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However, the spectral similarity between bamboo species makes this work extremely challenging through remote sensing technology. Existing related studies rarely integrate multiple feature variables and consider how to quantify the main factors affecting classification. Therefore, feature variables, such as spectra, topography, texture, and vegetation indices, were used to construct the XGBoost model to identify bamboo species using the Zhuhai-1 Orbita hyperspectral (OHS) imagery in the Southern Sichuan Bamboo Sea and its surrounding areas in Sichuan Province, China. The random forest and Spearman\u2019s rank correlation analysis were used to sort the main variables that affect classification accuracy and minimize the effects of multicollinearity among variables. The main findings were: (1) The XGBoost model achieved accurate and reliable classification results. The XGBoost model had a higher overall accuracy (80.6%), kappa coefficient (0.708), and mean F1-score (0.805) than the spectral angle mapper (SAM) method; (2) The optimal feature variables that were important and uncorrelated for classification accuracy included the blue band (B1, 464\u2013468 nm), near-infrared band (B27, 861\u2013871 nm), green band (B5, 534\u2013539 nm), elevation, texture feature mean, green band (B4, 517\u2013523 nm), and red edge band (B17, 711\u2013720 nm); and (3) the XGBoost model based on the optimal feature variable selection showed good adaptability to land classification and had better classification performance. Moreover, the mean F1-score indicated that the model could well balance the user\u2019s and producer\u2019s accuracy. Additionally, our study demonstrated that OHS imagery has great potential for land cover classification and that combining multiple features to enhance classification is an approach worth exploring. Our study provides a methodological reference for the application of OHS images for plant species identification.<\/jats:p>","DOI":"10.3390\/s22145434","type":"journal-article","created":{"date-parts":[[2022,7,21]],"date-time":"2022-07-21T03:34:40Z","timestamp":1658374480000},"page":"5434","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Identification of Bamboo Species Based on Extreme Gradient Boosting (XGBoost) Using Zhuhai-1 Orbita Hyperspectral Remote Sensing Imagery"],"prefix":"10.3390","volume":"22","author":[{"given":"Guoli","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"},{"name":"Key Laboratory of National Forestry and Grassland Administration\/Beijing for Bamboo & Rattan Science and Technology, Institute of Resources and Environment, International Centre for Bamboo and Rattan, Beijing 100102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7729-6610","authenticated-orcid":false,"given":"Zhongyun","family":"Ni","sequence":"additional","affiliation":[{"name":"College of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"},{"name":"Key Laboratory of National Forestry and Grassland Administration\/Beijing for Bamboo & Rattan Science and Technology, Institute of Resources and Environment, International Centre for Bamboo and Rattan, Beijing 100102, China"},{"name":"School of Geography, Archaeology & Irish Studies, National University of Ireland, Galway (NUIG), H91 CF50 Galway, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinbing","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu 610059, China"},{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"},{"name":"School of Geography, Archaeology & Irish Studies, National University of Ireland, Galway (NUIG), H91 CF50 Galway, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3848-4747","authenticated-orcid":false,"given":"Junwei","family":"Luan","sequence":"additional","affiliation":[{"name":"Key Laboratory of National Forestry and Grassland Administration\/Beijing for Bamboo & Rattan Science and Technology, Institute of Resources and Environment, International Centre for Bamboo and Rattan, Beijing 100102, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,20]]},"reference":[{"key":"ref_1","unstructured":"FAO (2020). Global Forest Resources Assessment 2020: Main Report, Food and Agriculture Organization of the United Nations."},{"key":"ref_2","first-page":"45","article-title":"Bamboo resources in China based on the ninth national forest inventory data","volume":"17","author":"Li","year":"2019","journal-title":"World Bamboo Ratt."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1007\/s12229-011-9082-z","article-title":"Review of carbon fixation in bamboo forests in China","volume":"77","author":"Zhou","year":"2011","journal-title":"Bot. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.foreco.2018.07.035","article-title":"Quantifying driving factors of vegetation carbon stocks of moso bamboo forests using machine learning algorithm combined with structural equation model","volume":"429","author":"Shi","year":"2018","journal-title":"For. Ecol. Manag."