malczewski 2006).

A further classification of MADA and MODA is concerned with the combination rule used in the analysis. One of the most common combination rules within MADA is the weighted summation that is also the combination rule used in weighted overlay analysis of many GIS software packages like ArcGIS (Malczewski 2006).

Another very common method within GIS-based MCDA is the analytical hierarchy process

(AHP) developed by Saaty. Due to the clear methodology AHP is very useful in complex decision analysis that contain a large number of attributes or alternatives (Saaty 1995).

GIS-based MCDA with slightly different methods was used in a large number of researches during the last decade. Location analysis and suitability analysis are the two very common research problems in which GIS-based MCDA was applied.

Nas et al. (2010) performed a multi-criteria evaluation combined with GIS. In their study a number of proposed land fill sites were evaluated concerning their suitability based on a limited number of evaluation criteria. Criteria were represented as layers within the GIS framework (Nas et al. 2009).

Similarly Babalola used GIS and Multi-criteria decision methods in his study of land suitability for land fill sites in Malaysia. Multiple data sets containing environmental and

12 policy factors were used to determine a risk free and environmentally friendly waste disposal site. (Babalola 2011)

Al Hanbali (2011) used the method of weighted linear combination to select the most suitable solid waste disposal site in Jordan. Similarly to Nas et al. and Babalola several criteria were considered in the research (Al-Hanbali 2011).

A number of diverse criteria were combined in the analysis of Arianoutsou (2011). Bio- indicators and geo-indicators were synthesized in a GIS to determine the post-fire resilience of forests in Greece. The different criteria included for example forest cover and relative species richness as well as fire history, parent soil material, and slope. (Arianoutsou et al.

2011)

Meng (2011) used in his study the analytical hierarchy process (AHP) to determine the relative importance of criteria. Meng emphasises that AHP is very helpful in understanding and solving complex problems. Within the AHP the complex problem gets decomposed into a hierarchy of elements. This helps in understanding the problem and simplifies the decision analysis by structuring. (Meng 2011)

Also Peng (2011) used AHP to determine the relative weights of criteria in the creation of potential hazard maps to be used in disaster prevention in Taiwan. Peng also shows the practical implementation of AHP in a questionnaire. The determined weights are then used in what Peng calls a map overlaying analysis. The description shows that the method is identical to the weighted overlay analysis, where weights are assigned to each criterion and the sum of these weighted criteria are calculated. (Peng et al. 2012)

Also in the agricultural context site suitability analysis are performed with the method of GIS- based MCDA. Das et al. (2014) performed a research on site suitability for pineapple and oranges in Meghalaya, India. Basis for the analysis were several layers consisting of evaluated soil criteria. For both fruits different suitability criteria were assigned to the attributes. An overlay analysis resulted in separate suitability maps. (Das & Sudhakar 2014)

Uribe et al. (2014) included stakeholder preferences in their research of land suitability.

Including stakeholders is getting more and more common in environmental decisions where

participation of stakeholders is a necessary or even compulsory precondition. Uribe et al.’s

approach is based on the definition and weighting of multiple criteria for evaluating land

suitability. The weights are defined according to preferences of stakeholders. (Uribe et al.

2014)

Teak is a very common species used in plantations to increase the household income in

rural Lao PDR. In 2005 Hansen (2005) described the current situation and best practices

concerning teak plantations in the area of Luang Prabang province in central Lao PDR

(Hansen et al. 2005). Also Keonakhone (2006) focuses in his assessment of the use of teak

plantations at landscape level on the area around Luang Prabang (Keonakhone 2006).

Newby et al. (2010) concentrates on northern Lao PDR in his study, while his main focus is

on Luang Prabang Province (Newby et al. 2010).

As far as the literature suggest Bokeo province was never focus of a suitability study for teak

plantations or any other agricultural or silvicultural usage. Nevertheless many statements

concerning teak plantations, especially the ecologic conditions and statements concerning

community based forest management or general policy can be transferred to the situation in

Bokeo province.

2 METHODOLOGY

2.1 METHODOLOGY

Typically multiple, conflicting and incommensurable evaluation criteria influence the decision and give rise to a large set of alternatives. In order to structure the decision making process to evaluate and prioritize alternative decisions a MCDA will be applied.

Location analysis is based on a multitude of criteria. The basic influencing factors comprise ecologic conditions, economic factors, socio-economic factors and the political and planning framework. Ecologic criteria are the topography with slope, aspect, altitude and soil conditions. Economic criteria are concerned with the transportation infrastructure and the relative location of sites to existing sawmills. Socio-economic criteria are related to population and land use. The political framework comprises existing conditions or limitations

(protected areas) and policy decisions.

In order to find the most sustainable and suitable locations for the promotion of teak plantations the ecological, economic, social and political criteria resulted from the MCDA will be combined in a weighted GIS location analysis.

Wade and Sommer (2006) define analysis as a “systematic examination of a problem or complex entity in order to provide new information from what is already known” (Wade &

Sommer 2006). Important is the systematic methodology that can be replicated. Equally important is the creation of new knowledge from available information.

GIS based multi criteria decision analysis is a process that combines geographic data with value judgement based on decision makers’ preferences. The analysis combines two different scientific methods: (1) the multi-criteria decision analysis, and (2) the GIS-based analysis.

Herbert Simon, the pioneer in studies of decision making (in (Uribe et al. 2014)) divides the process of decision making into three main stages: Intelligence, design and choice. These three stages correspond to the three key phases of MCDA: (1) Problem identification and structuring, (2) Model building and use and (3) Development of action plans (here: interpretation of results) (Belton 2002). Table 1 shows the sequence of steps in this study assigned to these three stages.

TABLE 1: STEPS AND ACTIVITIES

Sequences of steps and activities performed in this study. Stage Steps and Activities Tool Problem Define problem and main objective Analytical Hierarchy Process identification and Identify and evaluate relevant criteria Discussion with stakeholder and structuring literature work Define relevant criteria Analytical Hierarchy Process Assign attribute values of importance Analytical Hierarchy Process Assign weights to the criteria Pairwise Comparison Model building Create criteria layers GIS – ArcMap and use Create different constraint layers GIS – ArcMap Combine constraint layer = masks GIS – ArcMap Mask criteria layer GIS – ArcMap Transform every criteria layer to raster GIS – ArcMap Weighted analysis of raster layers GIS – ArcMap Interpretation of Interpretation of resulting raster layer results

Problem identification and structuring is one of the central themes in MCDA. Saaty’s

Analytical Hierarchy Process (AHP) is a structured technique for organizing and analysing complex decisions based on a hierarchical tree structure with three levels. The first step is to decompose the problem into a hierarchy that consists of all essential elements of the problem in question. At the top level the ultimate goal is places. The lowest level consists the specific elements of the problem, the criteria and attributes (Meng 2011). Saaty’s AHP results in a sound structure and understanding of the problem and enables decision making in an organised way (Saaty 2008).

Figure 1 show the hierarchical value tree of this study with the main objective at the top level, the criteria class at the second level and the actual criteria at the lowest level. This value tree is the result of literature work as well as discussions with consultants of the

Regional Community Forest Training Center for Asia and the Pacific (RECOFTC), GIS and forestry experts from the Ministry of Planning and Investment, Vientiane, Lao PDR.

FIGURE 1: HIERARCHY TREE

The first level contains the overall goal, namely the location analysis of finding the most suitable locations for farmer owned teak plantations in Bokeo, Lao PDR. The second level contains the categories of criteria. The relevant criteria that were identified can be sorted in the following four groups: ecological criteria, economic criteria, socio-economic criteria and the policy and national planning framework. Ecological criteria are the ecologic circumstances, like physiography and soil properties that are favourable for the commercial planting of teak. Economic criteria are factors related to the economic efficiency of teak plantations like distances to market facilities. Socio-economic criteria are related to the population and the current land use. Policy and national planning framework comprises mainly restrictions due to protected areas or considerations in order to promote a desired development.

Each criterion is discussed and assessed according to its attributes that influences teak plantations. From this assessment a scale of suitability is worked out. Afterwards the criteria are transformed into spatial attributes to be used in the GIS analysis process.

Ecologic criteria are based on the topographic conditions. The basic data set is a digital elevation model (DEM) that contains continuous elevation values over a topographic surface

(Wade & Sommer 2006). From this DEM the topographic criteria of slope and aspect can be derived. A second necessary data set contains major soils.

Economic criteria are based on the existing infrastructure. Layers of roads, rivers and other transportation networks are the basic source of information. Socio-economic criteria are based on the census data and on land use data.

Criteria within the political framework and policy are normally not directly represented by a single data set. Restrictions of protected area can be represented by one layer but policy decisions must be translated into a measurable spatial criterion. In this study the main political aim is poverty reduction. Areas classified as poor must therefore get preferential treatment. Poverty is measurable within the census data combined with location of the counted villages.

To enable the comparison of the different criteria and attributes, each criterion needs to be scored or standardized. This involves the conversion of original values into degrees of suitability. Scores do not necessarily have to be linear but depend on the criterion itself.

Sometimes reversing scales might be necessary as well. Standardization was done separately for each criterion explicitly in a subsequent step.

In the model building phase a layer was created for each criterion. The spatial attributes were reclassified into measures of suitability. During standardization it is important to distinguish between factors and constraints.

Factors are attributes that contribute to the location analysis and that possess a degree of suitability. The used standardized output values of factors range from 1 (least suitable) to 10

(most suitable). These standardized values are on a rational scale. The ratio scale has a reference point and the numbers within the scale are comparable. This means that for example 10 is twice as suitable as 5, and 4 is twice as suitable as 2. To comply with the program and to ensure comparability this evaluation system is used in reclassifying the different criterion layers. (Environmental Systems Research Institute, Inc. 2013)

Constraints are restrictions that exclude locations from the analysis. Output values are in a binary format either NoData (a “value” that excludes cells from the analysis) or 1 (suitable).

