{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T07:59:19Z","timestamp":1773388759187,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:00:00Z","timestamp":1657497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The knowledge of tree species distribution at a national scale provides benefits for forest management practices and decision making for site-adapted tree species selection. An accurate assignment of tree species in relation to their location allows conclusions about potential resilience or vulnerability to biotic and abiotic factors. Identifying areas at risk helps the long-term strategy of forest conversion towards a natural, diverse, and climate-resilient forest. In the framework of the national forest inventory (NFI) in Germany, data on forest tree species are collected in sample plots, but there is a lack of a full coverage map of the tree species distribution. The NFI data were used to train and test a machine-learning approach that classifies a dense Sentinel-2 time series with the result of a dominant tree species map of German forests with seven main tree species classes. The test of the model\u2019s accuracy for the forest type classification showed a weighted average F1-score for deciduous tree species (Beech, Oak, Larch, and Other Broadleaf) between 0.77 and 0.91 and for non-deciduous tree species (Spruce, Pine, and Douglas fir) between 0.85 and 0.94. Two additional plausibility checks with independent forest stand inventories and statistics from the NFI show conclusive agreement. The results are provided to the public via a web-based interactive map, in order to initiate a broad discussion about the potential and limitations of satellite-supported forest management.<\/jats:p>","DOI":"10.3390\/rs14143330","type":"journal-article","created":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T03:50:36Z","timestamp":1657597836000},"page":"3330","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Mapping Dominant Tree Species of German Forests"],"prefix":"10.3390","volume":"14","author":[{"given":"Torsten","family":"Welle","sequence":"first","affiliation":[{"name":"Naturwald Akademie gGmbH, Roeckstr. 40, 23568 L\u00fcbeck, Germany"}]},{"given":"Lukas","family":"Aschenbrenner","sequence":"additional","affiliation":[{"name":"Remote Sensing Solutions GmbH, Dingolfinger Str. 9, 81673 M\u00fcnchen, Germany"}]},{"given":"Kevin","family":"Kuonath","sequence":"additional","affiliation":[{"name":"Remote Sensing Solutions GmbH, Dingolfinger Str. 9, 81673 M\u00fcnchen, Germany"}]},{"given":"Stefan","family":"Kirmaier","sequence":"additional","affiliation":[{"name":"Remote Sensing Solutions GmbH, Dingolfinger Str. 9, 81673 M\u00fcnchen, Germany"}]},{"given":"Jonas","family":"Franke","sequence":"additional","affiliation":[{"name":"Remote Sensing Solutions GmbH, Dingolfinger Str. 9, 81673 M\u00fcnchen, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,11]]},"reference":[{"key":"ref_1","unstructured":"EEA (2016). European Forest Ecosystems: State and Trends, European Environment Agency Publications Office. EEA Report No 5\/2016."},{"key":"ref_2","unstructured":"(2022, April 28). Ergebnisse der Waldzustandserhebung 2020, Available online: https:\/\/www.bmel.de\/SharedDocs\/Downloads\/DE\/Broschueren\/ergebnisse-waldzustandserhebung-2020.pdf?__blob=publicationFile&v=7."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.baae.2020.04.003","article-title":"A First Assessment of the Impact of the Extreme 2018 Summer Drought on Central European Forests","volume":"45","author":"Schuldt","year":"2020","journal-title":"Basic Appl. Ecol."},{"key":"ref_4","unstructured":"BMEL (2022, April 28). The Forests in Germany. Selected Results of the Third National Inventory, Available online: https:\/\/www.bmel.de\/SharedDocs\/Downloads\/EN\/Publications\/ForestsInGermany-BWI.pdf?__blob=publicationFile&v=4."},{"key":"ref_5","unstructured":"European Commission (2022, April 28). Proposal for a Forest Observation, Reporting and Data Collection Framework. Available online: https:\/\/ec.europa.eu\/info\/law\/better-regulation\/have-your-say\/initiatives\/13396-EU-forests-new-EU-Framework-for-Forest-Monitoring-and-Strategic-Plans_en."},{"key":"ref_6","unstructured":"Zeug, G., Geltendorf, T., Immitzer, M., and Atzberger, C. (2018). Machbarkeitsstudie zur Nutzung von Satellitenfernerkundung (Copernicus) f\u00fcr Zwecke der Ableitung \u00f6kologischer Belastungsgrenzen und der Verifzierung von Indikatoren der Deutschen Anpassungsstrategie an den Klimawandel."