基于双变量统计和多准则决策分析的小尺度森林火险区划

doi:10.13287/j.1001-9332.202501.030

应用生态学报 ›› 2025, Vol. 36 ›› Issue (1): 187-196.doi: 10.13287/j.1001-9332.202501.030

基于双变量统计和多准则决策分析的小尺度森林火险区划

欧阳逸云^1,2, 李春辉^1,2, 倪荣雨^1,2, 赵平欣^1,2, 曾爱聪^1,2, 郭福涛^1,2*

¹福建农林大学林学院, 福州 350002;
²3S技术与资源优化利用福建省高校重点实验室, 福州 350002

收稿日期:2024-06-12 修回日期:2024-11-30 出版日期:2025-01-18 发布日期:2025-07-18
通讯作者: *E-mail: guofutao@126.com
作者简介:欧阳逸云, 男, 2000年生, 硕士研究生。主要从事林火生态与管理研究。E-mail: oyyy2023@126.com
基金资助:
国家重点研发计划战略性国际科技创新合作重点专项(2018YFE0207800)

Small-scale forest fire risk zoning based on bivariate statistics and multi-criteria decision analysis

OUYANG Yiyun^1,2, LI Chunhui^1,2, NI Rongyu^1,2, ZHAO Pingxin^1,2, ZENG Aicong^1,2, GUO Futao^1,2*

¹College of Fores-try, Fujian Agricultural and Forestry University, Fuzhou 350002, China;
²3S Technology and Resource Optimization Utilization Key Laboratory of Fujian Universities, Fuzhou 350002, China

Received:2024-06-12 Revised:2024-11-30 Online:2025-01-18 Published:2025-07-18

摘要/Abstract

摘要： 森林火灾对人类生命、森林环境和生物多样性等造成严重威胁,小尺度区域的森林火灾风险制图对于林火管理至关重要。本研究将双变量统计(证据权重WOE,统计指数SI)与多准则决策分析(层次分析法AHP,网络层次分析法ANP)结合构建新的WOE-ANP和SI-ANP综合模型,分析贵州省望谟县的森林火险等级区划。结果表明: 望谟县南部大部分地区、西部和北部的部分地区极易发生森林火灾,4级及以上火险等级区域占比达39.2%,该县火险情况较为严峻。综合模型有效提高了单一双变量统计模型的预测能力,相比于AHP,ANP在林火风险因子权重评估上更可靠。WOE-ANP和SI-ANP综合模型评估的森林火险具有较高的准确性(84.3%和83.8%),可为林火管理提供更可靠的决策支持和参考依据。

关键词: 森林火灾风险制图, 双变量统计, 基于GIS的多准则决策分析, 综合模型, 网络层次分析法

Abstract: Fires pose serious threats to human life, forest environments, and biodiversity. Small-scale regional forest fire risk mapping is crucial for fire management. We combined bivariate statistics (weight of evidence, WOE, statistical index, SI) with multi-criteria decision analysis (analytic hierarchy process, AHP, analytic network process, ANP) to construct new WOE-ANP and SI-ANP comprehensive models to conduct forest fire risk zoning in Wangmo County, Guizhou Province. The results showed that most areas in the southern, western, and northern parts of Wangmo County were highly prone to forest fires, with fire risk of regions classified as level 4 or above accounting for 39.2%. Fire risk was severe in the county. The comprehensive models effectively enhanced the predictive ability of single bivariate statistical models. Compared to AHP, ANP provided more reliable assessments for the weight of forest fire risk factors. The WOE-ANP and SI-ANP comprehensive models demonstrated high accuracy (84.3% and 83.8%, respectively) in assessing forest fire risk, offering more reliable decision support and reference for forest fire management.

Key words: forest fire risk mapping, bivariate statistics, GIS-based multi-criteria decision analysis, comprehensive model, analytic network process

欧阳逸云, 李春辉, 倪荣雨, 赵平欣, 曾爱聪, 郭福涛. 基于双变量统计和多准则决策分析的小尺度森林火险区划[J]. 应用生态学报, 2025, 36(1): 187-196.

OUYANG Yiyun, LI Chunhui, NI Rongyu, ZHAO Pingxin, ZENG Aicong, GUO Futao. Small-scale forest fire risk zoning based on bivariate statistics and multi-criteria decision analysis[J]. Chinese Journal of Applied Ecology, 2025, 36(1): 187-196.

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基于双变量统计和多准则决策分析的小尺度森林火险区划

Small-scale forest fire risk zoning based on bivariate statistics and multi-criteria decision analysis

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