In GIS constraints are pre-emptive criteria that screen out location alternatives before they are evaluated. They correspond to masks that exclude locations. (van Westen & Damen

2013; Mabin & Beattie 2006)

The aim of standardization is to enable comparability of different criteria. The scale of the criterion must be transformed into the suitability scale. This transformation describes the relationship between criterion and suitability for the specific question, here the suitability of areas for teak plantation promotion. This relationship is not necessarily straight forward and depends on the criterion itself.

With each criterion the following question must be asked: how does the change of a criterion impact on suitability? For some criteria it is a linear relationship. An increase in the original value increases the suitability for the specific aim. An example is the availability of manpower: the denser the population, the more workers are available. Other criteria have an exponential relationship. And some call for an assignment of suitability values based on a general judgement of experts. Additionally a mix of these relationships is possible. The relationship between criterion and suitability is discussed for each criterion separately below.

It is important to consider one criterion at a time. Mixing criteria distorts the analysis and might result in an emphasis on a single criterion. For example stakeholders suggested to

assign a high value to sparsely populated areas in the criterion “availability of manpower” to

support these areas. Instead of integrating this policy measure into the existing criterion this

called for an additional criterion and therefore an additional layer describing the policy of

supporting poor villages.

The last step before the calculation is the assignment of weights to the different criterion

based on the relative importance for the location analysis. To determine the relative

importance of each criterion a pairwise comparison was performed.

In the process of pairwise comparison all unique pairs are compared directly with each other

with the help of a matrix. Saathy’s weights as described in words in table 2 are assigned to

each combination in the matrix. The weighted values are then obtained by adding the values

in each row for each criterion; this sum is then divided by the total sum of all rows. (Saaty

2008)

TABLE 2: MEANING AND DESCRIPTION OF SCALES USED IN PAIRWISE COMPARISON

Scale Definition Description 1 Equally important The contribution of the two factors are equally important 3 Slightly important Experiences and judgement slightly tend to certain factor 5 Quite important Experiences and judgement strongly tend to certain factor 7 Extremely important Experiences and judgement extremely strongly tend to certain factor 9 Absolutely important There is sufficient evidence for absolutely tending to certain factor 2, 4, 6, 8 The median between In between two judgements two neighbouring scales (Saaty in Peng et al. 2012)

For the weighted overlay analysis process, the vector data was generally transformed into raster data by using the ‘polygon to raster’ - tool in ArcMap. The different raster were first reclassified according to the standardised suitability values for each criterion. In case of pre- emptive criteria a mask was created. The detailed processes for the single criterion are described in the chapter “Processes and Results”.

The final suitability map was created by using the raster calculation tool “weighted overlay”.

Resulting output values range from 1 to 10 similar to the input values. This is a function of

the calculation method that multiplied the suitability values with each corresponding weight

and then added the values of the different raster layers.

2.2 DATA

Lao PDR is collecting, preparing and supplying digital geographic data on a national scale.

Most of the data on national and province level was provided by the National Geographic

Department (NGD), Lao PDR. Data on district level was mainly provided by the Ministry of

Agriculture and Forestry (MAF) and their agricultural census of 2011. Data concerning the protected areas was provided by the Ministry of Natural Resources and Environment

(MoNRE), Lao PDR. Detailed census data was provided by the Provincial Department of

Planning and Investment (PDPI) of Bokeo province. The Center for Development and

Environment (CDE), SWISS Development, Lao PDR provided the data about main roads on all levels.

In general the timeliness of the data used was very good. Most of the provided shapefiles were from 2011. Road data was more recent with shapefiles from 2013. Unfortunately land use data was only available of 2008 and census data in English of 2009.

Despite the availability of GIS data and the mission of the government of Lao PDR to cover the entire country with GIS data (shapefiles), this collected data is up to now rarely used in any sophisticated spatial analysis. The use of GIS-based decision making tools to generate a map of suitable areas has so far not been tried in this region.

During the preparation of the data several small inconsistencies in census data like double allocation to the same village code were discovered during joining the census data with the shapefile of the villages in the 5 districts. Automatic cross-checking with the name of the villages was impossible due to the phonetic character of the Lao language. The English

names have different spellings in the different data sets. Correcting these inconsistencies by

hand was necessary.

The digital elevation model (DEM) was provided by the National Geographic Department

(NGD), Lao PDR. The DEM was developed in 2003 within a development project carried out

and funded by a Japanese organisation. Digitized contour lines from topographic maps with

a scale of 1:100,000 form the basis of the developed DEM.

A concise list of the different data sets used including their source and description is

provided in the Annex.

2.3 SOFTWARE

For all GIS operations ArcGIS 10.1 including the Spatial Analyst Extension was used.

3 PROCESSES AND RESULTS

3.1 MCDA PROCEDURE

The main partner during the procedure of finding and deciding on the criteria for a suitability analysis for teak plantations in Bokeo province were the employees of the Regional

Community Forest Training Center for Asia and the Pacific (RECOFTC). In discussions the hierarchy tree as displayed in figure 1 was worked out.

Literature review and further discussions led to the assessment of criteria and the assignment of suitability values for each criterion as shown in table 10 of the Annex.

Standardized suitability values range from 1 to 10 with 1 indicating a low suitability and 10 a high. The scale is a rational scale. As a general procedure several suitability values were assigned, the in between values were then calculated.

3.1.1 ALTITUDE

Altitude is defined as “the height or vertical elevation of a point above a reference surface”

(Wade & Sommer 2006). In other words altitude is the height of an area above sea level.

Map 2 shows the topography of Bokeo province in a natural colour scheme combined with hillshading to give an impression of the topography and altitudes in the study area.

Altitude influences the plant community. The limiting factor concerning altitude is the temperature. Teak is neither frost-resistant nor frost-tolerant. Therefore the lowest temperature limit is +2° Celsius for teak.

The most suitable altitude for teak plants in the relevant latitudes of 12 to 23 degrees North is according to Hansen below 700 to 900m (Hansen et al. 2005). The inability of teak plants, especially of seedlings and saplings to survive temperatures below +2° Celsius is the main limiting factor concerning altitude (Regional Seminar on Teak et al. 1998). Experiences shared during the AHP discussion process show that the altitude limit for teak is approximately 900m and the most suitable range lies between 400 and 700m. Above 700m suitability reduces linear with higher altitude. Below 400 m the suitability decreases at a lesser rate than above 700m. This was incorporated into the assignment of standardized suitability values.

Based on the discussion the following reclassification was decided:

Altitude 0 - 200 - suitability value 8

Altitude 200 – 400 - suitability value 9

Altitude 400 – 700 - suitability value 10

Altitude 700 – 800 - suitability value 8

Altitude 800 – 900 - suitability value 5

Altitude > 900m - suitability value ‘NoData’

Map 5 shows these altitude classes.

MAP 5: SUITABILITY OF ALTITUDES IN BOKEO PROVINCE, LAO PDR

3.1.2 SLOPE

ESRI defines slope as the incline or steepness of a surface. (Environmental Systems

Research Institute, Inc. 1995). ESRI uses two different units of measurement: degree and

percent. Degree slope is the angle between the surface and the horizontal plane. Values

range from 0 to 90 degree. Percent slope is the ratio between change in elevation (rise) to

the horizontal distance travelled (run) multiplied by 100. Values range from 0 to infinite.

Longley et al. (Longley et al. 2011) define a third unit of measurement, the ratio between the

elevation and the actual distance travelled with resulting slope values between 0 and 1.

Therefore it is important to know which one is used.

In ArcGIS software slope is defined and calculated as the rate of maximum change in z-

values from each cell. Important are the units in the data set. If the unit of the z-values is

different to the ground units (x and y) a z-factor needs to be defined.

The unit for x and y of the DEM in this study is similar to the unit of the elevation value:

meter. No z-value needs to be considered. Map 6 shows the slopes in Bokeo province.

MAP 6: SLOPES IN BOKEO PROVINCE, LAO PDR

The slope map was created with the spatial analysis tool “Slope”. Slope values range from

0° to 88.08°. Most authors in forestry literature and practice use the unit of percent to describe slopes. The description of the discussion of this criterion below follows this practice.

To calculate and create the suitability map from slopes the percentages were transformed into degree.

In timber harvesting slope is one of the main limiting factors. Greulich et al. (Greulich et al.

1985) define common slope classes in timber harvesting with < 30%, 30 – 70% and >70%.

However this number is given for the North-American context. During discussions it was discovered that the maximum usable slope is 50% and that slopes below 20% are the most

easy to cultivate. Additionally there was agreement that the relationship between slope and

suitability is not linear but that suitability decreases faster in steeper slopes.

Another aspect with slope is, that the lesser the slope, the higher the moisture content of the

slope. Therefore lesser slopes are more suitable.

Based on the above discussion the following classification is chosen:

Slope < 20% (< 11.31°) - very suitable - suitability value 10

Slope 20 – 30 % (11.31 – 16.70°) - suitable - suitability value 9

Slope 30 – 40% (16.7 – 21.8°) - moderately suitable - suitability value 7

Slope 40 – 50 % (21.8 – 26.57°) - less suitable - suitability value 5

Slope > 50% (> 26.57°) - unsuitable - suitability value ‘NoData’

Map 7 shows the resulting suitability classes based on the above discussed classification of

slopes.

MAP 7: SUITABILITY OF SLOPES IN BOKEO PROVINCE, LAO PDR

3.1.3 ASPECT

Aspect is defined as “the compass direction that a topographic slope faces” (Environmental

Systems Research Institute, Inc. 1995). The measurement unit is ‘degrees from North’. Map

8 displays the aspect map of the study area.

MAP 8: ASPECT IN BOKEO PROVINCE, LAO PDR

Micro climate conditions are influenced by the aspect of a surface. The direction of the slope influences the microclimate of an area. A very conspicuous example is viniculture in northern

Europe where the preferred hillside location for growing wine is south facing. As visible in this example, the latitude of the area of interest is important.

