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Dataset

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

Cite as:

Okujeni, Akpona; van der Linden, Sebastian; Hostert, Patrick (2016): Berlin-Urban-Gradient dataset 2009 - An EnMAP Preparatory Flight Campaign (Datasets). GFZ Data Services. https://doi.org/10.5880/enmap.2016.002

Status

I   N       R   E   V   I   E   W : Okujeni, Akpona; van der Linden, Sebastian; Hostert, Patrick (2016): Berlin-Urban-Gradient dataset 2009 - An EnMAP Preparatory Flight Campaign (Datasets). GFZ Data Services. https://doi.org/10.5880/enmap.2016.002

There is a new version of this dataset:

  • https://dx.doi.org/10.5880/enmap.2016.008
  • Abstract

    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.














    Additional Information

    The Environmental Mapping and Analysis Program (EnMAP) is a German hyperspectral satellite mission that aims at monitoring and characterizing 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. In the frame of the EnMAP preparatory phase, pre-flight campaigns including airborne and in-situ measurements in different environments and for several application fields are being conducted. The main purpose of these campaigns is to support the development of scientific applications for EnMAP. In addition, the acquired data are input in the EnMAP end-to-end simulation tool (EeteS) and are employed to test data pre-processing and calibration-validation methods. The campaign data are made freely available to the scientific community under a Creative Commons Attribution-ShareAlike 4.0 International License. An overview of all available data is provided in in the EnMAP Flight Campaigns Metadata Portal http://www.enmap.org/?q=flights.






    Authors

    • Okujeni, Akpona;Humboldt-Universität zu Berlin, Geography Department, Berlin, Germany
    • van der Linden, Sebastian;Humboldt-Universität zu Berlin, Geography Department, Berlin, Germany
    • Hostert, Patrick;Humboldt-Universität zu Berlin, Geography Department, Berlin, Germany

    Contact

    Contributors

    Foerster, Saskia; Elger, Kirsten

    Keywords

    imaging spectrometry, hyperspectral, EnMAP, HyMap, urban land cover, unmixing, classification, regression, support vector machines, multi-scale

    GCMD Science Keywords

    More Metadata

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            • affiliation: Humboldt-Universität zu Berlin, Geography Department, Berlin, Germany
          • creator
            • creatorName: van der Linden, Sebastian
            • affiliation: Humboldt-Universität zu Berlin, Geography Department, Berlin, Germany
          • creator
            • creatorName: Hostert, Patrick
            • affiliation: Humboldt-Universität zu Berlin, Geography Department, Berlin, Germany
        • titles
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        • publisher: GFZ Data Services
        • publicationYear: 2016
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          • subject: hyperspectral
          • subject: EnMAP
          • subject: HyMap
          • subject: urban land cover
          • subject: unmixing
          • subject: classification
          • subject: regression
          • subject: support vector machines
          • subject: multi-scale
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          License: CC BY-SA 4.0

          Dataset Description

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