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Forest cover change prediction using hybrid methodology of geoinformatics and Markov chain model: A case study on sub-Himalayan town Gangtok,India |
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| Authors: | Anirban Mukhopadhyay Arun Mondal Sandip Mukherjee Dipam Khatua Subhajit Ghosh Debasish Mitra Tuhin Ghosh |
| Institution: | 1. School of Oceanographic Studies, Jadavpur University, Kolkata, India 2. Department of Water Resources Management and Development, Indian Institute of Technology, Roorkee, India 3. TERI University, New Delhi, India 4. Department of Marine and Atmospheric Sciences, Indian Institute of Remote Sensing, Dehradun, India |
| Abstract: | In the Himalayan states of India, with increasing population and activities, large areas of forested land are being converted into other land-use features. There is a definite cause and effect relationship between changing practice for development and changes in land use. So, an estimation of land use dynamics and a futuristic trend pattern is essential. A combination of geospatial and statistical techniques were applied to assess the present and future land use/land cover scenario of Gangtok, the subHimalayan capital of Sikkim. Multi-temporal satellite imageries of the Landsat series were used to map the changes in land use of Gangtok from 1990 to 2010. Only three major land use classes (built-up area and bare land, step cultivated area, and forest) were considered as the most dynamic land use practices of Gangtok. The conventional supervised classification, and spectral indices-based thresholding using NDVI (Normalized Difference Vegetation Index) and SAVI (Soil Adjusted Vegetation Index) were applied along with the accuracy assessments. Markov modelling was applied for prediction of land use/land cover change and was validated. SAVI provides the most accurate estimate, i.e., the difference between predicted and actual data is minimal. Finally, a combination of Markov modelling and SAVI was used to predict the probable land-use scenario in Gangtok in 2020 AD, which indicted that more forest areas will be converted for step cultivation by the year 2020. |
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