{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T03:26:49Z","timestamp":1774927609470,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T00:00:00Z","timestamp":1704931200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science &amp; Technology Fundamental Resources Investigation Program","award":["2022FY101902"],"award-info":[{"award-number":["2022FY101902"]}]},{"name":"Science &amp; Technology Fundamental Resources Investigation Program","award":["42171367"],"award-info":[{"award-number":["42171367"]}]},{"name":"Science &amp; Technology Fundamental Resources Investigation Program","award":["FKLBDAITI202201"],"award-info":[{"award-number":["FKLBDAITI202201"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022FY101902"],"award-info":[{"award-number":["2022FY101902"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42171367"],"award-info":[{"award-number":["42171367"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["FKLBDAITI202201"],"award-info":[{"award-number":["FKLBDAITI202201"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Project Program of Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University","award":["2022FY101902"],"award-info":[{"award-number":["2022FY101902"]}]},{"name":"Open Project Program of Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University","award":["42171367"],"award-info":[{"award-number":["42171367"]}]},{"name":"Open Project Program of Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University","award":["FKLBDAITI202201"],"award-info":[{"award-number":["FKLBDAITI202201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate and reliable information on tree species composition and distribution is crucial in operational and sustainable forest management. Developing a high-precision tree species map based on time series satellite data is an effective and cost-efficient approach. However, we do not quantitatively know how the time scale of data acquisitions contributes to complex tree species mapping. This study aimed to produce a detailed tree species map in a typical forest zone of the Changbai Mountains by incorporating Sentinel-2 images, topography data, and machine learning algorithms. We focused on exploring the effects of the three-year time series of Sentinel-2 within monthly, seasonal, and yearly time scales on the classification of ten dominant tree species. A random forest (RF) and support vector machine (SVM) were compared and employed to map continuous tree species. The results showed that classification with monthly datasets (overall accuracy (OA): 83.38\u201387.45%) outperformed that with seasonal and yearly datasets (OA:72.38\u201385.91%), and the RF (OA: 81.70\u201387.45%) was better than the SVM (OA: 72.38\u201383.38%) at processing the same datasets. Short-wave infrared, the normalized vegetation index, and elevation were the most important variables for tree species classification. The highest classification accuracy of 87.45% was achieved by combining RF, monthly datasets, and topography information. In terms of single species\u2019 accuracy, the F1 scores of the ten tree species ranged from 62.99% (Manchurian ash) to 97.04% (Mongolian Oak), and eight of them obtained high F1 scores greater than 87%. This study confirmed that monthly Sentinel-2 datasets, topography data, and machine learning algorithms have great potential for accurate tree species mapping in mountainous regions.<\/jats:p>","DOI":"10.3390\/rs16020293","type":"journal-article","created":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T03:21:41Z","timestamp":1704943301000},"page":"293","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Evaluating the Potential of Sentinel-2 Time Series Imagery and Machine Learning for Tree Species Classification in a Mountainous Forest"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9056-5592","authenticated-orcid":false,"given":"Pan","family":"Liu","sequence":"first","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Key Laboratory of Wetland Ecology and Environment, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8798-3449","authenticated-orcid":false,"given":"Chunying","family":"Ren","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Key Laboratory of Wetland Ecology and Environment, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University, Nanping 354300, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9865-8235","authenticated-orcid":false,"given":"Zongming","family":"Wang","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Key Laboratory of Wetland Ecology and Environment, Chinese Academy of Sciences, Changchun 130102, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4548-899X","authenticated-orcid":false,"given":"Mingming","family":"Jia","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Key Laboratory of Wetland Ecology and Environment, Chinese Academy of Sciences, Changchun 130102, China"}]},{"given":"Wensen","family":"Yu","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Big Data Application and Intellectualization for Tea Industry, Wuyi University, Nanping 354300, China"}]},{"given":"Huixin","family":"Ren","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Key Laboratory of Wetland Ecology and Environment, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Chenzhen","family":"Xia","sequence":"additional","affiliation":[{"name":"Northeast Institute of Geography and Agroecology, Key Laboratory of Wetland Ecology and Environment, Chinese Academy of Sciences, Changchun 130102, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,11]]},"reference":[{"key":"ref_1","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."