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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.

Radiance Light Trends

Radiance Light Trends is a GIS web application that is designed to quickly display information about radiance trends at a specific location (available online at https://lighttrends.lightpollutionmap.info). It uses data from two satellite systems, DMSP-OLS and VIIRS DNB, with data processing by NOAA. New VIIRS layers are added automatically as soon as NOAA makes them available to public.The web application allows the user to examine changes in nighttime light emissions (nearly) worldwide, from 1992 up until last month. From 1992 to 2013, data comes from the Operational Linescan System of the Defense Meteorological Satellite Program (DMSP) satellites. From 2012 to the present, data comes from the Day/Night Band of the Visible Infrared Imaging Radiometer Suite instrument (VIIRS DNB). Due to significant differences in the instruments (as described by Miller et al., 2013), it is not possible to have a single record running from 1992 to today. A description of the VIIRS DNB night lights product used in this application was published by Elvidge et al. (2017), the data used in the app can be accessed from the NOAA Earth Observation Group (EOG) Website: https://ngdc.noaa.gov/eog/download.html

Karslruhe, Germany (2010) - a (hyperspectral) dataset for active participation in the HYPERedu MOOC on forest applications

This dataset accompanying the MOOC on forest applications contains an airborne hyperspectral HyMap image over the study site north of Karlsruhe in Southwest Germany which was recorded in August 2010. The surrounding area of Karlsruhe is characterized by its relatively warm climate due to the influence of the Upper-Rhine and its climate can be considered more continental than typical German conditions. Additionally it is characterized by its flat terrain. Here you can find a diversity of tree species growing in the mixed forests. These include coniferous trees such as Scots Pine, Douglas Fir, Norway Spruce, Silver Fir and Larch as well as deciduous tree species like European Beech, Oak and Red Oak. The image dataset is fully pre-processed –it was atmospherically and topographically corrected by the DLR using ATCOR4 and ORTH software – and provided in TIF format. In addition to the HyMap image, this dataset contains a point data shapefile with 250 sampling locations, which represents 5 tree species with 50 reference positions each. These reference positions were collected using visual interpretation of high-resolution images in combination with reference tree species maps provided by the local forest administration. These reference tree species maps are also provided as tif-files. The dataset is made publicly available as part of the Massive Open Online Course (MOOC) "Beyond the Visible - Imaging Spectroscopy for Forest Applications ", available from Summer 2025. Guidance on how to derive tree species classification maps using the EnMAP-Box (QGIS plugin) are provided as videos at the HYPERedu YouTube channel, the forest MOOC course pages and the regression workflow documentation. HYPERedu is an education initiative within the Environmental Mapping and Analysis Program (EnMAP), 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 variables, which provide information on the status and evolution of various terrestrial and aquatic ecosystems.

Demmin, Germany (2015) - a (hyperspectral) dataset for active participation in the HYPERedu MOOC on soil applications

This dataset accompanying the MOOC on soil applications contains an airborne hyperspectral HySpex image over the study site Demmin in Northern Germany which was recorded in October 2015. The surrounding area of Demmin is characterized by its glacial past and is largely used for agriculture. Here you can find relics of the ice age such as kettle holes - small, completely closed hollow shapes whose formation is attributed to the burial and subsequent thawing of an ice lens. Mostly overgrown nowadays by vegetation, SOC accumulates in these areas and higher contents are measured. The image dataset is fully pre-processed – all non-soil pixels are masked, the spectra were smoothed using a Savitzky-Golay Filter and transformed to first derivatives – and provided in BSQ format. In addition to the HySpex image, this dataset contains a point data shapefile with 27 sampling locations, as well as information on the soil organic carbon (SOC) contents [g/kg]. The dataset is made publicly available as part of the Massive Open Online Course (MOOC) "Beyond the Visible - Imaging Spectroscopy for Soil Applications ", available from Spring 2023. Guidance on how to derive quantitative soil maps (SOC content) using the EnMAP-Box (QGIS plugin) are provided as videos at the HYPERedu YouTube channel, the soil MOOC course pages and the regression workflow documentation.

Cabo de Gata-Nίjar Natural Park, Spain (2005) - a (hyperspectral) dataset for active participation in the HYPERedu MOOC on soil applications

The dataset contains a subset of an airborne hyperspectral HyMap image over the Cabo de Gata-Nίjar Natural Park in Spain from 15.06.2005, and soil wet chemistry data based on in-situ soil sampling. The Cabo de Gata-Nίjar Natural Park is a semi-arid mediterranean area in Southern Spain, sparsely populated and with a range of landscape patterns. The soils in this area are developed on volcanic and carbonatic bedrocks and are highly variable in their textural and mineralogical composition, offering interesting spectral variability. The airborne survey was acquired during a DLR / HyVista HyEurope campaign. The image dataset is fully processed for atmospheric and geometric correction with PARGE and ATCOR and is provided as orthorectified reflectance in BSQ format. Spatial resolution is 5 m and spectral coverage is 0.45-2.45 μm with 12-17 nm spectral sampling. In addition to the HyMap imagery, this dataset contains two soil reference datasets as CSV files, namely in-situ data for clay content and iron content. The dataset is made publicly available as part of the Massive Open Online Course (MOOC) "Beyond the Visible - Imaging Spectroscopy for Soil Applications ", available from Spring 2024. Guidance on how to derive semiquantitative and quantitative soil maps (clay and iron content) using the EnMAP-Box (QGIS plugin) EnSoMAP tool are provided as videos at the HYPERedu YouTube channel (https://www.youtube.com/@HYPERedu_GFZ/playlists) and the soil MOOC course pages (https://eo-college.org/courses/beyond-the-visible-imaging-spectroscopy-for-soil-applications/).

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