{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T06:49:49Z","timestamp":1781074189652,"version":"3.54.1"},"reference-count":93,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,7]],"date-time":"2023-04-07T00:00:00Z","timestamp":1680825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["FOR 5375\/1"],"award-info":[{"award-number":["FOR 5375\/1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["459717468"],"award-info":[{"award-number":["459717468"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring forest conditions is an essential task in the context of global climate change to preserve biodiversity, protect carbon sinks and foster future forest resilience. Severe impacts of heatwaves and droughts triggering cascading effects such as insect infestation are challenging the semi-natural forests in Germany. As a consequence of repeated drought years since 2018, large-scale canopy cover loss has occurred calling for an improved disturbance monitoring and assessment of forest structure conditions. The present study demonstrates the potential of complementary remote sensing sensors to generate wall-to-wall products of forest structure for Germany. The combination of high spatial and temporal resolution imagery from Sentinel-1 (Synthetic Aperture Radar, SAR) and Sentinel-2 (multispectral) with novel samples on forest structure from the Global Ecosystem Dynamics Investigation (GEDI, LiDAR, Light detection and ranging) enables the analysis of forest structure dynamics. Modeling the three-dimensional structure of forests from GEDI samples in machine learning models reveals the recent changes in German forests due to disturbances (e.g., canopy cover degradation, salvage logging). This first consistent data set on forest structure for Germany from 2017 to 2022 provides information of forest canopy height, forest canopy cover and forest biomass and allows estimating recent forest conditions at 10 m spatial resolution. The wall-to-wall maps of the forest structure support a better understanding of post-disturbance forest structure and forest resilience.<\/jats:p>","DOI":"10.3390\/rs15081969","type":"journal-article","created":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T03:19:54Z","timestamp":1681096794000},"page":"1969","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Forest Structure Characterization in Germany: Novel Products and Analysis Based on GEDI, Sentinel-1 and Sentinel-2 Data"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4538-8286","authenticated-orcid":false,"given":"Patrick","family":"Kacic","sequence":"first","affiliation":[{"name":"Department of Remote Sensing, Institute of Geography and Geology, University of W\u00fcrzburg, 97074 W\u00fcrzburg, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3371-7206","authenticated-orcid":false,"given":"Frank","family":"Thonfeld","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ursula","family":"Gessner","sequence":"additional","affiliation":[{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudia","family":"Kuenzer","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing, Institute of Geography and Geology, University of W\u00fcrzburg, 97074 W\u00fcrzburg, Germany"},{"name":"German Remote Sensing Data Center (DFD), German Aerospace Center (DLR), 82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,7]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"e2021EF002394","DOI":"10.1029\/2021EF002394","article-title":"The 2018\u20132020 Multi-year drought sets a new benchmark in Europe","volume":"10","author":"Rakovec","year":"2022","journal-title":"Earth\u2019s Future"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"6200","DOI":"10.1038\/s41467-020-19924-1","article-title":"Excess forest mortality is consistently linked to drought across Europe","volume":"11","author":"Senf","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_4","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_5","unstructured":"Statistical office of the European Union (Eurostat) (2023, January 26). Share of Timber Removals to Net Increment in EU Forests, Available online: https:\/\/ec.europa.eu\/eurostat\/statistics-explained\/index.php?title=File:Figure_3_Share_of_timber_removals_to_net_increment_in_EU_forests,_2020_(%25).png."