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TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements) is an Earth observation radar mission that consists of a SAR interferometer built by two almost identical satellites flying in close formation. With a typical separation between the satellites of 120 m to 500 m a global Digital Elevation Model (DEM) has been generated.

The TanDEM-X 90m DEM is a product variant of the global Digital Elevation Model (DEM) acquired in the frame of the German TanDEM-X mission between 2010 and 2014, and has a reduced pixel spacing of 3 arcseconds (arsec), which corresponds to 90m at the equator. It covers all Earth’s landmasses from pole to pole.

This map shows all 8 original TanDEM-X Information Layers as specified in the Product Specification:

• TDM90 Elevation (DEM): The DEM elevation values represent the ellipsoidal heights relative to the WGS84 ellipsoid in the WGS84-G1150 datum.
• TDM90 Height Error Map (HEM): The height error map (HEM) values represent for each DEM pixel the corresponding height error in form of the standard deviation.
• TDM90 Amplitute Min/Mean (AMP/AM2): The amplitude mosaic AMP and AM2 layers are a by-product generated for further processings, e.g. water body detection or DEM filtering. They represent the mean (AMP) and minimum value (AM2) of all calibrated amplitude values from the contributing DEM scenes (in general between 2 and up to 10 scenes).
• TDM90 Consistency Map (COM): The consistency mask (COM) indicates DEM pixels, which have height inconsistencies among the contributing DEM scenes.
• TDM90 Coverage Map (COV): The coverage map (COV) indicates how many valid height values from different DEM acquisitions were available for mosaicking.
• TDM90 Layover & Shadow Mask (LSM): The layover and shadow mask (LSM) is based on the SRTM-C DEM and the GLOBE DEM regarding the TanDEM-X geometry of each individual scene.
• TDM90 Water Indication Mask (WAM): The water indication mask (WAM) layer represents the water bodies indentified during processing.

The TanDEM-X PolarDEM is a project developed by the German Remote Sensing Data Center (DFD) at the German Aerospace Center (DLR) within the activities of the TanDEM-X mission. It is a framework for the provision of derivatives of the global Digital Elevation Model (DEM) of the TanDEM-X mission for Polar Regions. The current version of the global TanDEM-X DEM represents an unedited surface model that still contains noisy or void DEM values, some data gaps and a compilation of several acquisition campaigns performed at different times. The derivatives currently include the edited DEM product described below and will be supplemented with single year coverages and penetration bias corrected DEMs in the future.

TanDEM-X PolarDEM 90m of Antarctica: This is a gap-free and edited version of the TanDEM-X 90m DEM. The TanDEM-X PolarDEM 90m of Antarctica is provided in Antarctic Polar Stereographic projection (EPSG:3031) with a pixel spacing of 90 meters. The DEM elevation values represent the ellipsoidal heights relative to the WGS84 ellipsoid. The majority of the data were acquired between April 2013 to September 2014. The TanDEM-X PolarDEM 90m of Antarctica is split into 4 tiles. TanDEM-X PolarDEM High Resolution Coastline of Antarctica: This is a by-product to the DEM-editing process of the TanDEM-X PolarDEM of Antarctica in the original 12 m representation. It is provided as one polygon in shapefile-format. Every point of this polygon is given in Antarctic Polar Stereographic projection (EPSG:3031) with varying point distances with a minimum span of 10 meters.

For further information, please consult the PolarDEM Product Description.

Download:

TanDEM-X PolarDEM Antarctica: EOC Download Service (via GUI)
TanDEM-X High Resolution Coastline Antarctica: EOC Download Service (file-based)

License:

English: License for the utilization of the TanDEM-X PolarDEM 90m for Scientific Use
German: Lizenz für die Nutzung des TanDEM-X PolarDEMs 90m für wissenschaftliche Zwecke

The TanDEM-X Forest/Non-Forest Map is a project developed by the Microwaves and Radar Institute at the German Aerospace Center (DLR), within the activities of the TanDEM-X mission. The goal is the derivation of a global forest/non-forest classification mosaic from TanDEM-X bistatic interferometric synthetic aperture radar (InSAR) data, acquired for the generation of the global digital elevation model (DEM) in Stripmap single polarization (HH) mode.

The TanDEM-X Forest/Non-Forest Map (FNF) has been generated by processing and mosaicking more than 500,000 TanDEM-X bistatic images acquired from 2011 until 2015. The map has a spatial resolution of 50 m x 50 m. Forested and non-forested areas are depicted in green and white, respectively. Water bodies are depicted in blue and black is used for identifying urban areas and invalid pixels.

For further information, please consult the FNF50 Product Specification.

License:

License for the utilization of the TanDEM-X 50m FNF for Scientific Use (eng)
Lizenz für die Nutzung der TanDEM-X 50m Waldkarte für wissenschaftliche Zwecke (de)

This map contains elevation products at a spatial resolution of approximately 25 m x 25 m covering the globe between latitudes 60� North and 58� South. These products were generated from data collected by the German-Italian interferometric X-band radar system (X-SAR) onboard the Space Shuttle Endeavour during the Shuttle Radar Topography Mission (SRTM) between February 11 and February 22, 2000. The data have been processed into digital elevation models using radar interferometry, an innovative way for extracting surface height from the phase difference between two synthetic aperture radar datasets - a procedure somewhat resembling stereo viewing. The original tiled elevation products have been mosaicked into a single global elevation layer from which additional products have been derived. Shaded reliefs in grayscale and with color-coded elevation values provide a 3D impression of the global topography while a global error map, derived mainly from phase and baseline stability, helps to assess the relative accuracy of the measured elevation values.

The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission that aims at monitoring and characterising the Earth's environment on a global scale. EnMAP serves to measure and model key dynamic processes of the Earth's ecosystems by extracting geochemical, biochemical and biophysical parameters, which provide information on the status and evolution of various terrestrial and aquatic ecosystems. This collection includes Level 0 quicklook images of the mission. For more information, please see the mission website: https://www.enmap.org/mission/

The product is automatically derived from Aqua/Terra (MODIS) satellite imagery in near-real time. It is an incremental product, meaning that the retrieved results are updated as soon as new input data becomes available over a timespan of ten days. Besides the fire perimeter and detection time, each feature contains information about the severity of the burning.

General information:

• Satellite: Aqua / Terra
• Sensor: MODIS
• Product: Level 1b
• Period: Starting from November 2000. This product contains data for the recent 50 days.
• Coverage: Europe
• Temporal resolution: 2 - 4 times per day (Aqua & Terra), results are available within 30 minutes after satellite overpass (morning / afternoon)

Attributes:

• size: Number of affected pixels in original image resolution (250x250m)
• confidence: Robustness of the result, given in percent. The measure is derived from the number of present active fires in combination with the degree of burning
• name: Shortended name of the originating Aqua/Terra scene
• t_stamp: Time of detection
• region: Continental monitoring region
• avg_dndvi: Averaged degree of burning, addressing the decrease in vegetation fitness. Ranges from -2 (extremely severe) to > 0.
• median_dndvi: Median value of degree of burning, see 'avg_dndvi'
• min_dndvi: Minimal degree of burning, see 'avg_dndvi'
• max_dndvi: Maximal degree of burning, see 'avg_dndvi'
• area_ha: Size of affected area in hectares

Related datasets:

• Burnt Area Daily NRT Product - Europe, Sentinel-3

References:

• UKIS Data Tutorials demonstrating the access to these datasets with interactive Jupyter notebooks.

• Michael Nolde, Simon Plank, Torsten Riedlinger. (2020, July 6). An Adaptive and Extensible System for Satellite-Based, Large Scale Burnt Area Monitoring in Near-Real Time. Remote Sens. 2020, 12, 2162. https://doi.org/10.3390/rs12132162

License: Creative Commons Attribution-NonCommercial (CC BY-NC 4.0)

The product is automatically derived from Sentinel-3 (OLCI) satellite imagery in near-real time. It is an incremental product, meaning that the retrieved results are updated as soon as new input data becomes available over a timespan of ten days. Besides the fire perimeter, and detection time each feature contains information about the severity of the burning.

