Articles written in Journal of Earth System Science
Volume 126 Issue 1 February 2017 Article ID 0008
This study was carried out to simulate the forest cover changes in India using Land Change Modeler. Classified multi-temporal long-term forest cover data was used to generate the forest covers of 1880 and 2025. The spatial data were overlaid with variables such as the proximity to roads, settlements, water bodies, elevation and slope to determine the relationship between forest cover change and explanatory variables. The predicted forest cover in 1880 indicates an area of 10,42,008 km², which represents 31.7% of the geographical area of India. About 40% of the forest cover in India was lost during the time interval of 1880–2013. Ownership of majority of forest lands by non-governmental agencies and large scale shifting cultivation are responsible for higher deforestation rates in the Northeastern states. The six states of the Northeast (Assam, Manipur, Meghalaya, Mizoram, Nagaland, Tripura) and one union territory (Andaman & Nicobar Islands) had shown an annual gross rate of deforestation of >0.3 from 2005 to 2013 and has been considered in the present study for the prediction of future forest cover in 2025. The modelling results predicted widespread deforestation in Northeast India and in Andaman & Nicobar Islands and hence is likely to affect the remaining forests significantly before 2025. The multilayer perceptron neural network has predicted the forest cover for the period of 1880 and 2025 with a Kappa statistic of >0.70. The model predicted a further decrease of 2305 km2 of forest area in the Northeast and Andaman & Nicobar Islands by 2025. The majority of the protected areas are successful in the protection of the forest cover in the Northeast due to management practices, with the exception of Manas, Sonai-Rupai, Nameri and Marat Longri. The predicted forest cover scenario for the year 2025 would provide useful inputs for effective resource management and help in biodiversity conservation and for mitigating climate change.
Volume 131, 2022
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