{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T03:12:56Z","timestamp":1771470776647,"version":"3.50.1"},"reference-count":85,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,3]],"date-time":"2022-10-03T00:00:00Z","timestamp":1664755200000},"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":["2016YFC0503004"],"award-info":[{"award-number":["2016YFC0503004"]}]},{"name":"National Key R &amp; D Program of China","award":["2021FY100703"],"award-info":[{"award-number":["2021FY100703"]}]},{"name":"Special Project on National Science and Technology Basic Resources Investigation of China","award":["2016YFC0503004"],"award-info":[{"award-number":["2016YFC0503004"]}]},{"name":"Special Project on National Science and Technology Basic Resources Investigation of China","award":["2021FY100703"],"award-info":[{"award-number":["2021FY100703"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The expansion of plantation poses new challenges for mapping forest, especially in mountainous regions. Using multi-source data, this study explored the capability of the random forest (RF) algorithm for the extraction and mapping of five forest types located in Yanqing, north China. The Google Earth imagery, forest inventory data, GaoFen-1 wide-field-of-view (GF-1 WFV) images and DEM were applied for obtaining 125 features in total. The recursive feature elimination (RFE) method selected 32 features for mapping five forest types. The results attained overall accuracy of 87.06%, with a Kappa coefficient of 0.833. The mean decrease accuracy (MDA) reveals that the DEM, LAI and EVI in winter and three texture features (entropy, variance and mean) make great contributions to forest classification. The texture features from the NIR band are important, while the other texture features have little contribution. This study has demonstrated the potential of applying multi-source data based on RF algorithm for extracting and mapping plantation forest in north China.<\/jats:p>","DOI":"10.3390\/rs14194946","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T03:07:28Z","timestamp":1665371248000},"page":"4946","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Application of Multi-Source Data for Mapping Plantation Based on Random Forest Algorithm in North China"],"prefix":"10.3390","volume":"14","author":[{"given":"Fan","family":"Wu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0082-976X","authenticated-orcid":false,"given":"Yufen","family":"Ren","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China"},{"name":"Beijing Urban Ecosystem Research Station, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2421-3970","authenticated-orcid":false,"given":"Xiaoke","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,3]]},"reference":[{"key":"ref_1","unstructured":"Zhang, J., Yan, Z., and Liu, J. (2016). China Forestry Statistical Yearbook 2015, China Forestry Publishing House."},{"key":"ref_2","unstructured":"National Forestry and Grassland Administration (2013). Report on the Results of the Eighth National Forest Resources Inventory, China Forestry Publishing House."},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"355","DOI":"10.3390\/rs1030355","article-title":"A simple algorithm for large-scale mapping of evergreen forests in tropical America, Africa and Asia","volume":"1","author":"Xiao","year":"2009","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cheng, K., and Wang, J. (2019). Forest type classification based on integrated spectral-spatial-temporal features and random forest algorithm-A case study in the Qinling Mountains. Forests, 10.","DOI":"10.3390\/f10070559"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5660","DOI":"10.3390\/rs70505660","article-title":"Mapping species composition of forests and tree plantations in northeastern Costa Rica with an integration of hyperspectral and multitemporal landsat imagery","volume":"7","author":"Fagan","year":"2015","journal-title":"Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1407","DOI":"10.1007\/s11676-017-0530-4","article-title":"Forest type identification by random forest classification combined with SPOT and multitemporal SAR data","volume":"29","author":"Yu","year":"2018","journal-title":"J. For. Res."},{"key":"ref_8","first-page":"54","article-title":"Multi-source data for forest land type precise classication","volume":"52","author":"Chong","year":"2016","journal-title":"Sci. Silvae Sin."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1016\/j.rse.2007.06.003","article-title":"Estimating proportional change in forest cover as a continuous variable from multi-year MODIS data","volume":"112","author":"Hayes","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_10","first-page":"849","article-title":"Using spatial co-occurrence texture to increase forest structure and species composition classification accuracy","volume":"67","author":"Franklin","year":"2001","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.3390\/rs70101048","article-title":"Mapping deciduous rubber plantation areas and stand ages with PALSAR and landsat images","volume":"7","author":"Kou","year":"2015","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1016\/j.rse.2013.03.014","article-title":"Mapping deciduous rubber plantations through integration of PALSAR and multi-temporal Landsat imagery","volume":"134","author":"Dong","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Torbick, N., Ledoux, L., Salas, W., and Zhao, M. (2016). Regional mapping of plantation extent using multisensor imagery. Remote Sens., 8.","DOI":"10.3390\/rs8030236"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1080\/01431161.2014.995276","article-title":"Integrating multiple texture methods and NDVI to the Random Forest classification algorithm to detect tea and hazelnut plantation areas in northeast Turkey","volume":"36","author":"Akar","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/S0034-4257(01)00318-2","article-title":"Detection of forest harvest type using multiple dates of Landsat TM imagery","volume":"80","author":"Wilson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.foreco.2005.04.004","article-title":"Integration of vegetation inventory data and Landsat TM image for vegetation classification in the western Brazilian Amazon","volume":"213","author":"Lu","year":"2005","journal-title":"For. Ecol. Manag."