SIRIUS - Synthesized Inventory of CRitical Infrastructure and HUman-Impacted Areas in Permafrost Regions of AlaSka

• Main Authors: Kaiser, Soraya, Boike, Julia, Grosse, Guido, Langer, Moritz
• Format: Dataset
• Language: unknown
• Published: 2023
• Subjects: Arctic; The Sentinel; Sirius; permafrost; Alaska
• Online Access: https://zenodo.org/record/8311243; https://doi.org/10.5281/zenodo.8311243

The SIRIUS inventory integrates data from (i) the Sentinel-1/2 derived Arctic coastal human impact dataset (SACHI) (Bartsch et al., 2021), (ii) OpenStreetMap dataset for the infrastructure and land use information (OpenStreetMap Contributors and Geofabrik GmbH, 2018), (iii) the pan-Arctic catchments summary database (ARCADE) for the watersheds (Speetjens et al., 2022), (iv) the modeled Northern Hemisphere permafrost map by Obu et al. (2018), and (v) the contaminated sites database and reports by the State of Alaska Department of Environmental Conservation (2023) (DEC) to create a unified new dataset of critical infrastructure and human-impacted areas as well as permafrost and watershed information for Alaska. The dataset is deployed as a GeoPackage and can be imported to spatial databases (e.g. PostgreSQL/PostGIS), a Geographic Information System (e.g. QGIS), and used within geospatial processing libraries (e.g. Python's GeoPandas). All layers can be queried either in dependence or combination with one another. Each GeoPackage contains the following layers: ARCADE_WatershedsDB DEC_ContaminatedSitesAK OSM_Point_InfrastructureHIElements SACHI_OSM_InfrastructureHIElements SACHI_OSM_InfrastructureHIElements_RRNetwork UiO_MAGT UiO_PermafrostProbability UiO_PermafrostZones A corresponding manuscript, including application examples and a thorough description of the individual components, was submitted to be published in an open-access journal. Download Data Python Scripts 01_InfrastructureDataETL: reprojects the input Shapefiles and raster datasets to a common coordinate system (EPSG:5936) and then clips datasets to the boundary of Alaska. It also includes a step for filtering the permafrost probability raster dataset based on a minimum probability threshold of 50% and rounds the values in the mean annual ground temperature raster dataset. 02_OSM-aggregation: processes the OpenStreetMap (OSM) geospatial data. It imports and merges OSM polygon and point data, cleans and extracts unique values of "fclass" and "osm_type", ...