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Land cover

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  • 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 and the delivery of regular updates to register also the land cover and land use changes over time. 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. The first CLC data base CLC1990, which was finalized in the 1990s, consistently provided land use information comprising 44 classes, out of which 37 classes are relevant in Germany. The first two updates for Europe were based on the reference years 2000 and 2006. For Germany, DLR-DFD was responsible for the creation of CLC2000 and CLC2006 on behalf of the Federal Environment Agency. In addition to the updated land cover, change datasets were also parts of the project. For deriving a meaningful CLC2000 change product, it became necessary to re-interprete parts of the satellite data of 1990 and to create a revised product, called CLC1990 (rev). Further details: http://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-11882/20871_read-48836/

  • 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 and the delivery of regular updates to register also the land cover and land use changes over time. 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. The first CLC data base CLC1990, which was finalized in the 1990s, consistently provided land use information comprising 44 classes, out of which 37 classes are relevant in Germany. The first two updates for Europe were based on the reference years 2000 and 2006. For Germany, DLR-DFD was responsible for the creation of CLC2000 and CLC2006 on behalf of the Federal Environment Agency. In addition to the updated land cover, change datasets were also parts of the project. For deriving a meaningful CLC2000 change product, it became necessary to re-interprete parts of the satellite data of 1990 and to create a revised product, called CLC1990 (rev). Further details: http://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-11882/20871_read-48836/

  • 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 and the delivery of regular updates to register also the land cover and land use changes over time. 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. The first CLC data base CLC1990, which was finalized in the 1990s, consistently provided land use information comprising 44 classes, out of which 37 classes are relevant in Germany. The first two updates for Europe were based on the reference years 2000 and 2006. For Germany, DLR-DFD was responsible for the creation of CLC2000 and CLC2006 on behalf of the Federal Environment Agency. In addition to the updated land cover, change datasets were also parts of the project. For deriving a meaningful CLC2000 change product, it became necessary to re-interprete parts of the satellite data of 1990 and to create a revised product, called CLC1990 (rev). Further details: http://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-11882/20871_read-48836/

  • 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 and the delivery of regular updates to register also the land cover and land use changes over time. 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. The first CLC data base CLC1990, which was finalized in the 1990s, consistently provided land use information comprising 44 classes, out of which 37 classes are relevant in Germany. The first two updates for Europe were based on the reference years 2000 and 2006. For Germany, DLR-DFD was responsible for the creation of CLC2000 and CLC2006 on behalf of the Federal Environment Agency. In addition to the updated land cover, change datasets were also parts of the project. For deriving a meaningful CLC2000 change product, it became necessary to re-interprete parts of the satellite data of 1990 and to create a revised product, called CLC1990 (rev). Further details: http://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-11882/20871_read-48836/

  • 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 and the delivery of regular updates to register also the land cover and land use changes over time. 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. The first CLC data base CLC1990, which was finalized in the 1990s, consistently provided land use information comprising 44 classes, out of which 37 classes are relevant in Germany. The first two updates for Europe were based on the reference years 2000 and 2006. For Germany, DLR-DFD was responsible for the creation of CLC2000 and CLC2006 on behalf of the Federal Environment Agency. In addition to the updated land cover, change datasets were also parts of the project. For deriving a meaningful CLC2000 change product, it became necessary to re-interprete parts of the satellite data of 1990 and to create a revised product, called CLC1990 (rev). Further details: http://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-11882/20871_read-48836/

  • 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. For further information, please see the following publication: https://doi.org/10.1016/j.jag.2020.102065

  • GOME (Global Ozone Monitoring Experiment) stands for a family of satellite instruments named after the first GOME (https://wdc.dlr.de/sensors/gome/) instrument on ERS-2 launched in April 1995. Currently two GOME-2 instruments are operative on Metop-A and B (https://wdc.dlr.de/sensors/gome2/). The tropical tropospheric ozone is retrieved with convective cloud differential method (Valks et al., 2014 http://www.atmos-meas-tech.net/7/2513/2014/amt-7-2513-2014.html). The tropospheric column is retrieved by subtracting the stratospheric ozone column from the total column. The stratospheric ozone column is estimated as the column above high reaching convective clouds.

