An open-source Python package for DEM generation and landslide volume estimation based on Sentinel-1 imagery
Authors: Abad Lorena , Hölbling Daniel , Dabiri Zahra , Robson Benjamin 
Published: March 2022

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&lt;p&gt;&lt;span&gt;Landslide assessments&amp;#160;require timely, accurate and comprehensive information, where&amp;#160;Earth observation (EO) data&amp;#160;such as optical and radar satellite imagery&amp;#160;has played an important role.&amp;#160;Volume&amp;#160;estimates&amp;#160;are&amp;#160;important&amp;#160;to understand landslide characteristics and (post-failure) behaviour.&amp;#160;Pre- and post-event digital elevation model (DEM) differencing is a suitable method to estimate landslide volumes remotely, leveraging EO techniques.&amp;#160;However, high costs for commercial DEM products, limited temporal and spatial coverage and resolution, or insufficient accuracy&amp;#160;hamper the potential of this method.&amp;#160;Sentinel-1&amp;#160;synthetic&amp;#160;aperture&amp;#160;radar (SAR)&amp;#160;data from the European Union's Earth observation programme Copernicus&amp;#160;opens the opportunity to leverage free EO data to generate&amp;#160;multi-temporal topographic datasets.&amp;#160;&lt;/span&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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&lt;p&gt;&lt;span&gt;With the&amp;#160;project&amp;#160;&lt;/span&gt;&lt;em&gt;&lt;span&gt;SliDEM&lt;/span&gt;&lt;/em&gt;&lt;span&gt;&amp;#160;(Assessing the suitability of DEMs derived from Sentinel-1 for landslide volume estimation)&amp;#160;we&amp;#160;explore the potential of Sentinel-1&amp;#160;for the generation of DEMs for landslide assessment. Therefore,&amp;#160;we&amp;#160;develop&amp;#160;a semi-automated and transferable workflow&amp;#160;available through an&amp;#160;open-source Python package. The package consists&amp;#160;of&amp;#160;different modules to&amp;#160;1) query&amp;#160;Sentinel-1 image&amp;#160;pairs&amp;#160;that match a given geographical and temporal extent, and&amp;#160;based on&amp;#160;perpendicular&amp;#160;and temporal baseline thresholds; 2)&amp;#160;download and archive only suitable Sentinel-1 image pairs; 3) produce DEMs using&amp;#160;interferometric SAR (InSAR)&amp;#160;techniques&amp;#160;available in&amp;#160;the&amp;#160;open-source&amp;#160;Sentinel Application Platform (SNAP),&amp;#160;as well as performing necessary post-processing such as&amp;#160;terrain correction and&amp;#160;co-registration; 4)&amp;#160;perform DEM differencing of&amp;#160;pre- and post-event DEMs&amp;#160;to quantify landslide volumes; and 5)&amp;#160;assess the accuracy and&amp;#160;validate&amp;#160;the DEMs&amp;#160;and volume estimates&amp;#160;against reference data.&amp;#160;&lt;/span&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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&lt;p&gt;&lt;span&gt;We evaluate and validate our&amp;#160;workflow in terms of reliability, performance, reproducibility, and transferability over several&amp;#160;major&amp;#160;landslides in Austria and Norway.&amp;#160;We distribute our work within a Docker container, which allows the usage of the&amp;#160;&lt;/span&gt;&lt;em&gt;&lt;span&gt;SliDEM&lt;/span&gt;&lt;/em&gt;&lt;span&gt;&amp;#160;python package along with all its software dependencies in a structured&amp;#160;and convenient way, reducing usability problems related to software versioning.&amp;#160;The&amp;#160;&lt;/span&gt;&lt;em&gt;&lt;span&gt;SliDEM&amp;#160;&lt;/span&gt;&lt;/em&gt;&lt;span&gt;workflow&amp;#160;represents an important contribution to the field of natural hazard research by developing an open-source,&amp;#160;low-cost, transferable, and semi-automated method for DEM generation and landslide volume estimation.&amp;#160;&lt;/span&gt;&lt;span&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt;
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