000			03398nas|a2200385 i 4500
005			20260818145145.0
008			030530s2002 th u m rtt 00| a1eng d
035			_a.b11885130
099		9	_aAIT Thesis no.SR-02-02
100	1		_aAnwar, Sk. Morshed
245	1	0	_aLand use change dynamics : _ba dynamic spatial simulation
260			_aBangkok : _bAsian Institute of Technology, _c2002
300			_a77 p.
490	1		_aThesis ; _vno. SR-02-02
502			_aThesis (M.Sc.) - Asian Institute of Technology, 2002
500			_aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Science, School of Advanced Technologies
520			_aIt is important to study the driving forces of land use change to understand the change process. Spatially explicit simulation models help to test hypotheses about landscape evolution under several scenarios. This research presents a dynamic simulation model of land use change of Nong Chok area, Central Thailand. Simulation of land use change has been performed integrating remote sensing, Geographical Information Systems (GIS) and dynamic simulation toolkit. The model has been developed based on selected biophysical and human driving forces. It is a cellular automata model that presents vicinity based transitional functions. The study was conceived for the simulation of land use change dynamics, in particular from paddy fields to fishponds. The model was run for 19 years from 1981-2000. Data describing present and historic land use pattern were derived from aerial photographs. Transition functions were developed following entropy calculation by using ID3 algorithm of the land use change datasets. The model uses as its input a land use map (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 the simulation showed considerable performance of the model to diffuse fishponds except few mismatches. To validate this spatial simulation model of land use change dynamics, the simulated maps were compared with the reference land use map (2000) using a set of landscape indices: number of fishpond cells, patch density, mean patch size, edge density, fractal dimension, and mean nearest neighborhood. Further investigation by integrating other variables might allow the model to simulate land use change with greater accuracy.
650		0	_aLand use _xSimulation methods
700	1		_aBorne, Frederic, _eChairperson
700	1		_aSohan Wijesekera, _eExamination committee
700	1		_aFrancois Bousquet, _eExamination committee
710	2		_aGovernment of Japan, _eScholarship Donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. SR-02-02
856			_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B07511
907			_a.b11885130 _bmnait _cu
902			_a240403
998			_b0 _c030530 _dm _ea _fu _g0
945			_lmnait
945			_lmnarc
942			_c22
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909			_aBarcode : 30050120694384 _bCREATED : 2014-04-28 _cRECORD # : i12792627 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 1 _iTOT CHKOUT : 0 _jTOT RENEW : 0
909			_aBarcode : 30050160023577 _bCREATED : 2016-01-04 _cRECORD # : i12919366 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
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