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  • 1
    Publication Date: 2023-06-08
    Description: The data package compiles vegetation data of ten temperate pre-Alpine managed grasslands in southern Germany. The first dataset originates from a sampling campaign in April 2018. A 30 m x 30 m homogenous flat plot was selected at each of the ten grasslands and sampled at nine to twelve 0.25 m x 0.25 m subplots. After determining the bulk canopy height of the subplot, the vegetation was cut at 0.03 m. Afterwards the following parameters of the cut vegetation were determined in the laboratory: weight of the fresh and dry biomass; weight percentage of the plant functional types (PFT) non-green vegetation, legumes, graminoids, other forbs; total green area index (GAI) and PFT-specific GAI; carbon (C) and nitrogen (N) content of the PFT. Water content and mean C and N contents were calculated from measured values. Additional samples around the subplots were taken to determine the leaf mass per area (LMA) of PFTs that occur in the plot. The second data set is based on a vegetation survey from June 2020 at exactly the same ten plots and includes grassland type, plant species richness and species coverage of the plots. The data package was obtained within the framework of the SUSALPS project (https://www.susalps.de/) to provide in-situ data for the estimation of grassland parameters with unmanned aircraft system (UAS)-based and satellite-based remote sensing data.
    Keywords: Biomass; carbon content; forbs; graminoids; green area index; leaf mass per area; legumes; managed grassland; mesic grassland; nitrogen content; plant functional types; plants; SUSALPS; Sustainable use of alpine and pre-alpine grassland soils in a changing climate; temperate grassland; TERENO; Terrestrial Environmental Observatories; vegetation survey
    Type: Dataset
    Format: application/zip, 2 datasets
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  • 2
    Publication Date: 2023-06-08
    Keywords: Biomass; Biomass, dry mass per area; Biomass, wet mass per area; Calculated, see further details: Overview and description of data set variables; Canopy height; Carbon, organic; carbon content; Carbon content of forbs; Carbon content of graminoids; Carbon content of legumes; Carbon content of non-green vegetation; Date/Time of event; Element analyser CNS; Event label; forbs; Forbs; graminoids; Graminoids; green area index; Green area index, forbs; Green area index, graminoids; Green area index, legumes; Green area index, total; Latitude of event; leaf mass per area; Leaf mass per area; legumes; Legumes; Longitude of event; managed grassland; MULT; Multiple investigations; Nitrogen, organic; nitrogen content; Nitrogen content of forbs; Nitrogen content of graminoids; Nitrogen content of legumes; Nitrogen content of non-green vegetation; Non-green vegetation; Planimeter; Plant functional type; plant functional types; Plant water content; Plot; Rising plate meter; RPM; Site; Southern Germany; Subplot; SUSALPS; SUSALPS-RS2018_EL1-01; SUSALPS-RS2018_EL1-02; SUSALPS-RS2018_EL1-03; SUSALPS-RS2018_EL1-04; SUSALPS-RS2018_EL1-05; SUSALPS-RS2018_EL1-06; SUSALPS-RS2018_EL1-07; SUSALPS-RS2018_EL1-08; SUSALPS-RS2018_EL1-09; SUSALPS-RS2018_EL1-10; SUSALPS-RS2018_EL1-11; SUSALPS-RS2018_EL1-12; SUSALPS-RS2018_EL2-01; SUSALPS-RS2018_EL2-02; SUSALPS-RS2018_EL2-03; SUSALPS-RS2018_EL2-04; SUSALPS-RS2018_EL2-05; SUSALPS-RS2018_EL2-06; SUSALPS-RS2018_EL2-07; SUSALPS-RS2018_EL2-08; SUSALPS-RS2018_EL2-09; SUSALPS-RS2018_EL2-10; SUSALPS-RS2018_EL2-11; SUSALPS-RS2018_EL2-12; SUSALPS-RS2018_EL3-01; SUSALPS-RS2018_EL3-02; SUSALPS-RS2018_EL3-03; SUSALPS-RS2018_EL3-04; SUSALPS-RS2018_EL3-05; SUSALPS-RS2018_EL3-06; SUSALPS-RS2018_EL3-07; SUSALPS-RS2018_EL3-08; SUSALPS-RS2018_EL3-09; SUSALPS-RS2018_EL3-10; SUSALPS-RS2018_EL3-11; SUSALPS-RS2018_EL3-12; SUSALPS-RS2018_FE1-01; SUSALPS-RS2018_FE1-02; SUSALPS-RS2018_FE1-03; SUSALPS-RS2018_FE1-04; SUSALPS-RS2018_FE1-05; SUSALPS-RS2018_FE1-06; SUSALPS-RS2018_FE1-07; SUSALPS-RS2018_FE1-08; SUSALPS-RS2018_FE1-09; SUSALPS-RS2018_FE1-10; SUSALPS-RS2018_FE1-11; SUSALPS-RS2018_FE1-12; SUSALPS-RS2018_FE2-01; SUSALPS-RS2018_FE2-02; SUSALPS-RS2018_FE2-03; SUSALPS-RS2018_FE2-04; SUSALPS-RS2018_FE2-05; SUSALPS-RS2018_FE2-06; SUSALPS-RS2018_FE2-07; SUSALPS-RS2018_FE2-08; SUSALPS-RS2018_FE2-09; SUSALPS-RS2018_FE2-10; SUSALPS-RS2018_FE2-11; SUSALPS-RS2018_FE2-12; SUSALPS-RS2018_FE3-01; SUSALPS-RS2018_FE3-02; SUSALPS-RS2018_FE3-03; SUSALPS-RS2018_FE3-04; SUSALPS-RS2018_FE3-05; SUSALPS-RS2018_FE3-06; SUSALPS-RS2018_FE3-07; SUSALPS-RS2018_FE3-08; SUSALPS-RS2018_FE3-09; SUSALPS-RS2018_FE3-10; SUSALPS-RS2018_FE3-11; SUSALPS-RS2018_FE3-12; SUSALPS-RS2018_FE4-01; SUSALPS-RS2018_FE4-02; SUSALPS-RS2018_FE4-03; SUSALPS-RS2018_FE4-04; SUSALPS-RS2018_FE4-05; SUSALPS-RS2018