{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T05:33:48Z","timestamp":1774935228995,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,14]],"date-time":"2024-12-14T00:00:00Z","timestamp":1734134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2021YFB3901300"],"award-info":[{"award-number":["2021YFB3901300"]}]},{"name":"National Key R&amp;D Program of China","award":["E43302020D"],"award-info":[{"award-number":["E43302020D"]}]},{"name":"National Key R&amp;D Program of China","award":["2020132108"],"award-info":[{"award-number":["2020132108"]}]},{"name":"Talent Introduction Program Youth Project of the Chinese Academy of Sciences","award":["2021YFB3901300"],"award-info":[{"award-number":["2021YFB3901300"]}]},{"name":"Talent Introduction Program Youth Project of the Chinese Academy of Sciences","award":["E43302020D"],"award-info":[{"award-number":["E43302020D"]}]},{"name":"Talent Introduction Program Youth Project of the Chinese Academy of Sciences","award":["2020132108"],"award-info":[{"award-number":["2020132108"]}]},{"name":"2020 Report on Forestry Technological Developments and Monitoring and Assessment of Terrestrial Ecosystem Research","award":["2021YFB3901300"],"award-info":[{"award-number":["2021YFB3901300"]}]},{"name":"2020 Report on Forestry Technological Developments and Monitoring and Assessment of Terrestrial Ecosystem Research","award":["E43302020D"],"award-info":[{"award-number":["E43302020D"]}]},{"name":"2020 Report on Forestry Technological Developments and Monitoring and Assessment of Terrestrial Ecosystem Research","award":["2020132108"],"award-info":[{"award-number":["2020132108"]}]},{"name":"Research Funding for Academic Staff, Faculty of Social Sciences, Aimed at Publishing in International Journal","award":["2021YFB3901300"],"award-info":[{"award-number":["2021YFB3901300"]}]},{"name":"Research Funding for Academic Staff, Faculty of Social Sciences, Aimed at Publishing in International Journal","award":["E43302020D"],"award-info":[{"award-number":["E43302020D"]}]},{"name":"Research Funding for Academic Staff, Faculty of Social Sciences, Aimed at Publishing in International Journal","award":["2020132108"],"award-info":[{"award-number":["2020132108"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Timely and accurate tree species mapping is crucial for forest resource inventory, supporting management, conservation biology, and ecological restoration. This study utilized Sentinel-1 and Sentinel-2 data to classify five dominant tree species in Chengde and Beijing. To effectively capture the influence of multi-temporal data, data were acquired in March, June, September, and December 2020, extracting various features, including bands, spectral indices, texture features, and topographic variables. The optimal input variable combination was explored using 1519 field survey samples for training and testing datasets. Classification employed Random Forest, XGBoost, and deep learning models, with performance evaluated through out-of-bag estimation and cross-validation. The XGBoost model achieved the highest accuracy of 81.25% (kappa = 0.74) when using Sentinel-1 and Sentinel-2 bands, indices, texture features, and DEM data. Results demonstrate the effectiveness of using Sentinel data for tree species classification and emphasize the value of machine learning algorithms. This study underscores the potential of combining synthetic aperture radar (SAR) and optical data for large-scale tree species classification, with significant implications for forest monitoring and management.<\/jats:p>","DOI":"10.3390\/rs16244674","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T10:08:53Z","timestamp":1734343733000},"page":"4674","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Dominant Tree Species Mapping Using Machine Learning Based on Multi-Temporal and Multi-Source Data"],"prefix":"10.3390","volume":"16","author":[{"given":"Heyi","family":"Guo","sequence":"first","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2315-5622","authenticated-orcid":false,"given":"Sornkitja","family":"Boonprong","sequence":"additional","affiliation":[{"name":"Faculty of Social Sciences, Kasetsart University, Bangkok 10900, Thailand"}]},{"given":"Shaohua","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Zhidong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Academy of Forestry Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, China"}]},{"given":"Wei","family":"Liang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6154-9676","authenticated-orcid":false,"given":"Min","family":"Xu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0281-1179","authenticated-orcid":false,"given":"Xinwei","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]},{"given":"Kaimin","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Jingbo","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Xiaotong","family":"Gao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Yujie","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Ruichen","family":"Hu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Chunxiang","family":"Cao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Remote Sensing and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,14]]},"reference":[{"key":"ref_1","unstructured":"Sabins, F.F., and Ellis, J.M. (2020). Remote Sensing: Principles, Interpretation, and Applications, Waveland Press."