},{"key":"ref_6","unstructured":"Federal Ministry of Food and Agriculture (BMEL) (2023, January 25). Waldbericht der Bundesregierung 2021, Available online: https:\/\/www.bmel.de\/SharedDocs\/Downloads\/DE\/Broschueren\/waldbericht2021.pdf?__blob=publicationFile&v=11."},{"key":"ref_7","unstructured":"Statistisches Bundesamt (Destatis) (2023, January 25). Fl\u00e4chengr\u00f6\u00dfe des Waldes nach Bundesl\u00e4ndern, Available online: https:\/\/www.destatis.de\/DE\/Themen\/Branchen-Unternehmen\/Landwirtschaft-Forstwirtschaft-Fischerei\/Wald-Holz\/Tabellen\/waldflaeche-bundeslaender.html."},{"key":"ref_8","unstructured":"Statistisches Bundesamt (Destatis) (2023, January 25). Structural Survey of Forestry Holdings: Forest Area by Types of Forest Ownership, Available online: https:\/\/www.destatis.de\/EN\/Themes\/Economic-Sectors-Enterprises\/Agriculture-Forestry-Fisheries\/Forestry-Wood\/Tables\/structural-survey-of-forestry-holdings-forest-area-by-types-of-forest-ownership.html."},{"key":"ref_9","unstructured":"Statistical office of the European Union (Eurostat) (2023, January 26). Employment in Forestry and Logging, 2000 and 2020, Available online: https:\/\/ec.europa.eu\/eurostat\/statistics-explained\/index.php?title=File:Table_2_Employment_in_forestry_and_logging,_2000_and_2020.png."},{"key":"ref_10","unstructured":"Statistisches Bundesamt (Destatis) (2023, January 25). Exports of Raw Timber up 42.6% in 2020, Available online: https:\/\/www.destatis.de\/EN\/Press\/2021\/05\/PE21_N031_51.html."},{"key":"ref_11","unstructured":"Statistisches Bundesamt (Destatis) (2023, January 25). 2008 to 2018: Sawmills Increase Their Turnover and Now Earn One in Three Euros Abroad, Available online: https:\/\/www.destatis.de\/EN\/Press\/2019\/09\/PE19_377_412.html."},{"key":"ref_12","unstructured":"European Environment Agency (2023, February 03). Dominant Leaf Type 2018, Available online: https:\/\/land.copernicus.eu\/pan-european\/high-resolution-layers\/forests\/dominant-leaf-type\/status-maps\/dominant-leaf-type-2018."},{"key":"ref_13","unstructured":"Johann Heinrich von Th\u00fcnen Institute (Federal Research Institute for Rural Areas, Forestry and Fisheries)\u2014Institute of Forest Ecosystems (2023, January 12). Ergebnisse der Bundesweiten Waldzustandserhebung. Available online: https:\/\/wo-apps.thuenen.de\/apps\/wze\/."},{"key":"ref_14","unstructured":"Statistisches Bundesamt (Destatis) (2023, January 25). Impact of Extreme wind and Weather Conditions on the Forests, Available online: https:\/\/www.destatis.de\/EN\/Press\/2020\/02\/PE20_N006_413.html."},{"key":"ref_15","unstructured":"Statistisches Bundesamt (Destatis) (2023, January 25). Forest Damage: Logging of Timber Damaged by Insect Infestation Grew More than Tenfold within Five Years, Available online: https:\/\/www.destatis.de\/EN\/Press\/2021\/08\/PE21_N050_41.html."},{"key":"ref_16","unstructured":"Statistisches Bundesamt (Destatis) (2023, January 25). Total Timber Cutting by Cutting Cause and Forest Ownership Types, Available online: https:\/\/www.destatis.de\/EN\/Themes\/Economic-Sectors-Enterprises\/Agriculture-Forestry-Fisheries\/Forestry-Wood\/Tables\/timber-cutting-causes.html."},{"key":"ref_17","unstructured":"Federal Ministry of Food and Agriculture (BMEL) (2023, January 25). Ergebnisse der Waldzustandserhebung 2021, Available online: https:\/\/www.bmel.de\/SharedDocs\/Downloads\/DE\/Broschueren\/ergebnisse-waldzustandserhebung-2021.pdf?__blob=publicationFile&v=10."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1111\/1365-2664.12945","article-title":"Impacts of salvage logging on biodiversity: A meta-analysis","volume":"55","author":"Thorn","year":"2018","journal-title":"J. Appl. Ecol."