|Paper title||Assessing the suitability of DEMs derived from Sentinel-1 data for landslide volume estimation|
|Form of presentation||Poster|
Landslides are defined as the movement of rock, debris, or earth down a slope, which may cause numerous fatalities and significant infrastructure damages (Cruden & Varnes, 1996). Therefore, it is essential to have timely, accurate, and comprehensive information on the landslide distribution, type, magnitude, and evolution (Hölbling et al., 2020). In particular, volume estimates of landslides are critical for understanding landslide characteristics and their post-failure behaviour. Pre- and post-event digital elevation model (DEM) differencing is a suitable method to estimate landslide volumes remotely. However, such analyses are restricted by limitations to existing DEM products, such as limited temporal and spatial coverage and resolution or insufficient accuracy. The free availability of Sentinel-1 synthetic aperture radar (SAR) data from the European Union's Earth Observation Programme Copernicus opened a new era for generating such multi-temporal topographic datasets, allowing regular mapping and monitoring of land surface changes. However, the applicability of DEMs generated from Sentinel-1 for landslide volume estimation has not been fully explored yet (Braun, 2021; Dabiri et al., 2020). Within the project SliDEM (Assessing the suitability of DEMs derived from Sentinel-1 for landslide volume estimation) we address this issue and pursue the following objectives: 1) to develop a semi-automated and transferable workflow for DEM generation from Sentinel-1 data, 2) to assess the suitability of the generated DEMs for landslide volume estimation, and 3) to assess and validate the quality of the DEM results in comparison to reference elevation data and to evaluate the feasibility of the proposed workflow. This workflow is implemented within a Python package for easier reproducibility and transferability. We use the framework described by Braun (2020) for DEM generation from Sentinel-1 data, including: (1) querying for suitable Sentinel-1 image pairs based on the perpendicular baseline; (2) creating the interferogram using phase information of each Sentinel-1 SAR image pair; (3) phase filtering and removing the phase ambiguity by unwrapping the phase information using the SNAPHU toolbox; (4) converting the unwrapped phase values into height/elevation information; and (5) performing terrain correction to minimize the effect of topographic variations. The accuracy of the generated DEMs is assessed using very high-resolution reference DEMs and field reference data, collected for major landslides in Austria and Norway which serve as test sites. We use statistical measures such as the root mean square error (RMSE) to assess the vertical accuracy and autocorrelation Moran's-I index for quality assessment of the generated DEMs. The importance of the perpendicular baseline and temporal intervals on the quality of the generated DEMs is demonstrated. Moreover, we assess the influence of topography and environmental conditions on the quality of the generated DEMs. The results of this research will reveal the potential but also the challenges and limitations of DEM generation from Sentinel-1 data, and their applicability for geomorphological applications such as landslide volume estimation.
-Braun, A. (2020). DEM generation with Sentinel-1 Workflow and challenges. European Space Agency. http://step.esa.int/docs/tutorials/S1TBX DEM generation with Sentinel-1 IW Tutorial.pdf
Braun, A. (2021). Retrieval of digital elevation models from Sentinel-1 radar data–open applications, techniques, and limitations. Open Geosciences, 13(1), 532–569.
-Cruden, D. M., & Varnes, D. J. (1996). Landslide types and processes. In A. K. Turner & R. L. Schuster (Eds.), Landslides: Investigation and Mitigation. Transportation Research Board Special Report 247. National Research Council.
-Dabiri, Z., Hölbling, D., Abad, L., Helgason, J. K., Sæmundsson, Þ., & Tiede, D. (2020). Assessment of Landslide-Induced Geomorphological Changes in Hítardalur Valley, Iceland, Using Sentinel-1 and Sentinel-2 Data. Applied Sciences, 10(17), 5848. https://doi.org/10.3390/app10175848
-Hölbling, D., Abad, L., Dabiri, Z., Prasicek, G., Tsai, T., & Argentin, A.-L. (2020). Mapping and Analyzing the Evolution of the Butangbunasi Landslide Using Landsat Time Series with Respect to Heavy Rainfall Events during Typhoons. Applied Sciences, 10(2), 630. https://doi.org/10.3390/app10020630