Bokeo province is situated in the northern hemisphere between N 19° 47’ and N 20° 50’ latitude. The sun is predominantly shining from the South throughout most of the year apart from one to two months around the summer solstice in June. This results in a sunnier but also drier microclimate on the southern facing slopes. Teak is a pioneer species and needs

light conditions. However like in all plantations there is an increased danger to the

unprotected soil. These two factors were considered in the assessment of the aspect

conditions related to radiation.

Additionally the south-west monsoon influences the microclimate. South-west facing slopes

are directly exposed to the heavy rainfalls while north-east facing slopes are situated in the

precipitation shadow and receive less rainfall. The slopes directly exposed to sun and

monsoon rain are extremely prone to erosion and degradation.

Resulting from the position towards the sun, the east and west facing slopes are the most

suitable with less danger of drought and a much better soil moisture condition. Concerning

precipitation, the south-west facing slopes are less suitable due to high erosion susceptibility

and the north-west facing slopes due to their position in the rain shadow. The influence of

these two factors is displayed in figure 2.

The two factors were considered separately using a simple classification with three classes:

0 = less suitable, 1 = moderately suitable, 2 = suitable; Locations in between received mean

values. The values of the two factors were combined resulting in the combined suitability.

Finally this was transformed into the standardized value scheme.

Resulting from this discussion it was decided on the following classification:

TABLE 3: SUITABILITY OF ASPECT

Compass Aspect in Suitability Suitability Combined Standardized direction degree resulting from resulting from suitability value radiation precipitation North 0 – 22.5 and 1 1.5 2.5 9 337.5 - 360 North-East 22.5 – 67.5 1.5 2 3.5 10 East 67.5 – 112.5 2 1.5 3.5 10 South-East 112.5 – 157.5 1 1 2 8 South 157.5 – 202.5 0 0.5 0.5 6 South-West 202.5 – 247.5 1 0 1 7 West 247.5 – 292.5 2 0.5 2.5 9 North-West 292.5 – 337.5 1.5 1 2.5 9 Flat areas -1 10

FIGURE 2: INFLUENCE OF RADIATION AND PRECIPITATION ON ASPECT

MAP 9: SUITABILITY OF ASPECT IN BOKEO PROVINCE, LAO PDR

Map 9 shows the suitability categories resulting from the aspect as described above.

3.1.4 SOIL

Teak can grow on a variety of soils. The quality of the timber however depends on different soil properties. Keonakhone describes suitable soils as deep, well-drained and fertile. The range of soil pH in teak forests is with 5.0 to 8.0 very wide although the optimal soil pH is between 6.5 and 7.5 (Regional Seminar on Teak et al. 1998). Another important factor is high calcium content since Calcium deficiency may result in stunted growth. (Keonakhone

2006)

The soil map of Bokeo province is displayed in Map 10. Figure 3 shows a summary of existing major soil types. Acrisols is the most common major soil type in Bokeo province and cover 7686 km 2. Cambisols are the second most common covering an area of 2339 km 2.

The existing soils in the region were assessed concerning their suitability for teak

plantations. The following paragraphs outline this assessment.

MAP 10: MAJOR SOILS IN BOKEO PROVINCE, LAO PDR

The most common soils in Bokeo province are Acrisols. Acrisols are strongly weathered acid

soils with a low level of plant nutrients. They are not very productive and perform best under

acidity-tolerant crops such as pineapple, cashew, oil palm or rubber. Additionally they are

prone to erosion. These limited soil resources are the main problem for agricultural use. It is

suggested that the best way to use them is the very common slash and burn agriculture with

long fallow periods (Driessen et al. 2001)

With the general acidity, the low nutrient content and the susceptibility to erosion, these soils

are only moderately suitable for teak plantations.

FIGURE 3: MAJOR SOILS IN KM^2

Cambisols are soils with incipient soil formation. They can be found on a variety of base material. In the humid tropics and also in Bokeo province dystric and ferallic subtypes are predominant. Despite being poor in nutrients Cambisols are still richer than Acrisols or

Ferrasols. Suitability for teak is not very good but better than Agrisols or Ferrasols.

Luvisols are soils with favourable physical properties. Their good internal drainage, moderate state of weathering and high base saturation makes Luvisols potentially suitable

for a wide range of cultural usage. Gleyic and Ferric subtypes are less fertile but in general

Luvisols are very suitable for teak plantations (and therefore most probably in competition

with other agricultural uses).

Lixisols are strongly weathered soils with low level of available nutrients and low nutrient

reserves. Their moisture holding capacity is slightly better than with Acrisols. Additionally

their higher soil pH is more favourable for teak plantations. Since Lixisols are prone to

erosion perennial crops are preferred on these soils.

Fluvisols are young soils on alluvial deposits. Since Fluvisols are found in the floodplain of

major rivers, they are flooded periodically. This makes them unsuitable for teak plantations.

Leptosols are either shallow soils on hard rock or deeper soils on gravelly base material.

They are highly calcareous which makes them in general suitable for teak plantations.

Leptosols are prone to erosion and are best kept under forest. With a sound management

these soils are the most suitable for teak plantations in Bokeo province.

Table 4 shows the resulting classification of existing soils based on the discussion above.

Included is the classification “Water” that is present in the soil layer but obviously not suitable

for teak plantations.

TABLE 4: SOIL SUITABILITY

Major soil Suitability for teak Suitability value Acrisol Moderately suitable 6 Cambisol Moderately suitable 7 Luvisol Highly suitable 10 Lixisol Moderately suitable 8 Fluvisol Unsuitable NoData Leptosol Highly suitable 9 Water Unsuitable NoData

3.1.5 TRANSPORTATION INFRASTRUCTURE

There are three possible transportation paths for timber: roads, ferry routes and waterways.

The criterion ‘Transportation infrastructure’ encloses the distance to any of these

transportation paths. Distances to roads, to ferry routes and to waterways were combined

into one layer.

Timber transportation in Bokeo province on water is limited to the major rivers. Map 11

shows the transportation network in Bokeo.

MAP 11: TRANSPORTATION NETWORK IN BOKEO PROVINCE, LAO PDR

The road network in the road layer is classified into 7 different road types. Table 5 summarises the road types present:

TABLE 5: LENGTH AND SUITABILITY OF DIFFERENT TRANSPORTATION ROUTES

Transportation Suitability value of areas within 3000m distance of routes Length in km these routes Unpaved Road 1288.675 9 Trail 306.724 8 Collector Road 906.258 10 Interstate 806.18 10 Residential Road 58.085 10 Alley or Driveway 26.932 10 Arterial Road 3.914 10

In the discussion it was decided to assign a lesser suitability to areas close to roads that have an unpaved surface (unpaved road and trail), since they are more difficult or even impossible to travel on during the rainy season. Additionally ferry routes and major rivers received a suitability value of 10.

According to Mohns & Laity studies show that manual extraction of teak harvest is the predominant practice in farmer-owned teak plantations. This limits the financial and economic viability to a maximum distance of 500m to transportation infrastructure (Mohns &

Laity 2010).

At the moment there is an intensive research and educational campaign happening in Bokeo province with the aim to increase the transportation distance of teak. This increases the economically viable distance of teak plantations to transportation infrastructure to a maximum of up to 3000m.

Areas with a greater distance than 3000m to the transportation network are masked out with a suitability value of ‘NoData’. Areas within the 3000m buffer are divided into three classes according to the surface and type of road. Suitable areas are situated around paved roads like ‘Collector Road’, ‘Interstate’, ‘Residential Road’, ‘Alley or Driveway’ or ‘Arterial Road’. A lesser suitability is assigned to ‘unpaved road’ and to ‘trails’. Suitability values are displayed in table 5 above.

3.1.6 DISTANCE TO SAWMILLS

The main customers for teak timber are sawmills that prepare timber for the export market.

Within Bokeo province there is only a limited number of sawmills, most of them located in

Houay Xai district as displayed in map 12. Additionally timber is processed on the opposite side of the Mekong River in Thailand. The distance to these markets are one factor that needs to be considered.

MAP 12: LOCATION OF SAWMILLS IN BOKEO PROVINCE, LAO PDR

The distance to sawmills is closely linked to the transportation network. Distances should be measured along the transportation network to determine the suitability of locations. However during the analysis it was discovered that the road network is of poor quality. Gaps in the connectivity of the roads make this layer unusable for network analysis. To approximate the distance to the sawmills, an Euclidean distance layer was created

Euclidean distance or ‘distance as the crow flies’ is defined as the straight-line distance

between two points (Wade & Sommer 2006). ArcGIS calculates the Euclidean distance

within a raster based on a source layer. Map 13 displays the calculated distance layer. The

distances are classified into 10 classes with regular intervals according to the 10 suitability

classes.

For the distance to suitability a linear relationship was assumed. The calculated values of 0

to 70 km were divided equally on the suitability value range resulting in the reclassification

scheme displayed in the maps legend.

MAP 13: DISTANCE TO SAWMILLS IN BOKEO PROVINCE, LAO PDR

3.1.7 ACCESSIBILITY

The criterion of accessibility to the villages and therefore to manpower came up during discussing the criterion infrastructure. Some villages are difficult or even impossible to access during the rainy season due to bad road conditions. These villages are from an economic viewpoint less suitable for teak plantations.

The necessary data about accessibility is found in the census data of Bokeo province from

2009. Each village was assessed according the state of the access road. Resulting categories are “no road access”, “accessible during dry season only” and “accessible all year round during both seasons”.

Joining the census data tables with the village point layer enables a display of accessibility to villages. Since in Bokeo province the land area is continuously assigned to one village, the suitability was chosen to represent the village land. Map 14 shows the accessibility based on the census data.

It was decided on a suitability value of 10 for all-year accessible villages and a suitability value of 9 for possible disruptions of accessibility in the rainy season. Villages with no road access were classified with a suitability value of 5.

