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Automatic Landslide detection using Bi-Temporal Sentinel 2 Imagery
Sepideh Tavakkoli Piralilou1, Hejar Shahabi2,3, Robert Pazur2,4
1Department of Geoinformatics Z-GIS, University of Salzburg, Austria.; 2Institute of Geography, Slovak Academy of Sciences, Stefanikova 49, 814 73 Bratislava, Slovakia; 3Department of Remote Sensing and GIS, University of Tabriz, Tabriz, Iran.; 4Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Zürcherstrasse 111, CH-8903 Birmensdorf, Switzerland
Landslide inventory data sets are required for any landslide susceptibility mapping and prediction approaches. However, generating accurate landslide inventory data sets depends on applied methods and quality of input data, e.g., spatial resolution for satellite imagery. Therefore, the accuracy and availability of inventories vary in different studies. In this study, we evaluated a strategy of sudden landslide identification product (SLIP) for landslide detection using Bi-Temporal Sentinel 2 Imagery and ALOS Digital Elevation Model (DEM). The resulting landslide detection map was then compared with that of an improved version of SLIP. The accuracy assessment stage demonstrated that using the improved version increased the accuracy by 16%.
Comparing the Applicability of Sentinel-1 and Sentinel-2 for Monitoring the Evolution of Ice-marginal Lakes in Southeast Iceland
Zahra Dabiri1, Daniel Hölbling1, Lorena Abad1, Snævarr Guðmundsson2
1Department of Geoinformatics - Z_GIS, University of Salzburg, Schillerstrasse 30, 5020 Salzburg, Austria; 2South East Iceland Nature Research Center, 780 Höfn í Hornafirði, Iceland
Monitoring ice-marginal lakes is important for glaciological and geomorphological studies, as well as hazard and risk assessment. Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 optical data opened a new era for multi-temporal analysis and studying geomorphological changes. The purpose of this study is to compare the applicability of Sentinel-1 and Sentinel-2 data for mapping the changes in ice-marginal lake areas at the southern margin of the Vatnajökull ice cap, southeast Iceland, between 2016 and 2020. We semi-automatically mapped the ice-marginal lakes with object-based image analysis (OBIA) and based on image time series using 1) the polarization products derived from Sentinel-1 data, and 2) the spectral information of Sentinel-2 data, and compared the results. Our results show that Sentinel-1 performed better regarding the detection of the number of ice-marginal lakes, whereas Sentinel-2 performed better regarding lake delineation. Moreover, we discuss the applicability of optical and SAR data for mapping and monitoring the evolution of ice-marginal lakes.
One GUI to rule them all: Accessing multiple semantic EO data cubes in one graphical user interface (GUI)
Martin Sudmanns1, Hannah Augustin1, Lucas van der Meer1, Christian Werner1, Dirk Tiede1, Andrea Baraldi2
1Department of Geoinformatics - Z_GIS, University of Salzburg, Austria; 2Baraldi Consultancy in Remote Sensing (BACRES), Modena, Italy & Spatial Services GmbH, Austria
Spatio-temporal analysis capabilities of big Earth observation (EO) data are possible now on various infrastructures, but the transferability and interoperability of analyses remains challenging. This contribution provides a user-driven solution for interacting with multiple semantic EO data cubes, where for each observation at least one nominal (i.e., categorical) interpretation is available and can be queried in the same instance. Our in-house developed Web-based graphical user interface (GUI) facilitates access to multiple semantic EO data cubes, regardless of what infrastructure they are implemented on. Users create semantic models using a graphical language and an inference engine can then evaluate these models against existing semantic EO data cubes based on a user’s defined area and timespan of interest. Querying on a semantic level allows the transferability of semantic models across EO data cubes. Our contribution shows such a solution towards solving this open research gap and discusses relevant challenges such as transferability of semantic models, on-demand instantiation, and federated EO data cubes. We believe that such a solution offers new opportunities for improved semantic and syntactic interoperability in EO analyses and is better positioned to making semantically-enabled queries possible in a federated EO data cube context.