{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T21:08:57Z","timestamp":1774645737400,"version":"3.50.1"},"reference-count":62,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:00:00Z","timestamp":1722988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Federal Ministry of Agriculture, Forestry, Regions and Water Management","award":["BMLRT\/III-2021-M6 (FAI.35)"],"award-info":[{"award-number":["BMLRT\/III-2021-M6 (FAI.35)"]}]},{"name":"Federal Ministry of Agriculture, Forestry, Regions and Water Management","award":["BMLRT\/III-2021-M4\/2 (FAI.2)"],"award-info":[{"award-number":["BMLRT\/III-2021-M4\/2 (FAI.2)"]}]},{"name":"Federal Ministry of Agriculture, Forestry, Regions and Water Management","award":["ACRP14\u2013Austria Fire Futures\u2013KR21KB0K00001"],"award-info":[{"award-number":["ACRP14\u2013Austria Fire Futures\u2013KR21KB0K00001"]}]},{"name":"Austrian Climate Research Program","award":["BMLRT\/III-2021-M6 (FAI.35)"],"award-info":[{"award-number":["BMLRT\/III-2021-M6 (FAI.35)"]}]},{"name":"Austrian Climate Research Program","award":["BMLRT\/III-2021-M4\/2 (FAI.2)"],"award-info":[{"award-number":["BMLRT\/III-2021-M4\/2 (FAI.2)"]}]},{"name":"Austrian Climate Research Program","award":["ACRP14\u2013Austria Fire Futures\u2013KR21KB0K00001"],"award-info":[{"award-number":["ACRP14\u2013Austria Fire Futures\u2013KR21KB0K00001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The accurate large-scale classification of tree species is crucial for the monitoring, protection, and management of the Earth\u2019s invaluable forest ecosystems. Numerous previous studies have recognized the suitability of satellite imagery, particularly Sentinel-2 imagery, for this task. In this study, we utilized a dense phenology Sentinel-2 time series, which offered consistent data across multiple granules, to map tree species across the entire forested area in Austria. Aiming for the classification scheme to more accurately represent actual forest conditions, we included mixed tree species and sparsely populated classes (classes with sparse canopy cover) alongside pure tree species classes. To enhance the training data for the mixed and sparse classes, synthetic data creation was employed. Autocorrelation has significant implications for the validation of thematic maps. To investigate the impact of spatial dependency on validation data, two methods were employed at numerous split and buffer distances: spatial split validation and a validation method based on a buffered ground reference probability samples provided by the National Forest inventory (NFI). While a random training data holdout set yielded 99% accuracy, the spatial split validation resulted in 74% accuracy, emphasizing the importance of accounting for spatial autocorrelation when validating with holdout sets derived from polygon-based training data. The validation based on NFI data resulted in 55% overall accuracy, 91% post-hoc pure class accuracy, and 79% accuracy when confusions in phenological proximity were disregarded (e.g., spruce\u2013larch confused with spruce). The significant differences in accuracy observed between spatial split and NFI validation underscore the challenge for polygon-based training data to capture ground reference forest complexity, particularly in areas with diverse forests. This hardship is further accentuated by the pure class accuracy of 91%, revealing the substantial impact of mixed stands on the accuracy of tree species maps.<\/jats:p>","DOI":"10.3390\/rs16162887","type":"journal-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T07:01:25Z","timestamp":1723100485000},"page":"2887","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Evaluating Tree Species Mapping: Probability Sampling Validation of Pure and Mixed Species Classes Using Convolutional Neural Networks and Sentinel-2 Time Series"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8743-536X","authenticated-orcid":false,"given":"Tobias","family":"Schadauer","sequence":"first","affiliation":[{"name":"Department of Forest Inventory, Austrian Research Centre for Forests (BFW), Seckendorff-Gudent-Weg 8, 1130 Vienna, Austria"}]},{"given":"Susanne","family":"Karel","sequence":"additional","affiliation":[{"name":"Department of Forest Inventory, Austrian Research Centre for Forests (BFW), Seckendorff-Gudent-Weg 8, 1130 Vienna, Austria"}]},{"given":"Markus","family":"Loew","sequence":"additional","affiliation":[{"name":"Department of Forest Inventory, Austrian Research Centre for Forests (BFW), Seckendorff-Gudent-Weg 8, 1130 