_FE4-06; SUSALPS-RS2018_FE4-07; SUSALPS-RS2018_FE4-08; SUSALPS-RS2018_FE4-09; SUSALPS-RS2018_FE4-10; SUSALPS-RS2018_FE4-11; SUSALPS-RS2018_FE4-12; SUSALPS-RS2018_RB1-01; SUSALPS-RS2018_RB1-02; SUSALPS-RS2018_RB1-03; SUSALPS-RS2018_RB1-04; SUSALPS-RS2018_RB1-05; SUSALPS-RS2018_RB1-06; SUSALPS-RS2018_RB1-07; SUSALPS-RS2018_RB1-08; SUSALPS-RS2018_RB1-09; SUSALPS-RS2018_RB1-10; SUSALPS-RS2018_RB1-11; SUSALPS-RS2018_RB1-12; SUSALPS-RS2018_RB2-01; SUSALPS-RS2018_RB2-02; SUSALPS-RS2018_RB2-03; SUSALPS-RS2018_RB2-04; SUSALPS-RS2018_RB2-05; SUSALPS-RS2018_RB2-06; SUSALPS-RS2018_RB2-07; SUSALPS-RS2018_RB2-08; SUSALPS-RS2018_RB2-09; SUSALPS-RS2018_RB2-10; SUSALPS-RS2018_RB2-11; SUSALPS-RS2018_RB2-12; SUSALPS-RS2018_RB3-01; SUSALPS-RS2018_RB3-02; SUSALPS-RS2018_RB3-03; SUSALPS-RS2018_RB3-04; SUSALPS-RS2018_RB3-05; SUSALPS-RS2018_RB3-06; SUSALPS-RS2018_RB3-07; SUSALPS-RS2018_RB3-08; SUSALPS-RS2018_RB3-09; SUSALPS-RS2018_RB3-10; SUSALPS-RS2018_RB3-11; SUSALPS-RS2018_RB3-12; Sustainable use of alpine and pre-alpine grassland soils in a changing climate; temperate grassland; TERENO; Terrestrial Environmental Observatories
    Type: Dataset
    Format: text/tab-separated-values, 3480 data points
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  • 3
    Publication Date: 2020-09-28
    Description: The data set contains information on aboveground vegetation traits of 〉 100 georeferenced locations within ten temperate pre-Alpine grassland plots in southern Germany. The grasslands were sampled in April 2018 for the following traits: bulk canopy height; weight of fresh and dry biomass; dry weight percentage of the plant functional types (PFT) non-green vegetation, legumes, non-leguminous forbs, and graminoids; total green area index (GAI) and PFT-specific GAI; plant water content; plant carbon and nitrogen content (community values and PFT-specific values); as well as leaf mass per area (LMA) of PFT. In addition, a species specific inventory of the plots was conducted in June 2020 and provides plot-level information on grassland type and plant species composition. The data set was obtained within the framework of the SUSALPS project (“Sustainable use of alpine and pre-alpine grassland soils in a changing climate”; https://www.susalps.de/) to provide in-situ data for the calibration and validation of remote sensing based models to estimate grassland traits.
    Electronic ISSN: 2052-4463
    Topics: Nature of Science, Research, Systems of Higher Education, Museum Science
    Published by Springer Nature
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  • 4
    Publication Date: 2017-12-11
    Electronic ISSN: 2504-3900
    Topics: Technology
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  • 5
    Publication Date: 2018-11-10
    Description: The Copernicus Sentinel-1 mission provides synthetic aperture radar (SAR) acquisitions over large areas with high temporal and spatial resolution. This new generation of satellites providing open-data products has enhanced the capabilities for continuously studying Earth surface changes. Over the past two decades, several studies have demonstrated the potential of differential synthetic aperture radar interferometry (DInSAR) for detecting and quantifying land surface deformation. DInSAR limitations and challenges are linked to the SAR properties and the field conditions (especially in mountainous environments) leading to spatial and temporal decorrelation of the SAR signal. High temporal decorrelation can be caused by changes in vegetation (particularly in nonurban areas), atmospheric conditions, or high ground surface velocity. In this study, the kinematics of the complex and vegetated Corvara landslide, situated in Val Badia (South Tyrol, Italy), are monitored by a network of three permanent and 13 monthly measured benchmark points measured with the differential global navigation satellite system (DGNSS) technique. The slope displacement rates are found to be highly unsteady and reach several meters a year. This paper focuses firstly on evaluating the performance of DInSAR changing unwrapping and coherence parameters with Sentinel-1 imagery, and secondly, on applying DInSAR with DGNSS measurements to monitor an active and complex landslide. To this end, 41 particular SAR images, coherence thresholds, and 2D and 3D unwrapping processes give various results in terms of reliability and accuracy, supporting the understanding of the landslide velocity field. Evolutions of phase changes are analysed according to the coherence, the changing field conditions, and the monitored ground-based displacements.
    Electronic ISSN: 2072-4292
    Topics: Architecture, Civil Engineering, Surveying , Geography
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  • 6
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