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Liu, Y., Gong, W., Hu, X., and Gong, J. (2018). Forest type identification with random forest using Sentinel-1A, Sentinel-2A, multi-temporal Landsat-8 and DEM data. Remote Sens., 10.","DOI":"10.3390\/rs10060946"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1702","DOI":"10.3390\/rs70201702","article-title":"Mapping spatial distribution of larch plantations from multi-seasonal landsat-8 OLI imagery and multi-scale textures using random forests","volume":"7","author":"Gao","year":"2015","journal-title":"Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2189","DOI":"10.1080\/01431161.2017.1420933","article-title":"Monitoring rubber plantation distribution on Hainan Island using Landsat OLI imagery","volume":"39","author":"Han","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"905","DOI":"10.1080\/15481603.2018.1466441","article-title":"Expansion dynamics of deciduous rubber plantations in Xishuangbanna, China during 2000\u20132010","volume":"55","author":"Kou","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.foreco.2012.10.007","article-title":"Mapping local density of young Eucalyptus plantations by individual tree detection in high spatial resolution satellite images","volume":"301","author":"Zhou","year":"2013","journal-title":"For. Ecol. Manag."},{"key":"ref_24","first-page":"992","article-title":"Land-cover classification of random forest based on Sentinel-2A image feature optimization","volume":"41","author":"Yun","year":"2019","journal-title":"Resour. Sci."},{"key":"ref_25","first-page":"53","article-title":"Tree species classification using WorldView-2 images based on recursive texture feature elimination","volume":"37","author":"Huaipeng","year":"2015","journal-title":"J. Beijing For. Univ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6956","DOI":"10.1080\/01431161.2012.695095","article-title":"Classification of Landsat images based on spectral and topographic variables for land-cover change detection in Zagros forests","volume":"33","author":"Falkowski","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","first-page":"12","article-title":"Land Use\/cover Change Detection with Multi-Source Data","volume":"11","author":"Jinshui","year":"2007","journal-title":"J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.isprsjprs.2020.11.023","article-title":"Spruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery","volume":"172","author":"Bhattarai","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.rse.2018.07.006","article-title":"Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery","volume":"216","author":"Erinjery","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.rse.2013.06.004","article-title":"Vegetation index suites as indicators of vegetation state in grassland and savanna: An analysis with simulated SENTINEL 2 data for a North American transect","volume":"137","author":"Hill","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"41","DOI":"10.3897\/natureconservation.35.29588","article-title":"The utility of Sentinel-2 Vegetation Indices (VIs) and Sentinel-1 Synthetic Aperture Radar (SAR) for invasive alien species detection and mapping","volume":"35","author":"Rajah","year":"2019","journal-title":"Nat. Conserv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"6041","DOI":"10.3390\/rs70506041","article-title":"Phenology-based vegetation index differencing for mapping of rubber plantations using landsat OLI data","volume":"7","author":"Fan","year":"2015","journal-title":"Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1016\/j.rse.2011.01.009","article-title":"Object-based crop identification using multiple vegetation indices, textural features and crop phenology","volume":"115","author":"Ngugi","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"8207","DOI":"10.1080\/01431161.2010.532831","article-title":"Land-cover classification in a moist tropical region of Brazil with Landsat Thematic Mapper imagery","volume":"32","author":"Li","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_35","first-page":"32","article-title":"Forest cover classification using Landsat ETM+ data and time series MODIS NDVI data","volume":"33","author":"Jia","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2795","DOI":"10.3390\/rs5062795","article-title":"Mapping rubber plantations and natural forests in Xishuangbanna (Southwest China) using multi-spectral phenological metrics from modis time series","volume":"5","author":"Senf","year":"2013","journal-title":"Remote Sens."},{"key":"ref_37","first-page":"10","article-title":"The latest applications of optical image texture in forestry","volume":"37","author":"Ling","year":"2015","journal-title":"J. Beijing For. Univ."