Flood damage assessment with Sentinel-1 and Sentinel-2 data after Sardoba dam break with GLCM features and Random Forest method


TAVUS B., KOCAMAN GÖKÇEOĞLU S., GÖKÇEOĞLU C.

SCIENCE OF THE TOTAL ENVIRONMENT, vol.816, 2022 (Peer-Reviewed Journal) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 816
  • Publication Date: 2022
  • Doi Number: 10.1016/j.scitotenv.2021.151585
  • Journal Name: SCIENCE OF THE TOTAL ENVIRONMENT
  • Journal Indexes: Science Citation Index Expanded, Scopus, Academic Search Premier, PASCAL, Aerospace Database, Analytical Abstracts, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Chimica, Communication Abstracts, Compendex, EMBASE, Environment Index, Food Science & Technology Abstracts, Geobase, Greenfile, MEDLINE, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Keywords: Flood mapping, Synthetic aperture radar, Satellite optical data, Data fusion, Dam break, Sardoba (Uzbekistan), SAR DATA, MECHANICAL-PROPERTIES, LOESS, COEFFICIENT, VEGETATION, DYNAMICS, RIVER

Abstract

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. (c) 2021 Elsevier B.V. All rights reserved.