},{"key":"ref_2","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_3","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":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhong, L., Dai, Z., Fang, P., Cao, Y., and Wang, L. (2024). A Review: Tree Species Classification Based on Remote Sensing Data and Classic Deep Learning-Based Methods. Forests, 15.","DOI":"10.20944\/preprints202404.0569.v1"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Micha\u0142owska, M., and Rapi\u0144ski, J. (2021). A review of tree species classification based on airborne LiDAR data and applied classifiers. Remote Sens., 13.","DOI":"10.3390\/rs13030353"},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"100002","DOI":"10.1016\/j.ophoto.2021.100002","article-title":"A decision-level fusion approach to tree species classification from multi-source remotely sensed data","volume":"1","author":"Hu","year":"2021","journal-title":"ISPRS Open J. Photogramm. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1080\/15481603.2021.1952541","article-title":"Wetland mapping with multi-temporal sentinel-1 &-2 imagery (2017\u20132020) and LiDAR data in the grassland natural region of alberta","volume":"58","author":"Onojeghuo","year":"2021","journal-title":"GIScience Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Abdollahnejad, A., and Panagiotidis, D. (2020). Tree species classification and health status assessment for a mixed broadleaf-conifer forest with UAS multispectral imaging. Remote Sens., 12.","DOI":"10.3390\/rs12223722"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5236","DOI":"10.1080\/01431161.2017.1363442","article-title":"Deciduous tree species classification using object-based analysis and machine learning with unmanned aerial vehicle multispectral data","volume":"39","author":"Franklin","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Fujimoto, A., Haga, C., Matsui, T., Machimura, T., Hayashi, K., Sugita, S., and Takagi, H. (2019). An end to end process development for UAV-SfM based forest monitoring: Individual tree detection, species classification and carbon dynamics simulation. Forests, 10.","DOI":"10.3390\/f10080680"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"16917","DOI":"10.3390\/rs71215861","article-title":"Object-based urban tree species classification using bi-temporal WorldView-2 and WorldView-3 images","volume":"7","author":"Li","year":"2015","journal-title":"Remote Sens."},{"key":"ref_13","first-page":"106","article-title":"Evolution analysis and optimization research of ecosystem service value in Chengde City, Hebei Province of northern China based on land use\/land cover change (LUCC)","volume":"43","author":"Ming","year":"2021","journal-title":"J. Beijing For. Univ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Long, D., Liu, J., Han, Q., Wang, X., and Huang, J. (2016). Ectomycorrhizal fungal communities associated with Populus simonii and Pinus tabuliformis in the hilly-gully region of the Loess Plateau, China. Sci. Rep., 6.","DOI":"10.1038\/srep24336"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rse.2011.05.028","article-title":"GMES Sentinel-1 mission","volume":"120","author":"Torres","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s optical high-resolution mission for GMES operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1016\/j.rse.2018.08.009","article-title":"A first assessment of the Sentinel-2 Level 1-C cloud mask product to support informed surface analyses","volume":"217","author":"Coluzzi","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"399","DOI":"10.5194\/bg-15-399-2018","article-title":"An enhanced forest classification scheme for modeling vegetation\u2013climate interactions based on national forest inventory data","volume":"15","author":"Majasalmi","year":"2018","journal-title":"Biogeosciences"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1016\/j.rse.2007.08.025","article-title":"Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments","volume":"112","author":"Sesnie","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.isprsjprs.2010.05.002","article-title":"Backscatter coefficient as an attribute for the classification of full-waveform airborne laser scanning data in urban areas","volume":"65","author":"Alexander","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","first-page":"106","article-title":"Estimating tree species diversity in the savannah using NDVI and woody canopy cover","volume":"66","author":"Madonsela","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"112320","DOI":"10.1016\/j.rse.2021.112320","article-title":"Plant species classification in salt marshes using phenological parameters derived from Sentinel-2 pixel-differential