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, C., Huang, C., Li, H., Liu, Q., Li, J., Bridhikitti, A., and Liu, G. (2020). Effect of textural features in remote sensed data on rubber plantation extraction at different levels of spatial resolution. Forests, 11.","DOI":"10.3390\/f11040399"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4287","DOI":"10.1080\/0143116042000192367","article-title":"A multiscale texture analysis procedure for improved forest stand classification","volume":"25","author":"Coburn","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"3417","DOI":"10.1080\/01431160701601782","article-title":"Analysis of co-occurrence and discrete wavelet transform textures for differentiation of forest and non-forest vegetation in very-high-resolution optical-sensor imagery","volume":"29","author":"Ouma","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1080\/014311600210993","article-title":"Incorporating texture into classification of forest species composition from airborne multispectral images","volume":"21","author":"Franklin","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.rse.2007.02.014","article-title":"Application of high spatial resolution satellite imagery for riparian and forest ecosystem classification","volume":"110","author":"Johansen","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_43","first-page":"96","article-title":"Analysis of correlation between terrain and forest spatial distribution based on DEM","volume":"40","author":"Wu","year":"2012","journal-title":"J. North-East For. Univ."},{"key":"ref_44","first-page":"360","article-title":"Random forest classification of Landsat 8 imagery for the complex terrain area based on the combination of spectral, topographic and texture information","volume":"21","author":"Ma","year":"2019","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_45","first-page":"85","article-title":"Analysis on forest spatial distr ibution based on DEM","volume":"3","author":"Zeng","year":"2005","journal-title":"Territ. Resour. Study"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/S0378-1127(03)00113-0","article-title":"Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification","volume":"183","author":"Dorren","year":"2003","journal-title":"For. Ecol. Manag."},{"key":"ref_47","unstructured":"Strahler, A.H., Logan, T.L., and Bryant, N.A. (1978, January 20\u201326). Improving forest cover classification accuracy from Landsat by incorporating topographic information. Proceedings of the 12th International Symposium on Remote Sensing of Environment, Manila, Philippines."},{"key":"ref_48","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_49","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2011.12.003","article-title":"Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture","volume":"121","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Song, Q., Hu, Q., Zhou, Q., Hovis, C., Xiang, M., Tang, H., and Wu, W. (2017). In-season crop mapping with GF-1\/WFV data by combining object-based image analysis and random forest. Remote Sens., 9.","DOI":"10.3390\/rs9111184"},{"key":"ref_51","first-page":"235","article-title":"Object-oriented mapping of urban trees using random forestclassifiers","volume":"26","author":"Puissant","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"3621","DOI":"10.1007\/s11356-018-3824-y","article-title":"Mapping terrestrial oil spill impact using machine learning random forest and Landsat 8 OLI imagery: A case site within the Niger Delta region of Nigeria","volume":"26","author":"Ozigis","year":"2019","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Boonprong, S., Cao, C., Chen, W., and Bao, S. (2018). Random forest variable importance spectral indices scheme for burnt forest recovery monitoring-multilevel RF-VIMP. Remote Sens., 10.","DOI":"10.3390\/rs10060807"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1080\/01431161.2017.1395968","article-title":"A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis","volume":"39","author":"Shi","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1843","DOI":"10.1016\/j.rse.2009.04.015","article-title":"Monitoring of cropland practices for carbon sequestration purposes in north central Montana by Landsat remote sensing","volume":"113","author":"Watts","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_56","first-page":"1777","article-title":"The classification of urban greening tree species based on feature selection of random forest","volume":"20","author":"Wen","year":"2018","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.1016\/j.rse.2008.02.011","article-title":"Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery","volume":"112","author":"Chan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_58","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_59","doi-asserted-by":"crossref","unstructured":"Aygun, S., and Gunes, E.O. (2017, January 7\u201310). A benchmarking: Feature extraction and classification of agricultural textures using LBP, GLCM, RBO, Neural Networks, k-NN, and random forest. Proceedings of the 2017 6th International Conference on Agro-Geoinformatics, Agro-Geoinformatics, Fairfax, VA, USA.","DOI":"10.1109\/Agro-Geoinformatics.2017.8047000"},{"key":"ref_60","unstructured":"Qi, H. (2021, June 05). China High-Resolution Earth Observation System (CHEOS) and Its Latest Development. Available online: http:\/\/www.unoosa.org\/pdf\/pres\/stsc2014\/tech-47E.pdf."},{"key":"ref_61","unstructured":"(2020, July 06). Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2H Version 6 Product, Available online: https:\/\/ladsweb.modaps.eosdis.nasa.gov\/."