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Li, Y., Han, N., Li, X., Du, H., Mao, F., Cui, L., Liu, T., and Xing, L. (2018). Spatiotemporal estimation of bamboo forest aboveground carbon storage based on Landsat data in Zhejiang, China. Remote Sens., 10.","DOI":"10.3390\/rs10060898"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.agrformet.2017.09.001","article-title":"Observed high and persistent carbon uptake by moso bamboo forests and its response to environmental drivers","volume":"247","author":"Song","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"110329","DOI":"10.1016\/j.ecoenv.2020.110329","article-title":"Different strategies for lead detoxification in dwarf bamboo tissues","volume":"193","author":"Jiang","year":"2020","journal-title":"Ecotoxicol. Environ. Saf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"111500","DOI":"10.1016\/j.ecoenv.2020.111500","article-title":"Biomass allocation strategies and Pb-enrichment characteristics of six dwarf bamboos under soil Pb stress","volume":"207","author":"Cai","year":"2021","journal-title":"Ecotoxicol. Environ. Saf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1007\/BF02857909","article-title":"Ecological functions of bamboo forest: Research and application","volume":"16","author":"Zhou","year":"2005","journal-title":"J. For. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.jenvman.2015.03.030","article-title":"Current and potential carbon stocks in moso bamboo forests in China","volume":"156","author":"Li","year":"2015","journal-title":"J. Environ. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"995","DOI":"10.1016\/j.foreco.2010.12.015","article-title":"Comparing aboveground carbon sequestration between moso bamboo (Phyllostachys heterocycla) and China fir (Cunninghamia lanceolata) forests based on the allometric model","volume":"261","author":"Yen","year":"2011","journal-title":"For. Ecol. Manag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1139\/a11-015","article-title":"Carbon sequestration by Chinese bamboo forests and their ecological benefits: Assessment of potential, problems, and future challenges","volume":"19","author":"Song","year":"2011","journal-title":"Environ. Rev."},{"key":"ref_13","first-page":"654","article-title":"Managing woody bamboos for carbon farming and carbon trading","volume":"3","author":"Nath","year":"2015","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.foreco.2017.01.017","article-title":"Carbon stocks in bamboo ecosystems worldwide: Estimates and uncertainties","volume":"393","author":"Yuen","year":"2017","journal-title":"For. Ecol. Manag."},{"key":"ref_15","unstructured":"Yi, T. (2008). Iconographia Bambusoidearum Sinicarum, Science Press. (In Chinese)."},{"key":"ref_16","unstructured":"Fang, W. (2015). Chinese Economic Bamboo, Science Press. (In Chinese)."},{"key":"ref_17","first-page":"1","article-title":"Classification of tree species composition using a combination of multispectral imagery and airborne laser scanning data","volume":"63","author":"Sedliak","year":"2017","journal-title":"Cent. Eur. For. J."},{"key":"ref_18","first-page":"49","article-title":"A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales","volume":"26","author":"Ghosh","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.rse.2016.12.025","article-title":"Mapping the dynamics of eastern redcedar encroachment into grasslands during 1984\u20132010 through PALSAR and time series Landsat images","volume":"190","author":"Wang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4254","DOI":"10.1080\/01431161.2018.1452075","article-title":"Land cover 2.0","volume":"39","author":"Wulder","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"2223","DOI":"10.1007\/s11430-017-9143-3","article-title":"Continuous land cover change monitoring in the remote sensing big data era","volume":"60","author":"Dong","year":"2017","journal-title":"Sci. China Earth Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1080\/01431160701736471","article-title":"Species identification of individual trees by combining high resolution LiDAR data with multi-spectral images","volume":"29","author":"Holmgren","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_25","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_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2016.12.002","article-title":"Automatic tree species recognition with quantitative structure models","volume":"191","author":"Raumonen","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"535","DOI":"10.5721\/EuJRS20134631","article-title":"Tree species classification and input data evaluation","volume":"46","author":"Krahwinkler","year":"2013","journal-title":"Eur. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sheeren, D., Fauvel, M., Josipovi\u0107, 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_29","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/j.rse.2005.03.009","article-title":"Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales","volume":"96","author":"Clark","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.rse.2015.05.004","article-title":"A multi-temporal spectral library approach for mapping vegetation species across spatial and temporal phenological gradients","volume":"167","author":"Dudley","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ballanti, L., Blesius, L., Hines, E., and Kruse, B. (2016). Tree species classification using hyperspectral imagery: A comparison of two classifiers. Remote Sens., 8.","DOI":"10.3390\/rs8060445"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"7032","DOI":"10.1109\/JSTARS.2021.3090256","article-title":"Hyperspectral satellites, evolution, and development history","volume":"14","author":"Qian","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","first-page":"479","article-title":"Development status and literature analysis of China\u2019s earth observation remote sensing satellites","volume":"24","author":"Sun","year":"2020","journal-title":"J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Meng, X., Bao, Y., Ye, Q., Liu, H., Zhang, X., Tang, H., and Zhang, X. (2021). Soil organic matter prediction model with satellite hyperspectral image based on optimized denoising method. Remote Sens., 13.","DOI":"10.3390\/rs13122273"},{"key":"ref_36","first-page":"454","article-title":"Fine mineral identification of GF-5 hyperspectral image","volume":"24","author":"Dong","year":"2020","journal-title":"J. Remote Sens."},{"key":"ref_37","first-page":"24","article-title":"Greenhouse gases monitoring instrument(GMI) on GF-5 satellite (invited)","volume":"48","author":"Xiong","year":"2019","journal-title":"Infrared Laser Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5877","DOI":"10.1109\/TGRS.2017.2716401","article-title":"Land surface temperature estimate from Chinese Gaofen-5 satellite data using split-window algorithm","volume":"55","author":"Ye","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","first-page":"186","article-title":"Application evaluation of ZY-1-02D satellite hyperspectral data in geological survey","volume":"29","author":"Li","year":"2020","journal-title":"Spacecr. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Peng, Y., Zhao, L., Hu, Y., Wang, G., Wang, L., and Liu, Z. (2019). Prediction of soil nutrient contents using visible and near-infrared reflectance spectroscopy. ISPRS Int. J. Geo-inf., 8.","DOI":"10.3390\/ijgi8100437"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.isprsjprs.2017.12.003","article-title":"Hyperspectral sensing of heavy metals in soil and vegetation: Feasibility and challenges","volume":"136","author":"Wang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","first-page":"71","article-title":"Study on spectral unmixing model of chlorophyll-a concentration extraction based on HJ-1 HSI hyperspectral data","volume":"17","author":"Pan","year":"2017","journal-title":"Sci. Technol. Eng."},{"key":"ref_43","first-page":"53","article-title":"Study on monitoring freezing injury to winter wheat in overwinter period based on hyper-spectrometer and HJ1A-HSI image","volume":"26","author":"Li","year":"2017","journal-title":"J. Nat. Disasters"},{"key":"ref_44","first-page":"40","article-title":"Inversion of topsoil organic matter content by hyperspectral remote sensing of Zhuhai-1","volume":"35","author":"Sun","year":"2020","journal-title":"Remote Sens. Inf."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Kahaer, Y., Tashpolat, N., Shi, Q., and Liu, S. (2020). Possibility of Zhuhai-1 hyperspectral imagery for monitoring salinized soil moisture content using fractional order differentially optimized spectral indices. Water, 12.","DOI":"10.3390\/w12123360"},{"key":"ref_46","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_47","doi-asserted-by":"crossref","first-page":"014519","DOI":"10.1117\/1.JRS.15.014519","article-title":"Orbita hyperspectral satellite image for land cover classification using random forest classifier","volume":"15","author":"Mo","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"4042","DOI":"10.1080\/01431161.2021.1887543","article-title":"Evaluating satellite hyperspectral (Orbita) and multispectral (Landsat 8 and Sentinel-2) imagery for identifying cotton acreage","volume":"42","author":"Wang","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_49","first-page":"332","article-title":"A comparative study on wheat identification and growth monitoring based on multi-source remote sensing data","volume":"36","author":"Yin","year":"2021","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_50","first-page":"236","article-title":"Review of hyperspectral remote sensing image classification","volume":"20","author":"Du","year":"2016","journal-title":"J. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1062","DOI":"10.1109\/TGRS.2015.2473705","article-title":"Advancement of hyperspectral image processing and information extraction","volume":"20","author":"Zhang","year":"2016","journal-title":"J. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1111\/jbi.12002","article-title":"Historical fire and bamboo dynamics in western Amazonia","volume":"40","author":"McMichael","year":"2013","journal-title":"J. Biogeogr."