General information:

• Satellite: Sentinel-3 A/B
• Sensor: OLCI
• Product: Level 1, Near Real-Time (NRT)
• Period: Starting from March 2016. This product contains data for the recent 50 days.
• Coverage: Europe

Attributes:

• size: Number of affected pixels in original image resolution (300x300m)
• confidence: Robustness of the result, given in percent. The measure is derived from the number of present active fires in combination with the degree of burning
• name: Shortended name of the originating Sentinel-3 scene
• t_stamp: Time of detection
• region: Continental monitoring region
• avg_dndvi: Averaged degree of burning, addressing the decrease in vegetation fitness. Ranges from -2 (extremely severe) to > 0.
• median_dndvi: Median value of degree of burning, see 'avg_dndvi'
• min_dndvi: Minimal degree of burning, see 'avg_dndvi'
• max_dndvi: Maximal degree of burning, see 'avg_dndvi'
• area_ha: Size of affected area in hectares

Related datasets:

• Burnt Area Monthly Product - Europe, Sentinel-3
• Burnt Area Yearly Product - Europe, Sentinel-3

References:

• UKIS Data Tutorials demonstrating the access to these datasets with interactive Jupyter notebooks.

• Michael Nolde, Simon Plank, Torsten Riedlinger. (2020, July 6). An Adaptive and Extensible System for Satellite-Based, Large Scale Burnt Area Monitoring in Near-Real Time. Remote Sens. 2020, 12, 2162. https://doi.org/10.3390/rs12132162

License: Creative Commons Attribution-NonCommercial (CC BY-NC 4.0)

This data set represents the monthly, accumulated results of the final (10-day) version of the fire perimeters from the "Burnt Area Daily NRT Incremental Product - Europe, Sentinel-3" dataset. The burn perimeters are spatially and temporally correlated, so that interrelated detections from consecutive observations are combined into a single feature. A perimeter is interpreted as belonging to a given event if a spatial overlap exists within a time frame of 15 days. Besides the geometry, attribute information is also combined while considering the size of the perimeter as a weighting factor. Each feature contains information about the final fire perimeter, Date/Time of the first detection, and the averaged burn severity.

General information:

• Satellite: Sentinel-3 A/B
• Sensor: OLCI
• Product: Level 1, Near Real-Time (NRT) / Non-Time Critical (NTC)
• Period: starting from April 2016
• Coverage: Europe

Attributes:

• id: Generated unique ID of the burnt area
• first_seen. Date/Time of first detection
• burn_severity (avg_dndvi): Averaged degree of burning, addressing the decrease in vegetation fitness. Ranges from -2 (extremely severe) to > 0.
• confidence_total: Robustness of the result, given in percent. The measure is derived from the number of present active fires in combination with the degree of burning
• t_stamp_start: Startdate/time of the monthly monitoring period
• t_stamp_end: Enddate/time of the monthly monitoring period
• region: Continental monitoring region
• name: Shortened name of the originating Sentinel-3 scene
• area_ha: Size of affected area in hectares

Related datasets:

• Burnt Area Daily NRT Incremental Product - Europe, Sentinel-3
• Burnt Area Yearly Product - Europe, Sentinel-3

References:

• UKIS Data Tutorials demonstrating the access to these datasets with interactive Jupyter notebooks.

• Michael Nolde, Simon Plank, Torsten Riedlinger. (2020, July 6). An Adaptive and Extensible System for Satellite-Based, Large Scale Burnt Area Monitoring in Near-Real Time. Remote Sens. 2020, 12, 2162. https://doi.org/10.3390/rs12132162

License: Creative Commons Attribution-NonCommercial (CC BY-NC 4.0)

This data set represents the yearly, accumulated results of the final (10-day) version of the fire perimeters from the "Burnt Area Daily NRT Incremental Product - Europe, Sentinel-3" dataset. The burn perimeters are spatially and temporally correlated, so that interrelated detections from consecutive observations are combined into a single feature. A perimeter is interpreted as belonging to a given event if a spatial overlap exists within a time frame of 15 days. Besides the geometry, attribute information is also combined while considering the size of the perimeter as a weighting factor. Each feature contains information about the final fire perimeter, Date/Time of the first detection, and the averaged burn severity.

General information:

• Satellite: Sentinel-3 A/B
• Sensor: OLCI
• Product: Level 1, Near Real-Time (NRT) / Non-Time Critical (NTC)
• Period: starting from 2017
• Coverage: Europe

Attributes:

• id: Generated unique ID of the burnt area
• first_seen. Date/Time of first detection
• burn_severity (avg_dndvi): Averaged degree of burning, addressing the decrease in vegetation fitness. Ranges from -2 (extremely severe) to > 0.
• confidence_total: Robustness of the result, given in percent. The measure is derived from the number of present active fires in combination with the degree of burning
• t_stamp_start: Startdate/time of the monthly monitoring period
• t_stamp_end: Enddate/time of the monthly monitoring period
• region: Continental monitoring region
• name: Shortened name of the originating Sentinel-3 scene
• area_ha: Size of affected area in hectares

Related datasets:

• Burnt Area Daily NRT Incremental Product - Europe, Sentinel-3
• Burnt Area Monthly Product - Europe, Sentinel-3

References:

• UKIS Data Tutorials demonstrating the access to these datasets with interactive Jupyter notebooks.

• Michael Nolde, Simon Plank, Torsten Riedlinger. (2020, July 6). An Adaptive and Extensible System for Satellite-Based, Large Scale Burnt Area Monitoring in Near-Real Time. Remote Sens. 2020, 12, 2162. https://doi.org/10.3390/rs12132162

License: Creative Commons Attribution-NonCommercial (CC BY-NC 4.0)

This map shows daily products of different trace gases measured by the Global Ozone Monitoring Experiment-2 (GOME-2) which was launched on board EUMETSAT's Meteorological Operational Satellites (MetOp-A, -B and -C) in October 2006, September 2012, and November 2018, respectively. A wide range of atmospheric trace constituents are measured, with the emphasis on global ozone distributions. Furthermore cloud properties and intensities of ultraviolet radiation are retrieved. DLR generates operational GOME-2/MetOp Level 2 products in the framework of EUMETSAT's Satellite Application Facility on Atmospheric Composition Monitoring (AC SAF).

The Moderate Resolution Imaging Spectroradiometer (MODIS) is a key instrument aboard the American satellites Terra and Aqua. The MODIS-EU image mosaic is a seamless true color composite of all Terra and Aqua passes received at DLR during one day. Daily and Near Real Time (NRT) products are available. For the composite, MODIS channels 1, 4, 3 are used. The channels are re-projected, radiometrically enhanced, and seamlessly stitched to obtain a visually appealing result.

Terra passes from north to south across the equator in the morning, while Aqua passes the equator south to north in the afternoon. Both MODIS instruments are viewing the entire Earth surface every 1 to 2 days, acquiring data in 36 spectral bands. These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.

DLR's Earth Observation Centre produces and distributes Sentinel 2 Level 2A data over Germany, corrected for atmospheric effects thanks to the MAJA software developed in coordination between CNES/CESBIO and DLR. This processor uses multi-temporal information to detect clouds (and cloud shadows) and to estimate the optical properties of the atmosphere.

The MAJA product offers an alternative to the official ESA L2A product and has been processed with consideration of the characteristics of the Sentinel-2 mission (fast collection of time series, constant sensor perspective, and global coverage). Assumptions about the temporal constancy of the ground cover are taken into account for a robust detection of clouds and a more flexible determination of aerosol properties. As a result, an improved determination of the reflectance of sunlight at the earth's surface (pixel values of the multispectral image) is derived.

This service provides the following data layers:

• FRE: Surface reflectance, corrected for atmospheric effects, including adjacency effects and slope effects. All 10 and 20 m Sentinel 2 bands are available. The reflectance is scaled by a factor of 10000.
• MG2: Bitwise geophysical mask: Bit 0: Water, Bit 1: All Clouds (except the thinnest), Bit 2: Snow, Bit 3: Shadows, Bit 4: Topographic Shadows, Bit 5: Unseen due to topography, Bit 6: Sun too low, Bit 7: Sun direction tangent to slope
• CLM: Detailed cloud mask.

For a more detailed description, please refer to the MAJA L2A product format page.

Satellite: Sentinel-2
Sensor: MSI
Period: starting from July 2015
Coverage: Germany

References:

• Olivier Hagolle, Mireille Huc, Camille Desjardins, Stefan Auer, & Rudolf Richter. (2017, December 7). MAJA Algorithm Theoretical Basis Document (Version 1.0)

Digital Object Identifier (DOI): 10.15489/ifczsszkcp63

Related datasets:

• MAJA L2A Product Download (map client): EOC Download Service
• MAJA L2A Product Download (file-based): EOC Download Service
• WMS Service (Layer S2_L2A_MAJA): EOC Imagery WMS
• WCS Service (Coverage ID S2_L2A_MAJA): EOC Imagery WCS
• WASP L3A over Germany: Map Preview
• CNES provides L2A and L3A products for western Europe and some other regions on the Theia service.

