在精细规模的野火随机森林预测模型中评估空间自相关性和可扩展性
Madeleine Pascolini-Campbell1, Joshua B Fisher2, Kerry Cawse-Nicholson3
1NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA. madeleine.a.pascolini-campbell@jpl.nasa.gov.
Scientific reports
|July 2, 2025
概括
这项研究表明,使用卫星数据的机器学习模型可以准确地预测森林大火的严重程度和发生情况. 这些模型为加强野火管理策略提供了一个有希望的工具.
科学领域:
- 生态生态学 生态生态学
- 遥感 遥感 遥感 遥感
- 机器学习 机器学习
背景情况:
- 精细的野火预测模型对于有效的野火管理至关重要.
- 遥感可提供燃料特性和可燃性的动态测量.
- 机器学习,特别是随机森林,提供了准确,高效和可解释的生态建模.
研究的目的:
- 用遥感数据预测野火烧伤严重程度和发生情况在微小尺度 (<100米) 上.
- 为了评估单个随机森林模型在不同生态区域和野火类型的表现.
- 评估空间自相对应对模型准确性的影响.
主要方法:
- 利用来自ECOSTRESS的高分辨率 (70米) 卫星观测蒸发和蒸发应力指数.
- 集成地形和天气数据与ECOSTRESS数据来训练随机森林模型.
- 通过改变样本间距,结合空间结构预测因素和在多个野火中进行交叉验证来评估模型性能.
主要成果:
- 结合ECOSTRESS,天气和地形数据的单个模型实现了高预测准确度的烧伤严重程度 (R2 = 0.77).
- 随着样本间距的增加,模型准确性下降,但对训练数据大小的减少更为敏感,这表明捕获了微细规模的过程.
- 随机森林模型显示了很好的准确性来分类燃烧的像素发生 (67%的总像素准确性).
结论:
- 结合遥感,天气和地形数据的随机森林模型显示出强大的潜力,可以预测微型野火的严重程度和发生情况.
- 这些模型可以应用于不同地区和野火类型,为野火管理提供有价值的见解.
- 该研究强调了机器学习模拟复杂的生态关系以改善消防管理的能力.
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