  • The "AVHRR compatible Normalized Difference Vegetation Index derived from MERIS data (MERIS_AVHRR_NDVI)" was developed in a co-operative effort of DLR (German Remote Sensing Data Centre, DFD) and Brockmann Consult GmbH (BC) in the frame of the MAPP project (MERIS Application and Regional Products Projects). For the generation of regional specific value added MERIS level-3 products, MERIS full-resolution (FR) data are processed on a regular (daily) basis using ESA standard level-1b and level-2 data as input. The regular reception of MERIS-FR data is realized at DFD ground station in Neustrelitz. The Medium Resolution Imaging MERIS on Board ESA's ENVISAT provides spectral high resolution image data in the visible-near infrared spectral region (412-900 nm) at a spatial resolution of 300 m. For more details on ENVISAT and MERIS see http://envisat.esa.int The Advanced Very High Resolution Radiometer (AVHRR) compatible vegetation index (MERIS_AVHRR_NDVI) derived from data of the MEdium Resolution Imaging Spectrometer (MERIS) is regarded as a continuity index with 300 meter resolution for the well-known Normalized Difference Vegetation Index (NDVI) derived from AVHRR (given in 1km spatial resolution). The NDVI is an important factor describing the biological status of canopies. This product is thus used by scientists for deriving plant and canopy parameters. Consultants use time series of the NDVI for advising farmers with best practice. For more details the reader is referred to http://wdc.dlr.de/data_products/SURFACE/ndvi_meris.php and http://www.brockmann-consult.de/mapp/project_information.htm This product provides monthly maps.

  • The Global Urban Footprint® (GUF®) dataset is based on the radar (SAR) satellite imagery of the German satellites TerraSAR-X and TanDEM-X. By creating the GUF database, scientists at the German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR) have succeeded in using a newly developed method to generate a global raster map of the world’s built-up pattern in a so far unprecedented spatial resolution of about 12m per raster cell. Using a fully automated processing system, a global coverage of more than 180,000 very high resolution SAR images (3m ground resolution) has been analyzed acquired between 2010 and 2013. Thereby, the backscatter amplitudes of the SAR data have been used in combination with derived textural information to delineate human settlements in a highly automated, complex decision-making process. The evaluation procedure based mainly on radar signals detects the characteristic vertical structures of human habitations – primarily built-up areas. In addition, auxiliary data such as digital elevation models have been included to improve the classification process. In total, over 20 million datasets were processed with a combined volume of about 320 terabytes. The final global maps show three coverage categories (e. g. in a B&W representation): Built-up areas (vertical structures only) in black, non-built-up surfaces in white, areas of no coverage by TSX/TDX satellites (NoData) as most parts of the oceans in grey. The final product has been optimized for fast online access through web services by merging the 5° x 5° GUF tiles into a single global mosaic. Furthermore reduced resolution overviews have been generated with an interpolation algorithm, that computes the average value of all contribution pixels. The global mosaic uses PackBits compression to reduce file size. (GUF® and Global Urban Footprint® are protected as trademarks.)

  • This product consists of global gap free Leaf area index (LAI) time series, based on MERIS full resolution Level 1B data. It is produced as a series of 10-day composites in geographic projection at 300m spatial resolution. The processing chain comprises geometric correction, radiometric correction and pixel identification, LAI calculation with the BEAM MERIS vegetation processor, re-projection to a global grid, and temporal aggregation selecting the measurement closest to the mean value. After the LAI pre-processing we applied time series analysis to fill data gaps and filter outliers using the technique of harmonic analysis in combination with mean annual and multiannual phenological data. Data gaps are caused by clouds, sensor limitations due to the solar zenith angle (less than 10 degrees), topography and intermittent data reception. We applied our technique for the whole period of observation (Jul 2002 - Mar 2012). Validation, was performed using VALERI and BigFoot data.

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