An Open-Source-Based Workflow for Dem Generation From Sentinel-1 for Landslide Volume Estimation

Abstract

Abstract. Digital elevation models (DEMs) serve as a basis for various geomorphological applications, such as, landform mapping, natural hazard assessment, and landslide characterisation. The inter-comparison of DEMs can be used to provide estimates of topographic change, for example, volume changes. Sentinel-1 synthetic aperture radar (SAR) data can be used to generate multitemporal topographic datasets using interferometric SAR (InSAR). Such analyses are often conducted using commercial software; however, a well-structured workflow based on free and open-source software (FOSS) increases the applicability and transferability of the DEM generation method and can facilitate the use of multi-temporal topographic analysis within the Earth Sciences. In this study, we aim to develop an open-source-based workflow for the generation of multi-temporal DEMs from Sentinel-1 data, and to explore their potential for landslide volume estimation using FOSS. We implemented this semi-automated and transferable workflow bundled in the open-source Python package “SliDEM”, which relies on SNAP, SNAPHU, and several other open-source software for geospatial and geomorphological applications, all distributed within a Docker image. Two major landslides in Austria and Norway were selected to test, evaluate, and validate the workflow in terms of reliability, performance, reproducibility, and transferability. Even if the quality of the Sentinel-1-based DEMs varies among the study areas, the preliminary results are promising. However, there is a need for improvement and thorough analysis of the causes of elevation errors. A comprehensive evaluation of the influence of the perpendicular and temporal baselines, topography, land use/land cover, and other environmental conditions can be systematically assessed within the “SliDEM” workflow.

Lava Flow Mapping Using Sentinel-1 SAR Data at the Fagradalsfjall Volcano, Iceland

“…Future studies should further explore the potential of combining OBIA and automated change-detection analysis for lava flow mapping using the backscatter intensity information recorded by SAR data. Moreover, Sentinel-1 data can be used to create multi-temporal DEMs utilizing interferometric SAR (InSAR) techniques (Abad et al, 2022), which can support frequent monitoring of land surface changes and the characterization of lava flows. The resulting lava flow maps would be useful as reference data for future eruptions of Fagradalsfjall, could provide valuable information to support disaster risk management, and could be used as input data for lava flow modelling.…”