Browsing by Author "Sharma, Nayan"

An attempt has been made to assess the dynamics of land use land cover change (LULCC) in the study area. LANDSAT-5 TM, IRS-1C LISS III, IRS-P6 LISS III images of 1987, 1997 and 2007, respectively, were digitally classified for land use land cover (LULC) mapping. The dynamics of LULCC critically analyzed for the two time periods 1987–1997 and 1997–2007. The LULCC analyzed in terms of quantity of change and allocation of change. Relative changes; gross gains, gross losses and persistence; net change and swap changes of LULC of the study area examined carefully. The study provided a better understanding of the LULCC pattern. The total change during (1987–1997) was 68.40% and during (1997–2007) was 80.12%. Major exchanges of areas are in between degraded forest and built up land followed by dense forest and degraded forest. Others dominant systematic transitions are: degraded forest to built up land; dense forest to degraded forest; agricultural land to built up; degraded forest to land with or without scrub; land with or without scrub to built up; and in between river and sandy area. The transformation from forest to built up land especially built-up area constitutes a large percentage of the total landscape. The direct beneficiaries of this research will include resource managers and regional planners as well as others scientific community.

An attempt has been made to explore and evaluate the Cellular Automata (CA) Markov modelling to monitor and predict the future land use and land cover (LULC) scenario in a part of Brahmaputra River basin using LULC maps derived from multi-temporal satellite images. CA Markov is a combined cellular automata/Markov chain/multi-criteria/multi-objective land allocation (MOLA) LULC prediction procedure that adds an element of spatial contiguity as well as knowledge base of the likely spatial distribution of transitions to Markov chain analysis. Evidence likelihood map was used for as knowledge base of the likely spatial procedure in CA Markov model. The predicting quantity and predicting location change have been analysed and statistically evaluated. The validation statistics indicated how well the comparison map agreed and disagreed with the reference map. Predicted results accuracy is slightly higher when compare to others studies of LULC change using CA Markov approaches.