Monitoring and Prediction of Land Use/Land Cover Change using the Integration of Markov Chain Model and Cellular Automation in the Southeastern Tertiary Hilly Area of Bangladesh

Abstract

Optical satellite imagery has been used for analyzing the spatial distribution, temporal changes and simulation of land use/land cover (LULC) categories in the southeastern hill tracts of Bangladesh. This is the primary work in this study area where multi-temporal Landsat imagery have been used to generate LULC maps for the years of 1989, 2003 and 2014.In the first step, a total seven LULC categories were derived using the integration of NDVI and conventional supervise classification techniques which were further evaluated using the error matrix table and kappa statistics. The result obtained from the accuracy assessment process presented an overall accuracy of more than 90% for all three years with Kappa statistics 1989 (88.67), 2000 (92.33) and 2014 (89.67) respectively. A rigorous change analysis was carried out, using Land Change Modeler (LCM) where dramatic changes were noticed in hilly forest, shrub land and crop land categories for 1989-2014 time span. From 1989 to 2014 hilly forest decreased 3062 sq. km, shrub land and cropland increased 2990.82 sq.km and 258.74 sq.km area respectively. In the second stage, Markov chain-cellular automata was used to model and predict LULC in the study area. At first Markov chain was used to generate transition probability matrices between LULC categories and then cellular automata was used to predict 2014 LULC map. After successful validation of observed and predicted LULC maps of 2014 (Overall accuracy 91.43% and Kappa statistics 90.00), the combined procedure was employed to simulate land-use/land-cover for 2028 and 2042. The simulated result of 2014-2042 shown a dramatic decrease in hilly forest (1831 sq.km.) and water reservoirs (148 sq.km.) whereas a significant increase was noticed in the shrub land (1889 sq.km.), crop land (91 sq.km.) and settlement area (7.5 sq.km). All these findings from the research could offer an opportunity for the more skillful management and policy making on biodiversity, forest, land and other environmental resources in the study region.