10 documents found in 251ms
# 1
Rudenko, Sergei • Schöne, Tilo • Neumayer, Karl-Hans • Esselborn, Saskia • Raimondo, Jean-Claude • (et. al.)
Abstract: The data set provides GFZ VER11 orbits of altimetry satellites ERS-1 (August 1, 1991 - July 5, 1996),ERS-2 (May 13, 1995 - February 27, 2006),Envisat (April 12, 2002 - April 8, 2012),Jason-1 (January 13, 2002 - July 5, 2013) andJason-2 (July 5, 2008 - April 5, 2015)TOPEX/Poseidon (September 23, 1992 - October 8, 2005), derived at the time spans given at Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences within the Sea Level phase 2 project of the European Space Agency (ESA) Climate Change Initiative using "Earth Parameter and Orbit System - Orbit Computation (EPOS-OC)" software and the Altimeter Database and processing System (ADS, http://adsc.gfz-potsdam.de/ads/) developed at GFZ. The orbits were computed in the same (ITRF2008) terrestrial reference frame for all satellites using common, most precise models and standards available and described below. The ERS-1 orbit is computed using satellite laser ranging (SLR) and altimeter crossover data, while the ERS-2 orbit is derived using additionally Precise Range And Range-rate Equipment (PRARE) measurements. The Envisat, TOPEX/Poseidon, Jason-1 and Jason-2 orbits are based on Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) and SLR observations. The orbit files are available in the Extended Standard Product 3 Orbit Format (SP3-c, ftp://igscb.jpl.nasa.gov/igscb/data/format/sp3c.txt) Files are gzip-compressed. File names are given as sate_YYYYMMDD_SP3C.gz, where "sate" is the abbreviation (ENVI, ERS1, ERS2, JAS1, JAS2, TOPX) of the satellite name, YYYY stands for 4-digit year, MM stands for month and DD stands for day of the beginning of the file. More details on these orbits are provided in Rudenko et al. (2017)
# 2
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.
# 3
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.
# 4
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.
# 5
Eggert, Daniel • Sips, Mike • Dransch, Doris
Abstract: gms-vis is a web-based implementation of our visual-analytics approach for assessing remote-sensing data. It is implemented based on the GWT framework. Once deployed through a webserver it acts as the user interface for the GeoMultiSens (GMS) platform. Within the interface users can intuitively define spatial, temporal as well as quality constraints, for remote sensing scenes. A heatmap enables the user to assess the spatial distribution of selected scenes, while a time histogram allows the user to assess their temporal distribution. Finally, users can specify a workflow which will be executed by the GeoMultiSens platform. Though gms-vis is part of the GeoMultiSens platform, it is relatively self-contained and can be attached to different analysis frameworks and platforms with reasonable effort.
# 6
Eggert, Daniel • Sips, Mike • Dransch, Doris
Abstract: Gms-index-mediator is a standalone index for spatio-temporal data acting as a mediator between an application and a database. Even modern databases need several minutes to execute a spatio-temporal query to huge tables containing several million entries. Our index-mediator speeds the execution of such queries up by several magnitues, resulting in response times around 100ms. This version is tailored towards the GeoMultiSens database, but can be adapted to work with custom table layouts with reasonable effort.
# 7
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 https://taxonomy.openquake.org). 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
# 8
Eggert, Daniel • Köthur, Patrick • Dransch, Doris
Abstract: The processing of Persistent Scatterer Interferometry (PSI) data and the estimation of displacement is a nonlinear and user-driven procedure that can introduce large errors for noisy backscatter points. Results may differ significantly depending on chosen thresholds, filter settings, constraints and final interpretation. Thus the identification of valid PS with rather low errors in the SAR data is a crucial step in the PSI workflow. PSI-Explorer is a scientific prototype of our visual-analytics (VA) approach supporting this important task. The prototype is written in Java and operates on Matlab files.
# 9
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.)
# 10
Quinteros, Javier
Abstract: This service provides routing information for distributed data centres, in the case where multiple different seismic data centres offer access to data and products using compatible types of services. Examples of the data and product objects are seismic timeseries waveforms, station inventory, or quality parameters from the waveforms. The European Integrated Data Archive (EIDA) is an example of a set of distributed data centres (the EIDA „nodes“). EIDA have offered Arclink and Seedlink services for many years, and now offers FDSN web services, for accessing their holdings. In keeping with the distributed nature of EIDA, these services could run at different nodes or elsewhere; even on computers from normal users. Depending on the type of service, these may only provide information about a reduced subset of all the available waveforms. To be effective, the Routing Service must know the locations of all services integrated into a system and serve this information in order to help the development of smart clients and/or services at a higher level, which can offer the user an integrated view of the entire system (EIDA), hiding the complexity of its internal structure. The service is intended to be open and able to be queried by anyone without the need of credentials or authentication.
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