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  • 1
    Keywords: Forschungsbericht ; Satellitenfernerkundung ; Datenfusion
    Type of Medium: Online Resource
    Pages: 1 Online-Ressource (50 Seiten, 1,56 MB) , Illustrationen, Diagramme
    Language: German
    Note: Förderkennzeichen BMBF 01IS14010A-C. - Verbund-Nummer 01155122 , Autoren dem Berichtsblatt der Druck-Ausgabe entnommen , Unterschiede zwischen dem gedruckten Dokument und der elektronischen Ressource können nicht ausgeschlossen werden
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  • 2
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    PANGAEA
    In:  Supplement to: Pflugmacher, Dirk; Rabe, Andreas; Peters, Mathias; Hostert, Patrick (2019): Mapping pan-European land cover using Landsat spectral-temporal metrics and the European LUCAS survey. Remote Sensing of Environment, 221, 583-595, https://doi.org/10.1016/j.rse.2018.12.001
    Publication Date: 2023-01-13
    Description: The pan-European land cover map of 2015 was produced by combining the large European-wide land survey LUCAS (Land Use/Cover Area frame Survey) and Landsat-8 data. We used annual and seasonal spectral-temporal metrics and environmental features to map 12 land cover and land use classes across Europe (artificial land, seasonal cropland, perennial cropland, broadleaved forest, coniferous forest, mixed forest, shrubland, grassland, barren, water, wetland, and permanent snow/ice). The classification was based on Landsat-8 data acquired over three years (2014-2016). Overall map accuracy was 75.1%. The spatial resolution and minimum mapping unit is 30 x 30 m. The map can be downloaded as a single GeoTiff file of 874Mbyte. The produced pan-European land cover map compared favourably to the existing CORINE (Coordination of Information on the Environment) 2012 land cover dataset. The mapped country-wide area proportions strongly correlated with LUCAS-estimated area proportions (r=0.98). Differences between mapped and LUCAS sample-based area estimates were highest for broadleaved forest (map area was 9% higher). Grassland and seasonal cropland areas were 7% higher than the LUCAS estimate, respectively. In comparison, the correlation between LUCAS and CORINE area proportions was weaker (r=0.84) and varied strongly by country. CORINE substantially overestimated seasonal croplands by 63% and underestimated grassland proportions by 37%. Our study shows that combining current state-of-the-art remote sensing methods with the large LUCAS database imporves pan-European land cover mapping.
    Keywords: Europe
    Type: Dataset
    Format: image/tiff, 874.1 MBytes
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  • 3
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    PANGAEA
    In:  Supplement to: Rufin, Philippe; Frantz, David; Ernst, Stefan; Rabe, Andreas; Griffiths, Patrick; Özdoğan, Mutlu; Hostert, Patrick (2019): Mapping Cropping Practices on a National Scale Using Intra-Annual Landsat Time Series Binning. Remote Sensing, 11(3), 232, https://doi.org/10.3390/rs11030232
    Publication Date: 2023-01-13
    Description: Cropping practices underlie substantial spatial and temporal variability, which can be captured through the analysis of image time series. Temporal binning helps to overcome limitations concerning operability and repeatability for mapping large areas and can improve the thematic detail and consistency of maps in agricultural systems. We used eight-day temporal features for mapping five cropping practices on annual croplands at 30 m spatial resolution across Turkey. A total of 2,403 atmospherically corrected and topographically normalized Landsat Collection 1 L1TP images of 2015 were used to compute gap-filled eight-day time series of Tasseled Cap components and annual descriptions thereof. We used these features for binary cropland mapping, and subsequent discrimination of five cropping practices: Spring and winter cropping, summer cropping, semi-aquatic cropping, double cropping, and greenhouse cultivation. The map has an overall accuracy of 90%. Class accuracies of winter and spring, summer, and double cropping were robust, while omission errors for semi-aquatic cropping and greenhouse cultivation were high. Note that the map contains information on cropping practices for areas, which were identified as croplands with high certainty. The file is of GeoTiff format and contains the following classes: 1: Winter/spring cropping 2: Summer cropping 3: Semi-aquatic cropping 4: Double-cropping 6: Greenhouse cultivation For details, please see the publication or contact Philippe Rufin mailto:philippe.rufin@geo.hu-berlin.de.
    Keywords: MULT; Multiple investigations; Turkey
    Type: Dataset
    Format: image/tiff, 50.8 MBytes
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  • 4
    Publication Date: 2024-04-17
    Description: Phenology is the study of reoccurring events during a year or season. It can be linked to the behavior of animals, such as phases of mating, breeding, or movement and to events such as green-up, bud burst, flowering, or senescence when referring to vegetation, as a response to changing environmental factors throughout a season. While these changes can be tracked on the level of individual species, their observation is usually restricted to small spatial extents. To broaden the extent of the observed area remote sensing data have been proven useful. As remote sensing data capture the seasonal change rather on a pixel than on a species level, they enable to analyze the phenology of the observed vegetation on a different scale, which is known as land surface phenology. Land surface phenological metrics that can, for example, be derived from time series of vegetation indices, allow to analyze the observed spatial and temporal patterns in relation to ecosystem processes (e.g., primary productivity). Subsequently, the derived metrics can be grouped based on their similarities into land surface phenological archetypes (LSP), defined as areas with comparable phenologies. However, the spatial resolution of the data used is crucial, which becomes even more critical when looking at heterogeneous ecosystems such as the Brazilian savanna, known as the Cerrado. The Cerrado covers an extent of approximately 2 mio. km², hosts many endemic species and is considered as a biodiversity hotspot that provides several ecosystem services of national and even global importance. However, due to a lack of extensive conservation regulations the Cerrado is prone to land cover changes for agricultural expansion, highlighting the need for detailed mapping and monitoring approaches. To reveal and analyze the spatial patterns of the remaining share of natural vegetation based on their land surface phenology, we analyzed a dense 8-day time series of combined enhanced vegetation data derived from Landsat 7 ETM+ and Landsat 8 images. Data gaps that were due to cloud contamination or sensor errors were filled using a radial basis convolution filter, enabling to subsequently derive phenological metrics for the season 2013/2014 using TIMESAT (Eklundh and Jönsson 2017). As these variables, such as start and end of season, amplitude or the base value, relate to the seasonality and primary productivity of the observed vegetation, we clustered them based on their similarities and defined 8 land surface phenological archetypes (LSP) of the Cerrado. The GeoTiff file contains the 8 LSPs that are explained in detail in Schwieder et al. in prep. For further questions please contact Marcel Schwieder. Class labels: 0 = Unclassified 1 = FORMBMS 2 = SAFORMS 3 = FORHBLS 4 = GLSAVLB 5 = GLSAVHB 6 = FORHBHS 7 = VEGINMS 8 = VEGINLS
    Keywords: Cerrado; Cerrado_ecosystem_funct_types; Conservation; Land surface phenology; remote sensing; SAT; Satellite remote sensing; Timesat
    Type: Dataset
    Format: image/tiff, 365.1 MBytes
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