MAP 14: ACCESSIBILITY OF VILLAGE AREAS IN BOKEO PROVINCE, LAO PDR

3.1.8 AVAILABILITY OF MANPOWER

Manpower is defined as the labour force available for a specific task (Anon 2014). Manpower is therefore closely linked to the overall population. In forestry in Lao PDR the criterion of availability of manpower is influenced by several factors including total population, adult population and male population. The discussion of this criterion revealed that within the decades of growing teak coming up tending work like planting, weeding and cutting are performed by available workers within families disregarding age or sex. Only harvesting and the final cutting of timber is performed by groups of male population. It was therefore decided to use the total population as reference value for the criterion of availability of manpower.

Map 15 shows the population density within Bokeo province. The polygons display the

current borders of each village territory. The census data makes use of these boundaries as

basis for their data collection.

MAP 15: POPULATION DENSITY IN BOKEO PROVINCE, LAO PDR

In general a dense population suggests a high availability of manpower and a sparse population lower availability of manpower. Another factor that influences the availability of manpower is the available time of farmers. Since there is no data available on this aspect it was decided to translate population density directly into a scale of suitability.

The histogram of population density in Bokeo province is displayed in figure 4. The graph shows the population density on the x-axis and the number of villages related to the densities on the y-axis. The graph is extremely skewed to the left with several outlier values on the right. Only one village has a population density higher than 2000 ppl per km 2. And

only three villages have densities higher than 1000 ppl per km 2. More than 80 villages have

densities below 15 ppl per km 2.

FIGURE 4: HISTOGRAM OF POPULATION DENSITY 2009, BOKEO PROVINCE, LAO PDR

Based on these observations it was decided that natural breaks represent the inherent structure of the population densities most accurate. According to the range of the suitability values 10 classes were created using the classification along natural breaks within the dataset. Table 6 shows the resulting classes and the assigned suitability values used. Map

16 shows the distribution of the assigned suitability value based on this criterion.

TABLE 6: AVAILABILITY OF MANPOWER

Population density in people per km^2 Suitability value 0 – 14.90 1 14.90 – 28.94 2 28.94 – 53.23 3 53.23 – 92.44 4 92.44 -156.5 5 156.5 – 289.5 6 289.5 – 491.7 7 491.7 – 774.1 8 774.1 – 1173 9 1173 – 2574 10

MAP 16: SUITABILITY BASED ON POPULATION DENSITY IN BOKEO PROVINCE, LAO PDR

3.1.9 PROTECTED AREAS

Lao PDR is one of the few least developed countries that established an extensive set of protected areas as an integrated system. Protected areas cover approximately 21 % of the land area of the entire country.

In Bokeo province two types of protection area exist: (1) the national protection area Nam

Kan in the north-east and (2) protected forest areas on national and district level. Together they cover an area of 2885.7 km 2 or 41.41% of the province’s area.

Concerning the use in GIS, the protection areas form a pre-emptive criterion since plantation is a restricted use in these protected landscapes. Accordingly a value of “NoData” was assigned to the protected areas while all other areas received a value of 1.

MAP 17: PROTECTED AREAS AND PROTECTED FORESTS IN BOKEO PROVINCE, LAO PDR

3.1.10 SUSTAINABILITY

During the discussion of the different criteria experts suggested that to ensure sustainability

and protection of the environment the area around rivers and creeks should be restricted.

This pre-emptive criterion was incorporated into the GIS system by creating a buffer of 100m

from the sides of a river and 50m from the sides of a creek (Food and Agriculture

Organization of the United Nations (FAO) 1998).

3.1.11 LAND USE

Bokeo province is in general a rural province characterised by self-reliant subsistence

farming. Even district centres have populations well below 5000 people per km 2. Larger

settlements are mainly a conglomeration of neighbouring villages (Ireson 1995).

In Bokeo province the land use type is closely related to forests and agricultural activities.

Map 18 shows a map of the land use classification found in Bokeo province. The data

displays the land use classes of 2008. Conspicuous is the absence of settlements and

roads. The data set was provided from the National Geographic Department (NGD), Lao

PDR in Vientiane. After inquiry the exclusion of settlements and roads was explained with

the availability of separate datasets on these topics that can be overlayed on the land use

data layer. For this analysis the exclusion of roads and settlements was discussed during the

processes and judged as unproblematic. The low population density and the rural character

of the entire area were two of the most important reasons in this decision.

MAP 18: LAND USE IN BOKEO PROVINCE, LAO PDR, 2008

TABLE 7: AREA SIZES OF LANDUSE TYPES

Land use type Area in km^2 Total Current Forest Dry Evergreen Forest 46.93 Mixed Deciduous Forest 3223.33 Forest Plantation 1.28 3271.54 Potential Forest Bamboo 773.43 Un-stocked Forest Area 2471.53 Ray 208.53 3453.49 Permanent agricultural land Rice Paddy 164.88 Agricultural Plantation 7 171.88 Other non-forest land Grassland 3.85 3.86 Other Areas Water Bodies 78.41 78.41

As visible in table 7 and in map 18 most of the area in Bokeo province is covered by forests.

Bokeo is a rural province with no large settlements. Only 171 km 2 are covered by permanent

agricultural land. The predominant slash and burn agriculture is practiced in all areas around

settlements. There is quite an amount of change in landuse around villages. This has no

influence on the suitability for teak plantations.

Table 7 suggests a classification concerning the suitability for teak plantations. Water bodies

and Grassland will be excluded from the analysis. Grassland areas possess an inherent

unsuitability based on ecological conditions for teak plantations. This could be shallow soils,

water saturation or any other limiting factor that prevents the growth of trees.

Similarly it was decided to exclude permanent agricultural land. Bokeo is a rural province

with subsistence farming as the most common form or agriculture. Decreasing the

agricultural area would put unnecessary stress on the rural population. All other areas are

classified as suitable for teak plantations. A mask is created from the suitable landuse

categories.

3.1.12 POVERTY REDUCTION

The measure to promote teak plantations in Bokeo province is an attempt to reduce poverty

by creating income opportunities for the local communities and farmers. This fact leads to

the incorporation of another criterion: the poverty of the villages.

The census of Lao PDR evaluated the poverty of the villages on the basis of the following

criteria:

• Number of poor households in the village is higher than 51%,

• The village does not have health service

• The village does not have education service

• The village does not have water/gravity use

• The village does not have road access.

MAP 19: VILLAGES CLASSIFIED AS POOR IN BOKEO PROVINCE, LAO PDR

Map 19 shows the villages classified as poor in the census of 2009. To include this criterion

a suitability value of 10 was assigned to poor villages and a suitability value of 5 was

assigned to non-poor villages.

3.2 PAIRWISE COMPARISON

The next step after deciding on the criteria and their attributes was to assign weights to each criterion according to its relative importance. Weight is defined as a value that indicates the relative importance value of each criterion for a particular calculation. The larger the weight, the higher is the influence of this particular variable on the outcome (Environmental Systems

Research Institute, Inc. 1995). Weights are always numbers between 0 and 1. The sum of weights within a group equals 1. In the GIS analysis these weights will be used in the weighted overlay analysis. (Uribe et al. 2014)

It is very difficult to judge several factors simultaneously, especially with a number of varying criteria as discussed above. For this purpose Saaty developed the method of pairwise comparison within the AHP. The general process was described above. Table 2 shows

Saaty’s scale that was used in the comparison.

The matrix used in the pairwise comparison process was developed on basis of the discussed and decided on criteria. Table 8 shows this matrix. Together with Saaty’s scale and introductions this matrix was distributed to the groups of stakeholders who discussed and decided on the ranking of criteria. The weights for the analysis were calculated from the completed matrix.

TABLE 8: PAIRWISE COMPARISON MATRIX

Pairwise Comparison Matrix Distance Availability Transportation to Accessibility Poverty Elevation Slope Aspect Soil of network processing of villages reduction manpower facilities Elevation 1 Slope 1 Aspect 1 Soil 1

Transportation 1 network

Distance to processing 1 facilities

Accessibility of 1 villages Availability of 1 manpower Poverty 1 reduction

This matrix including a letter with instructions and explanations about the procedure was distributed to 4 groups of stakeholder. The pairwise comparison delivered no indisputable result. Table 9 shows the 4 different weight schemes calculated from the results of the pairwise comparison that was performed by the different groups of stakeholders.

TABLE 9: WEIGHTS RESULTING FROM PAIRWISE COMPARISON

Criteria Weights Ministry of RECOFTC Bokeo RECOFTC Independent Planning and Bangkok expert Investment, Vientiane Elevation 22 5 3 4 Slope 23 13 10 11 Aspect 14 2 1 2 Soil 19 26 16 18 Transportation 10 25 18 14 infrastructure Distance to 4 12 8 4 processing facilities Accessibility of 2 5 19 14 villages Availability of 4 5 4 9 manpower Poverty reduction 2 7 21 24

3.3 MODELBUILDER (STEPS WITHIN ARC GIS)

Esri’s ArcGIS software was used in the entire analysis. ArcMap 10.1 and the extension

“Spatial Analyst” formed the basis of the GIS steps and analysis described below.

3.3.1 REFERENCE SYSTEMS

The datum is defined by ESRI as ”The reference specifications of a measurement system, usually a system of coordinate positions on a surface (a horizontal datum) or heights above or below a surface (a vertical datum).” (Environmental Systems Research Institute, Inc.

1995) The provided data sets possess two different reference systems:

Lao97_UTM_zone_48 and WGS_1984_UTM_Zone_48N. UTM is the acronym for the projected coordinate system Universal Transverse Mercator that divides the world into 60 north and south zones 6 degrees wide. Both systems are based on UTM projection and the same zone 48.

In ArcMap the environment settings include the definition of a reference system. The projection of the digital elevation model (DEM) Lao97_UTM_zone48 was used. Similar this reference system was used as the output coordinate system. Setting the output coordinate system automatically transforms the results of tools used into this specified projection.

3.3.2 RESOLUTION

Resolution is defined as the detail with which a map depicts the location and shape of geographic features (Environmental Systems Research Institute, Inc. 1995). In a raster the resolution corresponds to the dimension represented by each cell.

