Comparison of CLUMondo Model and CA-Logistic Model For Land Use Prediction in Urban Area : A Case Study Mueang Chiang Rai District, Chiang Rai Province
Abstract
The purposes of this research were to study the land use change in A.D.2007, A.D.2010 and A.D.2016 and to anticipate the land use change in A.D.2016 in Mueang Chiang Rai district, Chiang Rai province. It is conducted by using CLUMondo model, CA-Logistic model and collected data of the anticipation of land use change from Land Development Department in A.D. 2007 and A.D. 2010 to produce probability analysis of quantitative and spatial transition land use. Land uses are classified into 5 categories; urban area, agriculture, forest area, water body and miscellaneous land, and 8 physical factors consist of distance from the stream, DEM (digital elevation model), slope, annual rainfall, distance from the road and the distance from the market. The finding of land use change in A.D. 2007 - A.D.2010, A.D.2007 - A.D.2016 and A.D.2010 – A.D.2016 found that urban area, agriculture, and water body were increase while forest area and miscellaneous land were decreased. Land use anticipation in A.D.2016 indicated that major land uses are agricultural, followed by forests area, urban area, miscellaneous land and water body. When verify the exactitude by using CLUMondo model, CA-Logistic model, with the correct data from Land Development Department in A.D.2016 showed that overall accuracy 87.71%, 92.01 and kappa coefficient 0.80 and 0.87, respectively. Keyword : land use ; CLUMondo model ; CA-Logistic model ; Logistic RegressionReferences
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Tonsiri, S., Arunpraparut, W., & Khunrattanasiri, W. (2018). Application of CA-Markov model to predict land use changes in Khao Soi Dao Wildlife Sanctuary, Chanthaburi province. Thai Journal of Forestry. 37(2): 138-150. (in Thai)
Van, J. V., & Malek. (2015). The CLUMondo Land Use Change Model Manual And Exercises. University Amsterdam.
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Wiyanont, T., Chompuchan, C., & Taesombat, W. (2020). Land use forecasting in Huai Phak subbasin, Phetchaburi province using CA-Markov model. The 21st Thai Society of Agricultural Engineering. Suranaree University of Technology. (in Thai)
Department of Provincial Administration. (2020). Official Statistics Registration Systems.The bureau of Registration Administration. (in Thai)
Hassan, G., Aliasghar, A., Mohammad, K., & Alireza, C. (2013). Spatio-Temporal Forest Fire Spread Modeling Using Cellular Automata, Honeybee Foraging and GIS. Bulletin of Environment, Pharmacology and Life Sciences. 3(1): 201-214
Iamchuen, N., & Thepwong, W. (2020). Relationship between Physical Factors and Land Use for the Future Land Use Prediction. Journal of Architectural/Planning Research and Studies. 17(2): 79-92. (in Thai)
Jensen, J.R. (2005), Introductory Digital Image Processing: A Remote Sensing Perspective, (New Jersey: Prentice Hall), 526.
Kafy, A. A., Khan, M-H H., Islam, M. A. & Sarker, M-H. S., (2020). Simulation of Future Development Pattern and Identify Its Impact on the Degradation of Agricultural Land: A Machine Learning Based Remote Sensing Approach in Rajshahi District. 1st International Student Research Conference 2020. Dhaka University Research Society (DURS), University of Dhaka, Bangladesh.
Limgomonvilas, T. (2014). Prediction of Lamtakong Watershed Land Use in 2024 with CA-MARKOV Model. Journal of Social Sciences Srinakharinwirot University. 17, 94-113. (in Thai)
Losiri, C. (2016). Land Use Change Model and Urban Area Prediction in the Future. Journal of Social Sciences Srinakharinwirot University, 19, 340-357. (in Thai)
Ninjun, W. (2017). Comparison of Land Use and Land cover prediction using CA-Markov model and Land change modeler model A Case Study of Uttaradit province. The 10th Thai Students Symposium on Geography and Geo-informatics. Buriram Rajabhat University. (in Thai)
Ongsomwang, S., & Iamchuen, N. (2014). Land use prediction using CLUE-S model a case study of Phayao lake, Phayao province. Journal of Remote Sensing and GIS Association of Thailand. 15(1), 26-31. (in Thai)
Russell, G.C. (1999). Accuracy assessment and validation of remotely sensed and other spatial information. International Journal of Wildland Fire, 10, 321-328.
Story, M., & Congalton, R., (1986), Accuracy Assessment: A User’s Perspective, Photogrammetric Engineering & Remote Sensing, 52(3): 397-399.
Tonsiri, S., Arunpraparut, W., & Khunrattanasiri, W. (2018). Application of CA-Markov model to predict land use changes in Khao Soi Dao Wildlife Sanctuary, Chanthaburi province. Thai Journal of Forestry. 37(2): 138-150. (in Thai)
Van, J. V., & Malek. (2015). The CLUMondo Land Use Change Model Manual And Exercises. University Amsterdam.
Verburg, P.H. (2009). Combining Top-Down And Bottom-Up Dynamics In Land Use Modeling: Exploring The Future Of Abandoned Farmlands In Europe With The Dyna-CLUE Model. Springer Netherlands.
Wiyanont, T., Chompuchan, C., & Taesombat, W. (2020). Land use forecasting in Huai Phak subbasin, Phetchaburi province using CA-Markov model. The 21st Thai Society of Agricultural Engineering. Suranaree University of Technology. (in Thai)
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Published
2022-09-01
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