Deforestation susceptibility assessment and prediction in hilltop mining-affected forest region

Narayan Kayet; Khanindra Pathak; Subodh Kumar; C P Singh; V M Chowdary; Abhisek Chakrabarty; Nibedita Sinha; Ibrahim Shaik; Amit Ghosh

doi:10.1016/j.jenvman.2021.112504

Deforestation susceptibility assessment and prediction in hilltop mining-affected forest region

J Environ Manage. 2021 Jul 1:289:112504. doi: 10.1016/j.jenvman.2021.112504. Epub 2021 Apr 8.

Authors

Narayan Kayet¹, Khanindra Pathak², Subodh Kumar², C P Singh³, V M Chowdary⁴, Abhisek Chakrabarty⁵, Nibedita Sinha⁶, Ibrahim Shaik⁷, Amit Ghosh⁵

Affiliations

¹ Department of Mining Engineering, Indian Institute of Technology, Kharagpur, India. Electronic address: narayankayet@gmail.com.
² Department of Mining Engineering, Indian Institute of Technology, Kharagpur, India.
³ Space Applications Centre (SAC), ISRO, Ahmedabad, India.
⁴ Regional Remote Sensing Centre (RRSC), ISRO, Delhi, India.
⁵ Vidyasagar University, Midnapore, India.
⁶ University of Alaska Fairbanks, USA.
⁷ National Remote Sensing Centre (NRSC), ISRO, Hyderabad, India.

PMID: 33839612
DOI: 10.1016/j.jenvman.2021.112504

Abstract

This work mainly focused on deforestation susceptibility (DS) assessment and its prediction based on statistical models (FR, LR & AHP) in the Saranda forest, India. Also, efforts had been made to quantify the effect of mining on deforestation. We had considered twenty-five (twenty present and five predicted) causative variables of deforestation, including climate, natural or geomorphological, forestry, topographical, environmental, and anthropogenic. The predicted variables have been generated from different simulation models. Also, very high-resolution, Google Earth imagery have been used in time series analysis for deforestation from 1987 to 2020 data and generated dependent variable. On deforestation analysis, it was observed that a total of 4197.84 ha forest areas were lost in the study region due to illegal mining, agricultural and tribal people allied activities. The DS results have shown that of total existing forest area, 11.22% area were under very high, 16.08% under high, 16.18% under moderate, 24.25% under low, and 32.27% falls very low categories. According to the DS assessment and predicted results, the very high susceptibility classes were found at and close to mines, agricultural, roads and settlement's surrounding sites. The sensitivity analysis results also shown that some causative variables (maximum temperature (2.95%), minimum temperature (0.51%), rainfall (2.69%), LST (4.56%), hot spot (7.36%), aspect (1.14%), NDVI (2.64%), forest density (3.78%), lithology (3.26%), geomorphology (3.00%), distance from agricultural (19.40%), soil type (2.05%), solar radiation (5.97%), LULC (3.26%), drought (3.16%), altitude (2.85%), slope (5.97%), distance from mines (18.05%), roads (2.17%), and settlements (5.18%)) were more sensitive to deforestation. Most of the sensitive parameters showed a positive correlation with DS. The AUC values of the ROC curve had shown a better fit for AHP (0.72) than (0.69) FR and LR (0.68) models for present DS results. The correlation results had shown a good inverse relationship between DS and distance from mines and foliar dust concentration. This work will espouse the future work in the effective planning and management of the mining-affected forest region and predicted deforestation susceptibility would be helpful for forest ecosystem study and policymaking.

Keywords: Deforestation susceptibility; GIS; Mining activities; Remote sensing; Statistical modeling.

MeSH terms

Conservation of Natural Resources*
Ecosystem*
Forestry
Forests
Humans
India
Trees