The specifications of the DEM were used as basis raster for any raster analysis performed.

This DEM was made available by the National Geographic Department (NGD), Lao PDR. It consists of 3851 rows and 3967 columns. The cells in this raster have a size of

30.79634901m x 30.79634901m. One cell covers therefore an area of 948.4151 m2. The

projection is a Transverse Mercator projection with a national datum of ‘D Lao National

Datum 1997’. Resampling the raster to a smaller cell size does not increase the detail of the map area.

Dieters (2014) gives the number of 1.4ha for woodlot sizes in farmer owned teak plantations in Luang Prabang (Dieters 2014). But he also concedes that this number is positively skewed due to a number of large plantations. RECOFTC in Bokeo calculated the average size of small-scale woodlots with 0.3 ha but also mentions a positive skew with 60% of plantations less than 0.3ha (Bianchi 2014). This number justifies the resolution which is 1/3 of this average size.

3.3.3 GIS DATA PREPARATION

The basis of this GIS-based suitability analysis is a weighted overlay analysis. Overlay analysis is a “technique for combining multiple raster by applying a common measurement scale of values to each raster, weighting each according to its importance, and adding them together to create an integrated analysis.” (Environmental Systems Research Institute, Inc.

1995) The precondition is that there exists one raster for each criterion discussed and decided on. The preparation of each raster is described below.

The step of creating a raster from the shapefiles to be used in the weighted overlay was performed with the tool “polygon to raster” and the environmental settings above. Each shapefile was projected to the Lao97-coordinate system before the “polygon to raster” tool was run to guarantee congruence of the different raster. Additionally the spatial references and the resolution of the digital elevation model bokeo_dem_utm_wgs84_z48n.tif that was provided by the NGD of Lao PDR, were used transforming polygon shapefiles into raster with a cell size of 30.8m x 30.8m based on the following coordinate system:

Lao97_UTM_zone_48.

The Spatial Analyst tool “Reclassify” was run on each raster to assign the agreed on suitability value to the attribute values. This was also necessary since the weighted overlay

49 tool only allows integer values in the input value field. The settings within the weighted overlay tool are simplified with this preceding reclassification.

In the following paragraphs the preparation of each raster layer in ArcMap will be described.

Each description is accompanied with a diagram of the steps performed in ArcMap. The symbolisation is similar to the one used in ArcGIS’ ModelBuilder. Tools are represented as yellow rectangles. Existing data is displayed in blue ovals and created data in green ovals.

3.3.3.1 ALTITUDE , SLOPE AND ASPECT

The DEM provided from the NGD of Lao PDR consists of a continuous surface of z-values representing the elevation in m as z-value at each cell. The specification of this DEM forms the basis of all the raster layers used in the analysis. The area of interest was extracted by masking the area with the shapefile of the province boundary using the tool “Extract by mask”. This new raster was used as source for creating raster layers related to the topography of the area.

To receive the slope raster the spatial analysis tool “slope” was performed. The tool extracts the maximum rate of change in z-values of each cell. The slope was calculated in percent as explained above.

Similarly the aspect layer was created by using the spatial analyst tool “aspect”. This tool identifies the direction of maximum change in z-values from each cell to its neighbour.

FIGURE 5: STEPS IN ARCMAP TO CREATE ELEVATION, SLOPE AND ASPECT RASTER

3.3.3.2 SOIL

The shapefile of the soil covers entire Lao PDR. As informed by the Ministry of Agriculture and Forestry (MAF), the data was digitized from the Atlas of Physical, Economic and Social

Resources of the lower Mekong Basin.

Similar to the masking of the DEM, the area of interest was retrieved by combining the soil shape file with the province boundary shapefile. The analysis tool “union” was used to create a geometric union of the two input features. To generate a raster from the vector layer, the tool “polygon to raster’ was run. The attribute of interest “MAJOR_SOIL” was specified as the fields used to assign values to the output raster. Since the input field contains string values, the output raster has an integer value field as well as a string field. Reclassification to the suitability value is not necessary but was done to simplify the settings in the weighted overlay tool.

FIGURE 6: STEPS IN ARCMAP TO CREATE SOIL RASTER

3.3.3.3 TRANSPORTATION INFRASTRUCTURE

The transportation infrastructure consists of roads, including ferry routes and waterways. To generate one road layer of the entire province, the 5 layers of the main roads of each district were combined. Since teak transportation on waterways is limited to major rivers the layer bko_main_river_pg represents the possible transportation routes on waterways.

The area suitable for teak plantations are areas within 3 km distance to these transportation routes. The suitability for teak plantation of the different buffer depends on the type of routes as described above. The analysis tool “buffer” with the specification of dissolving features according to the field “LAYER“ that contains the type of roads that is related to the type of transportation route created a polygon layer with overlapping polygons. These polygons were separated according to the route type as specified in the field “LAYER” by selecting each type of polygon and making a new layer from this selection. Additionally a 3 km buffer around the main rivers was created.

To remove the overlapping buffer areas the tool “erase” was used on the layers “unpaved roads”, “trails” and “main_rivers”. Then the 4 output layers were combined using the tool

“union”.

Before the final transformation to a raster layer was performed a field was added that contains the suitability value of each polygon. This field was used to as value field in the polygon to raster tool.

FIGURE 7: STEPS IN ARCMAP TO CREATE RASTER FOR INFRASTRUCTURE

3.3.3.4 DISTANCE TO SAWMILLS

The basis for this criterion layer formed a newly created point feature layer that contains the

existing sawmills in Bokeo province. To incorporate the entire area of the province two newly

created raster were combined: one from the point feature layer of sawmills and one from the

province boundaries. The new layer was reclassified to having a value of 1 at sawmill

locations and no data value at all other locations. This formed the input raster for the tool

“Euclidean distance” that calculated for each cell the distance to the closest source feature.

Additionally the province boundary was set as mask to exclude cells outside Bokeo province.

FIGURE 8: STEPS IN ARCMAP TO CREATE RASTER OF DISTANCE TO PROCESSING FACILITIES

3.3.3.5 ACCESSIBILITY , AVAILABILITY OF MANPOWER , POVERTY REDUCTION

All three of these criteria are related to the census data of the villages in Bokeo province.

The census data must be joined to a polygon shapefile of the village areas in Bokeo province. In ArcMap the command “join” appends attributes from one to the other based on a field common to both. The common field in both data sets is the village code that functions also as a unique identifier.

Before the table of the census data could be joined with the polygon shapefile of the villages of each district, the table was separated into one table for each district. Additionally the tables were simplified in simple columns for each attribute.

After joining a new column for the population density in peoples per km 2 “Pop_density” was

created in the attribute table of each layer. With the tool “Field calculator” the population

density in peoples per km 2 was calculated and filled into the respective field. The used formula divided the total population of each village area with the area size divided by

1,000,000.

The shapefiles of the 5 districts were merged into one dataset that covers the entire province.

To get the population density in raster format the merged shapefile was converted to a raster

specifying the newly created column as value field. Similarly the accessibility of each village

region needed specifying the column of accessibility as value field. A third raster showing the

poor villages was created by specifying the respective column as value field.

FIGURE 9: STEPS IN ARCMAP TO CREATE RASTER: MANPOWER, ACCESSIBILITY AND POVERTY

3.3.3.6 PROTECTED AREAS , SUSTAINABILITY AND LANDUSE

The remaining three criteria “Protected areas”, “Sustainability” and “Landuse” are pre-

emptive criteria that exclude areas from the analysis without classifying areas according to

their suitability. For these three criteria a mask layer was created that show the suitable

area.

To create a mask for the protected areas the two shapefiles

bko_national_protected_area_pg.shp and bko_national_protection_forest_pg.shp were

combined using the tool “union”. The resulting polygon layer shows the areas that needed to

be excluded from the analysis. A mask however shows the areas included in the analysis.

The tool “Erase” in combination with the polygon of the province boundary was used to

receive the areas to be used in the mask.

FIGURE 10: STEPS IN ARCMAP TO CREATE THE MASK

The mask for sustainability is created by buffering the layer of hydrology lines with a buffer of

50m and the layer of hydrology polygons with a buffer of 100m. These two polygon layers were merged. Again the tool “Erase” in combination with the province boundary was used to receive the areas to be used in the mask.

From the landuse layer all suitable areas were selected by attribute. A new layer was created from the selected features to result in a layer that masks the wanted areas.

The three created polygon layers were combined to form a final mask by using the tool

“intersect”. Map 20 shows the layer containing the mask. The purple area shows the area

that is suitable for teak plantations. Using a mask in ArcGIS excludes all areas not contained

in the polygon layer defined as mask.

MAP 20: MASK SHOWING SUITABLE AREAS FOR TEAK PLANTATIONS IN BOKEO PROVINCE, LAO PDR

3.3.3.7 WEIGHTED OVERLAY

The nine raster layers were used in the weighted overlay analysis. The tool “Weighted

Overlay” demands input raster with integer values. The reclassification of each raster data set to a suitability value according to the discussion above guaranteed a compliance with this condition.

The tool’s syntax is a weighted overlay table that lists all input raster with the chosen evaluation field. A value according to an evaluation scale is assigned within the tool to each attribute in the chosen field. These values correspond to the standardized suitability values

described above. The assignment of these values can be done directly in the “Weighted

Overlay” tool or each layer can be reclassified with the “Reclassify” tool to assign the

standardized suitability value directly.

The weights calculated in the pairwise comparison are assigned to each layer within the

weighted overlay table. All weights must sum up to 100% obviously. The weighted overlay

table was saved to enable a reuse in the weighted overlay tool while only the weights

needed to be changed.

The algorithm used by the tool multiplies the cell by their percentage influence, and then

adds the results together to create the output raster. The formula is

= ∗ where S = the final suitability value, X = suitability values of each cell, n = number of layers and W = the assigned weights.

