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# 6571
Hütt, Christoph
Abstract: This dataset contains the crop classification of 2017 for the German area of the CRC/Transregio 32: "Patterns in Soil-Vegetation-Atmosphere Systems: monitoring, modelling and data assimilation." The crop classification is derived from multitemporal remote sensing data analysis using Sentinel-1. In total 70 acquisitions, each covering the whole AOI were used. External data from open.NRW was used. The DGM1 and the ALKIS cadastre data. Accuracy assessment revealed a high overall accuracy of about 97% for the eleven crop classes. Only the German part of the TR32 is included in the data.
# 6572
Rosenau, Matthias • Pohlenz, Andre • Kemnitz, Helga • Warsitzka, Michael
Abstract: This dataset provides friction data from ring-shear tests (RST) for a quartz sand (“G12”). This material is used in various types of analogue experiments in the Helmholtz Laboratory for Tectonic Modelling (HelTec) at the GFZ German Research Centre for Geosciences in Potsdam for simulating brittle rocks in the upper crust. The material has been characterized by means of internal friction coefficients µ and cohesions C. According to our analysis the material shows a Mohr-Coulomb behaviour characterized by a linear failure envelope and peak, dynamic and reactivation friction coefficients of µP = 0.69, µD = 0.55 and µR = 0.62, respectively. Cohesions C are in the order of 50 – 110 Pa. The material shows a minor rate-weakening of <1% per ten-fold change in shear velocity. Further information about materical characteristics, measurement procedures, sample preparation, the RST (Ring-shear test) and VST (Velocity stepping test) procedure, as well as the analysed method is proviced in the data description file. The list of files explains the file and folder structure of the data set.
# 6573
Scherler, Dirk • Wulf, Hendrik • Gorelick, Noel
Abstract: This dataset is supplementary to the article of Scherler et al. (submitted), in which the global distribution of supraglacial debris cover is mapped and analyzed. For mapping supraglacial debris cover, we combined glacier outlines from the Randolph Glacier Inventory (RGI) version 6.0 (RGI consortium, 2017) with remote sensing-based ice and snow identification. Areas that belong to glaciers but that are neither ice nor snow were classified as debris cover. This dataset contains the outlines of the mapped debris-covered glaciers areas, stored in shapefiles (.shp). For creating this dataset, we used optical satellite data from Landsat 8 (for the time period 2013-2017), and from Sentinel-2A/B (2015-2017). For the ice and snow identification, we used three different algorithms: a red to short-wavelength infrared (swir) band ratio (RATIO; Hall et al., 1988), the normalized difference snow index (NDSI; Dozier, 1989), and linear spectral unmixing-derived fractional debris cover (FDC; e.g., Keshava and Mustard, 2002). For a detailed description of the debris-cover mapping and an analysis of the data, please see Scherler et al. (submitted). This dataset includes debris cover outlines based on either Landsat 8 (LS8; 30-m resolution) or Sentinel 2 (S2; 10-m resolution), and the three algorithms RATIO, NDSI, FDC. In total, there exist six different zip-files that each contain 19 shapefiles. The structure of the shapefiles follows that of the RGI version 6.0 (RGI consortium, 2017), with one shapefile for each RGI region. The original RGI shapefiles provide each glacier as one entry (feature) and include a variety of ancillary information, such as area, slope, aspect (RGI Consortium 2017a, Technical Note p. 12ff). Because the debris-cover outlines are based on the RGI v6.0 glacier outlines, all fields of the original shapefiles, which refer to the glacier, are retained, and expanded with four new fields: - DC_Area: Debris-covered area in m². Note that this unit for area is different from the unit used for reporting the glacier area (km²).- DC_BgnDate: Start of the time period from which satellite imagery was used to map debris cover.- DC_EndDate: End of the time period from which satellite imagery was used to map debris cover.- DC_CTSmean: Mean number of observations (CTS = COUNTS) per pixel and glacier. This number is derived from the number of available satellite images for the respective time period, reduced by filtering pixels due to cloud and snow cover. The dataset has a global extent and covers all of the glaciers in the RGI v. 6.0, but it exhibits poor coverage in the RGI region Subantarctic and Antarctic, where the debris cover extents are based on very few observations.