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern. SMC"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"203","DOI":"10.5194\/isprsarchives-XXXIX-B7-203-2012","article-title":"Random Forests-Based Feature Selection for Land-Use Classification Using Lidar Data and Orthoimagery","volume":"XXXIX-B7","author":"Guan","year":"2012","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"012071","DOI":"10.1088\/1755-1315\/17\/1\/012071","article-title":"Forest classification based on forest texture in Northwest Yunnan Province","volume":"17","author":"Wang","year":"2014","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_65","first-page":"3986","article-title":"Forest tree species identification and its response to spatial scale based on multispectral and multi-resolution remotely sensed data","volume":"29","author":"Xu","year":"2018","journal-title":"Chin. J. Appl. Ecol."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1016\/j.rse.2009.02.014","article-title":"A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification","volume":"113","author":"Pacifici","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1080\/01431160500295885","article-title":"On the optimization and selection of wavelet texture for feature extraction from high-resolution satellite imagery with application towards urban-tree delineation","volume":"27","author":"Ouma","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Wei, P., Zhu, W., Zhao, Y., Fang, P., Zhang, X., Yan, N., and Zhao, H. (2021). Extraction of Kenyan grassland information using PROBA-V based on RFE-RF algorithm. Remote Sens., 13.","DOI":"10.3390\/rs13234762"},{"key":"ref_69","unstructured":"Kuhn, M. (2020, April 17). The Caret Package. Available online: http:\/\/cran.r-project.org\/web\/packages\/caret\/."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Lou, P., Fu, B., He, H., Li, Y., Tang, T., Lin, X., Fan, D., and Gao, E. (2020). An optimized object-based random forest algorithm for marsh vegetation mapping using high-spatial-resolution GF-1 and ZY-3 data. Remote Sens., 12.","DOI":"10.3390\/rs12081270"},{"key":"ref_71","first-page":"1325","article-title":"Mapping the Spatial Distribution of Tea Plantations with 10m Resolution in Fujian Province Using Google Earth Engine","volume":"23","author":"Xiong","year":"2021","journal-title":"J. Geo-Inf. Sci."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2838","DOI":"10.3390\/rs5062838","article-title":"The performance of random forests in an operational setting for large area sclerophyll forest classification","volume":"5","author":"Mellor","year":"2013","journal-title":"Remote Sens."},{"key":"ref_73","first-page":"4","article-title":"Spafio\u2014Temporal Dynamic Analysis and Evaluation of Forest Resources in Yanqing County","volume":"26","author":"Zhang","year":"2010","journal-title":"For. Eng."},{"key":"ref_74","unstructured":"Ying, Z. (2018). Classification of Forest Types Based on Multi-Dimensional Features Using Multi-Seasonal Landsat-8 OLI Remote Sensing Images. [Doctoral Thesis, Beijing Forestry University]."},{"key":"ref_75","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_76","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_77","first-page":"3495","article-title":"The differences between extracting vegetation information from GF1-WFV and Landsat8-OLI","volume":"40","author":"Zhao","year":"2020","journal-title":"Acta Ecol. Sin."},{"key":"ref_78","unstructured":"National Forestry and Grassland Administration (2014). China Forest Resources Report (2009\u20132013), China Forestry Publishing House."},{"key":"ref_79","unstructured":"Sihan, L. (2020). Extraction of Larch Plantations Using Texture Features within High Spatial Resolution Images. [Masteral Thesis, Xi\u2019an University of Science and Technology]."},{"key":"ref_80","first-page":"52","article-title":"Identification of forest type with Landsat-8 image based on SVM","volume":"37","author":"Li","year":"2017","journal-title":"J. Cent. South Univ. For. Technol."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"3966","DOI":"10.1080\/01431161.2011.636081","article-title":"Google Earth as a virtual globe tool for Earth science applications at the global scale: Progress and perspectives","volume":"33","author":"Yu","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_82","first-page":"117","article-title":"Mapping tropical forests and deciduous rubber plantations in Hainan Island, China by integrating PALSAR 25-m and multi-temporal Landsat images","volume":"50","author":"Chen","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"2815","DOI":"10.1016\/S2095-3119(20)63208-7","article-title":"Comparison of machine learning algorithms for mapping mango plantations based on Gaofen-1 imagery","volume":"19","author":"Luo","year":"2020","journal-title":"J. Integr. Agric."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1007\/s11676-017-0518-0","article-title":"Using multi-source remote sensing data to classify larch plantations in Northeast China and support the development of multi-purpose silviculture","volume":"29","author":"Shang","year":"2018","journal-title":"J. For. Res."},{"key":"ref_85","first-page":"25","article-title":"Monitoring and Evaluation System of Ecological and Environmental Management of Plantation","volume":"12","author":"Lan","year":"2013","journal-title":"J. Beijing For. Univ. (Soc. Sci.)"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4946\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:46:01Z","timestamp":1760143561000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/19\/4946"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,3]]},"references-count":85,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["rs14194946"],"URL":"https:\/\/doi.org\/10.3390\/rs14194946","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,3]]}}}