The final step is the exclusion of unsuitable areas by running the “Extract by mask” tool with the created final mask polygon layer. This final step can be incorporated into the environment settings.

FIGURE 11: WEIGHTED OVERLAY: FINAL STEP IN ARCMAP

3.4 RESULTS OF THE LOCATION ANALYSIS

The result of the tool “Weighted Overlay” is a raster with a range of suitability values from 1 to 10. This corresponds to the input suitability value range used in the analysis. Cells that are excluded either within the input raster or with the final mask have the value “NoData” and are not displayed. The values assigned to each cell are calculated from the sum of the weighted input values.

The excluded areas cover approximately two third of the entire area of Bokeo province.

Suitable is an area of 2376 km 2 or 34.09% of the province. The values of the output raster

range from 5 to 10 with 8 having the highest frequency.

Maps 21 to 24 display the results of the weighted overlay analysis with the different weights

of table 9.

MAP 21: ANALYSIS RESULT (1) USING WEIGHTS DETERMINED BY THE MINISTRY OF PLANNING AND INVESTMENT

In result 1 (map 21) suitability values range from 6 to 10. Most suitable areas with a value of

10 cover 324 ha, 0.05% of the province Bokeo. These areas are situated near the district capital Houay Xai East of the Mekong River.

Two cluster of higher suitability are found on the map: One along the western border stretching from the Mekong River alongside the main road towards the north following the main road; the second at the southern border clustering around the district headquarter Pha

Oudom. A third smaller cluster can be found left of the Mekong river in the south around the

district headquarter Paktha.

Nearly 84 % of the suitable areas have a suitability value of 8 and higher. The value 8 has

the highest frequency, 9 the second highest. Less suitable areas with values of 6 and 7 are

found in a small cluster to the east of Pha Oudom district and along most of the edges of the

suitable area.

MAP 22: ANALYSIS RESULT (2) USING WEIGHTS DETERMINED BY RECOFTC BOKEO

The pattern of result 2 (map 22) is in many parts similar to result 1 (map 21). Clusters of high suitability can be found in Houay Xai east of the district headquarter and near Pha Oudom and to a lesser extent near Paktha.

In general suitability values are lower. Only slightly more than 60 % of the province area has a suitability value of 8 and higher. The second most frequent value is 7. Areas with this

suitability can be found in the east of Pha Oudom and Paktha district ant in the north of

Mueng district.

MAP 23: ANALYSIS RESULT (3) USING WEIGHTS DETERMINED BY RECOFTC BANGKOK

Result 3 (map 23) shows a slightly better suitability than result 2. The most frequent value is

8 and the second most frequent is 7. 63 % of the suitable area has a suitability value of 8 or above. The numbers in table 10 show a slightly lesser frequency of value 7 and 8 and a slightly higher frequency of 6 and 9 compared to result 2.

More interesting than these numbers is the map itself. There are several smaller clusters of higher suitability: two clusters in the southern district of Pha Oudom and two distinct clusters in the south of the most northern district Mueng. Additionally smaller clusters of high suitability can be found in all four northerly districts.

Areas of lesser suitability with a value of 7 are distributed in the eastern parts of the districts

Pha Oudom and Paktha and in the north of Mueng. The least suitable areas with a value of 6

are found at the northern edge of the suitable area in the east of Pha Oudom.

MAP 24: ANALYSIS RESULT (4) USING WEIGHTS DETERMINED BY THE INTERNATIONAL EXPERT

Result 4 (map 24) displays the most unsuitable result of the four analyses. Only 30% of the suitable area has values of 8 and above. The most frequent suitability value is 7. With nearly

64 % is this also the highest single suitability of all the analyses. This is also the only result containing a suitability value of 5.

The pattern of result 4 is in many parts similar to result 3. Clusters of moderately high suitability are in the southern centre of Pha Oudom district and in Mueng district in the north.

Areas with the lowest suitability are clustered in the north of Pha Oudom district.

Table 10 summarizes the results of the analysis of the four maps.

TABLE 10: ANALYSIS RESULT SUMMARY

Ministry Area in % of VALUE COUNT AREA in ha Area in % total province area 5 0 0.0000 0.00% 0.00% 6 32,063 3,041.6244 1.28% 0.44% 7 353,887 33,571.1364 14.13% 4.82% 8 1,469,751 139,426.4589 58.69% 20.00% 9 645,185 61,204.8298 25.76% 8.78% 10 3,412 323.6760 0.14% 0.05% Total 2,504,298 237,567.7255 100.00% 34.09% RECOFTC Bokeo Area in % of VALUE COUNT AREA in ha Area in % total province area 5 0 0.0000 0.00% 0.00% 6 22,462 2,130.8352 0.90% 0.31% 7 938,186 89,000.0767 37.46% 12.77% 8 1,456,589 138,177.8589 58.16% 19.83% 9 86,655 8,220.4399 3.46% 1.18% 10 406 38.5148 0.02% 0.00% Total 2,504,298 237,567.7255 100.00% 34.09% RECOFTC Bangkok Area in % of VALUE COUNT AREA in ha Area in % total province area 5 0 0.0000 0.00% 0.00% 6 37,298 3,538.2375 1.49% 0.51% 7 879,256 83,409.7412 35.11% 11.97% 8 1,421,930 134,889.9675 56.78% 19.35% 9 165,814 15,729.7793 6.62% 2.26% 10 0 0.0000 0.00% 0.00% Total 2,504,298 237,567.7255 100.00% 34.09% Independent Expert Area in % of VALUE COUNT AREA in ha Area in % total province area 5 688 65.2664 0.03% 0.01% 6 150,031 14,232.5408 5.99% 2.04% 7 1,597,044 151,501.9820 63.77% 21.74% 8 726,639 68,931.8821 29.02% 9.89% 9 29,896 2,836.0541 1.19% 0.41% 10 0 0.0000 0.00% 0.00% Total 2,504,298 237,567.7255 100.00% 34.09%

3.5 VALIDATION OF WEIGHTED OVERLAY ANALYSIS

The first step in the validation of the model is to check that the model works in the way expected. Assigning more weight to one specific criterion should result in a better scoring of the areas having a higher weight in this criterion (Mabin & Beattie 2006).

For this purpose the Weighted Overlay Analysis was run with equal weights of 11% assigned to all criteria to get a baseline result. In a second analysis the weight for the criterion of distance to sawmills was increased to 30% and the weights for all other criteria was reduced to 9 %. Map 25 compares both of the results.

MAP 25: VALIDATION OF WEIGHTED OVERLAY ANALYSIS

The right side of map 25 shows the result of the weighted overlay analysis with equal weights. Suitability values range from 5 to 9 with only 310 cells having a value of 5.

The left side of map 25 shows the validation map with a highly increased weight given to the

distance to sawmills. Sawmills are clustered around Houay Xai on the middle of the western

border of the province. With an increased distance the suitability value of the cell should be

decreased in the validation map. This pattern determined by the criterion is visible on the left

side of map 25. Especially the area far north in Meung district and far east of Pha Oudom

district have the lowest suitability values.

The strong influence of the criterion also increased the number of cells with low weights. A

suitability value of 4 emerged in far off areas in the east and the number of cells with a

suitability value of 5 increased to 44331. Additionally the area around sawmills received

higher suitability values.

A second step in validating the model and the analysis is to check whether the overall results

seem reasonable in hindsight (Mabin & Beattie 2006). The following interpretation of results

is equivalent to an examination of results according to their reasonability. No unreasonable

results were detected.

4 DISCUSSION

4.1 INTERPRETATION OF RESULTS

The extent of the suitable area is defined by the mask created from three criteria (protected areas, landuse and sustainability) and the areas excluded within the criteria. The biggest areas excluded correspond to the protected areas as visible in Map 17 where agriculture and silviculture is prohibited. Protected areas cover 41 % of the entire province.

Another strong influence on the excluded areas has the omission of distances of more than

3km from the transportation network of roads and main rivers. The suitable areas are situated within these 3000 m. This is especially apparent in the northern district Mueng

where the suitable area follows exactly the buffer along the main road as depicted in Map

26.

Slopes steeper than 50% and elevations above 900m make up most of the remaining

excluded areas. A smaller percentage was excluded due to the consideration of

sustainability and the concomitant exclusion of areas close to rivers and creeks. Jointly the

excluded areas sum up to 65.61% of the province.

MAP 26: INFLUENCE OF THE TRANSPORTATION INFRASTRUCTURE ON ANALYSIS RESULTS

Depending on the weight allocation the suitability of each cell in the raster was evaluated slightly different in the four analyses.

Conspicuous are the comparatively high suitability values in map 21. The weighing of the ministry clearly focuses on the ecologic criteria of elevation, slope, aspect and soil that have a combined weight of 78%. This focus is the reason for the overall higher suitability. The ecologic criteria have a minimum suitability value of 5. Criteria with low values are the

distance to processing facilities and the availability of manpower. The ministry judged these

two criteria as not very important. The resultant weight of 4 of each explains their low

influence on the map.

The reasoning behind this focus is the long time frame inherent in teak plantations.

Revenues are not immediate but can be expected only after a minimum 20 years and the

economic development as well as the situation concerning infrastructure and population

might be different then.

This focus on ecological criteria also explains the uneven pattern of suitability compared to

the other results. The suitability map is less smooth, more disjoint as visible in figure 11.

Both map frames show the same detail of the suitability maps near Houay Xai. The frame on

the left is a detail of result 2 while the right frame shows the described fuzzy pattern of result

1. Mainly slope and to a much lesser extent aspect account for this phenomenon.

FIGURE 12: COMPARISON OF RESULT 2 (LEFT) AND RESULT 1 (RIGHT)

Soil has with values of 16, 19, 18 and 26% generally the highest weights from the ecological criteria. The soil condition is reckoned as very important throughout the entire analyses. Soil was judged as the most important factor by RECOFTC Bokeo in result 2. Comparing this result as displayed in Map 22 directly with the soil map displayed in Map 10 it is clearly visible, that all the areas with a suitability of 9 correspond to the most suitable soil types

Luvisols, Lixisols and Leptosols. This correlation is to some degree visible in all four result maps.