# 6574
Unger, Andrea • Rabe, Daniela • Klemann, Volker • Eggert, Daniel • Dransch, Doris
Abstract: The validation of a simulation model is a crucial task in model development. It involves the comparison of simulation data to observation data and the identification of suitable model parameters. SLIVISU is a Visual Analytics framework that enables geoscientists to perform these tasks for observation data that is sparse and uncertain. Primarily, SLIVISU was designed to evaluate sea level indicators, which are geological or archaeological samples supporting the reconstruction of former sea level over the last ten thousands of years and are compiled in a postgreSQL database system. At the same time, the software aims at supporting the validation of numerical sea-level reconstructions against this data by means of visual analytics.
# 6575
Albert, Francisca
Abstract: This data set includes movies and images of sandbox experiments aiming at understainding the process of subduction erosion at active plate margins (Albert, 2013). Four experiments are documented by means of movies showing the evolution of a strong wedge (sand-sugar mix, “Reference experiment.avi”), a weak wedge (sand only, “F1 experiment.avi”) and two successive phases of a wedge that undergoes subduction erosion by subducting topographic highs (first stage without subducting topography= “HL.1 experiment.avi” and second stage with subducting topography = “HL.2 experiment.avi”). Images of preliminary tests and experiments not considered in Albert (2013) are given in “Appendix A2.2.pdf” (small box experiments) and “Appendix A3.3.pdf” (experiments varying friction and slope).
# 6576
Pittore, Massimiliano • Haas, Michael • Megalooikonomou, Konstantinos
Abstract: The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS (Remote Rapid Visual Screening) methodology in Alsace, France, within the framework of the DESTRESS project. The survey has been carried out between May and June 2017 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images from Google StreetView (vintage: February 2011) and footprints from OpenStreetMap.Surveyor: Konstantinos G. Megalooikonomou (GFZ German Research Centre for Geosciences)The attributes are encoded according to the GEM taxonomy v2.0 (see The following attributes are defined (not all are observable in the RRVS survey):code,descriptionlon, longitude in fraction of degreeslat, latitude in fraction of degreesobject_id, unique id of the building surveyedMAT_TYPE,Material TypeMAT_TECH,Material TechnologyMAT_PROP,Material PropertyLLRS,Type of Lateral Load-Resisting SystemLLRS_DUCT,System DuctilityHEIGHT,HeightYR_BUILT,Date of Construction or RetrofitOCCUPY,Building Occupancy Class - GeneralOCCUPY_DT,Building Occupancy Class - DetailPOSITION,Building Position within a BlockPLAN_SHAPE,Shape of the Building PlanSTR_IRREG,Regular or IrregularSTR_IRREG_DT,Plan Irregularity or Vertical IrregularitySTR_IRREG_TYPE,Type of IrregularityNONSTRCEXW,Exterior wallsROOF_SHAPE,Roof ShapeROOFCOVMAT,Roof CoveringROOFSYSMAT,Roof System MaterialROOFSYSTYP,Roof System TypeROOF_CONN,Roof ConnectionsFLOOR_MAT,Floor MaterialFLOOR_TYPE,Floor System TypeFLOOR_CONN,Floor Connections
# 6577
Renner, Maik • Wizemann, Hans-Dieter • Brenner, Claire • Mallick, Kaniska • Trebs, Ivonne • (et. al.)