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1714","DOI":"10.1080\/01431161.2016.1165885","article-title":"Tracking bamboo dynamics in Zhejiang, China, using time-series of Landsat data from 1990 to 2014","volume":"37","author":"Li","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Qi, S., Song, B., Liu, C., Gong, P., Luo, J., Zhang, M., and Xiong, T. (2022). Bamboo forest mapping in China using the dense Landsat 8 image archive and Google Earth Engine. Remote Sens., 14.","DOI":"10.3390\/rs14030762"},{"key":"ref_55","first-page":"298","article-title":"A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery","volume":"26","author":"Ghosh","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1786","DOI":"10.1109\/JSTARS.2013.2262767","article-title":"Coffee crop\u2019s biomass and carbon stock estimation with usage of high resolution satellites images","volume":"6","author":"Coltri","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Ma, Y., Fang, S., Peng, Y., Gong, Y., and Wang, D. (2019). Remote estimation of biomass in winter oilseed rape (Brassica napus L.) using canopy hyperspectral data at different growth stages. Appl. Sci., 9.","DOI":"10.3390\/app9030545"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.iswcr.2015.10.002","article-title":"Relationship between topography and the distribution of understory vegetation in a Pinus massoniana forest in southern China","volume":"3","author":"Wang","year":"2015","journal-title":"Int. Soil Water Conserv. Res."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.foreco.2007.01.056","article-title":"Overstory structure and topographic gradients determining diversity and abundance of understory shrub species in temperate forests in central Pyrenees (NE Spain)","volume":"242","author":"Gracia","year":"2007","journal-title":"For. Ecol. Manag."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/0341-8162(96)00005-7","article-title":"Influence of topography on some vegetation cover properties","volume":"27","author":"Florinsky","year":"1996","journal-title":"Catena"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"641","DOI":"10.2307\/3235880","article-title":"Predicting vegetation types at treeline using topography and biophysical disturbance variables","volume":"5","author":"Brown","year":"1994","journal-title":"J. Veg. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Cao, J., Leng, W., Liu, K., Liu, L., and Zhu, Y. (2018). Object-based mangrove species classification using unmanned aerial vehicle hyperspectral images and digital surface models. Remote Sens., 10.","DOI":"10.3390\/rs10010089"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1007\/s12524-017-0663-0","article-title":"Multi-feature classification approach for high spatial resolution hyperspectral images","volume":"46","author":"Yumin","year":"2018","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Thenkabail, P.S. (2016). Remote Sensing Handbook; Volume 1: Remotely Sensed Data Characterization, Classification, and Accuracies, Taylor & Francis.","DOI":"10.1201\/b19294"},{"key":"ref_65","first-page":"3010","article-title":"Hyperspectral Bambusoideae discrimination based on Mann-Whitney non-parametric test and SVM","volume":"31","author":"Chen","year":"2011","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_66","first-page":"1718","article-title":"Discriminant analysis of bamboo leaf types with NIR coupled with characteristic wavelengths","volume":"37","author":"Chu","year":"2017","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_67","first-page":"314","article-title":"Automatic identification of tree species based on airborne LiDAR and hyperspectral data","volume":"35","author":"Tao","year":"2018","journal-title":"J. Zhejiang Agric. For. Univ."},{"key":"ref_68","first-page":"75","article-title":"Comparison among methods that extract forest information from hyper-spectral remote sensing image","volume":"33","author":"Zhang","year":"2013","journal-title":"J. Central South Univ. For. Technol."},{"key":"ref_69","first-page":"1100","article-title":"Response and recognition of the spectral information of bamboo Phyllostachy sedulis based on HJ-1HIS data","volume":"38","author":"Liu","year":"2016","journal-title":"Acta Agric. Univ. Jiangxiensis"},{"key":"ref_70","unstructured":"Cai, L., Wu, D., Fang, L., and Zheng, X. (2019). Tree species identification using XGBoost based on GF-2 images. For. Resour. Manag., 44\u201351. (In Chinese)."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Lin, Y., Guo, H., and Hu, J. (2013, January 4\u20139). An SVM-based approach for stock market trend prediction. Proceedings of the 2013 International Joint Conference on Neural Networks (IJCNN), Dallas, TX, USA.","DOI":"10.1109\/IJCNN.2013.6706743"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1109\/LGRS.2008.915597","article-title":"Multiclass and binary SVM classification: Implications for training and classification users","volume":"5","author":"Mathur","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.neunet.2013.11.013","article-title":"Robust support vector machine-trained fuzzy system","volume":"50","author":"Forghani","year":"2014","journal-title":"Neural Netw."