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1641\/0006-3568(2004)054[0511:HSRRSD]2.0.CO;2","article-title":"High spatial resolution remotely sensed data for ecosystem characterization","volume":"54","author":"Wulder","year":"2004","journal-title":"BioScience"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-resolution global maps of 21st-century forest cover change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.isprsjprs.2008.09.004","article-title":"Pan-European forest\/non-forest mapping with Landsat ETM+ and Corine land cover 2000 data","volume":"64","author":"Pekkarinen","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","unstructured":"Copernicus Land Monitoring Service (CLMS) (2022, May 16). High Resolution Land Cover Characteristics. Tree-Cover\/Forest and Change 2015\u20132018. User Manual. Available online: https:\/\/land.copernicus.eu\/user-corner\/technical-library\/forest-2018-user-manual.pdf."},{"key":"ref_11","unstructured":"Houston Durrant, T., De Rigo, D., Mauri, A., Caudullo, G., and San-Miguel-Ayanz, J. (2016). European Atlas of Forest Tree Species, Publications Office."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nink, S., Hill, J., Stoffels, J., Buddenbaum, H., Frantz, D., and Langshausen, J. (2019). Using Landsat and Sentinel-2 Data for the Generation of Continuously Updated Forest Type Information Layers in a Cross-Border Region. Remote Sens., 11.","DOI":"10.3390\/rs11202337"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1982","DOI":"10.3390\/f6061982","article-title":"Satellite-Based Derivation of High-Resolution Forest Information Layers for Operational Forest Management","volume":"6","author":"Stoffels","year":"2015","journal-title":"Forests"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Vuolo, F., and Atzberger, C. (2016). First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe. Remote Sens., 8.","DOI":"10.3390\/rs8030166"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, Y., Gong, W., Hu, X., and Gong, J. (2018). Forest Type Identification with Random Forest Using Sentinel-1A, Sentinel-2A, Multi-Temporal Landsat-8 and DEM Data. Remote Sens., 10.","DOI":"10.3390\/rs10060946"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Persson, M., Lindberg, E., and Reese, H. (2018). Tree Species Classification with Multi-Temporal Sentinel-2 Data. Remote Sens., 10.","DOI":"10.3390\/rs10111794"},{"key":"ref_17","first-page":"102318","article-title":"Tree species classification using Sentinel-2 imagery and Bayesian inference","volume":"100","author":"Axelsson","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bjerreskov, K.S., Nord-Larsen, T., and Fensholt, R. (2021). Classification of Nemoral Forests with Fusion of Multi-Temporal Sentinel-1 and 2 Data. Remote Sens., 13.","DOI":"10.3390\/rs13050950"},{"key":"ref_19","first-page":"102208","article-title":"Exploring the potential of land surface phenology and seasonal cloud free composites of one year of Sentinel-2 imagery for tree species mapping in a mountainous region","volume":"94","author":"Kollert","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lim, J., Kim, K.-M., Kim, E.-H., and Jin, R. (2020). Machine Learning for Tree Species Classification Using Sentinel-2 Spectral Information, Crown Texture, and Environmental Variables. Remote Sens., 12.","DOI":"10.3390\/rs12122049"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Neuwirth, M., B\u00f6ck, S., Brenner, H., Vuolo, F., and Atzberger, C. (2019). Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data. Remote Sens., 11.","DOI":"10.3390\/rs11222599"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Grabska, E., Hostert, P., Pflugmacher, D., and Ostapowicz, K. (2019). Forest Stand Species Mapping Using the Sentinel-2 Time Series. Remote Sens., 11.","DOI":"10.3390\/rs11101197"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"112743","DOI":"10.1016\/j.rse.2021.112743","article-title":"Mapping temperate forest tree species using dense Sentinel-2 time series","volume":"276","author":"Hemmerling","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1139\/cjfr-2020-0170","article-title":"National mapping and estimation of forest area by dominant tree species using Sentinel-2 data","volume":"51","author":"Breidenbach","year":"2021","journal-title":"Can. J. For. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.isprsjprs.2021.08.017","article-title":"Mapping dominant leaf type based on combined Sentinel-1\/-2 data\u2014Challenges for mountainous countries","volume":"180","author":"Waser","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Lechner, M., Dost\u00e1lov\u00e1, A., Hollaus, M., Atzberger, C., and Immitzer, M. (2022). Combination of Sentinel-1 and Sentinel-2 Data for Tree Species Classification in a Central European Biosphere Reserve. Remote Sens., 14.","DOI":"10.3390\/rs14112687"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ma, M., Liu, J., Liu, M., Zeng, J., and Li, Y. (2021). Tree Species Classification Based on Sentinel-2 Imagery and Random Forest Classifier in the Eastern Regions of the Qilian Mountains. Forests, 12.","DOI":"10.3390\/f12121736"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wan, H., Tang, Y., Jing, L., Li, H., Qiu, F., and Wu, W. (2021). Tree Species Classification of Forest Stands Using Multisource Remote Sensing Data. Remote Sens., 13.","DOI":"10.3390\/rs13010144"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Holzwarth, S., Thonfeld, F., Abdullahi, S., Asam, S., Da Ponte Canova, E., Gessner, U., Huth, J., Kraus, T., Leutner, B., and Kuenzer, C. (2020). Earth Observation Based Monitoring of Forests in Germany: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12213570"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Thonfeld, F., Gessner, U., Holzwarth, S., Kriese, J., da Ponte, E., Huth, J., and Kuenzer, C. (2022). A First Assessment of Canopy Cover Loss in Germany\u2019s Forests after the 2018\u20132020 Drought Years. Remote Sens., 14.","DOI":"10.3390\/rs14030562"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1655","DOI":"10.5194\/bg-17-1655-2020","article-title":"Quantifying Impacts of the 2018 Drought on European Ecosystems in Comparison to 2003","volume":"17","author":"Buras","year":"2020","journal-title":"Biogeosciences"},{"key":"ref_32","unstructured":"European Space Agency (ESA) (2015). Sentinel-2 User Handbook, ESA. ESA Standard Document."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Main-Knorn, M., Pflug, B., Louis, J., Debaecker, V., M\u00fcller-Wilm, U., and Gascon, F. (2017, January 11\u201313). Sen2Cor for Sentinel-2. Proceedings of the Image and Signal Processing for Remote Sensing XXIII, Warsaw, Poland.","DOI":"10.1117\/12.2278218"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.rse.2016.08.013","article-title":"Review of studies on tree species classification from remotely sensed data","volume":"186","author":"Fassnacht","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"112103","DOI":"10.1016\/j.rse.2020.112103","article-title":"Evaluation of machine learning algorithms for forest stand species mapping using Sentinel-2 imagery and environmental data in the Polish Carpathians","volume":"251","author":"Grabska","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"9812624","DOI":"10.34133\/2021\/9812624","article-title":"Mapping Tree Species Using Advanced Remote Sensing Technologies: A State-of-the-Art Review and Perspective","volume":"2021","author":"Pu","year":"2021","journal-title":"J. Remote Sens."},{"key":"ref_37","unstructured":"Riedel, T., Hennig, P., Kroiher, F., Polley, H., Schmitz, F., and Schwitzgebel, F. (2017). Die dritte Bundesaldinventur (BWI 2012). Inventur- und Auswertemethoden; Eberswalde."},{"key":"ref_38","unstructured":"Welle, T., Sturm, K., and Bohr, Y. (2018). Der Alternative Waldzustandsbericht 2018, Naturwald Akademie. Available online: https:\/\/naturwald-akademie.org\/wp-content\/uploads\/2020\/06\/Alternativer-Waldzustandsbericht_Stand_24_04_2018_1.pdf."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Zamani Joharestani, M., Cao, C., Ni, X., Bashir, B., and Talebiesfandarani, S. (2019). PM2.5 Prediction Based on Random Forest, XGBoost, and Deep Learning Using Multisource Remote Sensing Data. Atmosphere, 10.","DOI":"10.3390\/atmos10070373"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Ma, Y., Quackenbush, L.J., and Zhen, Z. (2022). Estimation of Individual Tree Biomass in Natural Secondary Forests Based on ALS Data and WorldView-3 Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14020271"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, C., Li, M., and Liu, Z. (2019). Influence of Variable Selection and Forest Type on Forest Aboveground Biomass Estimation Using Machine Learning Algorithms. Forests, 10.","DOI":"10.3390\/f10121073"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"9952","DOI":"10.1038\/s41598-020-67024-3","article-title":"Forest