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"112742","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_3","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_4","doi-asserted-by":"crossref","unstructured":"Sheeren, D., Fauvel, M., Josipovic, V., Lopes, M., Planque, C., Willm, J., and Dejoux, J.-F. (2016). Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series. Remote Sens., 8.","DOI":"10.3390\/rs8090734"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"679","DOI":"10.2307\/3236575","article-title":"Classification trees: An alternative non-parametric approach for predicting species distributions","volume":"11","author":"Vayssieres","year":"2000","journal-title":"J. Veg. Sci."},{"key":"ref_6","first-page":"207","article-title":"Tree species classification using plant functional traits from LiDAR and hyperspectral data","volume":"73","author":"Shi","year":"2018","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.rse.2012.03.013","article-title":"Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral\/hyperspectral images and LiDAR data","volume":"123","author":"Dalponte","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Xi, Y., Ren, C., Wang, Z., Wei, S., Bai, J., Zhang, B., Xiang, H., and Chen, L. (2019). Mapping Tree Species Composition Using OHS-1 Hyperspectral Data and Deep Learning Algorithms in Changbai Mountains, Northeast China. Forests, 10.","DOI":"10.3390\/f10090818"},{"key":"ref_9","first-page":"101960","article-title":"Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data","volume":"84","author":"Modzelewska","year":"2020","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2019.01.019","article-title":"Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis","volume":"149","author":"Ferreira","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"112322","DOI":"10.1016\/j.rse.2021.112322","article-title":"Tree species classification from airborne hyperspectral and LiDAR data using 3D convolutional neural networks","volume":"256","author":"Mayra","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"113143","DOI":"10.1016\/j.rse.2022.113143","article-title":"Individual tree segmentation and tree species classification in subtropical broadleaf forests using UAV-based LiDAR, hyperspectral, and ultrahigh-resolution RGB data","volume":"280","author":"Qin","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.isprsjprs.2020.10.015","article-title":"Mapping forest tree species in high resolution UAV-based RGB-imagery by means of convolutional neural networks","volume":"170","author":"Schiefer","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"118397","DOI":"10.1016\/j.foreco.2020.118397","article-title":"Individual tree detection and species classification of Amazonian palms using UAV images and deep learning","volume":"475","author":"Ferreira","year":"2020","journal-title":"For. Ecol. Manag."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"565","DOI":"10.3390\/f12050565","article-title":"Analysis of Regional Distribution of Tree Species Using Multi-Seasonal Sentinel-1&2 Imagery within Google Earth Engine","volume":"12","author":"Xie","year":"2021","journal-title":"Forests"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.rse.2018.02.064","article-title":"Improved mapping of forest type using spectral-temporal Landsat features","volume":"210","author":"Pasquarella","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.isprsjprs.2019.11.007","article-title":"Comparison of multi-seasonal Landsat 8, Sentinel-2 and hyperspectral images for mapping forest alliances in Northern California","volume":"159","author":"Clark","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.rse.2019.02.015","article-title":"Current status of Landsat program, science, and applications","volume":"225","author":"Wulder","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1220253","DOI":"10.3389\/ffgc.2023.1220253","article-title":"Local-scale mapping of tree species in a lower mountain area using Sentinel-1 and -2 multitemporal images, vegetation indices, and topographic information","volume":"6","author":"Vorovencii","year":"2023","journal-title":"Front. For. Glob. Chang."