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自然资源遥感  2025, Vol. 37 Issue (3): 152-161    DOI: 10.6046/gyzyyg.2023383
  技术应用 本期目录 | 过刊浏览 | 高级检索 |
基于多因子的重庆市林火风险评价
陈艳英1(), 游扬声2(), 杨茜3, 汪艳波4
1.中国气象局气候资源经济转化重点开放实验室,重庆市气象科学研究所,重庆 401147
2.重庆大学土木工程学院,重庆 400045
3.重庆市气象台,重庆 401147
4.朝阳师范学院,朝阳 122000
Multifactor-based assessment of forest fire risk in Chongqing City, China
CHEN Yanying1(), YOU Yangsheng2(), YANG Qian3, WANG Yanbo4
1. CMA Key Open Laboratory of Transforming Climate Resources to Economy, Chongqing Institute of Meteorological Sciences, Chongqing 401147, China
2. School of Civil Engineering,Chongqing University Chongqing 400045, China
3. Chongqing Meteorological Station, Chongqing 401147, China
4. Chaoyang Normal University, Chaoyang 122000, China
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摘要 为了客观评价森林火灾对地形、植被和人类活动等因素的响应,为重庆森林防火及风险区划提供技术指导,该文以2000—2022年重庆市1 206个历史林火点数据作为因变量,以高程、坡度、地形起伏度、植被覆盖度、地表覆盖分类及路网距离等9种数据作为林火风险因子,首先建立分段函数,得到单因子林火风险概率;其次,基于CRITIC权重法计算各单因子林火风险概率的权重,经加权计算得到重庆市林火风险概率空间分布;最后,依据风险概率将重庆林火风险分为低、较低、较高、高和极高5个等级。结果表明: ①在高程、坡度、地形起伏度、植被覆盖度、地表覆盖及路网距离分类等9个因子中,林地、旱地及植被覆盖度对林火风险的贡献居前3位,坡度、高程及地形起伏度对林火风险的贡献偏低; ②基于单因子林火风险概率加权后得到的重庆林火风险分级效果较好,检验结果表明,落在较高风险区及以上等级区域的林火占比为83%,落在低风险区和较低风险区的林火分别占8.33%和8.67%; ③重庆市林火风险与地形走势、土地利用及人类活动关系密切,林火的高风险区与极高风险区主要分布在人类活动频繁的中低山林区附近,耕地、乡土路、住宅及墓地较近的林地周边区域,生产生活用火较多,易诱发林火,这些区域也属于高风险区域,低林火风险区域主要分布在地势低平的非林区及山势陡峭的林区,另外建筑用地、水体及距离林地较远的水田、旱地等区域也属于林火低风险区。该研究成果可用来评估森林火灾风险的空间状态,为森林防火研究提供科学指导。
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关键词 林火风险影响因子风险概率风险等级    
Abstract

By objectively assessing the response of forest fires to factors like terrain, vegetation, and human activities, this study aims to provide technical guidance for forest fire prevention and risk zoning in Chongqing City, China. In this study, 1 206 historical forest fire data of Chongqing City from 2000 to 2022 were used as dependent variables. The height, slope, terrain ruggedness, vegetation cover, land cover classification, and road network distance data were utilized as forest fire risk factors. With these data, a piecewise function was established to obtain the single-factor risk probabilities of forest fires. Based on the criteria importance through intercriteria correlation (CRITIC), the weights of the single-factor risk probabilities of forest fires were calculated to derive the spatial distribution of weighted forest fire risk probabilities in Chongqing City. Finally, according to the risk probabilities of forest fires, the forest fire risk in Chongqing City was divided into the low, relatively low, relatively high, high, and extremely high levels. The results indicate that among nine forest fire risk factors, the contributions of forest land, dry land, and vegetation cover to forest fire risk ranked top three, whereas the slope, height, and terrain ruggedness contributed little to forest fire risk. The forest fire risk levels of Chongqing City based on the weights of single-factor risk probabilities demonstrated satisfactory verification effects. Forest fires falling in zones at relatively high and above risk levels represented 83 %. In contrast, forest fires falling in zones at low and relatively low risk levels represented 8.33 % and 8.67 %, respectively. The forest fire risk in Chongqing City was intimately associated with the terrain trend, land use, and human activities. The high-risk and extremely high-risk zones were primarily distributed in low to middle mountain forest areas subjected to frequent human activities. Additionally, the areas surrounding forest land, located near farmland, rural roads, residential areas, and cemeteries, were also classified into high-risk zones since the frequent use of fire for production and daily life was prone to induce forest fires. The low-risk zones included primarily low and flat non-forest areas and steep forest areas, along with building land, water bodies, and paddy and dry lands that are far from forest land. Overall, the results of this study can be used to assess the spatial distribution of forest fire risk, providing scientific guidance for forest fire prevention.