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.enconman.2018.02.087","article-title":"Comparison of support vector machine and extreme gradient boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China","volume":"164","author":"Fan","year":"2018","journal-title":"Energy Convers. Manag."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","article-title":"Variable selection using random forests","volume":"31","author":"Genuer","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_77","unstructured":"Cooley, T., Anderson, G.P., Felde, G.W., Hoke, M.L., Ratkowski, A.J., Chetwynd, J.H., Gardner, J.A., Adler-Golden, S.M., Matthew, M.W., and Berk, A. (2002, January 24\u201328). FLAASH, a MODTRAN4-based atmospheric correction algorithm, its application and validation. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, Toronto, ON, Canada."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1109\/TGRS.2006.888937","article-title":"Automatic and precise orthorectification, coregistration, and subpixel correlation of satellite images, application to ground deformation measurements","volume":"45","author":"Leprince","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"2148","DOI":"10.1109\/TGRS.2005.852480","article-title":"SCS + C: A modified sun-canopy-sensor topographic correction in forested terrain","volume":"43","author":"Soenen","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.isprsjprs.2018.04.002","article-title":"A review of accuracy assessment for object-based image analysis: From per-pixel to per-polygon approaches","volume":"141","author":"Ye","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0034-4257(93)90013-N","article-title":"The spectral image processing system (SIPS)\u2014interactive visualization and analysis of imaging spectrometer data","volume":"44","author":"Kruse","year":"1993","journal-title":"Remote Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"111407","DOI":"10.1016\/j.rse.2019.111407","article-title":"Carotenoid based vegetation indices for accurate monitoring of the phenology of photosynthesis at the leaf-scale in deciduous and evergreen trees","volume":"233","author":"Wong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.rse.2014.03.018","article-title":"Urban tree species mapping using hyperspectral and lidar data fusion","volume":"148","author":"Alonzo","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.fcr.2013.09.023","article-title":"Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages","volume":"155","author":"Gnyp","year":"2014","journal-title":"Field Crops Res."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Yue, J., Yang, G., Li, C., Li, Z., Wang, Y., Feng, H., and Xu, B. (2017). Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sens., 9.","DOI":"10.3390\/rs9070708"},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Shoot, C., Andersen, H.-E., Moskal, L.M., Babcock, C., Cook, B.D., and Morton, D.C. (2021). Classifying forest type in the national forest inventory context with airborne hyperspectral and lidar data. Remote Sens., 13.","DOI":"10.3390\/rs13101863"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_88","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_89","doi-asserted-by":"crossref","first-page":"1763","DOI":"10.1213\/ANE.0000000000002864","article-title":"Correlation coefficients: Appropriate use and interpretation","volume":"126","author":"Schober","year":"2018","journal-title":"Anesth. Analg."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"8489","DOI":"10.3390\/rs70708489","article-title":"On the importance of training data sample selection in random forest image classification: A case study in peatland ecosystem mapping","volume":"7","author":"Millard","year":"2015","journal-title":"Remote Sens."},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the  22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v028.i05","article-title":"Building predictive models in R using the caret package","volume":"28","author":"Kuhn","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An Introduction to Statistical Learning, Springer.","DOI":"10.1007\/978-1-4614-7138-7"},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/S0034-4257(01)00295-4","article-title":"Status of land cover classification accuracy assessment","volume":"80","author":"Foody","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Dabija, A., Kluczek, M., Zagajewski, B., Raczko, E., Kycko, M., Al-Sulttani, A.H., Tard\u00e0, A., Pineda, L., and Corbera, J. (2021). Comparison of support vector machines and random forests for corine land cover mapping. Remote Sens., 13.","DOI":"10.3390\/rs13040777"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1007\/s11222-017-9746-6","article-title":"A note on using the F-measure for evaluating record linkage algorithms","volume":"28","author":"Hand","year":"2018","journal-title":"Stat. Comput."