Landcover classification map of Germany 2019 based on Sentinel-2 data

This landcover map was produced as an intermediate result in the course of the project incora (Inwertsetzung von Copernicus-Daten für die Raumbeobachtung, mFUND Förderkennzeichen: 19F2079C) in cooperation with ILS (Institut für Landes- und Stadtentwicklungsforschung gGmbH) and BBSR (Bundesinstitut für Bau-, Stadt- und Raumforschung) funded by BMVI (Federal Ministry of Transport and Digital Infrastructure). The goal of incora is an analysis of settlement and infrastructure dynamics in Germany based on Copernicus Sentinel data.

This classification is based on a time-series of monthly averaged, atmospherically corrected Sentinel-2 tiles (MAJA L3A-WASP: https://geoservice.dlr.de/web/maps/sentinel2:l3a:wasp; DLR (2019): Sentinel-2 MSI - Level 2A (MAJA-Tiles)- Germany). It consists of the following landcover classes:

10: forest

20: low vegetation

30: water

40: built-up

50: bare soil

60: agriculture

Potential training and validation areas were automatically extracted using spectral indices and their temporal variability from the Sentinel-2 data itself as well as the following auxiliary datasets:

- OpenStreetMap (Map data copyrighted OpenStreetMap contributors and available from htttps://www.openstreetmap.org)

- Copernicus HRL Imperviousness Status Map 2018 (© European Union, Copernicus Land Monitoring Service 2018, European Environment Agency (EEA))

- S2GLC Land Cover Map of Europe 2017 (Malinowski et al. 2020: Automated Production of Land Cover/Use Map of Europe Based on Sentinel-2 Imagery. Remote Sens. 2020, 12(21), 3523; https://doi.org/10.3390/rs12213523 )

- Germany NUTS administrative areas 1:250000 (© GeoBasis-DE / BKG 2020 / dl-de/by-2-0 / https://gdz.bkg.bund.de/index.php/default/nuts-gebiete-1-250-000-stand-31-12-nuts250-31-12.html )

- Contains modified Copernicus Sentinel data (2019), processed by mundialis

Processing was performed for blocks of federal states and individual maps were mosaicked afterwards.

For each class 100,000 pixels from the potential training areas were extracted as training data.

An exemplary validation of the classification results was perfomed for the federal state of North Rhine-Westphalia as its open data policy allows for direct access to official data to be used as reference. Rules to convert relevant ATKIS Basis-DLM object classes to the incora nomenclature were defined. Subsequently, 5.000 reference points were randomly sampled and their classification in each case visually examined and, if necessary, revised to obtain a robust reference data set. The comparison of this reference data set with the incora classification yielded the following results:

overall accurary: 91.9%

class: user's accuracy / producer's accurary (number of reference points n)

forest: 98.1% / 95.9% (1410)

low vegetation: 76.4% / 91.5% (844)

water: 98.4% / 92.8% (69)

built-up: 99.2% / 97.4% (983)

bare soil: 35.1% / 95.1% (41)

agriculture: 95.9% / 85.3% (1653)

Incora report with details on methods and results: pending

Simple

Date ( Publication )
2020-12-01
Identifier
dataset
Status
On going
Point of contact
mundialis GmbH & Co. KG -

Maintenance and update frequency
Irregular
Keywords ( Theme )
  • Sentinel-2
  • Classification
  • Land Cover
  • mFUND
  • MAJA
  • Infrastuktur
  • Umwelt
  • Regionen und Städte
  • mfund-projekt:incora
  • mfund-fkz:19F2079C
  • incora
Keywords ( Place )
  • Germany
GEMET - INSPIRE themes, version 1.0 ( Theme )
  • Land use
  • Land cover
Use limitation
None
Access constraints
Other restrictions
Use constraints
License
Other constraints
no limitations to public access
Other constraints
Data licence Germany - attribution - version 2.0 or later (DL-DE->BY-2.0) | Datenlizenz Deutschland - Namensnennung - Version 2.0 oder neuer
Other constraints
{ "id": "dl-by-de/2.0", "name": "Datenlizenz Deutschland Namensnennung 2.0", "url": "https://www.govdata.de/dl-de/by-2-0", "quelle": "Source: mundialis GmbH & Co. KG" }
Aggregate Datasetindentifier
db130a09-fc2e-421d-95e2-1575e7c4b45c
Association Type
Cross reference
Aggregate Datasetindentifier
9246503f-6adf-460b-a31e-73a649182d07
Association Type
Cross reference
Aggregate Datasetindentifier
5ef8565b-ea72-4f34-985b-71ab41959230
Association Type
Cross reference
Aggregate Datasetindentifier
ad2646e3-5667-40bd-b80b-73151949747c
Association Type
Cross reference
Spatial representation type
Grid
Distance
10 meters
Metadata language
en
Character set
UTF8
Topic category
  • Geoscientific information
Description
Germany
Begin date
2019-01-01
End date
2019-12-31
N
S
E
W
thumbnail


Reference system identifier
EPSG:32632 (UTM 32N)
Distribution format
  • GeoTIFF ( 1.0 )

OnLine resource
https://data.mundialis.de/geodata/lulc-germany/classification_2019/classification_map_germany_2019_v02.tif ( WWW:DOWNLOAD-1.0-http--download )
OnLine resource
https://data.mundialis.de/geodata/lulc-germany/classification_2019/LICENSE.html ( WWW:DOWNLOAD-1.0-http--download )
Hierarchy level
Dataset

Conformance result

Date ( Publication )
2010-12-08
Other citation details
http://data.europa.eu/eli/reg/2010/1089/2014-12-31
Explanation
See specified reference
Pass
Yes
Statement
derived from Sentinel-2 MSI - Level 3A-WASP

gmd:MD_Metadata

File identifier
36512b46-f3aa-4aa4-8281-7584ec46c813 XML
Metadata language
en
Character set
UTF8
Hierarchy level
Dataset
Date stamp
2021-10-25T11:55:14
Metadata standard name
ISO 19115:2003/19139
Metadata standard version
1.0
Point of contact
mundialis GmbH & Co. KG - ( )

Citation proposal


(2020) . Landcover classification map of Germany 2019 based on Sentinel-2 data.
https://data.opendatascience.eu/geonetwork/srv/api/records/36512b46-f3aa-4aa4-8281-7584ec46c813

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