Key wordsforest fire risk    impact factors    risk probability    risk level
收稿日期: 2023-12-14      出版日期: 2025-07-01
ZTFLH:  TP79  
基金资助:重庆市技术创新与应用发展专项“基于多源遥感的林火监测方法研究”(csc2019jscx-msxmX0289);重庆市气象部门业务技术攻关项目“基于多源卫星数据的重庆林火遥感监测方法探究”(YWJSGG-202102);重庆市气象局2022年业务技术攻关项目“应用遥感产品改进重庆市森林火险气象条件预报技术研究”(YWJSGG-202204)
通讯作者: 游扬声(1971-),男,博士,主要从事数据处理理论与应用、GIS不确定性理论、变形监测分析方面研究。Email: youyangsheng@126.com
作者简介: 陈艳英(1974-),女,硕士,主要从事遥感及地理信息系统在地表监测及反演中的应用研究。Email: chenyanying1618@163.com
引用本文:   
陈艳英, 游扬声, 杨茜, 汪艳波. 基于多因子的重庆市林火风险评价[J]. 自然资源遥感, 2025, 37(3): 152-161.
CHEN Yanying, YOU Yangsheng, YANG Qian, WANG Yanbo. Multifactor-based assessment of forest fire risk in Chongqing City, China. Remote Sensing for Natural Resources, 2025, 37(3): 152-161.
链接本文:  
https://www.gtzyyg.com/CN/10.6046/gyzyyg.2023383      或      https://www.gtzyyg.com/CN/Y2025/V37/I3/152
土地覆盖
类型
2021年各类土地
占比/%
林火数/次 林火数百分比/%
水田 14.560 193 16.0
旱地 25.972 358 29.7
林地 56.697 603 50.0
灌木 0.340 9 0.7
草地 0.062 2 0.2
水体 0.990 13 1.1
裸地 0.001 0 0.0
Tab.1  不同地表类型的林火次数统计
Fig.1  林火风险要素的分级
Fig.2  林火次数与地形要素的关系
林火与林地
距离/m
林地平均
距离/m
水田平均
距离/m
旱地平均
距离/m
林火数/次 林火
占比/%
0 0.0 383.0 204.0 614 50.9
(0,500] 149.1 92.6 31.2 481 39.9
(500,1000] 618.6 33.5 67.5 66 5.5
>1000 1 606.0 26.0 77.0 45 3.7
Tab.2  林地不同距离区间内林火次数及其与水田、旱地距离的关系
Fig.3  林火次数与林草地距离、水田距离及旱地距离的关系
Fig.4  林火次数与县道距离及居民点距离的关系
Fig.5  林火次数与植被覆盖度的关系
Fig.6-1  单要素林火风险等级划分
Fig.6-2  单要素林火风险等级划分
指标 权重/%
高程 9.71
坡度 8.77
起伏度 9.68
植被覆盖度 12.67
道路距离 10.21
建筑距离 11.55
林地距离 13.42
林地约束下的旱地距离 13.00
林地约束下的水田距离 10.79
Tab.3  各林火风险指标的权重值表
Fig.7  重庆市林火风险评估结果
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