Risk assessment and prediction of forest health for effective geo-environmental planning and monitoring of mining affected forest area in hilltop region

Abstract

This paper focuses on forest health risk (FHR) assessment and prediction in the mining-affected forest region using AHP model based on multi-criteria analysis in a GIS platform. We considered a total twenty-eight (twenty two present and six predicted) causative parameters including climate, natural or geomorphological, forestry, topographical, environmental, and anthropogenic. The assessment results of FHR show that of the total existing forest area, 2.85% area under very high, 13.63% high, 31.98% moderate, 32.68% low, and 18.87% are under very low categories. According to the assessment and prediction FHR results, the very high-risk classes were found at mines surrounding forest compartments. The sensitivity analysis showed that some factors were more sensitive to FHR. The correlation results showed a negative relationship between FHR and distance from mines and foliar dust concentration. This work will provide a basic guideline for effective planning and management in forestry studies for the mining-affected region.

Keywords:

• Forest health risk
• remote sensing and GIS
• AHP
• mining activities

Acknowledgements

The authors are thankful to Space Application Centre (SAC) ISRO, Ahmedabad for their financial support and providing necessary data. The authors are also thankful to DFO of Saranda forest, Raw material division (RMD) – SAIL, Kolkata and Forest department of Jharkhand for their kind support and providing necessary data. The authors would like to thanks, Indian Institute of Technology, Kharagpur and Vidyasagar University for their constant support and providing the wonderful platform for research.

Disclosure statement

No potential conflict of interest was reported by the author(s).