Preprints
https://doi.org/10.5194/nhess-2022-172
https://doi.org/10.5194/nhess-2022-172
 
23 Jun 2022
23 Jun 2022
Status: this preprint is currently under review for the journal NHESS.

Timing landslide and flash flood events from SAR satellite: a new method illustrated in African cloud-covered tropical environments

Axel Antonius Johannes Deijns1,2, Olivier Dewitte1, Wim Thiery2, Nicolas d'Oreye3,4, Jean-Philippe Malet5, and François Kervyn1 Axel Antonius Johannes Deijns et al.
  • 1Department of Earth Sciences, Royal Museum for Central Africa, 3080 Tervuren, Belgium
  • 2Department of Hydrology and Hydraulic Engineering, Earth System Science, Vrije Universiteit Brussel, 1050 Elsene, Belgium
  • 3Department of Geophysics/Astrophysics, National Museum of Natural History, 7256 Walferdange, Luxembourg
  • 4European Center for Geodynamics and Seismology, 7256 Walferdange, Luxembourg
  • 5École et Observatoire des Sciences de la Terre & Institut Terre et Environnement de Strasbourg, Centre National de la Recherche Scientifique, University of Strasbourg, F-67000 Strasbourg Cedex, France

Abstract. Landslides and flash floods are geomorphic hazards (GH) that often co-occur and interact. They generally occur very quickly, leading to catastrophic socioeconomic impacts. Understanding the temporal patterns of occurrence of GH events is essential for hazard assessment, early warning and disaster risk reduction strategies. However, temporal information is often poorly constrained, especially in frequently cloud-covered tropical regions, where optical-based satellite data is insufficient. Here we present a new method to accurately estimate GH event timing which requires no prior knowledge of the GH event timing, using Synthetic Aperture Radar (SAR) remote sensing. SAR can penetrate through clouds and therefore provides an ideal tool for constraining GH event timing. We use the open-access Copernicus Sentinel-1 (S1) SAR satellite that provides global coverage, high spatial resolution (~10–15 m) and a high repeat time (6–12 days) from 2016 to 2020. We investigate the amplitude, detrended amplitude, spatial amplitude correlation, coherence and detrended coherence time series in their suitability to constrain GH event timing. We apply the method on four recent large GH events located in Uganda, Rwanda, Burundi and DRC containing a total of about 2500 manually mapped landslides and flash flood features located in several contrasting landscape types. The GH event timing estimation accuracies vary among the GH events and the data products. Coherence and detrended coherence estimated timing accuracies range from a 1 day to a 47 day difference. The spatial amplitude correlation estimated timing accuracy ranges from a 1 day to an 85 day difference. The amplitude and detrended amplitude estimated timing accuracies range from a 13 to a 1000 day difference. The amplitude time series reflects the influence of seasonal dynamics, which causes the timing estimations to be further away from the actual GH event occurrence compared to the other data products. Timing estimations are generally closer to the actual GH event occurrence for GH events within homogenous densely vegetated landscape, and further for GH events within complex cultivated heterogenous landscapes. We believe that the complexity of the different contrasting landscapes we study is an added value for the transferability of the method and together with the open access and global coverage of S1 data it has the potential to be widely applicable.

Axel Antonius Johannes Deijns et al.

Status: open (until 04 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Axel Antonius Johannes Deijns et al.

Axel Antonius Johannes Deijns et al.

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Short summary
Landslides and flash floods are rainfall-induced processes that often co-occur and interact, generally very quickly. In mountainous cloud-covered environments, determining with accuracy when these processes occur remains challenging. Here we propose a new method using open-access satellite radar images. Our method allows to identify landslide and flash floods events in contrasting landscapes of tropical Africa with a time accuracy up to a few days. The methods show potential for transferability.
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