使用ALOS-2/PALSAR-2数据量化检测因大雨引起的斜坡失效的地面表面变化:日本的一项案例研究
Xuechen Wang1, Hiroyuki Honda2, Ibrahim Djamaluddin3
1Department of Civil Engineering, Graduate School of Engineering, Kyushu University, Fukuoka, Japan. wang.xuechen.662@s.kyushu-u.ac.jp.
Scientific reports
|October 4, 2024
概括
本研究介绍了使用合成孔径雷达 (SAR) 数据快速绘制斜坡故障区域的两种方法. 这些技术提高了准确性,并支持及时应对灾害和预防二次灾害.
科学领域:
- 地质科学地质科学是指地球科学.
- 遥感 遥感 遥感 遥感
- 灾害管理 灾害管理
背景情况:
- 像ALOS-2/PALSAR-2这样的合成孔径雷达 (SAR) 卫星越来越多地用于地质灾害监测.
- 快速识别斜坡故障造成的表面变化,特别是在大雨后,对于主动应对灾害至关重要.
研究的目的:
- 开发和评估使用L频段SAR数据提取斜坡故障区域的定量方法.
- 评估这些方法在支持紧急救援和预防二次灾害方面的有效性.
主要方法:
- 为斜坡故障区域提出了两种使用斜坡单位 (SU) 作为评估单位的定量提取方法.
- 方法1:基于值的方法,为各种条件 (土地使用,地形) 自动选择值.
- 方法2:通过特征选择优化使用多时间数据进行机器学习密度比率估计,用于通过特征选择优化检测异常值.
主要成果:
- 与值方法 (61.91%) 相比,密度比率方法在最短的观察期内获得了更高的F1得分 (65.87%).
- 这两种方法都有效地减轻了SAR图像中斑点噪声引起的准确性问题.
- 表面变化区域的提取在斜坡故障事件发生后的几个小时内实现.
结论:
- 提出的方法证明了使用L频段SAR数据快速提取斜坡故障区域的良好适用性.
- 这些技术是支持应急响应工作和减轻二次灾害风险的宝贵工具.
- 该研究强调了SAR数据在近乎实时地质灾害监测和管理方面的潜力.
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