Similarly the influence of the state of the transportation network was judged as important in all the results. In result 2 this criterion has with 25% the second highest weight, with only 1% less than the soil. Within the resulting map however the influence of this criterion is not clearly detectable. This could also be the result of the overall high suitability values assigned in this criterion of 8 and above.

The distance to the processing facilities was judged as of low importance in result 1 (4%) and 4 (9%) and of medium importance in result 2 (12%) and 3 (8%). The highest weight was assigned in result 2 with 12 %. The influence of this criterion is not clearly detectable in the result maps. There is in all maps a centre of high suitability in the western part of Houay Xai near the district headquarters, but there are several criteria that have a high suitability value in this area, like the transportation infrastructure or the availability of manpower.

Teak plantations are not labour intensive compared to other types of plantations like banana or rubber. Labour is reflected in the criterion of availability of manpower. This low importance is visible in the uniformly low weight of 4, 5 and 9% assigned to this criterion with less than

10 and on average 5.5.

Accessibility of village areas was judged either as of low importance in analyses 1 (2%) and

2 (5%) or of medium to high importance in analyses 3 (19%) and 4 (14%). These last two analyses are generally quite similar in their weight assignment and their focus on poverty reduction. The influence of this criterion is visible in the results maps 3 (map 23) and 4 (map

24) in the unsuitable areas in the northern part of Tonpheun and the north-western corner of

Meung district that received a lower suitability value that changed along the village area

border.

In both analyses 3 and 4 two there is also a clear focus on poverty reduction with a high

weight of the respective criterion of 21 and 24%. In both analyses poverty reduction received

the highest value. The influence is visible in higher suitability values assigned to areas within

villages classified as poor. This is most obvious in two distinct areas: in the south of Meung

district and one area at the western border of Tonpheun district.

The analysis of the results above shows that very often the influence of criteria with a high

weight is clearly visible in the resulting map.

4.2 DISCUSSION ON SUITABILITY OF METHODS USED

4.2.1 MULTI -CRITERIA DECISION ANALYSIS

The experience of this study shows that the structured method inherent in the MCDA is very

suitable for decisions with a multitude of criteria. The hierarchy tree developed at the

beginning of the process helps in clarifying not only questions and aims within the decision

taking process but also the influencing criteria.

Additionally the study shows that the method is very suitable in a problem solving

environment where different stakeholders have different views about significance of factors

and criteria, their measurement and their combination. The structured approach of MCDA

helps in clarifying matters and guides the discussion and decision process.

The study and the discussion on results however also show that especially the assignment

of standardized suitability values and weights are two of the most relevant and influential

steps within MCDA. Both need to be done with utmost care and by informed people or

stakeholders directly.

Allocation of standardized suitability values need to reproduce or have to describe the relationship of the criterion and the suitability. Clarifying and discussing this relationship is an essential part of the process. The most straightforward relationship is linear with a concurrent change of criterion and change of suitability value. But this is not always the correct relationship. Reversing scales might be necessary and very often the relationship is even more complicated. For example a cut-off value or a different rate of increase in suitability at different criterion values is necessary to consider.

In this study the allocation of suitability values was one of the most difficult tasks. Quite often the relationship was not obvious and long discussions emerged that helped in clarifying the influence of different criteria.

The allocation of standardized suitability values has a direct influence on results. In the study this is especially visible with the ecologic criteria. Their lowest suitability value is 5. Result 1 shows an overall better suitability when high suitability values and strong weights come together. This is a fact that needs to be kept in mind.

In this study pairwise comparison was condensed into one matrix. Originally the PA process is done at each level of the hierarchy tree separately. The weights of each criterion in the lowest level are then multiplied with the weight of their respective parent criterion (van

Westen & Damen 2013).

The reason for this merging was on one hand the short time frame and on the other the difficulty of the process itself. Despite a detailed explanation including instructions of the process and the filling in of the matrix difficulties arouse especially with unexperienced participants. Several inquiries and explanations were necessary to come to a sound and understandable result.

4.2.2 GIS

Silviculture belongs to the primary sector of the economy based directly on natural resources. The most influencing factors on silviculture are natural conditions and ecologic criteria like soil, climate and topography. Additionally economic and socio-economic criteria linked to a spatial location are considered. The spatial or geographic character and the regional differences inherent in these criteria explain the ideal suitability of GIS for location and management questions in silviculture. Sarah Elwood (2006) described GIS as a

“powerful mediator of spatial knowledge, social and political power, and intellectual practice” which summarizes the suitability of GIS for this kind of suitability analysis (Elwood 2006).

Despite the spatial character, the challenge in this study was to transform the influencing factors discussed in MCDA into spatial criteria and attributes. The ecologic criteria were defined by the most suitable conditions for teak plantations. They form the precondition and the limitations for the planting and growing of teak and are therefore explicitly spatial.

Much more challenging was the linking of economic influencing factors like the influence of the transportation network or the influence of manpower. These later factors are not directly impacting on the quality of teak but are only indirectly representable by spatial data. These kind of criteria are implicitly spatial.

As described above, the results depend strongly on the criteria and their weights. This relationship is clearly visualised and explained in the interpretation of results. The correct assignment of suitability values and weights is the precondition for the validity of the GIS analysis.

The quality of the GIS analysis is dependent on the availability of sound geographic digital data. In this study all the data was collected and supplied by the government of Lao PDR.

For several years the government of Lao PDR collects, generates and updates spatial data in form of vector shapefiles. The quality of this data increases with experience from year to year.

Using GIS in the decision process has two distinct advantages: the possible and easy

repetition of the analysis and the visualisation of results.

After the preparation of the necessary data raster used in the weighted overlay analysis the

repetition of the weighted overlay analysis is simple and quick. The weights can

straightforwardly be changed in the weighted overlay table. But also the classification of the

criteria can be changed within the weighted overlay table. This makes the model useful for

analysis of, for example, different plants. Nevertheless care needs to be taken during the

weighted overlay analysis. With a multitude of criteria it is important to recheck the

assignment of standardized values in the weighted overlay table to guarantee the

correctness of results.

This possible easy replication is especially useful in a problem solving environment where

different stakeholders are present. Alternatives can easily be produced, compared and

discussed as demonstrated in this study.

The visualisation of the results is one of the main advantages within a GIS. The visualisation

of location gives an easy to understand and conclusive illustration and comparison of the

results.

4.3 RECOMMENDATIONS

At the heart of the MCDA process is the discussion and decision on criteria, their attributes, standardized suitability values and their weights. A concerted effort of all stakeholders is a precondition. The nature of the MCDA procedure allows its application within a workshop that brings together the different stakeholders. MCDA as a participatory tool can be easily incorporated in participatory mapping or similar participatory processes. A facilitator is recommended to guide the processes.

During the discussions and the PA it was discovered that soils are regarded as very important. In this analysis due to the limited time available, the soils were analysed and

73 judged only according to their major soil types. A more detailed classification is present in the shapefile containing a more detailed classification. This could be one source of further information that should be incorporated into the analysis. Additionally it is recommended to research the connection between these soil types and teak plantations in more detail.

The recent development in Bokeo province shows a proliferation of banana and rubber plantations. Since this form of agriculture is very labour intensive and needs more water, areas close to rivers and in densely populated areas are preferred. A strong disadvantage of rubber and banana plantations are their intensive negative impact on soil fertility which makes both forms of agriculture unsustainable. It is the government’s assignment to promote sustainable methods of land use that counteracts this development.

The analysis only considers processing facilities in Bokeo province. To improve the accuracy and relevance of the analysis the incorporation of data from neighbouring provinces is necessary.

This study is only looking into the suitability of locations for teak plantations. The entire topic of teak plantations in Bokeo province comprises a much wider field ranging from policy measures, education about best practices up to research about seedlings and newest technologies.

For the practical aspect there is a definite need to continue the concerted effort of educating the farmers about best sustainable practices and also sustainable harvesting methods to ensure long-term revenue and sustainability.

This analysis is based on available data. For the final site selection the analysis should be accompanied by on-site evaluation and judgement. One possibility of this is the plant- indicator method where fast growing plants are used as indicator for suitability. Suitable plant indicators for teak plantations are Lagerstroemia calcylata (common Crape Myrtle), Xylia dolabriformis (Ironwood of Myanmar) or Bambuseae spp (Bamboo) all of which have similar site requirements but a much more rapid growth (Fogdestam & Galnander 2003).

5 CONCLUSIONS

5.1 SUMMARY

MCDA is a very useful technique for decision taking especially in a context with various stakeholders and a variety of influencing criteria. The structured approach helps in clarifying questions and can function as a guideline for the decision taking process and discussions.

The use of GIS is especially valuable in location based analysis. For analysis questions with a spatial character the use of GIS is recommendable due to easy reproducibility and visualisation.

This GIS based MCDA is grounded on a concerted effort of stakeholders. Within the MCDA process of this study 11 criteria were discussed and developed for a weighted overlay analysis to find the most suitable locations for teak plantations in Bokeo province. The criteria comprise ecologic, economic, socio-economic criteria and criteria concerning policy and the national framework.

Each criterion was translated into a spatial representation and transformed into an expressive raster layer based on the relationship of spatial topic and its suitability for teak plantations.

Weighs were assigned to each criterion in a pairwise comparison carried out by groups of stakeholders. For each groups a suitability map was generated by performing a weighted overlay analysis within the GIS. The model and the results were validated.

The resulting maps depict areas with a variable suitability for teak plantations in Bokeo province based on the multitude and the different weighing of considered criteria.

