Proximate causes of land-use and land-cover change in Bannerghatta National Park: A spatial statistical model

Land change modeling has become increasingly important in evaluating the unique driving factors and proximate causes that underlie a particular geographical location. In this article, a binary logistic regression analysis was employed to identify the factors influencing deforestation and simultaneous plantation driven reforestation in Bannerghatta National Park, located at the periphery of one of the fastest growing cities in India, i.e., Bangalore. Methodologically, this study explores the inclusion of different sub-regions and statistical population to address spatial autocorrelation in land change modeling. The results show negative relationship between deforestation and protected area status and edge of previous forest clearing. In addition, the deforestation models found differences in the processes that are affecting forest clearing in our two sub-periods of 1973-1992 and 1992-2007. The plantation driven reforestation in the region were attributed to distance to major towns, Bangalore city, rural centers and major and minor roads suggesting the importance of accessibility to market for heavy cash crops such as coconut palm and eucalyptus. Finally, the inclusion of different sub-regions and statistical population facilitated a better understanding of varying driving factors in different zones within the overall landscape.