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Immitzer, M., Neuwirth, M., Boeck, 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_21","doi-asserted-by":"crossref","first-page":"100032","DOI":"10.1016\/j.fecs.2022.100032","article-title":"Assessing Landsat-8 and Sentinel-2 spectral-temporal features for mapping tree species of northern plantation forests in Heilongjiang Province, China","volume":"9","author":"Wang","year":"2022","journal-title":"For. Ecosyst."},{"key":"ref_22","first-page":"103154","article-title":"Mapping tree species composition in a Caspian temperate mixed forest based on spectral-temporal metrics and machine learning","volume":"116","author":"Nasiri","year":"2023","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Phan, T.N., Kuch, V., and Lehnert, L.W. (2020). Land Cover Classification using Google Earth Engine and Random Forest Classifier\u2014The Role of Image Composition. Remote Sens., 12.","DOI":"10.3390\/rs12152411"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pratico, S., Solano, F., Di Fazio, S., and Modica, G. (2021). Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation. Remote Sens., 13.","DOI":"10.3390\/rs13040586"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2211881","DOI":"10.1080\/15481603.2023.2211881","article-title":"Regionalized classification of stand tree species in mountainous forests by fusing advanced classifiers and ecological niche model","volume":"60","author":"Fang","year":"2023","journal-title":"GISci. Remote Sens."},{"key":"ref_26","first-page":"7589","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.\u2014STARS"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hoscilo, 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_28","doi-asserted-by":"crossref","unstructured":"Abdollahnejad, A., Panagiotidis, D., Shataee Joybari, S., and Surov\u00fd, P. (2017). Prediction of Dominant Forest Tree Species Using QuickBird and Environmental Data. Forests, 8.","DOI":"10.3390\/f8020042"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2214994","DOI":"10.1080\/15481603.2023.2214994","article-title":"Contribution of topographic features and categorization uncertainty for a tree species classification in the boreal biome of Northern Ontario","volume":"60","author":"Pittman","year":"2023","journal-title":"GISci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pimple, U., Sitthi, A., Simonetti, D., Pungkul, S., Leadprathom, K., and Chidthaisong, A. (2017). Topographic Correction of Landsat TM-5 and Landsat OLI-8 Imagery to Improve the Performance of Forest Classification in the Mountainous Terrain of Northeast Thailand. Sustainability, 9.","DOI":"10.3390\/su9020258"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2020.04.001","article-title":"Google Earth Engine for geo-big data applications: A meta-analysis and systematic review","volume":"164","author":"Tamiminia","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1306","DOI":"10.1016\/j.scib.2023.05.004","article-title":"Mapping global distribution of mangrove forests at 10-m resolution","volume":"68","author":"Jia","year":"2023","journal-title":"Sci. Bull."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"113391","DOI":"10.1016\/j.rse.2022.113391","article-title":"Nationwide native forest structure maps for Argentina based on forest inventory data, SAR Sentinel-1 and vegetation metrics from Sentinel-2 imagery","volume":"285","author":"Silveira","year":"2023","journal-title":"Remote Sens. Environ."},{"key":"ref_34","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_35","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1387","DOI":"10.1016\/j.ecoleng.2011.03.011","article-title":"Forest ecosystem restoration due to a national conservation plan in China","volume":"37","author":"Yu","year":"2011","journal-title":"Ecol. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1007\/s11676-015-0085-1","article-title":"The influence of selective cutting of mixed Korean pine (Pinus koraiensis Sieb. et Zucc.) and broad-leaf forest on rare species distribution patterns and spatial correlation in Northeast China","volume":"26","author":"Kan","year":"2015","journal-title":"J. For. Res."},{"key":"ref_38","unstructured":"ESA (2015). Sentinel-2 User Handbook, Revision 2, ESA. ESA Standard Document."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1016\/S0034-4257(96)00112-5","article-title":"A Comparison of Vegetation Indices over a Global Set of TM Images for EOS-MODIS","volume":"59","author":"Huete","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/0034-4257(79)90013-0","article-title":"Red and photographic infrared linear combinations for monitoring vegetation","volume":"8","author":"Tucker","year":"1979","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1080\/10106049109354290","article-title":"Mapping burns and natural reforestation using thematic Mapper data","volume":"6","author":"Caselles","year":"1991","journal-title":"Geocarto Int."},{"key":"ref_42","unstructured":"(2011). Technical Regulations for Inventory for Forest Management Planning and Design (Standard No. GB\/T 26424-2010)."