通过将MaxEnt集成到尼泊尔戈尔卡区的地理空间森林火灾风险评估和分区
Gayatri Paudel1, Kabita Pandey2, Puspa Lamsal1
1Institute of Forestry, Tribhuvan University (TU), Nepal.
Heliyon
|June 3, 2024
概括
这项研究为尼泊尔开发了一种MaxEnt森林火灾风险模型,确定气候和土地覆盖是影响火灾发生的关键因素. 该模型准确预测高风险地区,有助于森林火灾预防策略.
科学领域:
- 环境科学 环境科学
- 林业 林业 林业 林业 林业
- 遥感 遥感 遥感 遥感
背景情况:
- 森林火灾对自然生态系统构成重大威胁.
- 有效的森林火灾风险评估和分区对于预防和减缓至关重要.
- 了解影响火灾发生的环境因素对于制定有针对性的战略至关重要.
研究的目的:
- 评估尼泊尔戈尔卡地区的森林火灾风险,使用MaxEnt模型.
- 分析环境变量对森林火灾发生的贡献和重要性.
- 为有针对性的预防工作确定森林火灾高风险地区.
主要方法:
- 利用Gorkha地区远程传感的高温火灾数据.
- 采用与GIS和R集成的MaxEnt模型进行风险评估.
- 纳入了九个环境变量:地形,气候,植被和人类干预.
主要成果:
- 马克森特模型在森林火灾风险评估方面表现出高准确度 (AUC培训:0.875,测试:0.861).
- 气候变量 (56.2%) 和土地利用土地覆盖面 (LULC) (32.9%) 是影响火灾发生的最重要因素.
- 环境因素以复杂的非线性方式影响森林火灾;特定变量,如最高温度和降水,显示出正相关性.
结论:
- 马克森特模型是森林火灾风险评估的实用工具.
- 气候和LULC因素是研究区域森林火灾的主要驱动因素.
- 风险存在地理差异,戈尔卡区的很大一部分被确定为高风险地区,需要集中管理干预.
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