Published July 29, 2024 | Version 7.5.0
Dataset Open

Forest condition anomaly index values covering Germany for 2016-2023

  • 1. Helmholtz-Centre for Environmental Research - UFZ
  • 2. Remote Sensing Centre for Earth System Research - RSC4Earth
  • 3. ROR icon Julius Kühn-Institut
  • 4. ROR icon Bavarian Forest National Park
  • 5. ROR icon University of Freiburg
  • 6. ROR icon Inland Norway University of Applied Sciences
  • 7. ROR icon German Center for Integrative Biodiversity Research

Description

General description:
In Lange et. al (2024) we utilised Sentinel-2 tree species-specific reflectance time series for extracting forest condition across Germany from 2016 to 2022. These time series' seasonal evolution - computed separately for seven natural regions - serves as reference when calculating a similarity metric – further called forest condition anomaly index (FCA). The FCA is computed between each single reflectance observation and the respective date within the reference time series, also considering the natural temporal deviations caused by phenology. FCA temporal aggregation allowed generating spatially comprehensive forest condition anomaly maps. FCA patterns in space and time are in line with dominant drivers like fires, storms and insect infestations and in agreement with state-of-the-art forest disturbance products using a threshold of FCA = −0.15 for forest loss. More information can be found in the related publication and in the UFZ Forest condition monitor web-application.


Data description:
Data is provided in GeoTiff format (projection EPSG:32632). Forest condition anomaly maps are available in a spatial resolution of 20 m for the years 2016 to 2023 as monthly (May to October), seasonal (spring, summer and fall) and yearly maps. Values are scaled by 10 000 to reduce the file size. Final FCA values are obtained by dividing the raw values by 10 000 and range from -1 to 1. A negative value generally indicates a poorer forest condition, for example, due to negative changes in chlorophyll or water content or due to crown defoliation. Through validation using forest surveys, data from the Copernicus Emergency Management System and other current maps of forest cover loss, it can be relatively accurate determined that a value below -0.15 indicates a heavily damaged or dead forest stand. Stronger damage (such as significant needle/leaf loss or tree mortality) is generally captured more precise than light damage (such as slight needle/leaf loss). Moderate forest condition values correspondingly show no anomaly and represent the expected normal condition for the respective tree species at the given time within the year. Positive forest condition values indicate a positive deviation from the expected state, which might stem from from positive chlorophyll or water content changes or from denser foliage or needle cover.

 

File descriptions
Data is provided in zip archives containing maps in GeoTiff format (projection EPSG:32632). 4 zip files are provided:

  • FCA_v0007-0005_Germany_2016-2023_yearly_R20m.zip contains 8 yearly FCA maps 
  • FCA_v0007-0005_Germany_2016-2023_seasonal_R20m.zip contains 24 seasonal FCA maps (spring, summer and fall for 2016 to 2023)
  • FCA_v0007-0005_Germany_2016-2019_monthly_R20m.zip  contains 24 monthly maps (May to October for 2016 to 2019)
  • FCA_v0007-0005_Germany_2020-2023_monthly_R20m.zip contains 24 monthly maps (May to October for 2020 to 2023)

 

Please note:
Forest pixels were selected according to the tree species map from Blickensdörfer et al. (2024)

Files

FCA_v0007-0005_Germany_2016-2023_yearly_R20m.zip

Files (44.0 GB)

Additional details

Related works

Is described by
Publication: 10.1016/j.rse.2024.114323 (DOI)

Funding

Initiative and Networking Fund WT-0207
Helmholtz Association of German Research Centres

References

  • Maximilian Lange, Sebastian Preidl, Anne Reichmuth, Marco Heurich, Daniel Doktor (2024). A continuous tree species-specific reflectance anomaly index reveals declining forest condition between 2016 and 2022 in Germany. Remote Sensing of Environment 312, 114323. https://doi.org/10.1016/j.rse.2024.114323
  • Lukas Blickensdörfer, Katja Oehmichen, Dirk Pflugmacher, Birgit Kleinschmit, Patrick Hostert (2024). National tree species mapping using Sentinel-1/2 time series and German National Forest Inventory data. Remote Sensing of Environment 304, 114069. https://doi.org/10.1016/j.rse.2024.114069