| 许定良,杨 跃,雷祖禄,李宏伟,朱立光,李金梦,段学斌,杨春平.滇东南森林火灾风险识别及驱动因素分析[J].林业调查规划,2025,50(3):142-150 |
| 滇东南森林火灾风险识别及驱动因素分析 |
| Forest Fire Risk Identification and Driving Factors Analysis inSoutheastern Yunnan |
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| DOI： |
| 中文关键词: 林火 林火驱动因素 发生概率 最大熵模型 火灾防控 滇东南地区 |
| 英文关键词: forest fire forest fire driving factors occurrence probability MaxEnt model fire prevention
and control Southeastern Yunnan |
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| 摘要点击次数: 49 |
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| 中文摘要: |
| 云南省东南部地区作为重要的物种聚集区和生态安全屏障,近年来却频繁遭受林火侵袭,防
火形势严峻。科学识别该地区的林火潜在风险区域,对于有效预防火灾发生及减轻灾害损失具有至关重要的意义。本研究基于2006—2020年中分辨率遥感图像提取的林火火点数据,结合多种潜在驱动因素,运用最大熵模型(MaxEnt) 来揭示滇东南地区的森林火灾发生概率,旨在划分火灾风险区域,并深入探究促进火灾发生的主要因素,以应对日益加剧的火灾防控压力。结果显示,MaxEnt模型的预测效果较好,且通过了模型精度检验。具体而言,红河州的火灾风险区总面积达18 761 km2,占该州总面积的57.43%,高风险区域主要集中于北部的建水、石屏县及弥勒市等地;而文山州的火灾风险区总面积为13 722 km2,占文山州面积的42.9%,高风险区则主要聚集于北部的广南、富宁、丘北县等地。影响两地林火发生的驱动因素类别几乎一致,其中日照时数、旱季降水量和国内生产总值是两个地区共同的林火发生主导因素。响应曲线上的差异表明,两地具体的驱动因素作用方式和机制存在地区性差异。 |
| 英文摘要: |
| As a critical biodiversity hotspot and ecological security barrier, southeastern Yunnan has frequently
suffered from forest fires in recent years, posing a severe fire prevention challenge. Scientifically identifying
potential fire risk zones in this region is of great significance for effective fire prevention and disaster
mitigation. Based on the forest fire point data extracted from moderate-resolution remote sensing images
during the period from 2006 to 2020 and multiple potential driving factors, this study employed the Maximum Entropy Model (MaxEnt) to assess forest fire occurrence probability in southeastern Yunnan, aiming
to delineate fire risk zones and explore the key factors contributing to fire incidents in response to increasing
fire prevention pressure. The results showed that the MaxEnt model performed well and passed accuracy
validation. Specifically, the total fire risk area in Honghe Prefecture reached 18 761 km2, accounting
for 57.43% of the prefecture′s total area, with high-risk zones mainly concentrated in the northern counties of
Jianshui, Shiping, and Mile. In Wenshan Prefecture, the total fire risk area was 13 722 km2, covering 42.9%
of the prefecture, with high-risk areas primarily clustered in the northern counties of Guangnan, Funing, and
Qiubei. The categories of driving factors influencing fire occurrence in both regions were nearly identical, with
sunshine duration, dry-season precipitation, and GDP being the dominant factors. Differences in response
curves indicated regional variations in the mechanisms and effects of these driving factors. |
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