Modeling Future Land Use and Land-Cover Change in the Asyut Region Using Markov Chains and Cellular Automata

2016 ◽  
pp. 99-112 ◽  
Author(s):  
Hatem Mahmoud ◽  
Prasanna Divigalpitiya
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Cover ◽  
Markov Chains ◽  
Land Cover Change

Markov chains–cellular automata modeling and multicriteria analysis of land cover change in the Lower Nhecolândia subregion of the Brazilian Pantanal wetland

2016 ◽  
Vol 10 (1) ◽  
pp. 016004 ◽  
Author(s):  
Vitor Matheus Bacani ◽  
Arnaldo Yoso Sakamoto ◽  
Hervé Quénol ◽  
Clémence Vannier ◽  
Samuel Corgne
Keyword(s):  
Cellular Automata ◽  
Land Cover ◽  
Markov Chains ◽  
Land Cover Change ◽  
Multicriteria Analysis ◽  
Brazilian Pantanal ◽  
Pantanal Wetland

Predicting Land Use and Land Cover Changes for Landscape Planning: An Integration of Markov Chains and Cellular Automata Using GIS

2018 ◽  
pp. 1-31
Author(s):  
Francisco Reis Sacramento Gutierres ◽  
Paulo Jorge Zuzarte de Mendonça Godinho-Ferreira ◽  
Eusebio Joaquim Marques dos Reis ◽  
Carlos Silva Neto
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Cover ◽  
Markov Chains ◽  
Landscape Planning ◽  
Land Cover Changes

scholarly journals CELLULAR AUTOMATA (CA) CONTIGUITY FILTERS IMPACTS ON CA MARKOV MODELING OF LAND USE LAND COVER CHANGE PREDICTIONS RESULTS

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1585-1591
Author(s):  
M. S. Mondal ◽  
N. Sharma ◽  
M. Kappas ◽  
P. K. Garg
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Cover ◽  
Land Cover Change ◽  
Correlation Coefficient ◽  
Satellite Images ◽  
Chain Model ◽  
Land Use Land Cover ◽  
Land Cover Map ◽  
Liss Iii

Abstract. In this study, attempts has been made to find out cellular automata (CA) contiguity filters impacts on Land use land cover change predictions results. Cellular Automata (CA) Markov chain model used to monitor and predict the future land use land cover pattern scenario in a part of Brahmaputra River Basin, India, using land use land cover map derived from multi-temporal satellite images. Land use land cover maps derived from satellite images of Landsat MSS image of 1987 and Landsat TM image of 1997 were used to predict future land use land cover of 2007 using Cellular Automata Markov model. The validity of the Cellular Automata Markov process for projecting future land use and cover changes calculates using various Kappa Indices of Agreement (Kstandard) predicted (results) maps with the reference map (land use land cover map derived from IRS-P6 LISS III image of 2007). The validation shows Kstandard is 0.7928. 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict LULC in 2007 using 1987 and 1997 LULC maps. Regression analysis have been carried out for both predicted quantity as well as prediction location to established the cellular automata (CA) contiguity filters impacts on predictions results. Correlation established that predicted LULC of 2007 and LULC derived from LISS III Image of 2007 are strongly correlated and they are slightly different to each-other but the quantitative prediction results are same for when 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict land use land cover. When we look at the quantity of predicted land use land cover of 2007 area statistics are derived by using 3x3, 5x5 and 7x7 CA contiguity filters, the predicted area statistics are the same. Other hands, the spatial difference between predicted LULC of 2007 and LULC derived from LISS III images of 2007 is evaluated and they are found to be slightly different. Correlation coefficient (r) between predicted LULC classes and LULC derived from LISS III image of 2007 using 3x3, 5x5, 7x7 are 0.7906, 0.7929, 0.7927, respectively. Therefore, the correlation coefficient (r) for 5x5 contiguity filters is highest among 3x3, 5x5, and 7x7 filters and established/produced most geographically / spatially distributed effective results, although the differences between them are very small.

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