Land Cover DE - Sentinel-2 - Germany, 2015

This land cover classification of Germany was created using Sentinel-2 imagery from the years 2015 to 2017 and LUCAS 2015 in-situ reference data ( https://ec.europa.eu/eurostat/web/lucas ). It contains seven land cover types: (1) artificial land, (2) open soil, (3) high seasonal vegetation, (4) high perennial vegetation, (5) low seasonal vegetation, (6) low perennial vegetation and (7) water with a spatial resolution of 10m x 10m. For further information, please see the following publication: https://doi.org/10.1016/j.jag.2020.10206 5

Date (Creation): 2020-02-05T12:48:00

Edition: 1.0

Citation identifier: https://geoservice.dlr.de/catalogue/srv/metadata/8205e1c9-14c3-47a7-ac12-8c890225680a

Presentation form: Digital map

Other citation details: DOI: 10.15489/1ccmlap3mn39

Status: Completed

Author

German Aerospace Center (DLR) -

Author

German Aerospace Center (DLR) -

Point of contact

German Aerospace Center (DLR)

Maintenance and update frequency: As needed

GEMET - INSPIRE themes, version 1.0

Land cover

Keywords

Sentinel-2
Land cover map
Germany

Use limitation: Nutzungseinschränkungen: Das DLR ist nicht haftbar für Schäden, die sich aus der Nutzung ergeben. / Use Limitations: DLR not liable for damage resulting from use.

Use limitation: Nutzungsbedingungen: Lizenz, https://creativecommons.org/licenses/by-nc/4.0 / Terms of use: License, https://creativecommons.org/licenses/by-nc/4.0

Access constraints: Other restrictions

Other constraints: License

Use constraints: License

Use constraints: Other restrictions

Other constraints: Nutzungsbedingungen: Lizenz, https://creativecommons.org/licenses/by-nc/4.0 / Terms of use: License, https://creativecommons.org/licenses/by-nc/4.0

Other constraints: {"id": "cc-by-nc/4.0",

"name": "Creative Commons Namensnennung - Nicht kommerziell 4.0 International (CC BY-NC 4.0)",

"url": " http://dcat-ap.de/def/licenses/cc-by-nc/4.0 ",

"quelle": "Copyright DLR (year of production)"}

Spatial representation type: Grid

Denominator: 20000

Language: English

Character set: UTF8

Topic category

Imagery base maps earth cover

Begin date: 2015-06-27T10:25:31

End date: 2017-09-29T10:20:07

Reference system identifier: EPSG / ETRS89 / LAEA Europe (EPSG:3035) / 8.6

Number of dimensions: 2

Dimension name: Column

Dimension size: 74592

Resolution: 10 m

Dimension name: Row

Dimension size: 90211

Resolution: 10 m

Cell geometry: Area

Transformation parameter availability: No

Distribution format

OnLine resource

LCC_DE_2015 ( OGC:WMS )

Land Cover DE, 2015

OnLine resource

EOC Geoservice Map Context ( WWW:LINK-1.0-http--link )

EOC Geoservice Map Context (de:lcc)

OnLine resource

HTTP Download ( WWW:LINK-1.0-http--link )

HTTP Download (Land Cover DE)

OnLine resource

Scientific publication ( WWW:LINK-1.0-http--link )

Scientific publication describing the Land Cover DE product

Hierarchy level: Dataset

Domain consistency

Measure identification: INSPIRE / Conformity_001

Conformance result

Date (Publication): 2010-12-08

Explanation: See the referenced specification.

Pass: Yes

Statement: Input data: Sentinel-2 multispectral imagery, LUCAS in-situ reference points, and imperviousness layers from OSM and the LoD1-de building model.

Description: This dataset was created using a median mosaic of all collected Sentinel-2 Scenes up until April 30, 2017 with a cloud cover of less than 60 percent. From these a total of 226 spectral and textural image features were derived. Additionally imperviousness from buildings, roads and railways were included. A supervised machine learning classifier was trained with in-situ reference points from the 2015 Land Use and Coverage Area Frame Survey (LUCAS). For further details we refer to the following publication: Weigand, M., Staab, J., Wurm, M., Taubenböck, H. (2020): Spatial and semantic effects of LUCAS samples on fully automated land use/land cover classification in high-resolution Sentinel-2 data, International Journal of Applied Earth Observation and Geoinformation, Volume 88, 102065, ISSN 0303-2434. https://doi.org/10.1016/j.jag.2020.10206 5