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","unstructured":"Wellbrock, N., and Bolte, A. (2019). Status and Dynamics of Forests in Germany: Results of the National Forest Monitoring, Springer.","DOI":"10.1007\/978-3-030-15734-0"},{"key":"ref_21","first-page":"1","article-title":"Monitoring plant functional diversity from space","volume":"2","author":"Jetz","year":"2016","journal-title":"Nat. Plants"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"111626","DOI":"10.1016\/j.rse.2019.111626","article-title":"Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models","volume":"239","author":"Randin","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.rse.2013.08.014","article-title":"Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)","volume":"140","author":"Hansen","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"667151","DOI":"10.3389\/ffgc.2021.667151","article-title":"Sentinel-2 analysis of spruce crown transparency levels and their environmental drivers after summer drought in the Northern Eifel (Germany)","volume":"4","author":"Montzka","year":"2021","journal-title":"Front. For. Glob. Chang."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Philipp, M., Wegmann, M., and K\u00fcbert-Flock, C. (2021). Quantifying the response of German forests to drought events via Satellite imagery. Remote Sens., 13.","DOI":"10.3390\/rs13091845"},{"key":"ref_27","unstructured":"European Environment Agency (2017). Forest Type 2015."},{"key":"ref_28","unstructured":"European Environment Agency (2020). Tree Cover Density 2018."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Welle, T., Aschenbrenner, L., Kuonath, K., Kirmaier, S., and Franke, J. (2022). Mapping Dominant Tree Species of German Forests. Remote Sens., 14.","DOI":"10.3390\/rs14143330"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"125013","DOI":"10.1088\/1748-9326\/ac3cec","article-title":"Challenges to aboveground biomass prediction from waveform lidar","volume":"16","author":"Bruening","year":"2021","journal-title":"Environ. Res. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1007\/s10712-019-09519-x","article-title":"The relevance of forest structure for biomass and productivity in temperate forests: New perspectives for remote sensing","volume":"40","author":"Fischer","year":"2019","journal-title":"Surv. Geophys."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1799","DOI":"10.1111\/geb.13158","article-title":"Evaluating the potential of full-waveform lidar for mapping pan-tropical tree species richness","volume":"29","author":"Marselis","year":"2020","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"045003","DOI":"10.1088\/1748-9326\/ac583f","article-title":"The use of GEDI canopy structure for explaining variation in tree species richness in natural forests","volume":"17","author":"Marselis","year":"2022","journal-title":"Environ. Res. Lett."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"115006","DOI":"10.1088\/1748-9326\/ab9e99","article-title":"Towards mapping the diversity of canopy structure from space with GEDI","volume":"15","author":"Schneider","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"112165","DOI":"10.1016\/j.rse.2020.112165","article-title":"Mapping global forest canopy height through integration of GEDI and Landsat data","volume":"253","author":"Potapov","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"112760","DOI":"10.1016\/j.rse.2021.112760","article-title":"Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles","volume":"268","author":"Lang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"100002","DOI":"10.1016\/j.srs.2020.100002","article-title":"The Global Ecosystem Dynamics Investigation: High-resolution laser ranging of the Earth\u2019s forests and topography","volume":"1","author":"Dubayah","year":"2020","journal-title":"Sci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"111779","DOI":"10.1016\/j.rse.2020.111779","article-title":"Biomass estimation from simulated GEDI, ICESat-2 and NISAR across environmental gradients in Sonoma County, California","volume":"242","author":"Duncanson","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1038\/s41559-021-01451-x","article-title":"Priority list of biodiversity metrics to observe from space","volume":"5","author":"Skidmore","year":"2021","journal-title":"Nat. Ecol. Evol."