Land cover fraction map of Austria at 10m spatial resolution based on Sentinel-1 and Sentinel-2 spectral temporal metrics

DOI

The increasing impact of humans on land and ongoing global population growth requires an improved understanding of land cover (LC) processes in general and of those related to settlements in particular. The heterogeneity of settlements and landscapes as well as the importance of not only mapping, but also characterizing anthropogenic and landscape structures suggests using a sub-pixel mapping approach for analysing related LC from space.This map has been created using a regression-based unmixing approach for mapping built-up surfaces and infrastructure, woody vegetation and non-woody vegetation for all of Austria at 10 m spatial resolution. Spectral-temporal metrics from all Sentinel-1 and Sentinel-2 observation in 2018 have been used to create synthetically mixed training data for regression. An elevation threshold of 1350m has been applied above which built-up surfaces and infrastructures were masked out.The mapping workflow has been established in the corresponding publication. This dataset is an enhanced dataset that uses an alternative set of spectral-temporal metrics for land cover modeling, including:- 25th, 50th and 75th quantile of Sentinel-2 reflectance- Average Sentinel-1 VH polarized backscatter- 90th quantile and standard deviation of Sentinel-2 Tasseled Cap GreennessThis enhanced set makes use of Sentinel-1 imagery, which reduces confusion of built-up features and seasonal soil-covered surfaces. Sentinel-2 Tasseled Cap Greenness is a more robust indicator for vegetation in temperate regions than the NDVI, which was used in the corresponding publication. The file is of GeoTiff format and contains three bands:Band 1 - Fraction of built-up surfaces and infrastructureBand 2 - Fraction of woody vegetationBand 3 - Fraction of non-woody vegetationFor further information, please see the publication or contact Franz Schug (franz.schug@geo.hu-berlin.de).Sentinel-1 data was kindly provided by TU Vienna (https://www.geo.tuwien.ac.at/) through EODC (https://www.eodc.eu/).This research has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 741950).

Identifier
DOI https://doi.org/10.1594/PANGAEA.923037
Related Identifier https://doi.org/10.1016/j.rse.2020.111810
Metadata Access https://ws.pangaea.de/oai/provider?verb=GetRecord&metadataPrefix=datacite4&identifier=oai:pangaea.de:doi:10.1594/PANGAEA.923037
Provenance
Creator Schug, Franz ORCID logo; Frantz, David ORCID logo; Okujeni, Akpona; van der Linden, Sebastian ORCID logo; Hostert, Patrick
Publisher PANGAEA
Publication Year 2020
Funding Reference Horizon 2020 https://doi.org/10.13039/501100007601 Crossref Funder ID 741950 https://cordis.europa.eu/project/id/741950 Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society
Rights Creative Commons Attribution 4.0 International; https://creativecommons.org/licenses/by/4.0/
OpenAccess true
Representation
Language English
Resource Type Dataset
Format application/zip
Size 1.9 GBytes
Discipline Earth System Research
Spatial Coverage (13.300 LON, 47.300 LAT)