Forest Fire Driving Factors and Fire Risk Zoning Based on an Optimal Parameter Logistic Regression Model: A Case Study of the Liangshan Yi Autonomous Prefecture, China

IF 3 3区 农林科学 Q2 ECOLOGY
Fuhuan Zhang, Bin Zhang, Jun Luo, Hui Liu, Qingchun Deng, Lei Wang, Ziquan Zuo
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Abstract

Planning the analyses of the spatial distribution and driving factors of forest fires and regionalizing fire risks is an important part of forest fire management. Based on the Landsat-8 active fire dataset of the Liangshan Yi Autonomous Prefecture from 2014 to 2021, this paper proposes an optimal parameter logistic regression (OPLR) model, conducts forest fire risk zoning research under the optimal spatial analysis scale and model parameters, and establishes a forest fire risk prediction model. The results showed that the spatial unit of the optimal spatial analysis scale in the study area was 5 km and that the prediction accuracy of the OPLR was about 81%. The climate was the main driving factor of forest fires, while temperature had the greatest influence on the probability of forest fires. According to the forest fire prediction model, mapping the fire risk zoning, in which the medium- and high-risk area was 6021.13 km2, accounted for 9.99% of the study area. The results contribute to a better understanding of forest fire management based on the local environmental characteristics of the Liangshan Yi Autonomous Prefecture and provide a reference for related forest fire prevention and control management.
基于最优参数Logistic回归模型的森林火灾驱动因素及火险区划——以凉山彝族自治州为例
规划分析森林火灾的空间分布和驱动因素,进行火灾风险区划是森林火灾管理的重要组成部分。基于2014 - 2021年凉山彝族自治州Landsat-8活火数据集,提出最优参数logistic回归(OPLR)模型,在最优空间分析尺度和模型参数下进行森林火情分区研究,建立森林火情预测模型。结果表明,研究区最优空间分析尺度的空间单元为5 km, OPLR的预测精度约为81%。气候是森林火灾的主要驱动因素,而温度对森林火灾发生概率的影响最大。根据森林火灾预测模型,绘制了火灾风险分区图,其中中高风险区域为6021.13 km2,占研究区面积的9.99%。研究结果有助于更好地了解基于凉山彝族自治州当地环境特征的森林火灾管理,并为相关的森林防火管理提供参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Fire-Switzerland
Fire-Switzerland Multiple-
CiteScore
3.10
自引率
15.60%
发文量
182
审稿时长
11 weeks
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