Abstract
Abstract. An attempt has been made to explore, evaluate and identify the sensitive parameter(s) of Cellular Automata Markov chain modeling to monitor and predict the future land use land cover pattern scenario in a part of Brahmaputra River Basin, India. For this purpose, 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. Sensitivity analysis has been carried out to identify the land use land cover parameter(s), which have the highest, lowest or intermediate influence on predicted results. The validity of the Cellular Automata Markov process for projecting future land use and cover changes in the study area calculates various Kappa Indices of Agreement (Kstandard) which indicate how well the comparison map agrees and disagrees with the reference map (land use land cover map derived from IRS-P6 LISS III image of 2007). The results shows that the land with or without scrub appeared to be most sensitive parameter as it has highest influences on predicted results of land use land cover of 2007. The second most sensitive parameter was lakes / reservoirs / ponds to predict land use land cover of 2007, followed by river, agricultural crop land, plantation, open land, marshy / swampy, sandy area, aquatic vegetation, built up land, dense forest, degraded forest, waterlogged area and agricultural fallow land. The least sensitive parameter is agricultural fallow land, which has minimum influence on predicted results of land use land cover of 2007. The validation of CA Markov land use land cover prediction results shows Kstandard is 0.7928.
Highlights
Markov chain analysis is a convenient tool for modeling land use and land cover change (LULCC) when changes and processing in the land use land cover (LULC) are difficult to describe
Markov chain analysis will describe LULC from one period to another and will use this as the basis to project future changes. This is accomplished by developing a transition probability matrix of land use and land cover change from time one to time two, which will be the basis for projecting to a later time periods
CA Markov LULCC simulation and forecast model is a meaningful exploration by combining of the process of CA and Markov chain analysis, which takes the complexity of combination CA, Markov chain, multi-criteria evaluation (MCE), and multi-objective land allocation (MOLA) into land use and land cover change account
Summary
Markov chain analysis is a convenient tool for modeling land use and land cover change (LULCC) when changes and processing in the land use land cover (LULC) are difficult to describe. According to the underlying land use and land cover change dynamics between years 1987 and 1997, a series of suitability maps (evidence likelihood map) consisting of built up land suitability, agricultural crop land suitability, agricultural fallow land suitability, plantation suitability, dense forest land suitability, degraded forest land suitability, land with or without scrub suitability, marshy / swampy land suitability, waterlogged area suitability, sandy area suitability, river suitability, lakes/reservoirs/ponds suitability, open land suitability, aquatic vegetation land suitability were standardized between 0 and 255 (Figure 3) The production of these images empirically derived, follows the same procedures of decision making exercise of multi-criteria evaluation (MCE). The total numbers of iterations are based on the number of time steps, for 10 years model will choose to complete run in 10 iterations
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