Sentinel Level 3A for Germany is a monthly, cloud-free surface reflectance syntheses produced at the DLR Earth Observation Center using the WASP processor. The Level 3A product provide a weighted average of the non-clouded observations obtained over a 45-day synthesis period, based on the Level 2A MAJA product. Conventional methods for creating time syntheses often use the "best available pixel" method, which selects for each pixel the date for which the best acquisition conditions are available. Best available pixel mosaics are prone to artifacts on the boundary lines between the different dates selected for each pixel. The weighted average method minimizes these artifacts by merging the different dates without clouds, but depends on good cloud masks, which are provided by the MAJA L2A processor.

This service provides the following layers:

• FRC: Monthly surface reflectance synthesis for all 10 and 20 m Sentinel 2 bands
• FLG: Bitwise classification map: Bit 0: No Data, Bit 1: Cloud, Bit 2: Snow, Bit 3: Water, Bit 4: Land
• DTS: Weighted average of the dates used by the synthesis, as number of days since January 1st, 2018

For a more detailed description, please refer to the WASP L3A product format page.

Satellite: Sentinel-2
Sensor: MSI
Period: starting from July 2015
Coverage: Germany
Known Issues:

• L3A production delayed for tiles with persistent cloud cover.

References:

• Olivier Hagolle, Mireille Huc, Camille Desjardins, Stefan Auer, & Rudolf Richter. (2017, December 7). MAJA Algorithm Theoretical Basis Document (Version 1.0)
• Olivier Hagolle, David Morin, & Mohamed Kadiri. (2018, August 21). Detailed Processing Model for the Weighted Average Synthesis Processor (WASP) for Sentinel-2 (Version 1.4)

Digital Object Identifier (DOI): 10.15489/4hcq6dgkj648

Related datasets:

• MAJA L2A over Germany: Map Preview
• MAJA L2A Product Tiles: EOC Download Service
• WMS Service (Layer S2_L2A_MAJA): EOC Imagery WMS
• WCS Service (Coverage ID S2_L2A_MAJA): EOC Imagery WCS
• CNES provides L2A and L3A products for western Europe and some other regions on the Theia service.

The World Settlement Footprint WSF 2015 version 2 (WSF2015 v2) is a 10m resolution binary mask outlining the extent of human settlements globally for the year 2015. Specifically, the WSF2015 v2 is a pilot product generated by combining multiple datasets, namely:

The WSF2015 v1 derived at 10m spatial resolution by means of 2014-2015 multitemporal Landsat-8 and Sentinel-1 imagery (of which ~217K and ~107K scenes have been processed, respectively); Publication

The High Resolution Settlement Layer (HRSL) generated by the Connectivity Lab team at Facebook through the employment of 2016 DigitalGlobe VHR satellite imagery and publicly released at 30m spatial resolution for 214 countries; Publication

The novel WSF2019 v1 derived at 10m spatial resolution by means of 2019 multitemporal Sentinel-1 and Sentinel-2 imagery (of which ~ 1.2M and ~1.8M scenes have been processed, respectively); Publication

The WSF2015 v1 demonstrated to be highly accurate, outperforming all similar existing global layers; however, the use of Landsat imagery prevented a proper detection of very small structures, mostly due to their reduced scale. Based on an extensive qualitative assessment, wherever available the HRSL layer shows instead a systematic underestimation of larger settlements, whereas it proves particularly effective in identifying smaller clusters of buildings down to single houses, thanks to the employment of 2016 VHR imagery. The WSF2015v v2 has been then generated by: i) merging the WSF2015 v1 and HRSL (after resampling to 10m resolution and disregarding the population density information attached); and ii) masking the outcome by means of the WSF2019 product, which exhibits even higher detail and accuracy, also thanks to the use of Sentinel-2 data and the proper employment of state-of-the-art ancillary datasets (which allowed, for instance, to effectively mask out all roads globally from motorways to residential).

General information:

• Product version: 2.0
• Satellite: Landsat-8, Sentinel-1
• Period: 2015
• Resolution: 10 x 10
• Coverage: Global

Download: HTTP download via EOC Download Service

Related datasets:

• World Settlement Footprint (WSF) 2019
• World Settlement Footprint (WSF) Evolution

References:

• Marconcini, M., Metz-Marconcini, A., Üreyen, S. et al. Outlining where humans live, the World Settlement Footprint 2015. Sci Data 7, 242 (2020). https://www.nature.com/articles/s41597-020-00580-5"

• Mattia Marconcini, Annekatrin Metz-Marconcini, Thomas Esch and Noel Gorelick. Understanding Current Trends in Global Urbanisation - The World Settlement Footprint Suite. GI_Forum 2021, Issue 1, 33-38 (2021). https://austriaca.at/0xc1aa5576%200x003c9b4c.pdf

Attribution: WSF2015 version 2 Data are licensed under: Attribution 4.0 International (CC BY 4.0)

The World Settlement Footprint (WSF) 2019 is a 10m resolution binary mask outlining the extent of human settlements globally derived by means of 2019 multitemporal Sentinel-1 (S1) and Sentinel-2 (S2) imagery. Based on the hypothesis that settlements generally show a more stable behavior with respect to most land-cover classes, temporal statistics are calculated for both S1- and S2-based indices. In particular, a comprehensive analysis has been performed by exploiting a number of reference building outlines to identify the most suitable set of temporal features (ultimately including 6 from S1 and 25 from S2). Training points for the settlement and non-settlement class are then generated by thresholding specific features, which varies depending on the 30 climate types of the well-established Köppen Geiger scheme. Next, binary classification based on Random Forest is applied and, finally, a dedicated post-processing is performed where ancillary datasets are employed to further reduce omission and commission errors. Here, the whole classification process has been entirely carried out within the Google Earth Engine platform. To assess the high accuracy and reliability of the WSF2019, two independent crowd-sourcing-based validation exercises have been carried out with the support of Google and Mapswipe, respectively, where overall 1M reference labels have been collected based photointerpretation of very high-resolution optical imagery.

The dataset is organized in 5138 GeoTIFF files (EPSG 4326 projection) each one referring to a portion of 2 x 2 degree size (~222 x 222 km) on the ground. Settlements are associated with value 255; all other pixels are associated with value 0.

A comprehensive publication with all technical details and accuracy figures is currently being finalized. For the time being, please refer to Marconcini et al,. 2021 .

General information:

• Satellite: Sentinel-1, Sentinel-2
• Period: 2019
• Resolution: 10 x 10
• Coverage: Global

Download: HTTP download via EOC Download Service

Related datasets:

• World Settlement Footprint (WSF) 2015
• World Settlement Footprint (WSF) Evolution

References:

• Mattia Marconcini, Annekatrin Metz-Marconcini, Thomas Esch and Noel Gorelick. Understanding Current Trends in Global Urbanisation - The World Settlement Footprint Suite. GI_Forum 2021, Issue 1, 33-38 (2021) https://austriaca.at/0xc1aa5576%200x003c9b4c.pdf

Attribution: WSF2019 data are licensed under: Attribution 4.0 International (CC BY 4.0)

The World Settlement Footprint (WSF) Evolution is a 30m resolution dataset outlining the global settlement extent on a yearly basis from 1985 to 2015. Based on the assumption that settlement growth occurred over time, all pixels categorized as non-settlement in the WSF2015 (Marconcini et al., 2020) are excluded a priori from the analysis. Next, for each target year in the past, all available Landsat-5/7 scenes acquired over the given area of interest are gathered and key temporal statistics (i.e., temporal mean, minimum, maximum, etc.) are then extracted for different spectral indices. Among others, these include: the normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI). Temporal features proved generally robust if computed over at least 7 clear cloud-/cloud-shadow-free observations; accordingly, if for a given pixel in the target year this constraint is not satisfied, the time frame is enlarged backwards (at 1-year steps) as long as the condition is met.

Starting backwards from the year 2015 - for which the WSF2015 is used as a reference - settlement and non-settlement training samples for the given target year t are iteratively extracted by applying morphological filtering to the settlement mask derived for the year t+1, as well as excluding potentially mislabeled samples by adaptively thresholding the temporal mean NDBI, MNDWI and NDVI. Finally, binary Random Forest classification in performed.

To quantitatively assess the high accuracy and reliability of the dataset, an extensive campaign based on crowdsourcing photointerpretation of very high-resolution airborne and satellite historical imagery has been performed with the support of Google. In particular, for the years 1990, 1995, 2000, 2005, 2010 and 2015, ~200K reference cells of 30x30m size distributed over 100 sites around the world have been labelled, hence summing up to overall ~1.2M validation samples.

