Citation

Bar S, Parida BR, Pandey AC, Shankar BU, Kumar P, Panda SK, Behera MD. Appl. Geogr. 2023; 151: e102867.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.apgeog.2022.102867

PMID

unavailable

Abstract

Forest fires are the result of complex interactions among human, geographic and weather conditions. Climate change would alter the link between forest fire and the controlling factors. The objective of the study is to model the forest fire occurrences and quantify the contribution of explanatory geographic, climatic and anthropogenic variables using satellite-derived historical fire data (2003-2019) and machine learning classifiers over the western Himalaya, India. The climatic variables were derived from a regional Earth system model (ROM). Along with the key selected explanatory variables, the conditions of neighbouring (3 × 3) pixels were incorporated to account for the contribution from the surrounding area. Out of the selected classifiers, random forest recorded the most promising performance in k-fold cross-validation (f2-score = 0.95 and f1-score = 0.94) as well as in the final model validation (f2-score = 0.85 and f1-score = 0.84). The elevation and mean neighbour elevation exhibited the highest influence (8.18% and 6.72%, respectively) in forest fire occurrences followed by near-surface temperatures (4.65-5.78%). We predicted the forest fire susceptibility [0, 1] for 2030, 2040 and 2050 using the future climate projections. The predicted map can be useful to plan effective fire management strategies to minimize damage to the forest ecosystem.

Language: en

Keywords

Climate; Forest fire; Himalaya; Human activities; Machine learning; Random forest