Abstract: This dataset provides half-hourly model output of sensible and latent heat fluxes simulated by three structurally different evapotranspiration schemes for a temperate grassland site in Luxembourg. All models use surface energy and meteorological observations as input. The observational data were collected during a field campaign in June and July 2015 and are distributed as complementary dataset by Wizemann et al., 2018. Two models are based on a parameterization of the sensible heat flux (OSEB, TSEB; see Brenner et al., 2017) and one model (STIC 1.2, Mallick et al., 2016) is a modification of the Penman-Monteith formulation using skin temperature as additional input variable. For details please see the reference article Renner et al., 2019, HESS. The data is provided as comma-separated-values (csv) format in a long table format. Columns represent Date, Time, variable, value, source. The column “variable” sets the name of the variable (following CEOP standards, Column “source” describes the data source with an acronym representing the models (OSEB, TSEB, STIC). The data contributes to the Joint Research Group "Catchments As Organized Systems" (CAOS) funded by the German Research Foundation. Methods: land-surface modelling, evapotranspiration schemes
# 6578
Franz, Daniela • Mammarella, Ivan • Boike, Julia • Kirillin, Georgiy • Vesala, Timo • (et. al.)
Abstract: The dataset comprises three tables: - Data Set 1: Half-hourly measurement dataset (quality controlled and filtered), derived variables and energy balance components (Franz_ds01.csv) - Data Set 2: Half-hourly fluxes and transfer coefficients derived by bulk aerodynamic transfer models (Franz_ds02.csv) - Data Set 3. Daily courses and cumulative sums of energy balance components for the average day per subperiod including measured and modelled H and LE (Franz_ds03.csv)
Eddy covariance measurements were conducted from 23 April to 16 August 2014 on a thermokarst lake in the Siberian Lena River Delta, yielding direct measurements of sensible (H) and latent (LE) heat flux on half-hourly basis. Ancillary measurements including meteorological variables and water temperature measurements were gathered during the campaign. We derived bulk aerodynamic transfer coefficients in order to parameterize the heat fluxes and compare this in-situ model with independent heat flux parameterization schemes, which are also based on the common bulk transfer algorithm. We further investigated the components of a simple energy balance including measured and modelled H and LE. The dataset was created within the framework of a publication of the study results in Journal of Geophysical Research - Atmospheres (Lake-atmosphere heat flux dynamics of a thermokarst lake in arctic Siberia, by Franz et al.)
# 6579
Wizemann, Hans-Dieter • Trebs, Ivonne • Wulfmeyer, Volker
Abstract: This dataset provides half-hourly surface energy balance measurements for a temperate grassland site in Luxembourg. The data were obtained during a field campaign in June and July 2015. The observations comprise multiple variables measurements by an Eddy-Covariance station, a net radiometer, soil moisture, temperature and soil heat flux probes and meteorological standard measurements. For details please see the reference article Renner et al. (2019, HESS) with the general setup described in Wizemann et al., 2015. The data are complemented by half-hourly model output of sensible and latent heat fluxes that are published as individual data publication (Renner et al., 2018).The data is provided as comma-separated-values (csv) format in a long table format. Columns represent Date, Time, variable, value, source. The column “variable” sets the name of the variable (following CEOP standards) with an information of the measurement depth for soil measurements. Column “source” describes the data source with an acronym(Observations “ObsEC”). The data contributes to the Joint Research Group "Catchments As Organized Systems" (CAOS) funded by the German Research Foundation. Methods: Eddy Covariance, Surface energy balance observations
# 6580
Sips, Mike • Dransch, Doris • Eggert, Daniel • Freytag, Johann-Christoph • Hollstein, Andre • (et. al.)
Abstract: GeoMultiSens developed an integrated processing pipeline to support the analysis of homogenized data from various remote sensing archives. The processing pipeline has five main components: (1) visual assessment of remote sensing Earth observations, (2) homogenization of selected Earth observation, (3) efficient data management with XtreemFS, (4) Python-based parallel processing and analysis algorithms implemented in a Flink cloud environment, and (5) visual exploration of the results. GeoMultiSens currently supports the classification of land-cover for Europe.
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