[1795] - Enhanced land use classification of 2017 for the Rur catchment

All available metadata of the dataset are listed below. Some features are available, e.g. download of dataset or additional description file.

Features
Citation
Waldhoff, G., Herbrecht, M., 2018. Enhanced land use classification of 2017 for the Rur catchment. CRC/TR32 Database (TR32DB). CRC/TR32 Database (TR32DB). DOI: 10.5880/TR32DB.27.
Identification
Title(s):Main Title: Enhanced land use classification of 2017 for the Rur catchment
Description(s):Abstract: This data set contains the Enhanced land use classification of 2017 for the study area of the CRC/Transregio 32: "Patterns in Soil-Vegetation-Atmosphere Systems: monitoring, modelling and data assimilation", which corresponds to the catchment of the river Rur. The study area is mainly situated in the western part of North Rhine-Westphalia (Germany) and parts of the Netherlands and Belgium. The classification is provided in GeoTIFF format. Spatial resolution: 15 m; Projection: WGS84, UTM Zone 32N.
Relation(s):References: DOI: 10.1016/j.jag.2017.04.009
References: DOI: 10.1016/B978-0-12-409548-9.09636-6
Citation Advice:Waldhoff, Guido & Herbrecht, Marina (2018): Enhanced land use classification of 2017 for the Rur catchment. TR32DB. DOI:10.5880/TR32DB.27.
Responsible Party
Creator(s):Author: Guido Waldhoff
Author: Marina Herbrecht
Contributor(s):Producer: University of Cologne, Institute of Geography
Producer: Transregional Collaborative Research Centre 32, Meteorological Institute, University of Bonn
Funding Reference(s):Deutsche Forschungsgemeinschaft (DFG): CRC/TRR 32: Patterns in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling and Data Assimilation
Publisher:CRC/TR32 Database (TR32DB)
Topic
TR32 Topic:Land Use
Related Sub-project(s):Z1
Subject(s):CRC/TR32 Keywords: Land Use, Agriculture, ATKIS, Remote Sensing, Crop/s
Topic Category:GeoScientificInformation
File Details
File Name:LU2017.zip
Data Type:Dataset
Size(s):1 Datasets
File Size:7411 kB (7.237 MB)
Date(s):Date Submitted: 2018-05-04
Mime Type:application/zip
Data Format:GeoTIFF
Language:English
Status:Completed
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Constraints
Download Permission:OnlyTR32
General Access and Use Conditions:According to the TR32DB data policy agreement.
Access Limitations:According to the TR32DB data policy agreement.
Licence:TR32DB Data policy agreement
Geographic
North:51.23091
East:6.80353
South:50.35942
West:5.83124
Measurement Region:RurCatchment
Measurement Location:--RurCatchment--
Specific Informations - Data
Temporal Extent:2016/01/01 - 2016/12/31
Lineage:The land use classification is derived from supervised, multi temporal remote sensing data analysis using Landsat 8 (L8) and RapidEye (RE). For the land use analysis datasets of the following acquisition dates were employed: May 17 (RE), May 25 (RE), May 29, (L8), June 14 (L8), and September 24 (RE). Full coverage of the study area was only available for the second L8 image and thus the crop classification is partly affected in its depth of information. For the assessment of the crop classification accuracy refer to the error matrix on the last page. To enhance the information content of the land use data product, the Multi-Data Approach (MDA) was applied to combine the remote sensing derived land use information with additional data sets like the ‘Authorative Topographic-Cartographic Information System’ (ATKIS Basis-DLM) and ‘Physical Block’ information. Furthermore, OpenStreetMap (OSM) data were integrated outside of the ATKIS coverage to enhance the information content on the road network, settlement areas and the course of the river Rur in the Netherlands and Belgium. ## The methodology of the MDA is described in more detail in Waldhoff et al. 2017, Bareth & Waldhoff (2018) and Waldhoff (2014). --- The classification is provided in GeoTIFF and in ASCII format. Spatial resolution: 15 m; Projection: WGS84, UTM Zone 32N. --- References: Waldhoff, G., Lussem, U., Bareth, G. (2017): Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment, Germany. International Journal of Applied Earth Observation and Geoinformation 61, 55-69, 10.1016/j.jag.2017.04.009. --- Bareth, G. and Waldhoff, G. (2018): 2.01 - GIS for Mapping Vegetation A2 - Huang, Bo. Comprehensive Geographic Information Systems, Elsevier, Oxford, 1-27, https://doi.org/10.1016/B978-0-12-409548-9.09636-6 --- Waldhoff, G. (2014): Multidaten-Ansatz zur fernerkundungs- und GISbasierten Erzeugung multitemporaler, disaggregierter Landnutzungsdaten. Methodenentwicklung und Fruchtfolgenableitung am Beispiel des Rureinzugsgebiets. Dissertation, University of Cologne, Germany, http://kups.ub.uni-koeln.de/id/eprint/5861. --- Acknowledgements: We thank Geobasis.NRW for the provision of the ATKIS-Basis-DLM. Additional spatial data for the Netherlands was obtained from geodata.nationaalgeoregister.nl. All OSM data were obtained from Geofabrik GmbH. Furthermore, we thank the Space Administration of the German Aerospace Center (DLR) and Planet Labs Germany GmbH for the provision of RapidEye data via the RapidEye Science Archive (RESA) and NASA for the provision of the Landsat 8 data.
Metadata Details
Metadata Creator:Guido Waldhoff
Metadata Created:2018-05-04
Metadata Last Updated:2018-05-04
Subproject:Z1
Funding Phase:3
Metadata Language:English
Metadata Version:V42
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Metadata Downloads:8
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