Enhanced land use classification of 2017 for the Rur catchment
This page lists all metadata that was entered for this dataset. You can download the dataset and view an additional description file.
Features
Citation
Citation Options
Identification
| Title: | Main Title: Enhanced land use classification of 2017 for the Rur catchment |
| Description: | 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. |
| Identifier: | 10.5880/TR32DB.27 (DOI) |
| Related Resources: | References 10.1016/j.jag.2017.04.009 (DOI) References 10.1016/B978-0-12-409548-9.09636-6 (DOI) |
| 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
| Creators: | Guido Waldhoff (Author), Marina Herbrecht (Author) |
| Contributors: | University of Cologne (Institute of Geography) (Producer), Transregional Collaborative Research Centre 32 (Meteorological Institute, University of Bonn) (Producer) |
| Funding Reference: | Deutsche Forschungsgemeinschaft (DFG): CRC/TRR 32: Patterns in Soil-Vegetation-Atmosphere Systems: Monitoring, Modelling and Data Assimilation |
| Publisher: | CRC/TR32 Database (TR32DB) |
| Publication Year: | 2018 |
Topic
| TR32 Topic: | Land Use |
| Related Subproject: | Z1 |
| Subjects: | Keywords: Land Use, Agriculture, ATKIS, Remote Sensing, Crop/s |
| Geogr. Information Topic: | Geoscientific Information |
File Details
| Filename: | LU2017.zip |
| Data Type: | Dataset - Dataset |
| Size: | 1 Datasets |
| File Size: | 7.2 MB |
| Date: | Submitted: 04.05.2018 |
| Mime Type: | application/zip |
| Data Format: | GeoTIFF |
| Language: | English |
| Status: | Completed |
Archive Content
Constraints
| Download Permission: | Free |
| 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
Specific Information - Data
| Temporal Extent: | 01.01.2016, 00:00:00 - 31.12.2016, 23:59:00 |
| 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. |
| Subtype: | Natural Science Data |
Metadata Details
| Metadata Creator: | Guido Waldhoff |
| Metadata Created: | 04.05.2018 |
| Metadata Last Updated: | 04.05.2018 |
| Subproject: | Z1 |
| Funding Phase: | 3 |
| Metadata Language: | English |
| Metadata Version: | V50 |
Metadata Export
| Metadata Schema: |
Dataset Statistics
| Page Visits: | 945 |
| Metadata Downloads: | 39 |
| Dataset Downloads: | 23 |
Dataset Activity
Features
By downloading this dataset you accept the license terms of [TR32DB] Data policy agreement and TR32DB Data Protection Statement
Adequate reference when this dataset will be discussed or used in any publication or presentation is mandatory. In this case please contact the dataset creator.
Adequate reference when this dataset will be discussed or used in any publication or presentation is mandatory. In this case please contact the dataset creator.
