Hacettepe University, Graduate School of Science and Engineering, 06800 Beytepe, Ankara, Turkey; Department of Geomatics Engineering, Hacettepe University, Ankara, Turkey.
Department of Geomatics Engineering, Hacettepe University, Ankara, Turkey.
Sci Total Environ. 2022 Apr 10;816:151585. doi: 10.1016/j.scitotenv.2021.151585. Epub 2021 Nov 9.
Accurate mapping and monitoring of flooded areas are immensely required for disaster management purposes, such as for damage assessment and mitigation. In this study, the flood damage mapping performances of two satellite Earth Observation sensors, i.e., European Space Agency's Sentinel-1 (S1) synthetic aperture radar (SAR) and Sentinel-2 (S2) multispectral optical instruments, were evaluated using the Random Forest (RF) supervised classification method and various feature types. The study area was Sardoba Reservoir (Uzbekistan) and its surroundings, in which a disastrous dam failure occurred on May 1, 2020. After the failure of a part of the earthfill dam, a large region with settlements and agricultural areas in Uzbekistan and Kazakhstan was flooded. S1 and S2 cloudless data with a short temporal interval acquired soon after the event were available for the area. Four different data availability scenarios, such as (i) only S1 pre- and post-flood data; (ii) only S2 pre- and post-flood data; (iii) S1 pre- and post-flood and S2 pre-flood data; and (iv) S1 and S2 pre- and post-flood data were evaluated in terms of classification accuracy. In addition to the polarization information of S1 and the intensity values of S2 bands, feature maps produced from these datasets, such as vegetation and water indices, textural information obtained from gray level co-occurrence matrix (GLCM), and the principal component analysis (PCA) bands were employed in the RF method. The results show that the fusion of S1 and S2 data exhibit very high classification accuracy for the flooded areas and can separate the inundated vegetation as well. The use of S2 pre-event data together with the S1 pre- and post-event data is recommended for obtaining high accuracy even when post-event optical data is not available.
准确测绘和监测洪水淹没区对于灾害管理至关重要，例如进行损失评估和减轻灾害影响。本研究使用随机森林（RF）监督分类方法和各种特征类型，评估了两颗卫星对地观测传感器，即欧洲航天局的 Sentinel-1（S1）合成孔径雷达（SAR）和 Sentinel-2（S2）多光谱光学仪器，在洪水灾害测绘中的性能。研究区域为萨尔多巴水库（乌兹别克斯坦）及其周边地区，2020 年 5 月 1 日该地区发生了灾难性的大坝决堤事件。土石坝的一部分决堤后，乌兹别克斯坦和哈萨克斯坦的大片居民区和农业区被洪水淹没。事件发生后不久，该地区就获得了无云的 S1 和 S2 数据。评估了四种不同的数据可用性情况，包括：（i）仅 S1 洪水前后数据；（ii）仅 S2 洪水前后数据；（iii）S1 洪水前后数据和 S2 洪水前数据；以及（iv）S1 和 S2 洪水前后数据，以评估分类精度。除了 S1 的极化信息和 S2 波段的强度值外，还使用了这些数据集生成的特征图，如植被和水指数，灰度共生矩阵（GLCM）获得的纹理信息，以及主成分分析（PCA）波段，用于 RF 方法。结果表明，S1 和 S2 数据的融合对洪水淹没区具有非常高的分类精度，并且可以分离淹没的植被。建议即使在没有后事件光学数据的情况下，也应使用 S2 前事件数据与 S1 前事件和后事件数据融合，以获得高精度。
