/* DATA DESCRIPTION:
Citation:	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.
Keyword(s):	Forest; forest condition; Germany; remote sensing
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):	Societal and Environmental impacts of complex ExtremeS in a chAnging World (SEESAW) (URI: https://www.ufz.de/index.php?en=51856)
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):	Germany_FCA * LATITUDE START: 47.240100 * LONGITUDE START: 6.098500 * LATITUDE END: 54.881900 * LONGITUDE END: 15.573540 * METHOD/DEVICE: Satellite remote sensing (SAT)
Parameter(s):	netCDF file (netCDF) * PI: Streitmatter, Denis (https://orcid.org/0000-0002-2204-945X, denis.streitmatter@ufz.de) * METHOD/DEVICE: Satellite imagery (SATI)
	netCDF file (File Size) [Bytes] (netCDF (Size)) * PI: Streitmatter, Denis (https://orcid.org/0000-0002-2204-945X, denis.streitmatter@ufz.de) * METHOD/DEVICE: Satellite imagery (SATI)
	File content (Content) * PI: Streitmatter, Denis (https://orcid.org/0000-0002-2204-945X, denis.streitmatter@ufz.de)
License:	Creative Commons Attribution 4.0 International (CC-BY-4.0) (URI: https://creativecommons.org/licenses/by/4.0/) (License comes into effect after moratorium ends)
Status:	Curation Level: Enhanced curation (URI: https://wiki.pangaea.de/wiki/Curation_levels)
Size:	4 data points
Files:	All files referred to in data matrix can be downloaded in one go as ZIP <https://download.pangaea.de/dataset/996294/allfiles.zip> or TAR <https://download.pangaea.de/dataset/996294/allfiles.tar> (PANGAEA user account required). To download single files, use the filename from data matrix appended to "https://download.pangaea.de/dataset/996294/files/".
*/
netCDF	netCDF (Size) [Bytes]	Content
fca_seasonal_2000-2022.nc	13.7 GBytes	This file contains the seasonal (spring, summer, fall) means of FCA across Germany. Notice that the file is spatially chunked (69 x 250 x 250, time, y, x), meaning one chunk contains the whole time series.
fca_yearly_2000-2022.nc	4.6 GBytes	This file contains the yearly means of FCA across Germany. Notice that the file is spatially chunked (23 x 250 x 250, time, y, x), meaning one chunk contains the whole time series.