It is worth noting that past Landsat-5/7 availability considerably varies across the world and over time. Independently from the implemented approach, this might then result in a lower quality of the final product where few/no scenes have been collected. Accordingly, to provide the users with a suitable and intuitive measure that accounts for the goodness of the Landsat imagery, we conceived the Input Data Consistency (IDC) score, which ranges from 6 to 1 with: 6) very good; 5) good; 4) fair; 3) moderate; 2) low; 1) very low. The IDC score is available on a yearly basis between 1985 and 2015 and supports a proper interpretation of the WSF evolution product.

The WSF evolution and IDC score datasets are organized in 5138 GeoTIFF files (EPSG4326 projection) each one referring to a portion of 2 x 2 degree size (~222 x 222 km) on the ground. WSF evolution values range between 1985 and 2015 corresponding to the estimated year of settlement detection, whereas 0 is no data. A comprehensive publication with all technical details and accuracy figures is currently being finalized. For the time being, please refer to Marconcini et al,. 2021 .

General information:

• Satellite: Landsat 5, 7
• Period: 1985 - 2015
• Resolution: 30 x 30
• Coverage: Global

Related datasets:

• World Settlement Footprint (WSF) 2015
• World Settlement Footprint (WSF) 2019

Download: HTTP download via EOC Download Service

References:

• Mattia Marconcini, Annekatrin Metz-Marconcini, Thomas Esch and Noel Gorelick. Understanding Current Trends in Global Urbanisation - The World Settlement Footprint Suite. GI_Forum 2021, Issue 1, 33-38 (2021) https://austriaca.at/0xc1aa5576%200x003c9b4c.pdf

• Marconcini, M., Metz-Marconcini, A., Üreyen, S. et al. Outlining where humans live, the World Settlement Footprint 2015. Sci Data 7, 242 (2020).https://www.nature.com/articles/s41597-020-00580-5

Attribution:WSF Evolution Data are licensed under: Attribution 4.0 International (CC BY 4.0)

The GUF® maps show two land cover categories (e. g. in a B&W representation): Built-up areas (vertical structures only) in black and non-built-up surfaces in white; in addition, areas of no coverage by theTSX/TDX satellites (NoData) are coded in grey (most parts of the oceans). The focus on two categories clearly highlights the settlement patterns, improving the ability to analyze and compare them with other built-up areas across the world, in an urban or in a rural context. Unlike previous approaches, the fully automatic evaluation procedure detects the characteristic vertical structures of human habitations are primarily buildings. In contrast, areas used for infrastructure purposes, like roads, are not mapped. This is why broad urban canyons or expanses of greenery within the cities are shown as white corridors and patches.

The GUF® maps show two land cover categories (e. g. in a B&W representation): Built-up areas (vertical structures only) in black and non-built-up surfaces in white; in addition, areas of no coverage by theTSX/TDX satellites (NoData) are coded in grey (most parts of the oceans). The focus on two categories clearly highlights the settlement patterns, improving the ability to analyze and compare them with other built-up areas across the world, in an urban or in a rural context. Unlike previous approaches, the fully automatic evaluation procedure detects the characteristic vertical structures of human habitations are primarily buildings. In contrast, areas used for infrastructure purposes, like roads, are not mapped. This is why broad urban canyons or expanses of greenery within the cities are shown as white corridors and patches.

The GWP maps show different water coverage categories which represent how often a location (pixel) was detected as open surface water. Dark blue color show lakes and rivers which can be considered as permanent open water, red color represents pixel which were rarely water over the year. Be aware that lake ice was not classified as open surface water. Therefore, e.g. Northern hemisphere lakes, indirectly visualize how long a lake was ice free

Information about extent and dynamics of open surface water are essential for climate research, hydrological applications, flood prediction and water management. Climate and environmental change as well as water management are influencing the characteristics and duration of surface water around the globe. Therefore, precise information about the different open surface water parameters and their development over time are of high importance for various research fields.

The Global WaterPack is a dataset containing information about open surface water cover parameters on a global scale. The water detection is derived from daily, operational MODIS datasets for every year since 2003. The negative effects of polar darkness and cloud coverage are compensated by applying interpolation processing steps. Thereby, a unique global dataset can be provided that is characterized by its high temporal resolution of one day and a spatial resolution of 250 meter.

Coverage: global
Resolution: 250 m x 250 m

Data access: Available upon request to waterpack@dlr.de

Attribution: The Global WaterPack data are licensed under: Attribution 4.0 International (CC BY 4.0)

This product shows the snow cover duration for a hydrological year. Its beginning differs from the calendar year, since some of the precipitation that falls in late autumn and winter falls as snow and only drains away when the snow melts in the following spring or summer. The meteorological seasons are used for subdivision and the hydrological year begins in autumn and ends in summer. The snow cover duration is made available for three time periods: the snow cover duration for the entire hydrological year (SCD), the early snow cover duration (SCDE), which extends from autumn to midwinter, and the late snow cover duration (SCDL), which in turn extends over the period from mid-winter to the end of summer. For the northern hemisphere SCD lasts from September 1st to August 31st, for the southern hemisphere it lasts from March 1st to February 28th/29th. The SCDE lasts from September 1st to January 14th in the northern hemisphere and from March 1st to July 14th in the southern hemisphere. The SCDL lasts from January 15th to August 31st in the northern hemisphere and from July 15th to February 28th/29th in the southern hemisphere.

The “Global SnowPack” is derived from daily, operational MODIS snow cover product for each day since February 2000. Data gaps due to polar night and cloud cover are filled in several processing steps, which provides a unique global data set characterized by its high accuracy, spatial resolution of 500 meters and continuous future expansion. It consists of the two main elements daily snow cover extent (SCE) and seasonal snow cover duration (SCD; full and for early and late season). Both parameters have been designated by the WMO as essential climate variables, the accurate determination of which is important in order to be able to record the effects of climate change. Changes in the largest part of the cryosphere in terms of area have drastic effects on people and the environment.

Product Information:

• Product Name: GSP - Global SnowPack Yearly
• Product Version: 2
• Release: 2022-03-01
• Contact: Sebastian Rößler

Data Information:

• Satellite: Terra (EOS-1)/Aqua (EOS-PM1)
• Sensor: MODIS
• Format: Cloud Optimized GeoTIFF
• Resolution: 460 x 460
• Period: 2000 - 2021
• Coverage: Global
• License: CC BY 4.0

Data:

• Layer: GSP - Snow Cover Duration (SCD), GSP - Snow Cover Duration Early Season (SCDE), GSP - Snow Cover Duration Late Season (SCDL)
• Related datasets:
• GSP - Global SnowPack - Daily
• GSP - Global SnowPack - Mean
• Download: HTTP download via EOC Download Service
• Metadata: ISO Collection

References:

• Dietz, A.J., Kuenzer, C., Conrad, C., 2013. Snow–cover variability in central Asia between 2000 and 2011 derived from improved MODIS daily snow-cover products. International Journal of Remote Sensing 34, 3879–3902. https://doi.org/10.1080/01431161.2013.767480

• Dietz, A.J., Kuenzer, C., Dech, S., 2015. Global SnowPack: a new set of snow cover parameters for studying status and dynamics of the planetary snow cover extent. Remote Sensing Letters 6, 844–853. https://doi.org/10.1080/2150704X.2015.1084551

• Dietz, A.J., Wohner, C., Kuenzer, C., 2012. European Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products. Remote Sensing 4. https://doi.org/10.3390/rs4082432

• Dietz, J.A., Conrad, C., Kuenzer, C., Gesell, G., Dech, S., 2014. Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data. Remote Sensing 6. https://doi.org/10.3390/rs61212752

• Rößler, S., Witt, M.S., Ikonen, J., Brown, I.A., Dietz, A.J., 2021. Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers. Geosciences 11, 130. https://doi.org/10.3390/geosciences11030130

DLR © 2022

This product shows globally the daily snow cover extent (SCE). The snow cover extent is the result of the Global SnowPack processor's interpolation steps and all data gaps have been filled. Snow cover extent is updated daily and processed in near real time (3 days lag). In addition to the near real-time product (NRT_SCE), the entire annual data set is processed again after the end of a calendar year in order to close data gaps etc. and the result is made available as a quality-tested SCE product. There is also a quality layer for each day (SCE_Accuracy), which reflects the quality of the snow determination based on the time interval to the next "cloud-free" day, the time of year and the topographical/geographical location.

