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

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 Greenness This 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 infrastructure Band 2 - Fraction of woody vegetation Band 3 - Fraction of non-woody vegetation For 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).

Surrogate playground

Surrogate playground is an automated machine learning approach written for rapidly screening a large number of different models to serve as surrogates for a slow running simulator. This code was written for a reactive transport application where a fluid flow model (hydrodynamics) is coupled to a geochemistry simulator (reactions in time and space) to simulate scenarios such as underground storage of CO2 or hydrogen storage for excess energy from wind farms. The challenge for such applications is that the geochemistry simulator is typically slow compared to fluid dynamics and constitutes the main bottleneck for producing highly detailed simulations of such application scenarios. This approach attempts to find machine learning models that can replace the slow running simulator when trained on input-output data from the geochemistry simulator. The code may be of more general interest as this prototype can be used to screen many different machine learning models for any regression problem in general. To illustrate this it also includes a demonstration example using the Boston housing standard data-set.

Berlin-Urban-Gradient dataset 2009 - An EnMAP Preparatory Flight Campaign (Datasets)

Berlin-Urban-Gradient is a ready-to-use imaging spectrometry dataset for multi-scale unmixing and hard classification analyses in urban environments. The dataset comprises two airborne HyMap scenes at 3.6 and 9 m resolution, a simulated spaceborne EnMAP scene at 30 m resolution, an im-age endmember spectral library and detailed land cover reference information. All images are pro-vided as geocoded reflectance products and cover the same subset along Berlin’s urban-rural gra-dient. The variety of land cover and land use patterns captured make the dataset an ideal play-ground for testing the transfer of methods and research approaches at multiple spatial scales. Version HIstory: This version of the Berlin-Urban-Gradient-Dataset was updated to account for errors in the spatial referencing. This included six updated header files (.hdr) and two updated shapte files. See details in the new version and the associated data report. 27 Feb 2025: change to CC BY 4.0 License.

Berlin-Urban-Gradient dataset 2009 - An EnMAP Preparatory Flight Campaign (Datasets)

Berlin-Urban-Gradient is a ready-to-use imaging spectrometry dataset for multi-scale unmixing and hard classification analyses in urban environments. The dataset comprises two airborne HyMap scenes at 3.6 and 9 m resolution, a simulated spaceborne EnMAP scene at 30 m resolution, an im-age endmember spectral library and detailed land cover reference information. All images are pro-vided as geocoded reflectance products and cover the same subset along Berlin’s urban-rural gradient. The variety of land cover and land use patterns captured make the dataset an ideal play-ground for testing the transfer of methods and research approaches at multiple spatial scales.Version HIstory:This version of the Berlin-Urban-Gradient-Dataset was updated to account for errors in the spatial referencing. The following files were updated:Folder “BerlinUrbGrad2009_01_image_products\01_image_products”Replacement of header files of the four image products: (1) EnMAP01_Berlin_Urban_Gradient_2009.hdr, (2) EnMAP02_Berlin_Urban_Gradient_2009.hdr, (3) HyMap01_Berlin_Urban_Gradient_2009.hdr, (4) HyMap02_Berlin_Urban_Gradient_2009.hdr.Folder “BerlinUrbGrad2009_02_additional_data\02_additional_data\land_cover”:Replacement of header files of the two reference land cover images (Land-Cov_Layer_Level1_Berlin_Urban_Gradient_2009.hdr, Lan d-Cov_Layer_Level2_Berlin_Urban_Gradient_2009.hdr).Replacement of the shapefile (incl. extensions) representing the references polygons (LandCov_Vec_polygons_Berlin_Urban_Gradient_2009.shp, *.dbf, *.prj, *.sbn, *.sbx, *.shp.xml, *.shx).

Ergaenzung hydrologischer Datenreihen

Da in den Laendern der dritten Welt Beobachtungsreihen des Abflusses haeufig zeitliche Luecken aufweisen, sollen Methoden zur Ergaenzung fehlender Beobachtungswerte entwickelt werden. Dazu wurden in Ghana (West Afrika) drei verschiedene Einzugsgebiete mit verschiedenen klimatischen Charakteristika ausgewaehlt. Unter Beachtung der unterschiedlichen Laenge der Luecken (von einem Tag bis zu einem Jahr) und der unterschiedlichen klimatischen Charakteristika sollen allgemeingueltige Verfahren zur Ergaenzung der Beobachtungsreihen entwickelt werden. Folgende Verfahren werden untersucht: Autoregression, Rekursive Modelle, Interpolation, Regression mit benachbarten Pegeln und Niederschlag, Regression nur mit Niederschlag, Speichermodelle, Unit Hydrograph, Event-based Modelle und die Anwendung von Satellitenbildern. Im Ergebnis ist ein Entscheidungsbaum zu entwickeln, anhand dessen fuer verschiedene klimatische Charakteristika und bei unterschiedlicher Laenge der Luecken in den Beobachtungsreihen geeignete Methoden zur Ergaenzung der fehlenden Beobachtungsdaten ausgewiesen werden.

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