Vienna, Austria"}]},{"given":"Ursula","family":"Knieling","sequence":"additional","affiliation":[{"name":"Department of Forest Inventory, Austrian Research Centre for Forests (BFW), Seckendorff-Gudent-Weg 8, 1130 Vienna, Austria"}]},{"given":"Kevin","family":"Kopecky","sequence":"additional","affiliation":[{"name":"Department of Forest Inventory, Austrian Research Centre for Forests (BFW), Seckendorff-Gudent-Weg 8, 1130 Vienna, Austria"}]},{"given":"Christoph","family":"Bauerhansl","sequence":"additional","affiliation":[{"name":"Department of Forest Inventory, Austrian Research Centre for Forests (BFW), Seckendorff-Gudent-Weg 8, 1130 Vienna, Austria"}]},{"given":"Ambros","family":"Berger","sequence":"additional","affiliation":[{"name":"Department of Forest Inventory, Austrian Research Centre for Forests (BFW), Seckendorff-Gudent-Weg 8, 1130 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4239-7812","authenticated-orcid":false,"given":"Stephan","family":"Graeber","sequence":"additional","affiliation":[{"name":"Department of Forest Inventory, Austrian Research Centre for Forests (BFW), Seckendorff-Gudent-Weg 8, 1130 Vienna, Austria"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8229-1160","authenticated-orcid":false,"given":"Lukas","family":"Winiwarter","sequence":"additional","affiliation":[{"name":"Research Unit Photogrammetry, Department of Geodesy and Geoinformation, TU Wien, Wiedner Hauptstra\u00dfe 8-10, 1040 Vienna, Austria"},{"name":"Department of Basic Sciences in Engineering Sciences, University of Innsbruck, Technikerstra\u00dfe 13, 6020 Innsbruck, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2043","DOI":"10.1038\/s41467-023-37796-z","article-title":"High economic costs of reduced carbon sinks and declining biome stability in Central American forests","volume":"14","author":"Baumbach","year":"2023","journal-title":"Nat. Commun."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1023\/B:NHAZ.0000048468.67886.e5","article-title":"Mountain Protection Forests against Natural Hazards and Risks: New French Developments by Integrating Forests in Risk Zoning","volume":"33","author":"Berger","year":"2004","journal-title":"Nat. Hazards"},{"key":"ref_3","first-page":"53","article-title":"Forests as Protection from Natural Hazards","volume":"Volume 2","author":"Evans","year":"2001","journal-title":"The Forests Handbook"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1016\/j.jenvman.2007.03.035","article-title":"Assessing the ecosystem service of air pollutant removal by urban trees in Guangzhou (China)","volume":"88","author":"Jim","year":"2008","journal-title":"J. Environ. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"104647","DOI":"10.1016\/j.worlddev.2019.104647","article-title":"Forests as pathways to prosperity: Empirical insights and conceptual advances","volume":"125","author":"Miller","year":"2020","journal-title":"World Dev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"127707","DOI":"10.1016\/j.ufug.2022.127707","article-title":"Ecological functions and human benefits of urban forests","volume":"75","author":"Urbanek","year":"2022","journal-title":"Urban For. Urban Green."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1646","DOI":"10.1016\/j.ecolecon.2010.03.011","article-title":"The value of urban tree cover: A hedonic property price model in Ramsey and Dakota Counties, Minnesota, USA","volume":"69","author":"Sander","year":"2010","journal-title":"Ecol. Econ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Teich, M., Accastello, C., Perzl, F., and Kleemayr, K. (2022). Protective Forests as Ecosystem-Based Solution for Disaster Risk Reduction (Eco-DRR), IntechOpen. Available online: https:\/\/www.intechopen.com\/books\/10812.","DOI":"10.5772\/intechopen.95014"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.ufug.2004.09.001","article-title":"The urban forest in Beijing and its role in air pollution reduction","volume":"3","author":"Yang","year":"2005","journal-title":"Urban For. Urban Green."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1111\/gcb.16531","article-title":"Significant increase in natural disturbance impacts on European forests since 1950","volume":"29","author":"Patacca","year":"2023","journal-title":"Glob. Chang. Biol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1038\/nclimate3303","article-title":"Forest disturbances under climate change","volume":"7","author":"Seidl","year":"2017","journal-title":"Nat. Clim. Chang."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1016\/j.foreco.2009.09.023","article-title":"Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems","volume":"259","author":"Lindner","year":"2010","journal-title":"For. Ecol. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1016\/j.foreco.2009.09.001","article-title":"A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests","volume":"259","author":"Allen","year":"2010","journal-title":"For. Ecol. Manag."},{"key":"ref_14","first-page":"1","article-title":"Two unprecedented outbreaks of the European spruce bark beetle, Ips typographus L. (Col., Scolytinae) in Austria since 2015: Different causes and different impacts on forests","volume":"70","author":"Hallas","year":"2024","journal-title":"Cent. Eur. For. J."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1111\/geb.12558","article-title":"Biotic disturbances in Northern Hemisphere forests\u2014A synthesis of recent data, uncertainties and implications for forest monitoring and modelling","volume":"26","author":"Kautz","year":"2017","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"e12823","DOI":"10.1111\/efp.12823","article-title":"The pine pathogen Diplodia sapinea is associated with the death of large Douglas fir trees","volume":"53","author":"Ritzer","year":"2023","journal-title":"For. Pathol."},{"key":"ref_18","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_19","unstructured":"Hallas, T., Netherer, S., Pennerstorfer, J., Karel, S., Schadauer, T., L\u00f6w, M., Baier, P., Bauerhansl, C., Kessler, D., and Englisch, M. (2024, July 23). The Bark Beetle Dashboard\u2014Towards a Holistic Risk Assessment of Ips typographus. Available online: https:\/\/rgdoi.net\/10.13140\/RG.2.2.11420.09603."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"172","DOI":"10.25518\/1780-4507.16524","article-title":"Forest mapping and species composition using supervised per pixel classification of Sentinel-2 imagery","volume":"22","author":"Bolyn","year":"2018","journal-title":"BASE"},{"key":"ref_22","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_23","first-page":"32","article-title":"Use of Sentinel-2 for forest classification in Mediterranean environments","volume":"42","author":"Puletti","year":"2018","journal-title":"Ann. Silvic. Res."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wessel, M., Brandmeier, M., and Tiede, D. (2018). Evaluation of Different Machine Learning Algorithms for Scalable Classification of Tree Types and Tree Species Based on Sentinel-2 Data. Remote Sens., 10.","DOI":"10.3390\/rs10091419"},{"key":"ref_25","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_26","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_27","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_28","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_29","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_30","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_31","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_32","doi-asserted-by":"crossref","unstructured":"Zagajewski, B., Kluczek, M., Raczko, E., Njegovec, A., Dabija, A., and Kycko, M. (2021). Comparison of Random Forest, Support Vector Machines, and Neural Networks for Post-Disaster Forest Species Mapping of the Krkono\u0161e\/Karkonosze Transboundary Biosphere Reserve. Remote Sens., 13.","DOI":"10.3390\/rs13132581"},{"key":"ref_33","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":"267","author":"Hemmerling","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_34","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_35","unstructured":"Delwart, S. (2024, February 12). ESA SENTINEL-2 User Handbook. Available online: https:\/\/sentinels.copernicus.eu\/documents\/247904\/685211\/Sentinel-2_User_Handbook."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"L\u00f6w, M., and Koukal, T. (2020). Phenology Modelling and Forest Disturbance Mapping with Sentinel-2 Time Series in Austria. Remote Sens., 12.","DOI":"10.21203\/rs.3.rs-26379\/v1"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"113205","DOI":"10.1016\/j.rse.2022.113205","article-title":"Mapping tree species proportions from satellite imagery using spectral\u2013spatial deep learning","volume":"280","author":"Bolyn","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1111\/ecog.02881","article-title":"Cross-validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure","volume":"40","author":"Roberts","year":"2017","journal-title":"Ecography"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4540","DOI":"10.1038\/s41467-020-18321-y","article-title":"Spatial validation reveals poor predictive performance of large-scale ecological mapping models","volume":"11","author":"Ploton","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"109692","DOI":"10.1016\/j.ecolmodel.2021.109692","article-title":"Spatial cross-validation is not the right way to evaluate map accuracy","volume":"457","author":"Wadoux","year":"2021","journal-title":"Ecol. Model."