Wang Xuechen, Honda Hiroyuki, Djamaluddin Ibrahim, Taniguchi Hisatoshi, Mitani Yasuhiro
Department of Civil Engineering, Graduate School of Engineering, Kyushu University, Fukuoka, Japan.
Disaster Risk Reduction Research Center, Graduate School of Engineering, Kyushu University, Fukuoka, Japan.
Sci Rep. 2024 Oct 4;14(1):23110. doi: 10.1038/s41598-024-73372-1.
Many SAR images have been utilized for geologic disasters investigations with the continuous launch of new Synthetic Aperture Radar (SAR) satellites such as ALOS-2/PALSAR-2. However, to proactively respond to transient slope failures caused by heavy rainfall, rapid extraction of areas of surface change accompanying slope failures is required. This study proposes two methods for quantitatively extracting slope failure areas using L-band SAR observations with slope units (SUs) as the evaluation units. The first method is based on the threshold method, which automates the selection of thresholds for various disaster-affected conditions, such as land use and topography. The second method is a machine-learning-based density ratio estimation method, which uses multi-temporal periodic observation data and pre- and post-disaster data to detect outliers through feature selection optimization. In the observation direction with the shortest satellite observation period, the F1 score (The F1 score is the harmonic mean of the precision and recall) of the threshold method for accuracy evaluation is 61.91%, and the F1 score of the density ratio method is 65.87%. Both methods can reduce the problem of low extraction accuracy caused by the effect of speckle noise. When slope failure occurs, both methods can extract the area of surface change within hours of a disaster. The method proposed in this study displays good applicability in supporting emergency rescue and the prevention of secondary disasters.
随着诸如ALOS - 2/PALSAR - 2等新型合成孔径雷达（SAR）卫星的不断发射，许多SAR图像已被用于地质灾害调查。然而，为了积极应对暴雨引发的瞬态边坡失稳，需要快速提取伴随边坡失稳的地表变化区域。本研究提出了两种以坡段（SUs）作为评估单元，利用L波段SAR观测定量提取边坡失稳区域的方法。第一种方法基于阈值法，该方法能自动为诸如土地利用和地形等各种受灾条件选择阈值。第二种方法是基于机器学习的密度比估计法，它使用多期周期性观测数据以及灾前和灾后数据，通过特征选择优化来检测异常值。在卫星观测周期最短的观测方向上，用于精度评估的阈值法的F1分数（F1分数是精确率和召回率的调和均值）为61.91%，密度比法的F1分数为65.87%。两种方法都能减少由斑点噪声影响导致的提取精度低的问题。当边坡失稳发生时，两种方法都能在灾害发生后的数小时内提取地表变化区域。本研究提出的方法在支持应急救援和预防次生灾害方面显示出良好的适用性。