5.2 OUTLOOK

As mentioned above this study covers only the suitability of areas for teak plantations according to discussed and decided on criteria. Additional fields of study that are related and can complement this study could be:

- Implementation of results, especially the recommended use of teak plantations in

poverty reduction and development. Additional factors that could be included are the

ethnicity of population, their habits, their education and willingness to participate in

poverty reduction measures like teak plantations;

- Suitability of this method in a rural participatory setting;

- Inclusion of cost surface modelling in emphasising the economic criteria;

- More detailed consideration of availability and influence of labour with research in the

actual time family members on one hand have available and on the other hand are

needed for teak plantations;

- Research in suitability and the availability of seedlings;

- Research in the possibility of intercropping with teak;

- Development measures available to increase the overall area of possible locations by

for example infrastructure development;

- Inclusion of satellite data especially for current landuse and spreading of competing

banana and rubber plantations.

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ANNEX

TABLE 11: ANALYSIS CRITERIA

Criteria Layer Attribute Suitability value Elevation Bokeo_DEM 0 - 200m 8 200 - 400m 9 400 - 700m 10 700 - 800m 8 800 - 900m 5 > 900 m NoData

Slope Boeko_DEM_slope < 20% 10 20 - 30% 9 30 - 40% 7 40 - 50% 5 > 50% NoData

Aspect Bokeo_DEM_aspect 0 – 22.5 and 337.5 - 360 9 22.5 – 67.5 10 67.5 – 112.5 10 112.5 – 157.5 8 157.5 – 202.5 6 202.5 – 247.5 7 247.5 – 292.5 9 292.5 – 337.5 9 -1 (flat) 10

Soil bko_soil Acrisol 6 Cambisol 7 Luvisol 10 Lixisol 8 Fluvisol NoData Leptosol 9 Water NoData

Distance to bko_transport Unpaved Road 9 transportation Trail 8 infrastructure Paved Road 10 Waterways 10

Distance to bko_sawmills < 7 km 10 processing 7 -14 km 9

facilities 14 - 21 km 8 21 -28 km 7 28 - 35 km 6 35 - 42 km 5 42 - 49 km 4 49 - 56 km 3 56 - 63 km 2 63 - 70 km 1

Accessibility bko_access both seasons 10 dry season 9 no raod access 5 non response 7

Availability of bko_population 0 – 14.90 ppl/km^2 1 manpower 14.90 – 28.94 ppl/km^2 2 28.94 – 53.23 ppl/km^2 3 53.23 – 92.44 ppl/km^2 4 92.44 -156.5 ppl/km^2 5 156.5 – 289.5 ppl/km^2 6 289.5 – 491.7 ppl/km^2 7 491.7 – 774.1 ppl/km^2 8 774.1 – 1173 ppl/km^2 9 1173 – 2574 ppl/km^2 10

Protected Areas bko_protected protected areas NoData all other areas 1

Sustainability bko_hydrology_li buffer areas NoData all other areas 1

Landuse bko_landuse Current forest 1 Potential Forest 1 Permanent agricultural NoData land Other non -forest land NoData Other Areas NoData

Poverty - bko_population poor villages 10 reduction non -poor villages 5

TABLE 12: DATA SOURCES

Topic Filename Description Spatial Reference Date Format Source Elevation bokeo_dem_utm_wgs84_z48n.tif Bokeo, digital elevation, cell size 30m, Lao97_UTM_zone_48 2003 Raster National Geographic elevation in m, province level, based on Department (NGD), Lao PDR topographic maps (1:100,000) Soil la_soil.shp Laos, polygon shapefile of soil types, WGS_1984_UTM_Zone_48N Vector, Ministry of Agriculture and national level polygon Forestry (MAF), Lao PDR Districts bko_districst_pg2011.shp Bokeo districts, province level WGS_1984_UTM_Zone_48N 2011 Vector, National Geographic polygon Department (NGD), Lao PDR Landuse bko_general_landuse_2008.shp Bokeo general landuse of 2008, WGS_1984_UTM_Zone_48N 2008 Vector, National Geographic province level polygon Department (NGD), Lao PDR River bko_hydrology_lines.shp Bokeo hydrology network of rivers, WGS_1984_UTM_Zone_48N Vector, National Geographic province level polylines Department (NGD), Lao PDR Main rivers, bko_hydrology_pg.shp Bokeo main rivers, province level WGS_1984_UTM_Zone_48N Vector, National Geographic lakes polygon Department (NGD), Lao PDR Protected bko_national_protected_area_pg.shp Bokeo National Protection area Nam WGS_1984_UTM_Zone_48N Vector, Ministry of Natural areas Kan, province level polygon Resources and Environment (MoNRE), Lao PDR Protected bko_national_protection_forest_pg.shp Bokeo protected forest areas, province WGS_1984_UTM_Zone_48N Vector, Ministry of Natural areas level polygon Resources and Environment (MoNRE), Lao PDR Province bko_province_boundary_pg.shp Bokeo province boundary, province WGS_1984_UTM_Zone_48N Vector, National Geographic level polygon Department (NGD), Lao PDR Village Areas bko_village_pg2011.shp Bokeo village areas, province level WGS_1984_UTM_Zone_48N 2011 Vector, Agricultural Census 2011, polygon MAF, Lao PDR Village bko_village_pt2011.shp Bokeo location of villages, province WGS_1984_UTM_Zone_48N 2011 Vector, Agricultural Census, MAF, locations level point Lao PDR District hxy_district_pg2011.shp Houay Xai district boundary, district WGS_1984_UTM_Zone_48N 2011 Vector, National Geographic level polygon Department (NGD), Lao PDR Village Areas hxy_village_pg2011.shp Houay Xai village areas, district level WGS_1984_UTM_Zone_48N 2011 Vector, Agricultural Census 2011, polygon MAF, Lao PDR Village hxy_village_pt2011.shp Houay Xai location of villages, district WGS_1984_UTM_Zone_48N 2011 Vector, Agricultural Census 2011, locations level point MAF, Lao PDR Main roads hxy_main_road_ln2013 Houay Xai main roads, district level WGS_1984_UTM_Zone_48N 2013 Vector, CDE, SWISS Development, polylines Lao PDR

District me_district_pg2011.shp Meung district boundary, district level WGS_1984_UTM_Zone_48N 2011 Vector, National Geographic polygon Department (NGD), Lao PDR Village Areas me_village_pg2011.shp Meung village areas, district level WGS_1984_UTM_Zone_48N 2011 Vector, Agricultural Census 2011, polygon MAF, Lao PDR Village me_village_pt2011.shp Meung location of villages, district level WGS_1984_UTM_Zone_48N 2011 Vector, Agricultural Census, MAF, locations point Lao PDR Main roads me_main_road_ln2013 Meung main roads, district level WGS_1984_UTM_Zone_48N 2013 Vector, CDE, SWISS Development, polylines Lao PDR District pkt_district_pg2011.shp Paktha district boundary, district level WGS_1984_UTM_Zone_48N 2011 Vector, National Geographic polygon Department (NGD), Lao PDR Village Areas pkt_village_pg2011.shp Paktha village areas, district level WGS_1984_UTM_Zone_48N 2011 Vector, Agricultural Census 2011, polygon MAF, Lao PDR Village pkt_village_pt2011.shp Paktha location of villages, district level WGS_1984_UTM_Zone_48N 2011 Vector, Agricultural Census 2011, locations point MAF, Lao PDR Main roads pkt_main_road_ln2013 Paktha main roads, district level WGS_1984_UTM_Zone_48N 2013 Vector, CDE, SWISS Development, polylines Lao PDR District phdm_district_pg2011.shp Pha Oudom district boundary, district WGS_1984_UTM_Zone_48N 2011 Vector, National Geographic level polygon Department (NGD), Lao PDR Village Areas phdm_village_pg2011.shp Pha Oudom village areas, district level WGS_1984_UTM_Zone_48N 2011 Vector, Agricultural Census 2011, polygon MAF, Lao PDR Village phdm_village_pt2011.shp Pha Oudom location of villages, district WGS_1984_UTM_Zone_48N 2011 Vector, Agricultural Census, MAF, locations level point Lao PDR Main roads phdm_main_road_ln2013 Pha Oudom main roads, district level WGS_1984_UTM_Zone_48N 2013 Vector, CDE, SWISS Development, polylines Lao PDR District tnpg_district_pg2011.shp Tonpheun district boundary, district WGS_1984_UTM_Zone_48N 2011 Vector, National Geographic level polygon Department (NGD), Lao PDR Village Areas tnpg_village_pg2011.shp Tonpheun village areas, district level WGS_1984_UTM_Zone_48N 2011 Vector, Agricultural Census 2011, polygon MAF, Lao PDR Village tnpg_village_pt2011.shp Tonpeun location of villages, district WGS_1984_UTM_Zone_48N 2011 Vector, Agricultural Census, MAF, locations level point Lao PDR Main roads tpng_main_road_ln2013 Tonpheun main roads, district level WGS_1984_UTM_Zone_48N 2013 Vector, CDE, SWISS Development, polylines Lao PDR Census data Data Bokeo Pop_15.01.2009english.xls Census data of Bokeo of 2009 2009 Excle Provincial Department of sheet, Planning and Investment table (PDPI), Bokeo

Sawmills bko_sawmills.shp Location of sawmills in Bokeo, province WGS_1984_UTM_Zone_48N 04- Vector, Collected data data 2014 point Temperature tmean_29_tif.zip mean annual temperature based on WGS_1984_UTM_Zone_48N 2005 Raster Hijmans, R.J., Cameron, S.E., global climate layer, resolution 30 Parra, J.L., Jones, P.G., Jarvis, seconds, tmean = average monthly A., 2005. Very high mean temperature resolution interpolated climate surface for global land areas. http://www.worldclim.org/ Precipitation prec_29_tif.zip mean annual precipitaion on global WGS_1984_UTM_Zone_48N 2005 Raster Hijmans, R.J., Cameron, S.E., climate layer, resolution 30 seconds, Parra, J.L., Jones, P.G., Jarvis, prec = average monthly precipitation A., 2005. Very high (mm) resolution interpolated climate surface for global land areas. http://www.worldclim.org/