Abstract:
This repository contains tree canopy cover loss information between January 2018 and April 2021 for Germany at monthly resolution. The analysis is based on monthly image composites of the disturbance index (DI) derived from Sentinel-2 and Landsat-8 time series. Deviations from a 2017 reference median DI image exceeding a threshold are recorded as losses.
The method used to derive this product as well as the mapping results are described in detail in Thonfeld et al. (2022). The map depicts areas of natural disturbances (windthrow, fire, droughts, insect infestation) as well as sanitation and salvage logging, and regular forest harvest without explicitly differentiating these drivers.
Product Information:
- Product Name: Tree Canopy Cover Loss - Germany, 2018-2021
- Release: 2022-08-11
- Contact: Dr. Frank Thonfeld
Data Information:
Data:
Layer: Tree Canopy Cover Loss Germany 2018-2021
- Metadata: ISO Collection
- Attributes:
- 0: intact forest
- 1: Jan 2018
- 2: Feb 2018
- 3: Mar 2018
- 4: Apr 2018
- 5: May 2018
- 6: Jun 2018
- 7: Jul 2018
- 8: Aug 2018
- 9: Sep 2018
- 10: Oct 2018
- 11: Nov 2018
- 12: Dec 2018
- 13: Jan 2019
- 14: Feb 2019
- 15: Mar 2019
- 16: Apr 2019
- 17: May 2019
- 18: Jun 2019
- 19: Jul 2019
- 20: Aug 2019
- 21: Sep 2019
- 22: Oct 2019
- 23: Nov 2019
- 24: Dec 2019
- 25: Jan 2020
- 26: Feb 2020
- 27: Mar 2020
- 28: Apr 2020
- 29: May 2020
- 30: Jun 2020
- 31: Jul 2020
- 32: Aug 2020
- 33: Sep 2020
- 34: Oct 2020
- 35: Nov 2020
- 36: Dec 2020
- 37: Jan 2021
- 38: Feb 2021
- 39: Mar 2021
- 40: Apr 2021
- 100: non-forested
Layer: Tree Canopy Cover Loss Germany 2018-2021 per district
- Metadata: ISO Collection
- Attributes:
- NAME_2: district name
- TYPE_2: district type
- NAME_1: federal state
- nr: number
- lk_area: district area (ha)
- forestr: initial forest area per district (ha)
- decall: losses in deciduous forest from January 2018 to April 2021 (ha)
- dec2018: losses in deciduous forest in 2018 (ha)
- dec2019: losses in deciduous forest in 2019 (ha)
- dec2020: losses in deciduous forest in 2020 (ha)
- dec2021: losses in deciduous forest in 2021 (January to April) (ha)
- conall: losses in coniferous forest from January 2018 to April 2021 (ha)
- con2018: losses in coniferous forest in 2018 (ha)
- con2019: losses in coniferous forest in 2019 (ha)
- con2020: losses in coniferous forest in 2020 (ha)
- con2021: losses in coniferous forest in 2021 (January to April) (ha)
- allall: losses in all forest types from January 2018 to April 2021 (ha)
- all2018: losses in all forest types in 2018 (ha)
- all2019: losses in all forest types in 2019 (ha)
- all2020: losses in all forest types in 2020 (ha)
- all2021: losses in all forest types in 2021 (January to April) (ha)
- p_frstr: initial forest area per district (%)
- p_decll: losses in deciduous forest from January 2018 to April 2021 (%)
- p_d2018: losses in deciduous forest in 2018 (%)
- p_d2019: losses in deciduous forest in 2019 (%)
- p_d2020: losses in deciduous forest in 2020 (%)
- p_d2021: losses in deciduous forest in 2021 (January to April) (%)
- p_conll: losses in coniferous forest from January 2018 to April 2021 (%)
- p_c2018: losses in coniferous forest in 2018 (%)
- p_c2019: losses in coniferous forest in 2019 (%)
- p_c2020: losses in coniferous forest in 2020 (%)
- p_c2021: losses in coniferous forest in 2021 (January to April) (%)
- p_allll: losses in all forest types from January 2018 to April 2021 (%)
- p_l2018: losses in all forest types in 2018 (%)
- p_l2019: losses in all forest types in 2019 (%)
- p_l2020: losses in all forest types in 2020 (%)
- p_l2021: losses in all forest types in 2021 (January to April) (%)
- Download: HTTP download via EOC Download Service
References:
- Healey, S., Cohen, W., Zhiqiang, Y., Krankina, O., 2005. Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection. Remote Sensing of Environment 97, 301-310. https://doi.org/10.1016/j.rse.2005.05.009
- Thonfeld, F., Gessner, U., Holzwarth, S., Kriese, J., da Ponte, E., Huth, J., Kuenzer, C., 2022. A First Assessment of Canopy Cover Loss in Germany’s Forests after the 2018-2020 Drought Years. Remote Sensing 14, 562. https://doi.org/10.3390/rs14030562
DLR © 2022
Keywords:
DLR, EOC, Forest, Canopy Cover Loss, Drought, Disturbance Index, Landsat-8, Sentinel-2, time-series, Germany
Map Projection:
Bounds: 4000000, 2650000, 4700000, 3550000