Simulating Forest Cover Changes of Bannerghatta National Park Based on a CA-Markov Model: A Remote Sensing Approach
Establishment of protected areas has been a primary response to deforestation and land-use change in tropical forests. However, few empirical evaluations have conducted a comparison of pre- and post-intervention landscapes.
Research goals & methods
This study provides a method to empirically evaluate pre-and post-establishment of protected areas by using a cellular automata-Markov model. This method is tested using remotely sensed data of Bannerghatta National park (BNP) and surrounding areas, which have experienced various national policy interventions, such as the Indian National Forest Policy of 1988, and rapid land cover change between 1973 and 2007. The model constructs a hypothetical land cover scenario of BNP and its surroundings in 1999 and 2007 in the absence of any policy intervention. The models predicted a decline in native forest cover and an increase in non-forest cover post 1992, whereas the actual observed landscape experienced the reverse trend.
Conclusions & takeaways
The models also show a higher deforestation and lower reforestation than the observed deforestation and reforestation patterns for BNP post 1992. These results suggest the usefulness of modeling in evaluation of conservation efforts and demonstrate the implication of national level policy changes on forest cover.
Simulating Forest Cover Changes of Bannerghatta National Park Based on a CA-Markov Model: A Remote Sensing Approach. Remote Sensing. 2012;4:3215–3243. doi:10.3390/rs4103215..
- Department of Geography, Land-Use and Environmental Change Institute, University of Florida