Predictive modelling of the spatial pattern of past and future forest cover changes in India

Abstract

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 km 2 , 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 km 2 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.

Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

“…The important properties of CA is that they demonstrate the spatial and dynamic process and that is why they have been broadly used in land use simulation [46]. Besides, the state of each cell depends on the spatial and temporal state of its neighbours [47].…”

Predicting Land Use Changes in Philadelphia Following Green Infrastructure Policies

“…To evaluate the consequences of urbanization and the validity of possible NBS, social and environmental scientists are increasingly using highly detailed LULCC models [11,12]. Landcover models have been used to address general questions of landcover change and urbanization around the world [2,9,10,[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]; however, only one other study models LULCCs under GI policies [28]. To predict precise landcover transitions and to answer specific questions of policy, future LULCCs need to be modeled at finer scales.…”

“…Most landcover models are created at a 30 m resolution using Landsat imagery [2,9,13,[15][16][17][18]27]; yet, small landcover features, like GI, require modeling at a much higher resolution, as GI projects can be smaller than 30 m. Similarly, urban models have been created at different levels of detail with varying numbers of landcover classes. Some studies present a broad overview of urbanization with only two landcover classes [10,19,[21][22][23][24][25][26][27], usually "urban" and "nature" or "nonurban." Other studies present more realistic models with seven to ten landcover classes representing many of the features in the urban system [13,15,16,18], such as buildings, roads, trees, and grass.…”

Predictive Modeling of Future Forest Cover Change Patterns in Southern Belize

“…A plethora of research has focused on the locations and rates of forest cover change based on remote sensing technology. Recently, the results of these studies have been used to identify the predictors of change and to assess specific areas where forest loss is likely to occur in the future [11][12][13][14][15][16][17][18][19][20].…”