time-series","volume":"256","author":"Sun","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/j.rse.2013.09.006","article-title":"Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data","volume":"140","author":"Dalponte","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7847","DOI":"10.1080\/01431161.2010.531783","article-title":"Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil","volume":"32","author":"Arvor","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1007\/s10533-009-9369-x","article-title":"Tree species impact the terrestrial cycle of silicon through various uptakes","volume":"97","author":"Cornelis","year":"2010","journal-title":"Biogeochemistry"},{"key":"ref_26","first-page":"102727","article-title":"Estimating species-specific leaf area index and basal area using optical and SAR remote sensing data in Acadian mixed spruce-fir forests, USA","volume":"108","author":"Bhattarai","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Otsu, K., Pla, M., Duane, A., Cardil, A., and Brotons, L. (2019). Estimating the threshold of detection on tree crown defoliation using vegetation indices from UAS multispectral imagery. Drones, 3.","DOI":"10.3390\/drones3040080"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"229","DOI":"10.3354\/cr01026","article-title":"Predicting forest cover changes in future climate using hydrological and thermal indices in South Korea","volume":"49","author":"Choi","year":"2011","journal-title":"Clim. Res."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3247946","DOI":"10.1155\/2019\/3247946","article-title":"Tree species classification by employing multiple features acquired from integrated sensors","volume":"2019","author":"Yang","year":"2019","journal-title":"J. Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2661","DOI":"10.3390\/rs4092661","article-title":"Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data","volume":"4","author":"Immitzer","year":"2012","journal-title":"Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhou, G., Ni, Z., Zhao, Y., and Luan, J. (2022). Identification of bamboo species based on Extreme Gradient Boosting (XGBoost) using Zhuhai-1 orbita hyperspectral remote sensing imagery. Sensors, 22.","DOI":"10.3390\/s22145434"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"He, T., Zhou, H., Xu, C., Hu, J., Xue, X., Xu, L., Lou, X., Zeng, K., and Wang, Q. (2023). Deep learning in forest tree species classification using sentinel-2 on google earth engine: A case study of Qingyuan County. Sustainability, 15.","DOI":"10.3390\/su15032741"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Miyoshi, G.T., Arruda, M.d.S., Osco, L.P., Marcato Junior, J., Gon\u00e7alves, D.N., Imai, N.N., Tommaselli, A.M.G., Honkavaara, E., and Gon\u00e7alves, W.N. (2020). A novel deep learning method to identify single tree species in UAV-based hyperspectral images. Remote Sens., 12.","DOI":"10.3390\/rs12081294"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"117186","DOI":"10.1016\/j.eswa.2022.117186","article-title":"Machine learning models for estimating above ground biomass of fast growing trees","volume":"199","author":"Wongchai","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_35","first-page":"464","article-title":"The use of airborne hyperspectral data for tree species classification in a species-rich Central European forest area","volume":"52","author":"Richter","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Pal, K., and Patel, B.V. (2020, January 11\u201313). Data classification with k-fold cross validation and holdout accuracy estimation methods with 5 different machine learning techniques. Proceedings of the 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), Erode, India.","DOI":"10.1109\/ICCMC48092.2020.ICCMC-00016"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"105012","DOI":"10.1016\/j.compag.2019.105012","article-title":"Classification of tree species and stock volume estimation in ground forest images using Deep Learning","volume":"166","author":"Liu","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1507","DOI":"10.1016\/j.foreco.2011.06.052","article-title":"Topography related spatial distribution of dominant tree species in a tropical seasonal rain forest in China","volume":"262","author":"Lan","year":"2011","journal-title":"For. Ecol. Manag."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Gini, R., Sona, G., Ronchetti, G., Passoni, D., and Pinto, L. (2018). Improving tree species classification using UAS multispectral images and texture measures. ISPRS Int. J. Geo-Inf., 7.","DOI":"10.3390\/ijgi7080315"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"8900","DOI":"10.1109\/JSTARS.2023.3313251","article-title":"Forest Volume Estimation Method Based on Allometric Growth Model and Multi-source Remote Sensing Data","volume":"16","author":"Wu","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4674\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:55:25Z","timestamp":1760115325000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/24\/4674"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,14]]},"references-count":40,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["rs16244674"],"URL":"https:\/\/doi.org\/10.3390\/rs16244674","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,14]]}}}