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biota

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  • This product is a vector file of the districts of the Paraguayan Chaco. It contains information on the forest cover within each district for the years 1986 until 2020. Hence, this product aggregates the information of 34 annual forest maps of the Paraguayan Chaco to a district level and provides the basis for further analysis as conducted in the following publication: https://doi.org/10.3390/f13010025

  • This raster dataset shows forest canopy cover loss (FCCL) in Germany at a monthly resolution from September 2017 to September 2024. It is similar to the product developed by Thonfeld et l. (2022) but was fully reprocessed and updated to reveal the most recent forest disturbance dynamics. The combination of Sentinel-2A/B and Landsat-8/9 data allows for a high temporal resolution while the pixel size of the product is 10 m. The results are clipped to the stocked area 2018 mapped by the Johann-Heinrich-von-Thünen Institute (Langner et al. 2022, https://doi.org/10.3220/DATA20221205151218). The dataset contains predominantly larger canopy openings resulting from different drivers but also larger clusters of standing deadwood. FCCL can result from abiotic (e.g. wind, fire, drought, hail) drivers, biotic (e.g. insects, funghi) drivers or a combination of both as well as from sanitary and salvage logging and planned harvest. The first version with canopy cover losses from January 2018 - April 2021 (Thonfeld et al. 2022) can be accessed here: https://geoservice.dlr.de/web/datasets/tccl.

  • The dataset is based on the analysis of forest cover dynamics in the Paraguayan Chaco (northeastern part of Paraguay) between 1987 and 2020. The underlying forest masks were derived through annual forst classifications with a Random-Forest-Classifier trained on Landsat data from 1987 until 2020. The map shows the year in which the forest area was lost.

  • 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).

  • This product is a vector file of the protected areas of the Paraguayan Chaco. It contains information on the forest cover within each protected area and a 5, 10, and 15 km buffer zone around these areas, for the years 2000 until 2020. Hence, this product aggregates the information of 21 annual forest maps of the Paraguayan Chaco to the level of protected areas and provides the basis for further analysis as conducted in the following publication: https://doi.org/10.3390/f13010025

  • This product consists of global gap free Leaf area index (LAI) time series, based on MERIS full resolution Level 1B data. It is produced as a series of 10-day composites in geographic projection at 300m spatial resolution. The processing chain comprises geometric correction, radiometric correction and pixel identification, LAI calculation with the BEAM MERIS vegetation processor, re-projection to a global grid, and temporal aggregation selecting the measurement closest to the mean value. After the LAI pre-processing we applied time series analysis to fill data gaps and filter outliers using the technique of harmonic analysis in combination with mean annual and multiannual phenological data. Data gaps are caused by clouds, sensor limitations due to the solar zenith angle (less than 10 degrees), topography and intermittent data reception. We applied our technique for the whole period of observation (Jul 2002 - Mar 2012). Validation, was performed using VALERI and BigFoot data.

  • Standorte der Gehegewildhalter im Landkreis Diepholz