UDTR — Radar mondial des droits de la Nature

Dominant Tree Species for Germany (2022/2023)

Identification

Clé publique

radar:r-ce26ba7369b17110aca5ea80ba1f7b44

Auteurs

Blickensdörfer, Lukas · Oehmichen, Katja · Bender, Susann · Erasmi, Stefan

Année déclarée

2026

Langue déclarée

en

Type source

Dataset

Première détection

2026-11-13T15:28:25.241393+00:00

Dernière observation des métadonnées

2026-11-13T15:28:25.241393+00:00

Dernière vérification HTTP

Non effectuée

Institutions déclarées par la source

Johann Heinrich von Thünen-Institut · Thünen-Institute of Forest Ecosystems

Résumé documentaire — langue originale

Detailed and spatially explicit information on the condition and development of forests forms the basis for climate-resilient, forward-looking forest management and forest ecosystem research. Tree species are a fundamental silvicultural parameter in the analysis and assessment of forests. Consequnetly, tree species composition serves as the basis information for all following calculations regarding the condition of forests and changes within them, such as shifts in tree species composition, timber utilisation and timber stocks, as well as carbon sequestration in forests. Maps showing the current tree species composition and its spatial distribution are used, for example, in forest damage analyses and form part of resilience assessments and forest restructuring measures. This product presents eleven dominant tree species groups in the German forest as at 2022/23 at a spatial resolution of 10x10 meters. The tree species map was produced with a random‑forest classifier trained on ground truth from the German National Forest Inventory (NFI) 2022 in accordance with the methodology described in Blickensdörfer et al. (2024). It is a temporal update of the data set Dominant Tree Species for Germany (2017/2018) (Blickensdörfer et al., 2026b).  Sentinel-2, Sentinel-1 and environmental data 2022/23 were used to map 11 tree species groups, including two broader deciduous tree classes (ODH: other deciduous trees with high life expectancy, ODL: other deciduous trees with low life expectancy). This tree species mapping was applied within the stocked forest area (Blickensdörfer et al., 2026a). To reduce input data, the Sentinel-2 time series interpolation interval was extended from 5 to 10 days, and the RADOLAN precipitation data was excluded. The reference data for model training and map validation for 2022/2023 is based on the German National Forest Inventory (NFI) 2022. A 10-fold cross-validation replaced the former 70/30 split to maximise the use of NFI 2022 data more efficiently. To exclude inventory plots where tree species composition might have changed after the inventory field visit, plots disturbed in 2022 and 2023 were excluded by using maps by Viana-Soto et al. (2025).  To enforce temporal consistency between the 2017/18 (Blickensdörfer et al., 2022) and to the 2022/23 classification, a time series post-classification was applied using the random forest outputs together with a Hidden Markov Model (HMM) solved via the Forward-Backward-Algorithm (Abercrombie et al., 2016). The HMM improves temporal consistency between the maps and reduces spurious tree species changes due to classification errors within each random forest classification. The random forest classifier votes are used as initial probabilities of class membership for each pixel and time step. A transition probability matrix defined the likelihood of pixels changing between different tree species classes from 2017/18 to 2022/23. The transition matrix was derived from tree species changes at plot level between the Carbon Inventory 2017 and NFI 2022 observations. All trees with a diameter at breast height greater 7 cm and that fall into the angle count sample with basal area factor of 4 (Riedel et al., 2021) were recorded by species. The dominant tree species per plot and year was derived, and the transition probability was estimated by comparing the plots dominant species between inventory intervals. The species maps for 2017/18 and 2022/23 was generated by assigning, for each pixel, the class with the maximum posterior probability after the HMM post-processing. This post-processing step does also modify the initially published 2017/18 tree species map. The new version is available under Blickensdörfer et al. (2026b). Please note that the map errors within the two processed maps (2017/18 and 2022/23) remain in the same magnitude as expected tree species changes over the 5-year time span. While the HMM improved consistency between the two maps, they remain a best estimate of the tree species composition within each year. The data was not designed nor validated for the purpose of performing analysis of tree species change between the 2017/18 and 2022/23 maps. Map accuracies were estimated for pure species stands based on a stratified estimator according to Stehman et al. (2014). Further, accuracies were calculated for the entire map, including mixed species stands. For the dominant tree species map of 2022/23, the overall accuracy of pure-species stands is 92.89 ± 0.2 % and 74.57 ± 0.2 % for the entire map, including mixed-species stands. The map is provided as cloud optimized GeoTIFF. Additionally, the tree species code and the accuracies can be accessed. ----------------------------------------------------------------------------------------------------------------------------------------- References: Abercrombie, S.P., Friedl, M.A., 2016. Improving the Consistency of Multitemporal Land Cover Maps Using a Hidden Markov Model. IEEE Trans. Geosci. Remote Sensing 54 (2), 703–713. https://doi.org/10.1109/TGRS.2015.2463689 Blickensdörfer, L., Oehmichen, K., 2026a. Stocked forest area map 2022 [dataset]. Zenodo, v2.0. https://doi.org/10.5281/zenodo.22688926  Blickensdörfer, L., Oehmichen, K., Pflugmacher, D., Kleinschmit, B., Hostert, P., 2026b. Dominant Tree Species for Germany (2017/2018) [dataset]. Zenodo, v2.0. https://doi.org/10.5281/zenodo.22688654 Blickensdörfer, L., Oehmichen, K., Pflugmacher, D., Kleinschmit, B., Hostert, P., 2024. National tree species mapping using Sentinel-1/2 time series and German National Forest Inventory data. Remote Sens. Environ. 304, 11406. https://doi.org/10.1016/j.rse.2024.114069 Blickensdörfer, L., Oehmichen, K., Pflugmacher, D., Kleinschmit, B., Hostert, P., 2022. Dominant Tree Species for Germany (2017/2018) [dataset]. OpenAgrar, Version 1. https://doi.org/10.3220/DATA20221214084846  Riedel, T., Hennig, H., Polley, H., Schwitzgebel, F., 2021. Aufnahmeanweisung für die vierte Bundeswaldinventur (BWI 2022) (2021 - 2022), fourth ed. Bundesministerium für Ernährung und Landwirtschaft (BMEL), Bonn. Stehman, S.V., 2014. Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes. Int. J. Remote Sens. 35 (13), 4923–4939. https://doi.org/10.1080/01431161.2014.930207 Viana-Soto, A., Senf, C., 2025. The European Forest Disturbance Atlas: a forest disturbance monitoring system using the Landsat archive. Earth Syst. Sci. Data 17 (6), 2373–2404. https://doi.org/10.5194/essd-17-2373-2025 ----------------------------------------------------------------------------------------------------------------------------------------- Funding was provided by the German Federal Ministry for the Environment, Climate Action, Nature Conservation and Nuclear Safety as part of the joint project “EO4Nature – Tree species” (The national portal for monitoring and applications in nature conservation and natural climate protection.)

Identifiants

DOI

10.5281/zenodo.22689195

Repères documentaires

Famille documentaire

dataset

Types déclarés par les sources

Dataset

Éditeurs

Zenodo

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Sources

DataCite · Document 1

• Document · https://zenodo.org/doi/10.5281/zenodo.22689195

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Bibliothèque

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