},{"key":"ref_41","first-page":"36","article-title":"An unsupervised two-stage clustering approach for forest structure classification based on X-band InSAR data\u2014A case study in complex temperate forest stands","volume":"57","author":"Abdullahi","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_42","first-page":"1","article-title":"Definition of tomographic SAR configurations for forest structure applications at L-band","volume":"19","author":"Pardini","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1111\/1365-2435.14188","article-title":"High-resolution 3D forest structure explains ecomorphological trait variation in assemblages of saproxylic beetles","volume":"37","author":"Drag","year":"2022","journal-title":"Funct. Ecol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Pardini, M., Cazcarra-Bes, V., and Papathanassiou, K.P. (2021). TomoSAR mapping of 3D forest structure: Contributions of L-band configurations. Remote Sens., 13.","DOI":"10.3390\/rs13122255"},{"key":"ref_45","first-page":"101904","article-title":"Canopy height estimation with TanDEM-X in temperate and boreal forests","volume":"82","author":"Schlund","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"2101","DOI":"10.1007\/s10980-020-01085-7","article-title":"The response of canopy height diversity to natural disturbances in two temperate forest landscapes","volume":"35","author":"Senf","year":"2020","journal-title":"Landsc. Ecol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3402","DOI":"10.1109\/JSTARS.2018.2859050","article-title":"Forest structure characterization from SAR tomography at L-band","volume":"11","author":"Tello","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"113134","DOI":"10.1016\/j.rse.2022.113134","article-title":"Forest canopy stratification based on fused, imbalanced and collinear LiDAR and Sentinel-2 metrics","volume":"279","author":"Wernicke","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Pucher, C., Neumann, M., and Hasenauer, H. (2022). An Improved Forest Structure Data Set for Europe. Remote Sens., 14.","DOI":"10.3390\/rs14020395"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kacic, P., Hirner, A., and Da Ponte, E. (2021). Fusing Sentinel-1 and-2 to Model GEDI-Derived Vegetation Structure Characteristics in GEE for the Paraguayan Chaco. Remote Sens., 13.","DOI":"10.3390\/rs13245105"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Mullissa, A., Vollrath, A., Odongo-Braun, C., Slagter, B., Balling, J., Gou, Y., Gorelick, N., and Reiche, J. (2021). Sentinel-1 SAR Backscatter Analysis Ready Data Preparation in Google Earth Engine. Remote Sens., 13.","DOI":"10.3390\/rs13101954"},{"key":"ref_52","unstructured":"Louis, J., Debaecker, V., Pflug, B., Main-Knorn, M., Bieniarz, J., Mueller-Wilm, U., Cadau, E., and Gascon, F. (2016, January 9). Sentinel-2 Sen2Cor: L2A processor for users. Proceedings of the Proceedings Living Planet Symposium 2016, Spacebooks Online."},{"key":"ref_53","first-page":"37","article-title":"Sen2Cor for sentinel-2","volume":"Volume 10427","author":"Pflug","year":"2017","journal-title":"Proceedings of the Image and Signal Processing for Remote Sensing XXIII"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_55","unstructured":"Dubayah, R., Hofton, M., Blair, J., Armston, J., Tang, H., and Luthcke, S. (2022, October 11). GEDI L2A Elevation and Height Metrics Data Global Footprint Level V002, Available online: https:\/\/lpdaac.usgs.gov\/products\/gedi02_av002\/."},{"key":"ref_56","unstructured":"Dubayah, R., Tang, H., Armston, J., Luthcke, S., Hofton, M., and Blair, J. (2022, October 11). GEDI L2B Canopy Cover and Vertical Profile Metrics Data Global Footprint Level V002, Available online: https:\/\/lpdaac.usgs.gov\/news\/release-of-gedi-v2-data-for-february-through-june-2021\/."},{"key":"ref_57","unstructured":"Dubayah, R. (2022, October 11). GEDI L2B Description Update Release 2. Canopy Cover and Vertical Profile Metrics Data Global Footprint Level 2021, Available online: https:\/\/lpdaac.usgs.gov\/products\/gedi02_bv001\/."},{"key":"ref_58","unstructured":"Dubayah, R., Armston, J., Kellner, J., Duncanson, L., Healey, S., Patterson, P., Hancock, S., Tang, H., Bruening, J., and Hofton, M. (2022). GEDI L4A Footprint Level Aboveground Biomass Density, Version 2.1."