印度森林火灾的空间预测:用于改进风险评估和预警系统的机器学习方法
Utsav Biswas1, Susanta Mahato2, Pawan K Joshi3,4
1Theoretical Ecology and Evolution Laboratory, Centre for Ecological Sciences, Indian Institute of Science, Bangalore, 560012, Karnataka, India.
Environmental science and pollution research international
|February 1, 2025
概括
机器学习可以预测印度森林火灾的概率,识别关键风险因素和脆弱地区. 这有助于制定关键的预防措施和生态保护早期预警系统.
科学领域:
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 森林火灾是一个日益严重的全球威胁,印度正在经历越来越频繁和严重的事件,破坏自然资源和野生动物息地.
- 有效的森林火灾管理策略对于减轻生态和环境影响至关重要.
研究的目的:
- 利用机器学习技术开发印度森林火灾的空间预测模型.
- 确定导致森林火灾发生的关键因素,并绘制高火灾概率的区域.
主要方法:
- 对2001-2020年森林火灾的空间模式和趋势的分析.
- 利用了中等分辨率成像光谱辐射仪 (MODIS) 和欧洲航天局气候变化倡议 (ESA-CCI) 的土地使用数据.
- 采用了最大 (MaxEnt) 模型,结合了气候,生物物理和近距离因素.
主要成果:
- 在印度东北部,乌塔拉罕德 - 希马查尔邦和德坎高原确定了森林火灾的主要集中.
- 开发了一个概率地图,突出显示森林火灾发生的脆弱地区.
- 森林火灾与几个可持续发展目标 (SDGs) 之间建立了联系.
结论:
- 该研究提供了印度森林火灾的强大空间预测,对于风险评估至关重要.
- 调查结果使得有针对性地确定易受伤害的地区,以便实施预防措施.
- 增强的理解有助于改进预警系统和为森林火灾管理做出明智的政策决策.
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