Deforestation and forest fragmentation in the highlands of Eastern Ghats, India

Abstract

Tropical forest cover change along with increasing fragmentation has detrimental effects on the global biodiversity. In the current study change in both forest cover and fragmentation of Koraput district have been assessed in the past three decades (1987 − 2017) and future decade (2017 − 2027), which has been modelled using logistic regression showing a gradual decrease in the forest cover and increase in fragmentation. The long term deforestation rates from 1987 to 2017 (current period) and from 1987 to 2027 (predicted period) were found to be -0.018 and -0.012, respectively. Out of the total geographical area, 2027 number of grids (1 km²) out of 8856 grids were found to have shown extinction of forest in the study period. The conversion of forested lands into other land uses has been one of the major causes of deforestation in Koraput, especially because of the increasing mining activities and establishment of three major industries namely National Aluminium Company (NALCO), Damanjodi, Hindustan Aeronautics Limited (HAL), Sunabeda and Ballarpur Industries Limited (BILT). The forest fragmentation reveals a negative trend, recording highest conversion from large core fragments to edge (191.33 km²) and the predicted period has also shown the same trend of negative change, which poses serious danger to the structure of the forests. Out of all the landscape matrices calculated, number of patches will increase to 214 in 2027 from 93 in 1987. In the test between geographically weighted regression (GWR) and ordinary least square regression (OLS), GWR was the better fit model for drawing a spatial relationship between forest cover and fragmentation changes. The study confirmed that the forest cover change has impacted the forest fragmentation in the study area. The programmes like REDD + should be implemented along with the experiences of Community Forest Management and the joint forest management should be intensified at community level in order to develop better management practices to conserve habitats in biodiversity rich areas.

Keywords

Forest cover / Forest ecosystem / Predictive model / Forest fragmentation / Grid based change / Geographically weighted regression

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