Streitmatter, Denis; Lange, Maximilian; Doktor, Daniel: Long Term High Resolution Forest Condition Anomalies of Germany (2000-2022) [dataset]. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.996294 (dataset in review)
Abstract:
This dataset provides estimates of forest condition anomalies across Germany at 30m spatial resolution and 4-day temporal resolution, spanning the period from 2000 to 2022. Forest condition is quantified as Forest Condition Anomaly index (FCA), a continuous index ranging from -1 (strongly negative anomaly) to +1 (strongly positive anomaly), based on the methods proposed by Lange et al. (2024). The dataset is published as NetCDF files containing yearly and seasonal means for spring (March-May), summer (June-August), and fall (September-November); winter months were excluded due to snow contamination.
The dataset was generated to extend the temporal coverage of existing high-resolution forest condition products, which are limited to the Sentinel-2 era (from 2017 onwards), in order to enable the study of forest condition across multiple disturbance events over a longer time horizon. Due to that, the datasets are chunked spatially (250 x 250 pixels), with one chunk containing the whole time line. Forest condition was estimated from the Seamless Data Cube (SDC) by Chen et al. (2024), a global daily 30m surface reflectance product derived from the fusion of Landsat and MODIS satellite observations. For each observation, a per-pixel anomaly was computed relative to species- and region-specific reflectance reference statistics. Reference statistics (for deciduous species) were further adjusted for annual phenological variation using the Near-Infrared Vegetation Index (NIRv) to extract yearly species- and region-specific phenological events. Tree species information was obtained from the national tree species map of Germany by Blickensdörfer et al. (2024), and Germany was divided into seven climatic landscape regions. The anomaly index was computed as a weighted combination of per-band deviations in the red, near-infrared, and shortwave infrared bands.
References:
Blickensdörfer, Lukas; Oehmichen, Katja; Pflugmacher, Dirk; Kleinschmit, Birgit; Hostert, Patrick (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
Chen, Shuang; Wang, Jie; Liu, Qiang; Liang, Xiangan; Liu, Ruiying; Qin, Peng; Yuan, Jincheng; Wei, Junbo; Yuan, Shuai; Huang, Huabing; Gong, Peng (2024): Global 30 m seamless data cube (2000–2022) of land surface reflectance generated from Landsat 5, 7, 8, and 9 and MODIS Terra constellations. Earth System Science Data, 16(11), 5449-5475, https://doi.org/10.5194/essd-16-5449-2024
Lange, Maximilian; Preidl, Sebastian; Reichmuth, Anne; Heurich, Marco; Doktor, Daniel (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
Project(s):
Coverage:
Median Latitude: 51.061000 * Median Longitude: 10.836020 * South-bound Latitude: 47.240100 * West-bound Longitude: 6.098500 * North-bound Latitude: 54.881900 * East-bound Longitude: 15.573540
Event(s):
Parameter(s):
| # | Name | Short Name | Unit | Principal Investigator | Method/Device | Comment |
|---|---|---|---|---|---|---|
| 1 | netCDF file | netCDF | Streitmatter, Denis | Satellite imagery (SATI) | ||
| 2 | netCDF file (File Size) | netCDF (Size) | Bytes | Streitmatter, Denis | Satellite imagery (SATI) | |
| 3 | File content | Content | Streitmatter, Denis |
License:
Creative Commons Attribution 4.0 International (CC-BY-4.0) (License comes into effect after moratorium ends)
Status:
Curation Level: Enhanced curation (CurationLevelC)
Size:
4 data points