},{"key":"ref_42","unstructured":"(2024, February 12). Climate Austria: Average Temperature, Weather by Month & Weather for Austria. Available online: https:\/\/en.climate-data.org\/europe\/austria-4\/?utm_content=cmp-true."},{"key":"ref_43","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Navab","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","article-title":"Deep learning for time series classification: A review","volume":"33","author":"Forestier","year":"2019","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_46","unstructured":"(2024, February 12). Klimamittel\u2014ZAMG. Available online: https:\/\/www.zamg.ac.at\/cms\/de\/klima\/klimauebersichten\/klimamittel-1971-2000."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"034026","DOI":"10.1088\/1748-9326\/8\/3\/034026","article-title":"Atlantic influence on spring snowfall over the Alps in the past 150 years","volume":"8","author":"Zampieri","year":"2013","journal-title":"Environ. Res. Lett."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Differentiation of Data by Simplified Least Squares Procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1353691","DOI":"10.1155\/2017\/1353691","article-title":"Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications","volume":"2017","author":"Xue","year":"2017","journal-title":"J. Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1109\/36.134076","article-title":"Atmospherically resistant vegetation index (ARVI) for EOS-MODIS","volume":"30","author":"Kaufman","year":"1992","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2455","DOI":"10.1016\/j.biombioe.2011.02.028","article-title":"A review of remote sensing methods for biomass feedstock production","volume":"35","author":"Ahamed","year":"2011","journal-title":"Biomass Bioenergy"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1016\/j.ecolind.2018.04.010","article-title":"Automatic mapping afforestation, cropland reclamation and variations in cropping intensity in central east China during 2001\u20132016","volume":"91","author":"Qiu","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"259","DOI":"10.5194\/isprs-annals-IV-2-W4-259-2017","article-title":"Improved topographic models via concurrent airborne lidar anddense image matching","volume":"IV-2\/W4","author":"Mandlburger","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_54","unstructured":"(2024, January 25). Trimble Inpho|Office Software. Available online: https:\/\/geospatial.trimble.com\/products\/software\/trimble-inpho."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic Minority Over-sampling Technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Vidal, C., Alberdi, I.A., Hern\u00e1ndez Mateo, L., and Redmond, J.J. (2016). Austria. National Forest Inventories, Springer International Publishing.","DOI":"10.1007\/978-3-319-44015-6"},{"key":"ref_57","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, The MIT Press. Adaptive Computation and Machine Learning."},{"key":"ref_58","unstructured":"Maas, A.L., Hannun, A.Y., and Ng, A.Y. (2013, January 17\u201319). Rectifier Nonlinearities Improve Neural Network Acoustic Models. Proceedings of the 30th International Conference on Machine Learning, Atlanta, GA, USA."},{"key":"ref_59","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_60","unstructured":"(2024, January 25). Austrian National Forest Inventory\u2014Tree Species Map. Available online: https:\/\/www.waldinventur.at\/?x=1486825&y=6059660&z=7.75968&r=0&l=1111#\/map\/1\/mBaumartenkarte\/Bundesland\/erg9."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Figueira, A., and Vaz, B. (2022). Survey on Synthetic Data Generation, Evaluation Methods and GANs. Mathematics, 10.","DOI":"10.3390\/math10152733"},{"key":"ref_62","unstructured":"S. Clerc & MPC Team (2024, June 20). L1C Data Quality Report. Available online: https:\/\/sentinel.esa.int\/documents\/247904\/685211\/Sentinel-2_L1C_Data_Quality_Report."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/16\/2887\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:31:47Z","timestamp":1760110307000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/16\/2887"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,7]]},"references-count":62,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["rs16162887"],"URL":"https:\/\/doi.org\/10.3390\/rs16162887","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,7]]}}}