Temporal mapping of deforestation and forest degradation in Nepal: Applications to forest conservation

Abstract

Deforestation and forest degradation are associated and progressive processes resulting in the conversion of forest area into a mosaic of mature forest fragments, pasture, and degraded habitat. Monitoring of forest landscape spatial structures has been recommended to detect degenerative trends in forest conditions. GIS and remote sensing play an important role in the generation of such data to identify degraded and deforested areas as well as potential areas for conservation. In this study we analyzed forest degradation and deforestation trends in Chitwan district in Nepal, which contains key habitat elements for wildlife in the region. An artificial neural network was used to predict forest canopy density in five classes using Landsat images of the year 2001. Forest canopy density was predicted with 82% overall accuracy. Except riverine forest, forest area of all other forest types was reduced. Terai Shorea robusta forest, which has high commercial value, showed a loss of 23% between 1976 and 1989 and an overall loss of 15% forest covers between the year 1976 and 2001. Deforestation and forest degradation disproportionately reduced the sizes of the different forest types, a finding that has important management implications. The maps presented in this article could be useful to prioritize limited resources for conservation.