Spatial analysis and machine learning prediction of forest fire susceptibility: a comprehensive approach for effective management and mitigation

随机森林 环境科学 支持向量机 森林生态学 气候变化 环境资源管理 生物多样性 点模式分析 防火 生态系统 自然地理学 遥感 空间分布 地理 生态学 计算机科学 机器学习 建筑工程 工程类 生物
作者
Manoranjan Mishra,Rajkumar Guria,Besnik Baraj,Ambika Prasad Nanda,Celso Augusto Guimarães Santos,Richarde Marques da Silva,FX Anjar Tri Laksono
出处
期刊:Science of The Total Environment [Elsevier BV]
卷期号:926: 171713-171713
标识
DOI:10.1016/j.scitotenv.2024.171713
摘要

Forest fires (FF) in tropical seasonal forests impact ecosystem. Addressing FF in tropical ecosystems has become a priority to mitigate impacts on biodiversity loss and climate change. The escalating frequency and intensity of FF globally have become a mounting concern. Understanding their tendencies, patterns, and vulnerabilities is imperative for conserving ecosystems and facilitating the development of effective prevention and management strategies. This study investigates the trends, patterns, and spatiotemporal distribution of FF for the period of 2001-2022, and delineates the forest fire susceptibility zones in Odisha State, India. The study utilized: (a) MODIS imagery to examine active fire point data; (b) Kernel density tools; (c) FF risk prediction using two machine learning algorithms, namely Support Vector Machine (SVM) and Random Forest (RF); (d) Receiver Operating Characteristic and Area Under the Curve, along with various evaluation metrics; and (e) a total of 19 factors, including three topographical, seven climatic, four biophysical, and five anthropogenic, to create a map indicating areas vulnerable to FF. The validation results revealed that the RF model achieved a precision exceeding 94 % on the validation datasets, while the SVM model reached 89 %. The estimated forest fire susceptibility zones using RF and SVM techniques indicated that 20.14 % and 16.72 % of the area, respectively, fall under the "Very High Forest Fire" susceptibility class. Trend analysis reveals a general upward trend in forest fire occurrences (R2 = 0.59), with a notable increase after 2015, peaking in 2021. Notably, Angul district was identified as the most affected area, documenting the highest number of forest fire incidents over the past 22 years. Additionally, forest fire mitigation plans have been developed by drawing insights from forest fire management strategies implemented in various countries worldwide. Overall, this analysis provides valuable insights for policymakers and forest management authorities to develop effective strategies for forest fire prevention and mitigation.
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