The “Global SnowPack” is derived from daily, operational MODIS snow cover product for each day since February 2000. Data gaps due to polar night and cloud cover are filled in several processing steps, which provides a unique global data set characterized by its high accuracy, spatial resolution of 500 meters and continuous future expansion. It consists of the two main elements daily snow cover extent (SCE) and seasonal snow cover duration (SCD; full and for early and late season). Both parameters have been designated by the WMO as essential climate variables, the accurate determination of which is important in order to be able to record the effects of climate change. Changes in the largest part of the cryosphere in terms of area have drastic effects on people and the environment.

Product Information:

• Product Name: GSP - Global SnowPack Daily
• Product Version: 2
• Release: 2022-03-01
• Contact: Sebastian Rößler

Data Information:

• Satellite: Terra (EOS-1)/Aqua (EOS-PM1)
• Sensor: MODIS
• Format: Cloud Optimized GeoTIFF
• Resolution: 460 x 460
• Period: 2000 - today
• Coverage: Global
• License: CC BY 4.0

Data:

• Layer: GSP - Snow Cover Extent (SCE), GSP - Snow Cover Extent Near Real-Time (SCE NRT)
• Related datasets:
• GSP - Global SnowPack - Yearly
• GSP - Global SnowPack - Mean
• Download: HTTP download via EOC Download Service
• Metadata: ISO Collection

Attributes:

The individual bit positions indicate the class to which they belong (bits 4-8) or the interpolation level (bits 1-3). The meaning of the bytes is as follows (little-endian):

• 1: Classified by 3-day interpolation
• 2: Classified according to topographical interpolation of absolute snow lines
• 4: Classified according to seasonal interpolation of the previous days
• 8: Class: ocean
• 16: Class: inland water
• 32: Class: snow-free land
• 64: Class: snow covered with low NDSI (greater than 0.1, smaller than 0.4) - snow in the forest
• 128: snow covered with high NDSI (greater than 0.4)

The value of the pixels can be read as follows: A value of 132 (128+4) indicates that there is snow cover (value 128), but this was only detected with the seasonal interpolation (value 4). If a binary mask is required, all pixels with a value greater than or equal to 64 should be selected.

References:

• Dietz, A.J., Kuenzer, C., Conrad, C., 2013. Snow–cover variability in central Asia between 2000 and 2011 derived from improved MODIS daily snow-cover products. International Journal of Remote Sensing 34, 3879–3902. https://doi.org/10.1080/01431161.2013.767480

• Dietz, A.J., Kuenzer, C., Dech, S., 2015. Global SnowPack: a new set of snow cover parameters for studying status and dynamics of the planetary snow cover extent. Remote Sensing Letters 6, 844–853. https://doi.org/10.1080/2150704X.2015.1084551

• Dietz, A.J., Wohner, C., Kuenzer, C., 2012. European Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products. Remote Sensing 4. https://doi.org/10.3390/rs4082432

• Dietz, J.A., Conrad, C., Kuenzer, C., Gesell, G., Dech, S., 2014. Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data. Remote Sensing 6. https://doi.org/10.3390/rs61212752

• Rößler, S., Witt, M.S., Ikonen, J., Brown, I.A., Dietz, A.J., 2021. Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers. Geosciences 11, 130. https://doi.org/10.3390/geosciences11030130

DLR © 2022

This product shows the mean snow cover duration (SCDmean), which is updated each year and consists of the arithmetic mean for the entire time series since the hydrological year 2001. The hydrological year begins in the meteorological autumn (October 1 of the previous year in the northern hemisphere or March 1 of the reference year in the southern hemisphere) and ends with the meteorological summer (northern hemisphere: August 31 of the reference year; southern hemisphere: February 28/29 of the following year). Analogous to the annual products for snow cover duration, the entire year as well as the early season (until mid-winter) and the late season (from mid-winter) are taken into account here.

The “Global SnowPack” is derived from daily, operational MODIS snow cover product for each day since February 2000. Data gaps due to polar night and cloud cover are filled in several processing steps, which provides a unique global data set characterized by its high accuracy, spatial resolution of 500 meters and continuous future expansion. It consists of the two main elements daily snow cover extent (SCE) and seasonal snow cover duration (SCD; full and for early and late season). Both parameters have been designated by the WMO as essential climate variables, the accurate determination of which is important in order to be able to record the effects of climate change. Changes in the largest part of the cryosphere in terms of area have drastic effects on people and the environment.

Product Information:

• Product Name: GSP - Global SnowPack Mean
• Product Version: 2
• Release: 2022-03-01
• Contact: Sebastian Rößler

Data Information:

• Satellite: Terra (EOS-1)/Aqua (EOS-PM1)
• Sensor: MODIS
• Format: Cloud Optimized GeoTIFF
• Resolution: 460 x 460
• Period: 2000 - 2021
• Coverage: Global
• License: CC BY 4.0

Data:

• Layer: GSP - Snow Cover Duration Mean, GSP - Snow Cover Duration Early Season Mean, GSP - Snow Cover Duration Late Season Mean
• Related datasets:
• GSP - Global SnowPack - Yearly
• GSP - Global SnowPack - Daily
• Download: HTTP download via EOC Download Service
• Metadata: ISO Collection

References:

• Dietz, A.J., Kuenzer, C., Conrad, C., 2013. Snow–cover variability in central Asia between 2000 and 2011 derived from improved MODIS daily snow-cover products. International Journal of Remote Sensing 34, 3879–3902. https://doi.org/10.1080/01431161.2013.767480

• Dietz, A.J., Kuenzer, C., Dech, S., 2015. Global SnowPack: a new set of snow cover parameters for studying status and dynamics of the planetary snow cover extent. Remote Sensing Letters 6, 844–853. https://doi.org/10.1080/2150704X.2015.1084551

• Dietz, A.J., Wohner, C., Kuenzer, C., 2012. European Snow Cover Characteristics between 2000 and 2011 Derived from Improved MODIS Daily Snow Cover Products. Remote Sensing 4. https://doi.org/10.3390/rs4082432

• Dietz, J.A., Conrad, C., Kuenzer, C., Gesell, G., Dech, S., 2014. Identifying Changing Snow Cover Characteristics in Central Asia between 1986 and 2014 from Remote Sensing Data. Remote Sensing 6. https://doi.org/10.3390/rs61212752

• Rößler, S., Witt, M.S., Ikonen, J., Brown, I.A., Dietz, A.J., 2021. Remote Sensing of Snow Cover Variability and Its Influence on the Runoff of Sápmi’s Rivers. Geosciences 11, 130. https://doi.org/10.3390/geosciences11030130

DLR © 2022

The TimeScan Landsat 2015 layer is a higher-processing level baseline product providing a harmonized representation of the spectral and temporal properties of the land surface. The global TimeScan Landsat 2015 dataset was derived from over 450,000 Landsat-8 images (~500TB) collected from 2013 to 2015. The product condenses the information content of the original images to a 20th of their original size. The TimeScan Landsat 2015 layer can be analyzed in the form of a single, global, cloud-free dataset based on statistical ranges of 6 indices for such aspects as the state of vegetation, water cover, or human settlement (built-up areas). For each of the recorded indices the statistics provide the minimal, maximal and mean values along with the standard deviation and the mean slope determined for the entire period from 2013 to 2015. The TimeScan procedure is designed to help end users exploit information from masses of data that until now were too unwieldy for them to handle.

IceLines is an automated calving front monitoring service providing monthly ice shelf front time series of major Antarctic ice shelves. The provided time series allows to discover the dynamics of ice shelf front changes and calving events. The front positions are automatically derived from Sentinel-1 data based on a deep neuronal network called HED-U-Net. The time series covers the timespan 2014 to today (partly limited due to Sentinel-1 data availability). Incorrectly extracted fronts are truncated which might lead to gaps in the time series especially between December to March due to strong surface melt. Annual averages are calculated based on the extracted monthly fronts (excluding the summer months) and provide more robust results due to temporal aggregation.

Product Information:

• Product Name: IceLines - Ice Shelf and Glacier Front Time Series
• Release: 01-06-2022
• Contact: Celia Baumhoer

Data Information:

• Satellite: Sentinel-1
• Sensor: C-band synthetic aperture radar (SAR)
• Format: GeoPackage
• Period: 2014 - today
• Coverage: Antarctica
• License: CC BY 4.0
• Digital Object Identifier (DOI): 10.15489/btc4qu75gr92

Data:

• Layer: Icelines Yearly, Icelines Monthly, Icelines Shelfnames, TanDEM-X PolarDEM High Resolution Coastlines of Antarctica 2013-2014, TanDEM-X PolarDEM-X 90m Hillshade Combined of Antarctica
• Attributes:
• gid: Shelf ID
• date: Ice Shelf position date
• shelf_name: Name of the Ice Shelf ID
• updated: date of file update
• version: processing version
• Download: HTTP download via EOC Download Service
• Metadata: ISO Collection

References:

• Baumhoer, C. A.: Glacier Front Dynamics of Antarctica - Analysing Changes in Glacier and Ice Shelf Front Position based on SAR Time Series, Dissertation, Julius-Maximilians Universität Würzburg, Würzburg, 2021.

