Tree Canopy Cover Loss Monthly - Landsat-8/Sentinel-2 - Germany, 2018-2021

The product shows tree canopy cover loss in Germany between January 2018 and April 2021 at monthly temporal and 10 m spatial resolution. The basic principle behind this map is to compute monthly composites of the disturbance index (DI, Healey et al. 2005), a spectral index sensitive to forest disturbance, from all available Sentinel-2 and Landsat-8 data with less than 80 % cloud cover. These monthly composites are then compared to a median composite of the DI for 2017, which serves as a reference. After applying a threshold to the difference image, the time series of detected losses is checked for consistency. Only losses recorded continuously in all observations of a pixel until the end of the time series are considered. The dataset does not differentiate between the drivers of the losses. It depicts areas of natural disturbances (windthrow, fire, droughts, insect infestation) as well as sanitation and salvage logging, and regular forest harvest. The full description of the method and results can be found in Thonfeld et al. (2022).

Citation proposal
German Aerospace Center (DLR). Tree Canopy Cover Loss Monthly - Landsat-8/Sentinel-2 - Germany, 2018-2021. https://gdk.gdi-de.org/geonetwork/srv/api/records/3a5f8f67-aede-4325-aee6-83330ea046f1

Simple

Date ( Creation ): 2022-08-11T00:00:00

Edition

Identifier: https://geoservice.dlr.de/catalogue/srv/metadata/3a5f8f67-aede-4325-aee6-83330ea046f1

Presentation form: Digital map

Other citation details: DOI: 10.15489/z0g96v5kwg14

Purpose: This map shows the tree canopy cover losses in Germany for the period of January 2018 until April 2021.

Status: Completed

Author

German Aerospace Center (DLR) -

Point of contact

German Aerospace Center (DLR)

Maintenance and update frequency: As needed

GEMET - INSPIRE themes, version 1.0 ( Theme )

Land cover

Spatial scope ( Place )

National

Keywords

opendata
Forest
Canopy Cover Loss
Drought
Disturbance Index
Sentinel-2
Landsat-8
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.

Access constraints: Other restrictions

Use constraints: Other restrictions

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

Other constraints

{"id": "cc-by-4.0",

"name": "Creative-Commons - Attribution 4.0 International (CC BY 4.0)",

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

"quelle": "Copyright DLR (2022)"}

Spatial representation type: Grid

Denominator: 20000

Metadata language: English

Character set: UTF8

Topic category

Biota

Number of dimensions: 2

Dimension name: Column

Dimension size: 71084

Resolution: 10 m

Dimension name: Row

Dimension size: 90002

Resolution: 10 m

Cell geometry: Area

Transformation parameter availability: false

Distribution format

GeoTIFF ()

OnLine resource: EOC Geoservice Dataset
EOC Geoservice Dataset

OnLine resource: TREE_CANOPY_COVER_LOSS_P1M
WMS Access: Tree Canopy Cover Loss Germany 2018-2021

OnLine resource: HTTP download Tree Canopy Cover Loss Monthly - Germany, 2018-2021
HTTP download Tree Canopy Cover Loss Monthly - Germany, 2018-2021

OnLine resource: Sorge um den deutschen Wald
DLR News - Webpage

Hierarchy level: Dataset

Domain consistency

Measure identification: INSPIRE / Conformity_001

Conformance result

Date ( Publication ): 2010-12-08

Explanation: See the referenced specification.

Pass: true

Statement: All Sentinel-2 and Landsat-8 data since 2017 available through the Google Earth Engine (GEE) with less than 80 % cloud cover were used.

Description

Tree Canopy Cover Loss - Germany 2018-2021 - Processing

Processing:

The Sentinel-2 level 2 surface reflectance dataset available from GEE was processed using sen2cor and the Landsat-8 level 2 dataset is composed of surface reflectance data processed using the land surface reflectance code (LaSRC).

Quality Assurance:

With regard to the spatial accuracy, our assessment based on pixel counts revealed an overall accuracy of 92.8%. The sample-based accuracy assessment revealed an overall accuracy of 98% ± 1% and an area estimate of 643,735 ha ± 120,726 ha canopy cover loss. This map is a modified version of the paper, where we removed low-confidence losses, particularly during early 2018 and the 2020/21 winter.