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"111265","DOI":"10.1016\/j.rse.2019.111265","article-title":"Mapping moso bamboo forest and its on-year and off-year distribution in a subtropical region using time-series Sentinel-2 and Landsat 8 data","volume":"231","author":"Li","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_98","first-page":"116","article-title":"Bamboo mapping of Ethiopia, Kenya and Uganda for the year 2016 using multi-temporal Landsat imagery","volume":"66","author":"Zhao","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1458","DOI":"10.1109\/JSTARS.2018.2800127","article-title":"Mapping global bamboo forest distribution using multisource remote sensing data","volume":"11","author":"Du","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_100","unstructured":"Girouard, G., Bannari, A., El Harti, A., and Desrochers, A. (2004, January 12\u201323). Validated spectral angle mapper algorithm for geological mapping: Comparative study between QuickBird and Landsat-TM. Proceedings of the XXth ISPRS Congress, Geo-Imagery Bridging Continents, Istanbul, Turkey."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1080\/17538947.2012.671378","article-title":"Evaluation of diverse classification approaches for land use\/cover mapping in a Mediterranean region utilizing Hyperion data","volume":"7","author":"Elatawneh","year":"2014","journal-title":"Int. J. Digit. Earth"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"DeLancey, E.R., Kariyeva, J., Bried, J.T., and Hird, J.N. (2019). Large-scale probabilistic identification of boreal peatlands using Google Earth Engine, open-access satellite data, and machine learning. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0218165"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Samat, A., Li, E., Wang, W., Liu, S., Lin, C., and Abuduwaili, J. (2020). Meta-XGBoost for hyperspectral image classification using extended MSER-guided morphological profiles. Remote Sens., 12.","DOI":"10.3390\/rs12121973"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1080\/01431160310001598971","article-title":"Using artificial neural networks to map the spatial distribution of understorey bamboo from remote sensing data","volume":"25","author":"Linderman","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Jafarzadeh, H., Mahdianpari, M., Gill, E., Mohammadimanesh, F., and Homayouni, S. (2021). Bagging and boosting ensemble classifiers for classification of multispectral, hyperspectral and PolSAR data: A comparative evaluation. Remote Sens., 13.","DOI":"10.3390\/rs13214405"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A computer movie simulating urban growth in the Detroit region","volume":"46","author":"Tobler","year":"1970","journal-title":"Econ. Geogr."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"2604","DOI":"10.1109\/TCYB.2019.2905793","article-title":"Dimensionality reduction of hyperspectral imagery based on spatial-spectral manifold learning","volume":"50","author":"Huang","year":"2020","journal-title":"IEEE Trans. Cybern."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"1685","DOI":"10.1007\/s12145-021-00621-6","article-title":"Hyperspectral and multispectral image fusion techniques for high resolution applications: A review","volume":"14","author":"Sara","year":"2021","journal-title":"Earth Sci. Inform."},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Dashti, H., Poley, A., Glenn, N.F., Ilangakoon, N., Spaete, L., Roberts, D., Enterkine, J., Flores, A.N., Ustin, S.L., and Mitchell, J.J. (2019). Regional scale dryland vegetation classification with an integrated lidar-hyperspectral approach. Remote Sens., 11.","DOI":"10.3390\/rs11182141"},{"key":"ref_110","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_111","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.rse.2017.05.006","article-title":"Enhanced forest cover mapping using spectral unmixing and object-based classification of multi-temporal Landsat imagery","volume":"196","author":"Pontius","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1007\/s10342-011-0577-2","article-title":"Satellite-based stand-wise forest cover type mapping using a spatially adaptive classification approach","volume":"131","author":"Stoffels","year":"2012","journal-title":"Eur. J. For. Res."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"175","DOI":"10.5589\/m05-004","article-title":"Issues in species classification of trees in old growth conifer stands","volume":"31","author":"Leckie","year":"2005","journal-title":"Can. J. Remote Sens."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Abdollahnejad, A., Panagiotidis, D., Joybari, S.S., and Surovy, P. (2017). Prediction of dominant forest tree species using QuickBird and environmental data. Forests, 8.","DOI":"10.3390\/f8020042"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5434\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:55:00Z","timestamp":1760140500000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/14\/5434"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,20]]},"references-count":114,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["s22145434"],"URL":"https:\/\/doi.org\/10.3390\/s22145434","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,20]]}}}