气候变化领域集成服务门户(CCPortal): Spatial analysis and machine learning prediction of forest fire susceptibility: a comprehensive approach for effective management and mitigation

CCPortal

DOI	10.1016/j.scitotenv.2024.171713
	Spatial analysis and machine learning prediction of forest fire susceptibility: a comprehensive approach for effective management and mitigation
	Mishra, Manoranjan; Guria, Rajkumar; Baraj, Biswaranjan; Nanda, Ambika Prasad; Santos, Celso Augusto Guimaraes; da Silva, Richarde Marques; Laksono, F. X. Anjar Tri
发表日期	2024
ISSN	0048-9697
EISSN	1879-1026
起始页码	926
卷号	926
英文摘要	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 sus- ceptibility zones using RF , 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 (R 2 = 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 man- agement 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.
英文关键词	Susceptibility map; Climate and anthropogenic factors; Spatiotemporal trends; Machine learning; Wildfire management
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:001217950000001
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/303794
作者单位	Fakir Mohan University; Universidade Federal da Paraiba; Universidade Federal da Paraiba; University of Pecs; Universitas Jenderal Soedirman
推荐引用方式 GB/T 7714	Mishra, Manoranjan,Guria, Rajkumar,Baraj, Biswaranjan,et al. Spatial analysis and machine learning prediction of forest fire susceptibility: a comprehensive approach for effective management and mitigation[J],2024,926.
APA	Mishra, Manoranjan.,Guria, Rajkumar.,Baraj, Biswaranjan.,Nanda, Ambika Prasad.,Santos, Celso Augusto Guimaraes.,...&Laksono, F. X. Anjar Tri.(2024).Spatial analysis and machine learning prediction of forest fire susceptibility: a comprehensive approach for effective management and mitigation.SCIENCE OF THE TOTAL ENVIRONMENT,926.
MLA	Mishra, Manoranjan,et al."Spatial analysis and machine learning prediction of forest fire susceptibility: a comprehensive approach for effective management and mitigation".SCIENCE OF THE TOTAL ENVIRONMENT 926(2024).