},{"key":"ref_59","unstructured":"Tang, H., and Armston, J. (2022, October 11). Algorithm Theoretical Basis Document (ATBD) for GEDI L2B Footprint Canopy Cover and Vertical Profile Metrics, Available online: https:\/\/lpdaac.usgs.gov\/documents\/588\/GEDI_FCCVPM_ATBD_v1.0.pdf."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Kellner, J.R., Armston, J., and Duncanson, L. (2021). Algorithm theoretical basis document for GEDI footprint aboveground biomass density (1.0). Earth Space Sci., e2022EA002516.","DOI":"10.31223\/X5V93D"},{"key":"ref_61","first-page":"103175","article-title":"Assessing GEDI-NASA system for forest fuels classification using machine learning techniques","volume":"116","author":"Lamelas","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1080\/07038992.2015.1089401","article-title":"Large Area Mapping of Annual Land Cover Dynamics Using Multitemporal Change Detection and Classification of Landsat Time Series Data","volume":"41","author":"Franklin","year":"2015","journal-title":"Can. J. Remote Sens."},{"key":"ref_63","first-page":"61","article-title":"Long-term deforestation dynamics in the Brazilian Amazon\u2014Uncovering historic frontier development along the Cuiab\u00e1\u2013Santar\u00e9m highway","volume":"44","author":"Griffiths","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_64","unstructured":"Zanaga, D., Van De Kerchove, R., De Keersmaecker, W., Souverijns, N., Brockmann, C., Quast, R., Wevers, J., Grosu, A., Paccini, A., and Vergnaud, S. (2022, December 04). ESA WorldCover 10 m 2020 V100. Available online: https:\/\/developers.google.com\/earth-engine\/datasets\/catalog\/ESA_WorldCover_v100."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"Isprs J. Photogramm. Remote. Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote. Sens."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Sothe, C., Gonsamo, A., Louren\u00e7o, R.B., Kurz, W.A., and Snider, J. (2022). Spatially Continuous Mapping of Forest Canopy Height in Canada by Combining GEDI and ICESat-2 with PALSAR and Sentinel. Remote Sens., 14.","DOI":"10.3390\/rs14205158"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Rishmawi, K., Huang, C., and Zhan, X. (2021). Monitoring Key Forest Structure Attributes across the Conterminous United States by Integrating GEDI LiDAR Measurements and VIIRS Data. Remote Sens., 13.","DOI":"10.3390\/rs13030442"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Rishmawi, K., Huang, C., Schleeweis, K., and Zhan, X. (2022). Integration of VIIRS Observations with GEDI-Lidar Measurements to Monitor Forest Structure Dynamics from 2013 to 2020 across the Conterminous United States. Remote Sens., 14.","DOI":"10.3390\/rs14102320"},{"key":"ref_72","unstructured":"(2022, December 28). Bundesamt f\u00fcr Kartographie und Geod\u00e4sie. GeoBasis-DE\/BKG Digitales Landschaftsmodell 1:250,000 (DLM250). Available online: https:\/\/gdz.bkg.bund.de\/index.php\/default\/digitales-landschaftsmodell-1-250-000-ebenen-dlm250-ebenen.html."},{"key":"ref_73","unstructured":"Lang, N., Schindler, K., and Wegner, J.D. (2023, January 05). High carbon stock mapping at large scale with optical satellite imagery and spaceborne LIDAR, Available online: http:\/\/xxx.lanl.gov\/abs\/2107.07431."},{"key":"ref_74","unstructured":"Verheyen, R. (2020). Rechtliche Optionen f\u00fcr den Dannenr\u00f6der Wald: Rodungsstopp, Erg\u00e4nzungsverfahren-Ist das Wirklich unm\u00f6glich?, Greenpeace eV Hamburg."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Jung, C., and Schindler, D. (2019). Historical winter storm atlas for Germany (GeWiSA). Atmosphere, 10.","DOI":"10.3390\/atmos10070387"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Adam, M., Urbazaev, M., Dubois, C., and Schmullius, C. (2020). Accuracy Assessment of GEDI Terrain Elevation and Canopy Height Estimates in European Temperate Forests: Influence of Environmental and Acquisition Parameters. Remote Sens., 12.","DOI":"10.3390\/rs12233948"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"100024","DOI":"10.1016\/j.srs.2021.100024","article-title":"The impact of geolocation uncertainty on GEDI tropical forest canopy height estimation and change monitoring","volume":"4","author":"Roy","year":"2021","journal-title":"Sci. Remote Sens."