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LAND USE CHANGE DYNAMICS: A DYNAMIC SPATIAL SIMULATION
Sk. Morshed Anwar
A thesis submitted in partial fulfillment of the requirements for the degree ofMaster of Science.
Examination Committee: Dr. Frdric Borne (Chairman)Dr. Sohan WijesekeraDr. Franois Bousquet
Nationality: BangladeshiPrevious degree: Bachelor of Science (Hons.) in Forestry
Scholarship Donor: Government of Japan
Asian Institute of TechnologySchool of Advanced Technologies
First of all, all praise goes to the almighty Allah, the sustainer who has been showering Hisendless blessings on me throughout my life. The leaves of the tree cannot even wavewithout his consent.
I would like to extend my gratitude to the Government of Japan for granting me thescholarship to complete my masters study at AIT and bring this research a success.
It is a great pleasure and privilege to convey my deepest gratefulness and immenseadmiration to my academic advisor and eventually my thesis supervisor, Dr. FrdricBorne for his constant guidance, invaluable inspiration, encouragement, and continuoussupports in taking the challenge of this research idea and give this thesis a final shape.
My sincere appreciation is also extended to my committee member, Dr. Sohan Wijesekerafor his constructive criticism, guidance, and continuous encouragement from the verybeginning day when I started to work with him as a student assistant.
I am extremely grateful to Dr. Francois Bousquet, committee member for his continuousinspiration, technical suggestions and endless supports in understanding simulationmodelling, multi-agents based modelling, in learning Smalltalk, Visual Works anddynamic simulation toolkit, CORMAS. Without his support this study might not end upwith this fruitation.
Sincere thanks to Ms. Ornuma, CIRAD and Dr. Guy Trebuil, Ms. Wipawan, IRRI-Thailand for their cooperation and supports during field survey and my work at IRRI-Thailand office.
My whole hearted acknowledgement goes to my friends Anis, Rony, Azhar, Nila, and allother friends who helped me directly and indirectly by their valuable suggestions andencouragement.
Finally, deepest part in my heart, the faces always shine are my mother, father, brothersand sister. I am lucky in having such great parents. Thanks are due to them for their endlessencouragement, inspiration, and moral supports to start my study at AIT and finish thisresearch.
Here, I would like to dedicate this research work to my great lovely Mother and Fatherwho is the lighthouse in my way towards achieving any success in my life.
It is important to study the driving forces of land use change to understand the changeprocess. Spatially explicit simulation models help to test hypotheses about landscapeevolution under several scenarios. This research presents a dynamic simulation model ofland use change of Nong Chok area, Central Thailand. Simulation of land use change hasbeen performed integrating remote sensing, Geographical Information Systems (GIS) anddynamic simulation toolkit. The model has been developed based on selected biophysicaland human driving forces. It is a cellular automata model that presents vicinity basedtransitional functions. The study was conceived for the simulation of land use changedynamics, in particular from paddy fields to fishponds. The model was run for 19 yearsfrom 1981-2000. Data describing present and historic land use pattern were derived fromaerial photographs. Transition functions were developed following entropy calculation byusing ID3 algorithm of the land use change datasets. The model uses as its input a land usemap (1981), spatial and human variables: distance to canal, age, ownership, religion,education, and family size of the farmers before the simulation starts. The result of thesimulation showed considerable performance of the model to diffuse fishponds except fewmismatches. To validate this spatial simulation model of land use change dynamics, thesimulated maps were compared with the reference land use map (2000) using a set oflandscape indices: number of fishpond cells, patch density, mean patch size, edge density,fractal dimension, and mean nearest neighborhood. Further investigation by integratingother variables might allow the model to simulate land use change with greater accuracy.
TABLE OF COTENTS
Chapter Title Page
Title page iAcknowledgement iiAbstract iiiTable of Contents ivList of Figures viList of Tables viiList of Appendices viii
1. Introduction1.1 Background 11.2 Statement of the problem 11.3 Objectives of the study 21.4 Research questions 21.5 Rationale of the study 21.6 Scope and limitations of the study 31.7 Organization of the thesis 3
2. Study Area2.1 Profile of the study area 42.2 General topography 42.3 Climate 42.4 Land use pattern 4
3. Literature Review3.1 Preprocessing of remote sensing data 7
3.1.1 Geometric correction 73.1.2 Image mosaicing 7
3.2 Elements of aerial photo interpretation 83.3 Spatially explicit land use change simulation 83.4 Cellular automata (CA) 103.5 ID3 algorithm 10
3.5.1 Entropy 113.5.2 Information gain 11
3.6 Landscape pattern analysis 113.6.1 Components of pattern 12
188.8.131.52 Landscape composition 184.108.40.206 Landscape configuration 12
3.6.2 Landscape pattern indices 220.127.116.11 Number of patches (NP) 18.104.22.168 Patch density (PD) 22.214.171.124 Mean patch size (MPS) 126.96.36.199 Edge density (ED) 188.8.131.52 Fractal dimension (FD) 184.108.40.206 Mean nearest neighborhood (MNN) 15
TABLE OF COTENTS (COND.)