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"111811","DOI":"10.1016\/j.rse.2020.111811","article-title":"Discriminating tree species at different taxonomic levels using multi-temporal WorldView-3 imagery in Washington DC, USA","volume":"246","author":"Fang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2632","DOI":"10.1109\/TGRS.2012.2216272","article-title":"Tree Species Classification in Boreal Forests with Hyperspectral Data","volume":"51","author":"Dalponte","year":"2013","journal-title":"IEEE Geosci. Remote"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1016\/j.rse.2018.11.032","article-title":"Deep learning based multi-temporal crop classification","volume":"221","author":"Zhong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2013.05.001","article-title":"Urban vegetation classification: Benefits of multitemporal RapidEye satellite data","volume":"136","author":"Tigges","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1080\/15481603.2021.1974275","article-title":"Urban tree species classification using UAV-based multi-sensor data fusion and machine learning","volume":"58","author":"Hartling","year":"2021","journal-title":"GISci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1093\/biomet\/asac017","article-title":"Mean decrease accuracy for random forests: Inconsistency, and a practical solution via the Sobol-MDA","volume":"109","author":"Scornet","year":"2022","journal-title":"Biometrika."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"119085","DOI":"10.1016\/j.foreco.2021.119085","article-title":"Modelling leaf phenology of some trees with accumulated temperature in a temperate forest in northeast China","volume":"489","author":"Xu","year":"2021","journal-title":"For. Ecol. Manag."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"407","DOI":"10.14358\/PERS.82.6.407","article-title":"An Assessment of Algorithmic Parameters Affecting Image Classification Accuracy by Random Forests","volume":"82","author":"Shi","year":"2016","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.rse.2016.10.010","article-title":"Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas","volume":"187","author":"Pelletier","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2016.03.021","article-title":"Mapping tree species in tropical seasonal semi-deciduous forests with hyperspectral and multispectral data","volume":"179","author":"Ferreira","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2207","DOI":"10.1080\/01431161003692040","article-title":"Segmented canonical discriminant analysis of in situ hyperspectral data for identifying 13 urban tree species","volume":"32","author":"Pu","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","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_55","doi-asserted-by":"crossref","first-page":"2088","DOI":"10.1016\/j.rse.2007.10.011","article-title":"Classification of Australian forest communities using aerial photography, CASI and HyMap data","volume":"112","author":"Lucas","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1016\/j.rse.2010.10.010","article-title":"Multi-sensor data fusion for estimating forest species composition and abundance in northern Minnesota","volume":"115","author":"Wolter","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1007\/s10980-012-9703-x","article-title":"Detection of relative differences in phenology of forest species using Landsat and MODIS","volume":"27","author":"Isaacson","year":"2012","journal-title":"Landscape Ecol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1007\/s00442-014-3055-y","article-title":"Linking belowground and aboveground phenology in two boreal forests in Northeast China","volume":"176","author":"Du","year":"2014","journal-title":"Oecologia"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"110190","DOI":"10.1016\/j.ecolmodel.2022.110190","article-title":"Improving species distribution models for dominant trees in climate data-poor forests using high-resolution remote sensing","volume":"475","author":"Ahmadi","year":"2023","journal-title":"Ecol. Model."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1080\/10549811.2019.1598443","article-title":"Evaluating the potential of sentinel-2, landsat-8, and irs satellite images in tree species classification of hyrcanian forest of iran using random forest","volume":"38","author":"Soleimannejad","year":"2019","journal-title":"J. Sustain. Forest."},{"key":"ref_61","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/293\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:44:25Z","timestamp":1760103865000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/2\/293"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,11]]},"references-count":61,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["rs16020293"],"URL":"https:\/\/doi.org\/10.3390\/rs16020293","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,11]]}}}