BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.osgeo.org//foss4g-2022-academic-track//speaker//ZR KRY7 BEGIN:VTIMEZONE TZID:CET BEGIN:STANDARD DTSTART:20001029T040000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:CET TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:CEST TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-2022-academic-track-XTSQPL@talks.osgeo.org DTSTART;TZID=CET:20220826T093000 DTEND;TZID=CET:20220826T100000 DESCRIPTION:Digital elevation models (DEMs) are a representation of the top ography of the Earth\, stored as elevation values in regular raster grid c ells. These data serve as basis for various geomorphological applications\ , for example\, for landslide volume estimation. Access to timely\, accura te and comprehensive information is crucial for landslide analysis\, chara cterisation and for understanding (post-failure) behaviours. This informat ion can subsequently be used to effectively assess and manage potential ca scading hazards and risks\, such as landslide dam outburst floods or debri s flows. Freely available DEM data has been an important asset for landsli de volume estimation. Earth observation (EO) techniques\, such as DEM diff erencing\, can be leveraged for volume estimation. However\, their applica bility is reduced by high costs for commercial DEM products\, limited temp oral and spatial coverage and resolution\, or insufficient accuracy. \n\n Sentinel-1 synthetic aperture radar (SAR) data from the European Union's E arth observation programme Copernicus opens the opportunity to leverage fr ee SAR data to generate on-demand multi-temporal topographic datasets. Sen tinel-1 A & B data provide a new opportunity to tackle some of the problem s related to data costs and spatio-temporal availability. Moreover\, the E uropean Space Agency (ESA) guarantees the continuity of the Sentinel-1 mis sion with the planned launch of another two satellites\, i.e.\, Sentinel-1 C & D. Interferometric SAR (InSAR) approaches based on Sentinel-1 have of ten been used to detect surface deformation\; however\, few studies have a ddressed DEM generation (Braun\, 2021). For example\, Dabiri et al. (2020) tested Sentinel-1 for landslide volume estimation\, but highlighted the n eed to further research and systematically assess the accuracy of the gene rated DEMs. InSAR analysis is often conducted using commercial software\; however\, a well-structured workflow based on free and open-source softwar e (FOSS) increases the applicability and transferability of the DEM genera tion method. Although a general workflow for DEM generation from Sentinel- 1 imagery based on InSAR has been described and documented (ASF DAAC\, 201 9\; Braun\, 2020\, 2021)\, there is still a need for improvement\, harmoni sation and automation of the required steps based on open-source tools. \n \nWithin the project SliDEM (Assessing the suitability of DEMs derived fro m Sentinel-1 for landslide volume estimation)\, we explore the potential o f Sentinel-1 for the generation of multi-temporal DEMs for landslide asses sment leveraging FOSS. Relying on the open-source Sentinel Application Pla tform (SNAP) developed by the ESA\, the Statistical-Cost\, Network-Flow Al gorithm for Phase Unwrapping (SNAPHU) developed by Stanford University\, a nd several other open-source software publicly available for geospatial an d geomorphological applications\, we work on a semi-automated and transfer able workflow bundled in an open-source Python package that is currently u nder active development. The workflow uses available Python SNAP applicati on programming interfaces (APIs)\, such as snappy and snapista. We distrib ute the SliDEM package within a Docker container\, which allows its usage along with all its software dependencies in a structured and straightforwa rd way\, reducing usability problems related to software versioning and di fferent operating systems. The final package will be released under an ope n-source license on a public GitHub repository. \n\nThe package consists o f different modules to 1) query Sentinel-1 image pairs based on perpendicu lar and temporal baseline thresholds that also match a given geographical and temporal extent\; 2) download and archive suitable Sentinel-1 image pa irs\; 3) produce DEMs using InSAR techniques and perform necessary post-pr ocessing such as terrain correction and co-registration\; 4) perform DEM d ifferencing of pre- and post-event DEMs to quantify landslide volumes\; an d 5) assess the accuracy and validate the generated DEMs and volume estima tes against reference data. The core module focusses on DEM generation fro m Sentinel-1 using InSAR techniques available in SNAP. The script co-regis ters and debursts Sentinel-1 image pairs before generating and filtering a n interferogram. Phase unwrapping is performed using SNAPHU. The unwrapped phase is then converted into elevation values\, which are finally geometr ically corrected and co-registered to a reference DEM. Co-registration is based on assessing the normalised elevation biases over stable terrain (af ter Nuth and Kääb\, 2011). \n\nWe assess errors and uncertainties for e ach step and the quality of the Sentinel-1 derived DEMs using reference da ta and statistical approaches. The semi-automated workflow allows for the generation of DEMs in an iterative and structured manner\, where a systema tic evaluation of the resulting DEM quality can be performed by testing th e influence of different temporal and perpendicular baselines\, the usage of ascending and descending passes\, distinct land use/land cover and topo graphy\, among other factors. Several major landslides in Austria and Norw ay have been selected to evaluate and validate the workflow in terms of re liability\, performance\, reproducibility\, and transferability. \n\nThe SliDEM workflow represents an important contribution to the field of natur al hazard research by developing an open-source\, low-cost\, transferable\ , and semi-automated method for DEM generation and landslide volume estima tion. From a practical perspective\, disaster risk management can benefit from efficient methods that deliver added-value information. From a techni cal point of view\, SliDEM tackles scientific questions on the validity of EO-based methods and the quality of results related to the assessment of geomorphological characteristics of landslides. DTSTAMP:20260907T030616Z LOCATION:Room Modulo 3 SUMMARY:An open-source-based workflow for DEM generation from Sentinel-1 fo r landslide volume estimation - Lorena Abad URL:https://talks.osgeo.org/foss4g-2022-academic-track/talk/XTSQPL/ END:VEVENT END:VCALENDAR