Chapter Title Page
4. Materials and Methodology4.1 Land use change analysis 17
4.1.1 Data acquisition and analysis tools 220.127.116.11 Data collection 18.104.22.168 Software 18
4.1.2 Data processing and analysis 184.1.3 Land use change map 20
4.2 Development of the model 204.2.1 Simulation model of land use change dynamics 204.2.2 Model structure 244.2.3 Development of decision rules 29
22.214.171.124 Decision tree construction using ID3 algorithm 294.2.4 Conversion of decision tree into equivalent set of rules 30
4.3 Measuring landscape fragmentation and validation of the model 30
5. Results and Discussion5.1 Data collection and preparation 325.2 Geo-rectification 325.3 Demographic and socio-economic variables 325.4 Land use change map 345.5 Development of decision rules 36
5.5.1 Entropy calculation of Nong Chok dataset 365.5.2 Information gain of Nong Chok dataset 37
5.6 Simulation of land use change 465.7 Validation of the model 49
5.7.1 Area of fishpond 495.7.2 Patch density (PD) 495.7.3 Mean patch size (MPS) 505.7.4 Edge density (ED)505.7.5 Fractal dimension (FD) 515.7.6 Mean nearest neighborhood (MNN) 51
6. Conclusions 53
LIST OF FIGURES
Figure No. Title Page
2.1 Location map of Nong Chok 52.2 Map of the study area, Nong Chok 2000 64.1 Methodological framework of the study 164.2 Flow diagram showing methodology of land use change map preparation 194.3a Photograph shows paddy field of Nong Chok 214.3b Photograph shows fishpond of Nong Chok 214.3c Photograph shows resident & orchard of Nong Chok 224.3d Photograph shows waterbody of Nong Chok 224.3e Photograph shows others of Nong Chok 234.4 Interface of CORMAS and initial state of Nong Chok model 254.5 Spatial entity elements of Nong Chok model 264.6 Dynamic spatial simulation modelling diagram 274.7 Rules of initialization of random fishponds into the model 284.8 Flow diagram of measuring landscape fragmentation and validation of the model 315.1 Land use change map of the study area 355.2 Graph shows land use conversion from 1981-2000 365.3 Decision tree structure 385.4a Decision tree structure 415.4b Decision tree structure 425.4c Decision tree structure 445.5 Transition probability for each time step 465.6 Initialization of the model with random fishponds 475.7 Different maps produced by simulation 485.8 Fishpond areas of simulated map 495.9 Patch density of simulated map 505.10 Mean patch size of simulated map 505.11 Edge density of simulated map 515.12 Fractal dimension of simulated map 515.13 Mean nearest neighborhood of simulated map 52
LIST OF TABLES
Table No. Title Page
4.1 Particulars of multi-temporal aerial photographs 174.2 Decision variables with class ranking 305.1 Driving forces of spatially explicit land use change in Central America 335.2 Areas of different land use types from 1981-2000 365.3 Information gain against each decision variables 395.4 Analysis of landscape indices of the simulated and reference change map 52
LIST OF APPENDICES
Appendix Title Page
T1 Ground control point (GCPs) with RMS 59T2 Cross tabulation between decision variables and Change index 59T3 Calculation of entropy and information gain of Nong Chok change datasets 61T4 Nong Chok land use change datasets 67
Change is a continuous process, but learning is optional. Resources, ecosystem,biophysical environment, and land use/cover on the surface of the earth undergo changesover time. Land cover is the layer of soil and biomass, including natural vegetation, cropsand man made infrastructures that cover the land surface. Land use is the purpose forwhich human exploit the land cover (Fresco, 1994, cited in Verburg et al., 2000). Land usechange is the modification in the purpose of the land, which is not necessarily only thechange in land cover but also changes in intensity and management (Verburg, 2000, citedin Soepboer, 2001). Land use and land cover change are critical issues due to their greatinfluence in globa