• Baumhoer, C. A., Dietz, A. J., Kneisel, C., Paeth, H., and Kuenzer, C.: Environmental drivers of circum-Antarctic glacier and ice shelf front retreat over the last two decades, The Cryosphere, 15, 2357­2381, https://doi.org/10.5194/tc-15-2357-2021, 2021.

• Heidler, K., Mou, L., Baumhoer, C., Dietz, A., and Zhu, X. X.: HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline, IEEE Trans. Geosci. Remote Sensing, 1­14, https://doi.org/10.1109/TGRS.2021.3064606 , 2021.

• Baumhoer, C. A., Dietz, A. J., Kneisel, C., and Kuenzer, C.: Automated Extraction of Antarctic Glacier and Ice Shelf Fronts from Sentinel-1 Imagery Using Deep Learning, Remote Sensing, 11, 2529, https://doi.org/10.3390/rs11212529, 2019.

• Baumhoer, C., Dietz, A., Dech, S., and Kuenzer, C.: Remote Sensing of Antarctic Glacier and Ice-Shelf Front Dynamics­A Review, 10, 1445, https://doi.org/10.3390/rs10091445 , 2018.

DLR © 2022

The objective of the pan-European project CORINE Land Cover (CLC) is the provision of a unique and comparable data set of land cover for Europe. It is part of the European Union programme CORINE (Coordination of Information on the Environment). The mapping of the land cover and land use was performed on the basis of satellite remote sensing images on a scale of 1:100,000. The first CLC data base CLC1990, which was finalised in the 1990s, consistently provided land use information comprising 44 classes, out of which 37 classes are relevant in Germany.

In the project CORINE Land Cover 2000 (CLC2000), an update of the database and a mapping of changes have been accomplished using the year 2000 as reference. The project CLC2000, which resulted in area-wide land use and land use change maps of Germany, was led by the German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR) on behalf of the Federal Environmental Agency (UBA). With CLC2000 a reliable, objective and comparable data base for the description of the current situation (at 2000) and the analysis of changes during the decade between 1990 and 2000 is available.

Integrated in the European GMES activities, a further update of CORINE Land Cover was done in 37 European countries with the reference year 2006. The project CLC2006 in Germany was again performed by the German Remote Sensing Data Center, on behalf of the Federal Environment Agency (UBA). The update CORINE Land Cover 2006 for Germany is available since February 2010. Besides the status in 2006, an analysis of the changes between 2000 and 2006 is available. Read more at the DFD Corine Website

The CLC2000 project in Germany was executed using financial support by DG Regio and the German Federal Ministry on Environment, Nature Conservation and Nuclear Safety (BMU) on behalf of the German Federal Environmental Agency (UBA), project no. UBA FKZ 201 12 209. The CLC2006 project in Germany was under the responsibility of the Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) and was performed on behalf of the Federal Environment Agency (UBA), project no. UBA FKZ 3707 12 200 and UBA FKZ 3708 12 200.

This land cover classfication of Germany was created using Sentinel-2 imagery from the years 2015 to 2017 and LUCAS 2015 in-situ reference data (https://ec.europa.eu/eurostat/web/lucas). It contains seven land cover types: (1) artificial land, (2) open soil, (3) high seasonal vegetation, (4) high perennial vegetation, (5) low seasonal vegetation, (6) low perennial vegetation and (7) water with a spatial resolution of 10m x 10m.

Sensor: Sentinel-2 MSI
Period: 2015 - 2017
Product Version: 1.0

Image preprocessing: This dataset was created using a median mosaic of all collected Sentinel-2 Scenes up until April 30, 2017 with a cloud cover of less than 60%. From these a total of 226 spectral and textural image features were derived. Additionally imperviousness from buildings, roads and railways were included. A supervised machine learning classifier was trained with in-situ reference points from the 2015 Land Use and Coverage Area Frame Survey (LUCAS).

Digital Object Identifier (DOI): 10.15489/1ccmlap3mn39

Download: HTTP download via EOC Download Service

Attribution: The creation of this dataset was partly funded by the German Federal Environmental Foundation (DBU). Funding for the projects "meinGruen" and "SAUBER" (funding codes 19F2073B and 19F2064B, respectively) were granted by the German Federal Ministry of Transport and Digital Infrastructure (BMVI), and funding for the project "Monitoring des Stadtgrüns (Monitoring of urban green)" was granted by the German Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR). The dataset is produced and distributed by the DLR under the CC-BY-NC 4.0 license. This product contains processed and modified Copernicus Sentinel data (2015-2017).

This repository contains tree canopy cover loss information between January 2018 and April 2021 for Germany at monthly resolution. The analysis is based on monthly image composites of the disturbance index (DI) derived from Sentinel-2 and Landsat-8 time series. Deviations from a 2017 reference median DI image exceeding a threshold are recorded as losses. The method used to derive this product as well as the mapping results are described in detail in Thonfeld et al. (2022). The map depicts areas of natural disturbances (windthrow, fire, droughts, insect infestation) as well as sanitation and salvage logging, and regular forest harvest without explicitly differentiating these drivers.

Product Information:

• Product Name: Tree Canopy Cover Loss - Germany, 2018-2021
• Release: 2022-08-11
• Contact: Dr. Frank Thonfeld

Data Information:

• Satellite: Landsat-8/Sentinel-2
• Sensor: Operational Land Imager (OLI), Multispectral Instrument (MSI)
• Format: GeoTIFF, GeoPackage, Shapefile
• Spatial Resolution: 10 m
• Period: January 2018 - April 2021
• Coverage: Germany
• License: CC BY 4.0

Data:

Layer: Tree Canopy Cover Loss Germany 2018-2021
• Metadata: ISO Collection
• Attributes:
• 0: intact forest
• 1: Jan 2018
• 2: Feb 2018
• 3: Mar 2018
• 4: Apr 2018
• 5: May 2018
• 6: Jun 2018
• 7: Jul 2018
• 8: Aug 2018
• 9: Sep 2018
• 10: Oct 2018
• 11: Nov 2018
• 12: Dec 2018
• 13: Jan 2019
• 14: Feb 2019
• 15: Mar 2019
• 16: Apr 2019
• 17: May 2019
• 18: Jun 2019
• 19: Jul 2019
• 20: Aug 2019
• 21: Sep 2019
• 22: Oct 2019
• 23: Nov 2019
• 24: Dec 2019
• 25: Jan 2020
• 26: Feb 2020
• 27: Mar 2020
• 28: Apr 2020
• 29: May 2020
• 30: Jun 2020
• 31: Jul 2020
• 32: Aug 2020
• 33: Sep 2020
• 34: Oct 2020
• 35: Nov 2020
• 36: Dec 2020
• 37: Jan 2021
• 38: Feb 2021
• 39: Mar 2021
• 40: Apr 2021
• 100: non-forested

Layer: Tree Canopy Cover Loss Germany 2018-2021 per district
• Metadata: ISO Collection
• Attributes:
• NAME_2: district name
• TYPE_2: district type
• NAME_1: federal state
• nr: number
• lk_area: district area (ha)
• forestr: initial forest area per district (ha)
• decall: losses in deciduous forest from January 2018 to April 2021 (ha)
• dec2018: losses in deciduous forest in 2018 (ha)
• dec2019: losses in deciduous forest in 2019 (ha)
• dec2020: losses in deciduous forest in 2020 (ha)
• dec2021: losses in deciduous forest in 2021 (January to April) (ha)
• conall: losses in coniferous forest from January 2018 to April 2021 (ha)
• con2018: losses in coniferous forest in 2018 (ha)
• con2019: losses in coniferous forest in 2019 (ha)
• con2020: losses in coniferous forest in 2020 (ha)
• con2021: losses in coniferous forest in 2021 (January to April) (ha)
• allall: losses in all forest types from January 2018 to April 2021 (ha)
• all2018: losses in all forest types in 2018 (ha)
• all2019: losses in all forest types in 2019 (ha)
• all2020: losses in all forest types in 2020 (ha)
• all2021: losses in all forest types in 2021 (January to April) (ha)
• p_frstr: initial forest area per district (%)
• p_decll: losses in deciduous forest from January 2018 to April 2021 (%)
• p_d2018: losses in deciduous forest in 2018 (%)
• p_d2019: losses in deciduous forest in 2019 (%)
• p_d2020: losses in deciduous forest in 2020 (%)
• p_d2021: losses in deciduous forest in 2021 (January to April) (%)
• p_conll: losses in coniferous forest from January 2018 to April 2021 (%)
• p_c2018: losses in coniferous forest in 2018 (%)
• p_c2019: losses in coniferous forest in 2019 (%)
• p_c2020: losses in coniferous forest in 2020 (%)
• p_c2021: losses in coniferous forest in 2021 (January to April) (%)
• p_allll: losses in all forest types from January 2018 to April 2021 (%)
• p_l2018: losses in all forest types in 2018 (%)
• p_l2019: losses in all forest types in 2019 (%)
• p_l2020: losses in all forest types in 2020 (%)
• p_l2021: losses in all forest types in 2021 (January to April) (%)
• Download: HTTP download via EOC Download Service