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1080\/15481603.2022.2085354","article-title":"Factors affecting relative height and ground elevation estimations of GEDI among forest types across the conterminous USA","volume":"59","author":"Wang","year":"2022","journal-title":"Gisci. Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Hirschmugl, M., Lippl, F., and Sobe, C. (2023). Assessing the Vertical Structure of Forests Using Airborne and Spaceborne LiDAR Data in the Austrian Alps. Remote Sens., 15.","DOI":"10.3390\/rs15030664"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Arekhi, M., Goksel, C., Balik Sanli, F., and Senel, G. (2019). Comparative evaluation of the spectral and spatial consistency of Sentinel-2 and Landsat-8 OLI data for Igneada longos forest. Isprs Int. J.-Geo-Inf., 8.","DOI":"10.3390\/ijgi8020056"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F. (2023, January 20). World map of the K\u00f6ppen-Geiger Climate Classification Updated. Available online: https:\/\/www.schweizerbart.de\/papers\/metz\/detail\/15\/55034\/World_Map_of_the_Koppen_Geiger_climate_classificat?af=crossref.","DOI":"10.1127\/0941-2948\/2006\/0130"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.5194\/hess-11-1633-2007","article-title":"Updated world map of the K\u00f6ppen-Geiger climate classification","volume":"11","author":"Peel","year":"2007","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Hl\u00e1sny, T., Krokene, P., Liebhold, A., Montagn\u00e9-Huck, C., M\u00fcller, J., Qin, H., Raffa, K., Schelhaas, M., Seidl, R., and Svoboda, M. (2019). Living with Bark Beetles: Impacts, Outlook and Management Options, European Forest Institute. Number 8.","DOI":"10.36333\/fs08"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1007\/s40725-021-00142-x","article-title":"Bark beetle outbreaks in Europe: State of knowledge and ways forward for management","volume":"7","author":"Krokene","year":"2021","journal-title":"Curr. For. Rep."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.foreco.2016.06.006","article-title":"Effects of natural disturbances and salvage logging on biodiversity\u2013Lessons from the Bohemian Forest","volume":"388","author":"Thorn","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"112845","DOI":"10.1016\/j.rse.2021.112845","article-title":"Aboveground biomass density models for NASA\u2019s Global Ecosystem Dynamics Investigation (GEDI) lidar mission","volume":"270","author":"Duncanson","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"095001","DOI":"10.1088\/1748-9326\/ac8694","article-title":"GEDI launches a new era of biomass inference from space","volume":"17","author":"Dubayah","year":"2022","journal-title":"Environ. Res. Lett."},{"key":"ref_88","unstructured":"(2023, January 27). GEDI Ecosystem Lidar. GEDI could Get Extension under New Proposal. Available online: https:\/\/gedi.umd.edu\/gedi-could-get-extension-under-new-proposal\/."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"101195","DOI":"10.1016\/j.ecoinf.2020.101195","article-title":"From local spectral species to global spectral communities: A benchmark for ecosystem diversity estimate by remote sensing","volume":"61","author":"Rocchini","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1007\/s42974-022-00113-7","article-title":"Double down on remote sensing for biodiversity estimation: A biological mindset","volume":"23","author":"Rocchini","year":"2022","journal-title":"Community Ecol."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"e2022JG007026","DOI":"10.1029\/2022JG007026","article-title":"The spectral species concept in living color","volume":"127","author":"Rocchini","year":"2022","journal-title":"J. Geophys. Res. Biogeosci."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"111218","DOI":"10.1016\/j.rse.2019.111218","article-title":"Remote sensing of terrestrial plant biodiversity","volume":"231","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Kacic, P., and Kuenzer, C. (2022). Forest Biodiversity Monitoring Based on Remotely Sensed Spectral Diversity\u2014A Review. 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