aboveground biomass estimation using Landsat 8 and Sentinel-1A data with machine learning algorithms","volume":"10","author":"Li","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Samat, A., Li, E., Wang, W., Liu, S., Lin, C., and Abuduwaili, J. (2020). Meta-XGBoost for Hyperspectral Image Classification Using Extended MSER-Guided Morphological Profiles. Remote Sens., 12.","DOI":"10.3390\/rs12121973"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Rousset, G., Despinoy, M., Schindler, K., and Mangeas, M. (2021). Assessment of Deep Learning Techniques for Land Use Land Cover Classification in Southern New Caledonia. Remote Sens., 13.","DOI":"10.3390\/rs13122257"},{"key":"ref_46","first-page":"3885","article-title":"Assessment of Machine Learning Algorithms for Land Cover Classification Using Remotely Sensed Data","volume":"33","author":"Park","year":"2021","journal-title":"Sens. Mater."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"\u0141o\u015b, H., Sousa Mendes, G., Cordeiro, D., Grosso, N., Costa, H., Benevides, P., and Caetano, M. (2021, January 11\u201316). Evaluation of Xgboost and Lgbm Performance in Tree Species Classification with Sentinel-2 Data. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553031"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s13040-017-0155-3","article-title":"Ten quick tips for machine learning in computational biology","volume":"10","author":"Chicco","year":"2017","journal-title":"BioData Min."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Boughorbel, S., Jarray, F., and El-Anbari, M. (2017). Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0177678"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.patrec.2020.03.030","article-title":"On the performance of Matthews correlation coefficient (MCC) for imbalanced dataset","volume":"136","author":"Qiuming","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_51","unstructured":"Kurth, H. (1994). Forsteinrichtung. Nachhaltige Regelung des Waldes, Deutscher Landwirtschaftsverlag."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"7589","DOI":"10.1109\/JSTARS.2021.3098817","article-title":"Exploitation of Time Series Sentinel-2 Data and Different Machine Learning Algorithms for Detailed Tree Species Classification","volume":"14","author":"Xi","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ho\u015bci\u0142o, A., and Lewandowska, A. (2019). Mapping Forest Type and Tree Species on a Regional Scale Using Multi-Temporal Sentinel-2 Data. Remote Sens., 11.","DOI":"10.3390\/rs11080929"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"112165","DOI":"10.1016\/j.rse.2020.112165","article-title":"Mapping and monitoring global forest canopy height through integration of GEDI and Landsat data","volume":"253","author":"Potapov","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_55","unstructured":"SaarForst Landesbetrieb (2022, May 16). Staatswaldinventur des Saarlandes 2018. Ein Faktencheck. Available online: https:\/\/www.saarland.de\/saarforst\/DE\/service\/publikationen\/publikationen\/publ_staatswaldinventur.pdf?__blob=publicationFile&v=1."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1038\/s41558-022-01343-3","article-title":"Glasgow forest declaration needs new modes of data ownership","volume":"12","author":"Nabuurs","year":"2022","journal-title":"Nat. Clim. Chang."},{"key":"ref_57","unstructured":"Jucker, T., Fischer, F., Chave, J., Coomes, D., Caspersen, J., Ali, A., Loubota Panzou, G.J., Feldpausch, T., Falster, D., and Usoltsev, V. (2022). Tallo Database (1.0.0). Zenodo, Available online: https:\/\/zenodo.org\/record\/6637599."},{"key":"ref_58","unstructured":"(2022, April 28). Mehr Fortschritt wagen. B\u00fcndnis f\u00fcr Freiheit, Gerechtigkeit und Nachhaltigkeit. Koalitionsvertrag zwischen SPD, B\u00fcndnis 90\/Die Gr\u00fcnen und FDP. Berlin, SPD, Die Gr\u00fcnen, FDP. 2022. p. 144. Available online: https:\/\/www.spd.de\/fileadmin\/Dokumente\/Koalitionsvertrag\/Koalitionsvertrag_2021-2025.pdf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3330\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:47:53Z","timestamp":1760140073000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/14\/3330"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,11]]},"references-count":58,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14143330"],"URL":"https:\/\/doi.org\/10.3390\/rs14143330","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,11]]}}}