References:

• Healey, S., Cohen, W., Zhiqiang, Y., Krankina, O., 2005. Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection. Remote Sensing of Environment 97, 301-310. https://doi.org/10.1016/j.rse.2005.05.009
• Thonfeld, F., Gessner, U., Holzwarth, S., Kriese, J., da Ponte, E., Huth, J., Kuenzer, C., 2022. A First Assessment of Canopy Cover Loss in Germany’s Forests after the 2018-2020 Drought Years. Remote Sensing 14, 562. https://doi.org/10.3390/rs14030562

DLR © 2022

The RapidEye RESA Germany Mosaic provides a nearly cloud-free view of the country's geography, natural resources, and infrastructure. It is composed of 374,240 sqkm of multi-spectral RapidEye imagery, acquired between April and October 2015. The product is being provided in the framework of the RapidEye Science Archive (RESA) agreement. Co-funded by the German Federal Government, the fleet of RapidEye satellites were launched from the Baikonur cosmodrome in Kazakhstan in 2008. The satellites are now owned by Planet Labs, Inc. The RapidEye Earth observation system comprises five satellites equipped with high-resolution optical sensors. With a spatial resolution of 6.5 m the 5-band instruments operate in the visible and near-infrared portions of the electromagnetic spectrum. With its high repetition rate the RapidEye constellation can image each point on the Earth's at least once per day.

For more information see the DLR Earth Observation projects page or the Planet Website.

The Soil Composite Mapping Processor (SCMaP) is a new approach designed to make use of per-pixel compositing to overcome the issue of limited soil exposure due to vegetation. Primary products are reflectance composites that will allow for a long term assessment of exposed soils. Further products include the distribution of exposed soils and statistical information related to soil use and intensity. The resulting reflectance soil composites correlate well with existing soil maps and the underlying geological structure.

Version: V 1.0
Sensor: Landsat 4, 5, 7
Period: 1984 - 2014 (6 x 5-year composites)
Image preprocessing: Cloud-removal (FMask), ATM-Correction + Haze removal (ATCOR), Reprojection, Image filtering
Reference: Rogge, D., Bauer, A., Zeidler, J., Müller, A., Esch, T., Heiden, U. (2018). Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984-2014). Remote Sensing of Environment, 205, Seiten 1-17. DOI: 10.1016/j.rse.2017.11.004 ISSN 0034-425.

The Soil Composite Mapping Processor (SCMaP) is a new approach designed to make use of per-pixel compositing to overcome the issue of limited soil exposure due to vegetation. Primary products are reflectance composites that will allow for a long term assessment of exposed soils. Further products include the distribution of exposed soils and statistical information related to soil use and intensity. The resulting reflectance soil composites correlate well with existing soil maps and the underlying geological structure.

Version: V 1.0
Sensor: Landsat 4, 5, 7
Period: 1984 - 2014
Image preprocessing: Cloud-removal (FMask), ATM-Correction + Haze removal (ATCOR), Reprojection, Image filtering
Reference: Rogge, D., Bauer, A., Zeidler, J., Müller, A., Esch, T., Heiden, U. (2018). Building an exposed soil composite processor (SCMaP) for mapping spatial and temporal characteristics of soils with Landsat imagery (1984-2014). Remote Sensing of Environment, 205, Seiten 1-17. DOI: 10.1016/j.rse.2017.11.004 ISSN 0034-425.

This inventory of traffic areas in the city of Brunswick, Germany, is based on image sequences acquired during six flight campaigns at different times of the day and year in 2019 and 2020. Each aerial image is segmented by a neural network into the classes (1) Parking area, (2) Road, and (3) Access way, with the latter two classes differing in terms of their primary transportation function (mobility versus access). The individual segmentations are subsequently merged, since in addition to dedicated parking areas, those traffic areas that are regularly used for parking a motorized vehicle (e.g., at the curbside) are also to be classified as such. Furthermore, the multitemporal fusion enhances the robustness and completeness of the traffic area map (TAM). Potential applications include: urban planning, traffic modeling, and parking management.

Product Information:

• Product Name: UrMo - Traffic Area Map (TAM) - Brunswick, Germany
• Release: 01-08-2022
• Contact: Jens Hellekes

Data Information:

• Version: V 1.0
• Sensor: 3K camera system, 10 cm ground sampling distance
• Format: Cloud Optimized GeoTIFF
• Period: 2019 - 2020 (composite)
• Coverage: Germany
• License: CC BY-NC 4.0
• Digital Object Identifier (DOI): 10.15489/8zt0ay2yke58

Data:

• Layer: Traffic Area Map (TAM) - Brunswick, Germany
• Attributes:
• 1: Parking Area
• 2: Road
• 3: Access Way
• Download: HTTP download via EOC Download Service
• Metadata: ISO Collection

References:

• Hellekes, Jens und Merkle, Nina Marie und Lopez Diaz, Maria und Henry, Corentin und Heinrichs, Matthias und Azimi, Seyedmajid und Kurz, Franz (2021) Assimilation of parking space information derived from remote sensing data into a transport demand model. In: ITS World Congress 2021: Book of Abstracts, Seiten 2579-2590. ITS World Congress 2021, 11.-15. Okt. 2021, Hamburg, Deutschland. Publication

DLR © 2022

This repository contains data on the forest structure and forest cover dynamics in the Paraguayan Chaco (northeastern part of Paraguay) between 1987 and 2020. The products displaying results on the forest cover dynamic base on annual forest cover masks at 30 m resolution. These were derived through a classification of forest and non-forest areas using a random forest classifier trained on Landsat data. The annual forest masks were used to generate further products which are also contained in this repository: Annual forest cover per district, forest cover changes in protected areas, information on the degree of forest fragmentation and a detailed overview map showing where and when forest was lost in the Paraguayan Chaco. Additionally, this repository comprises the result of a forest structure modelation based on Sentinel-1, Sentinel-2 and GEDI data, and provides information on e.g. the plant area index, or the canopy height.

Product Information:

• Product Name: Geo-ForPy - Forest cover Paraguayan Chaco
• Release: 2022-09-01
• Contact: Jennifer Kriese, Patrick Kacic

Data Information:

• Satellite: Landsat-5, Landsat-7, Landsat-8, Sentinel-1, Sentinel-2, ISS
• Sensor: Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), Operational Land Imager (OLI), Multispectral Instrument (MSI), Global Ecosystem Dynamics Investigation (GEDI)
• Format: GeoTIFF, GeoPackage, Shapefile
• Spatial Resolution: 10 m / 30 m
• Period: 1987 - 2020
• Coverage: Paraguayan Chaco
• License: CC BY 4.0

Data:

Layer: Forest cover changes in the Paraguayan Chaco 1987-2020
• Metadata: ISO Collection Metadata
• Attributes:
• 0: intact forest
• 1987: non-forest area since 1987
• 1988: non-forest area since 1988
• ...
• 2020: non-forest area since 2020
• 3000: areas that were already non-forest areas in 1986
• 4000: no data, areas outside the Paraguayan Chaco

Layer: Protected Areas
• Metadata: ISO Collection Collection
• Attributes:
• Name: Name of the portected area
• Zone: Protected or Buffer
• Category: type of protected area, NULL for buffer areas
• Subsystem: administrative organisation, either "public" or "private", NULL for buffer areas
• Creation: date on which the protected area was created, NULL for buffer areas
• Area: area of each zone [m²]
• Loss [m²]: forest cover change [m^2] between 2020 and 2000: FA_2020-FA_2000
• Loss [%]: forest cover change in percent between 2020 and 2000 with respect to the forest cover of 2000: 100*(FA_2020-FA_2000)/FA_2000
• FA_2000: forest cover in the protected area or buffer zone (see column "Zone") in 2000 [m²]
• ...
• FA_2020: forest cover in the protected area or buffer zone (see column "Zone") in 2020 [m²]

