基于机器学习的森林火灾易感性测绘南米佐拉姆,印度缅甸生物多样性热点的一部分
Priyanka Gupta1, Arun Kumar Shukla2, Dericks Praise Shukla3
1DExtER Lab, School of Civil and Environmental Engineering, A-11 Building, North Campus, IIT Mandi, Mandi, Himachal Pradesh, India, 175075.
概括
这项研究使用机器学习绘制了米佐拉姆森林火灾的易感性,识别了高风险地区以及高度和角度等因素. 发现有助于管理环境影响和保护生物多样性热点.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 地理信息学是一种地理信息学.
背景情况:
- 森林火灾对全球环境和经济构成重大威胁,特别是在印度米佐拉姆等生物多样性热点地区.
- 米佐拉姆州在2012年至2021年期间因野火遭受了相当大的经济损失 (891万美元).
- 由于密佐拉姆南部的生态脆弱性,迫切需要进行高分辨率的森林火灾易感性测绘.
研究的目的:
- 为南部Mizoram创建高分辨率的森林火灾易感性地图.
- 评估六种机器学习算法的性能,以预测森林火灾的易感性.
- 确定影响该地区森林火灾发生的关键因素.
主要方法:
- 采用了六个机器学习算法 (AdaBoost,决策树,高斯过程,K-最近邻居,随机森林,支持向量机器).
- 分析了十个野火条件因素,包括地形,植被指数,土地使用和太阳辐射.
- 使用高分辨率卫星图像开发了森林火灾库存,并使用行星图像验证了地图.
主要成果:
- 随机森林算法显示出最好的性能 (AUC 0.84-0.91,准确率为 0.74-0.81).
- 确定了主要影响因素:规范差异湿度指数 (NDMI),增强植被指数 (EVI),数字高度模型 (DEM),角度和太阳辐射.
- 拉恩格特莱地区的敏感性最高 (64%),研究区总面积的37.01%被归类为高度敏感.
结论:
- 基于机器学习的易感性映射对于自然资源管理是有效的.
- 较低的高度和特定的面向 (东,东南,西南,南) 显著增加了火灾易感性.
- 该研究为实施积极措施以减轻米佐拉姆森林火灾影响提供了关键数据.
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