Layer: Forest Cover Change per District
• Metadata: ISO Collection Metadata
• Attributes:
• Department: name of administrative department
• District: name of administrative department
• District_a: name of administrative district
• FA_1986: forest cover in the district in 1986 [m²] based on 1986 forst cover map (external source: INFONA)
• FA_1987: forest cover in the district in 1987 [m²]
• FA_1988: forest cover in the district in 1988 [m²]
• ...
• FA_2020: forest cover in the district in 2020 [m²]
• loss_pct: forest cover change in percent

Layer: Forest Fragmentation
• Metadata: ISO Collection Metadata
• Attributes:
• gridcode: 100 for every polygon, gridcode 100 stands for "forest"
• Area: forest patch area [m²]
• Perimeter: forest patch perimeter [m]
• Paratio: Perimeter-Area-ratio (= Perimeter / Area) describes the shape of the forest patch
• Shape_Idx: Shape index describes the shape of the forest patch
• Frac_Dim: Fractal dimension describes the shape of the forest patch
• Prox_100 Proximity, all neighboring patches within a 100 m distance were considered for calculation
• ID: unique id for all forest patches
• CA_500: Core area, area of a forest patch that has at least 500m distance to the patch's border
• CAI: Core area index (= CA_500 / Area), share of core area in forest patch area

Layer: Forest Structure
• Metadata: ISO Collection Metadata
• Attributes - Cover:
• 0: No Data
• 0.2: 0.2 % in total canopy coverage
• ...:
• 78.06: 78.06 % in total canopy coverage
• Attributes - FHD:
• 0: No Data
• 0.65: 0.65 in Foliage height diversity index
• ...:
• 2.80: 2.80 Foliage height diversity index
• Attributes - PAI:
• 0: No Data
• 0.006: 0.006 in Plant area index
• ...:
• 3.96: 3.96 in Plant area index
• Attributes - RH95:
• 0: No Data
• 1.84: 1.84 m in canopy height
• ...:
• 3.96: 3.96 in Plant area index

• Download: HTTP download via EOC Download Service

References:

• Da Ponte, E.; García-Calabrese, M.; Kriese, J.; Cabral, N.; Perez de Molas, L.; Alvarenga, M.; Caceres, A.; Gali, A.; García, V.; Morinigo, L.; Ríos, M.; Salinas, A. Understanding 34 Years of Forest Cover Dynamics across the Paraguayan Chaco: Characterizing Annual Changes and Forest Fragmentation Levels between 1987 and 2020. Forests 2022, 13, 25. https://doi.org/10.3390/f13010025
• Kacic, P.; Hirner, A.; Da Ponte, E. Fusing Sentinel-1 and -2 to Model GEDI-Derived Vegetation Structure Characteristics in GEE for the Paraguayan Chaco. Remote Sens. 2021, 13, 5105. https://doi.org/10.3390/rs13245105

DLR © 2022

Information about residential heat demand is crucial for sustainable climate mitigation and adaptation as approximately 30% of the heat demand stem from residential heating which contribute to one third of the nationwide CO2 emissions. This service provides information on heat demand in kWh/a based on three different refurbishment scenarios (no refurbishment, advanced refurbishment, usual refurbishment).
Residential heat demand is calculated based on a method using the constructional elements of the buildings, as well as on building type and construction period and climatic properties. The constructional elements (exterior wall surface, roof surface, ground surface and volume) are extracted from LoD2 data, whereas building type (single-family house, terraced house or multi-family house) and construction period are predicted using the machine learning algorithm Random Forest. For the climatic properties degree days are used from the German Weather Service stations and are derived area-wide using a linear regression. Three different heat demand scenarios, differing based on the assumed amount of refurbishment conducted are calculated. The three heat demand scenarios are referred to as: no refurbishment, usual refurbishment and advanced refurbishment. The building-specific heat demand is aggregated to a 100x100 m grid and due to privacy protection, cells containing less than 3 residential buildings are not depicted.
This is a product of the project "Entwicklung eines standardisierten Analyse- und Ergebnisrasters für Wärmepläne zur Umsetzung der Energiewende im kommunalen Bereich" (ANSWER-Kommunal) funded by the Federal Ministry for Economic Affairs and Climate Action (Bundesministerium für Wirtschaft und Klimaschutz (BMWK)). Grant number: 020E-SY 48192

Coverage: Baden Wuerttemberg
Spatial Resolution: 100x100m
Attribution: Residential Heat Demand Data are licensed under: Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

Publications:

• Wurm M, Droin A, Stark T, Geiß C, Sulzer W, Taubenböck H (2021): Deep Learning-Based Generation of Building Stock Data from Remote Sensing for Urban Hear Demand Modeling. ISPRS Int. J. Geo-Inf. 10, 23. https://doi.org/10.3390/ijgi10010023
• Garbasevschi OM, Estevam Schmiedt J, Verma T, Lefter I, Korthals Altes W, Droin A, Schiricke B & Wurm M (2021): Spatial factors influencing building age prediction and implications for urban energy modelling. Computers, Environment and Urban Systems 88. https://doi.org/10.1016/j.compenvurbsys.2021.101637
• Droin A, Wurm M, Sulzer, W (2020). Semantic labelling of building types. A comparison of two approaches using Random Forest and Deep Learning. Wissenschaftlich-Technische Jahrestagung der DGPF, Band 29.
• Wurm, M, Schmitt, A, Taubenböck, H (2015): Building Types' Classification Using Shape-Based Features and Linear Discriminant Functions. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9 (5). https://doi.org/10.1109/JSTARS.2015.2465131

Contact: Michael Wurm, michael.wurm@dlr.de

ECoLaSS is an EU Horizon2020 project to develop several prototypes of new or enhanced Copernicus Land services by making full use of dense time series of SAR and optical Sentinel EO data at a high spatial resolution of 10m. These prototypes shall be suggested for qualifying as candidates for operational integration into the future Copernicus Land Monitoring Service from 2020 onwards. The high resolution layer (HRL) prototypes fall into six main categories: improved imperviousness HRLs, improved forest HRLs, improved grassland HRLs, improved landcover HRLs, novel agriculture HRLs as well as novel time-series indicator HRLs.

The prototypes displayed here are the final results of the ECoLaSS project:

• Forest tree cover mask: binary forest / non-forest mask for 2018.
• Forest tree cover density: continuous tree cover for 2018.
• Forest dominant leaf type: broadleaved vs. coniferous trees for 2018.
• Forest tree cover change: forest loss and gain from 2017 to 2018.
• Agriculture cropland mask: binary cropland / non-cropland mask for 2018.
• Agriculture crop type: discrete crop type classification for 2018.
• Crop growth condition: time-series derived anomalies in agricultural fields for 2018.
• Time series indicators: potential change indicators (2016-2019) for Copernicus Grassland HRL of 2015.
• Imperviousness built-up area: binary built-up mask for 2018.
• Imperviousness degree: continuous imperviousness density for 2018.
• Imperviousness change classified: increase and decrease of imperviousness from 2017 to 2018.
• Grassland mask: binary mask of permanent grasslands for 2018.
• Grassland change: gain and loss from 2017 to 2018.
• Grassland use intensity: intensively and extensively managed grassland in 2018.

The ECoLaSS Consortium is composed of five companies/institutions: GAF AG (Germany), SIRS (France), JOANNEUM RESEARCH (Austria), Universite Catholiqe De Louvain (Belgium), and the German Aerospace Center - DLR (Germany). This project has received funding from the European Union's Horizon 2020 research and innovation programme, under grant agreement no 730008.

• Version: V2.0
• Sensor: Sentinel 1 & Sentinel 2
• Period: 2017-2018
• Reference: ECoLaSS EU

The Sentinel-2 fractional vegetation cover (fCover) product for the Netherlands was produced as part of the NextGEOSS project at the German Aerospace Center (DLR). The goal is to derive abundance maps from atmospherically corrected Sentinel-2 multispectral images for: photosynthetically active vegetation (PV); and for combined non-photosynthetically active vegetation (NPV) and bare soil (BS).

Satellite / Sensor: Sentinel-2 MSI

Image preprocessing: The fCover product for the Netherlands has been generated by processing 10 cloud-free Sentinel-2 tiles which covered the country on 8 September 2016. The map has a spatial resolution of 60m x 60m. The Sentinel-2 scene classification layer was used to ensure that the spectral unmixing was only performed on areas of vegetation or soil.

Digital Object Identifier (DOI): 10.15489/d4eigqgpz945