{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T03:58:08Z","timestamp":1775102288603,"version":"3.50.1"},"reference-count":94,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,11]],"date-time":"2018-01-11T00:00:00Z","timestamp":1515628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mangroves are one of the most important coastal wetland ecosystems, and the compositions and distributions of mangrove species are essential for conservation and restoration efforts. Many studies have explored this topic using remote sensing images that were obtained by satellite-borne and airborne sensors, which are known to be efficient for monitoring the mangrove ecosystem. With improvements in carrier platforms and sensor technology, unmanned aerial vehicles (UAVs) with high-resolution hyperspectral images in both spectral and spatial domains have been used to monitor crops, forests, and other landscapes of interest. This study aims to classify mangrove species on Qi\u2019ao Island using object-based image analysis techniques based on UAV hyperspectral images obtained from a commercial hyperspectral imaging sensor (UHD 185) onboard a UAV platform. First, the image objects were obtained by segmenting the UAV hyperspectral image and the UAV-derived digital surface model (DSM) data. Second, spectral features, textural features, and vegetation indices (VIs) were extracted from the UAV hyperspectral image, and the UAV-derived DSM data were used to extract height information. Third, the classification and regression tree (CART) method was used to selection bands, and the correlation-based feature selection (CFS) algorithm was employed for feature reduction. Finally, the objects were classified into different mangrove species and other land covers based on their spectral and spatial characteristic differences. The classification results showed that when considering the three features (spectral features, textural features, and hyperspectral VIs), the overall classification accuracies of the two classifiers used in this paper, i.e., k-nearest neighbor (KNN) and support vector machine (SVM), were 76.12% (Kappa = 0.73) and 82.39% (Kappa = 0.801), respectively. After incorporating tree height into the classification features, the accuracy of species classification increased, and the overall classification accuracies of KNN and SVM reached 82.09% (Kappa = 0.797) and 88.66% (Kappa = 0.871), respectively. It is clear that SVM outperformed KNN for mangrove species classification. These results also suggest that height information is effective for discriminating mangrove species with similar spectral signatures, but different heights. In addition, the classification accuracy and performance of SVM can be further improved by feature reduction. The overall results provided evidence for the effectiveness and potential of UAV hyperspectral data for mangrove species identification.<\/jats:p>","DOI":"10.3390\/rs10010089","type":"journal-article","created":{"date-parts":[[2018,1,11]],"date-time":"2018-01-11T13:36:15Z","timestamp":1515677775000},"page":"89","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":224,"title":["Object-Based Mangrove Species Classification Using Unmanned Aerial Vehicle Hyperspectral Images and Digital Surface Models"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1239-2203","authenticated-orcid":false,"given":"Jingjing","family":"Cao","sequence":"first","affiliation":[{"name":"Center of Integrated Geographic Information Analysis, Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}]},{"given":"Wanchun","family":"Leng","sequence":"additional","affiliation":[{"name":"Center of Integrated Geographic Information Analysis, Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1829-7557","authenticated-orcid":false,"given":"Kai","family":"Liu","sequence":"additional","affiliation":[{"name":"Center of Integrated Geographic Information Analysis, Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7202-3418","authenticated-orcid":false,"given":"Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"Center of Geographic Information Analysis for Public Security, School of Geographic Sciences, Guangzhou University, Guangzhou 510006, China"},{"name":"Department of Geography, University of Cincinnati, Cincinnati, OH 45221-0131, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9568-7076","authenticated-orcid":false,"given":"Zhi","family":"He","sequence":"additional","affiliation":[{"name":"Center of Integrated Geographic Information Analysis, Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0474-5945","authenticated-orcid":false,"given":"Yuanhui","family":"Zhu","sequence":"additional","affiliation":[{"name":"Center of Integrated Geographic Information Analysis, Guangdong Key Laboratory for Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,11]]},"reference":[{"key":"ref_1","first-page":"592","article-title":"A review on the mangrove research in China","volume":"40","author":"Peng","year":"2001","journal-title":"J. Xiamen Univ. Nat. Sci."},{"key":"ref_2","first-page":"229","article-title":"Impact of the tsunami and earthquake of 26th December 2004 on the vital coastal ecosystems of the Andaman and Nicobar Islands assessed using RESOURCESAT AWIFS data","volume":"10","author":"Bahuguna","year":"2008","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_3","unstructured":"Food and Agriculture Organization (FAO) (2007). The World\u2019s Mangroves 1980\u20132005, FAO."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/j.ecolind.2016.03.036","article-title":"Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system","volume":"67","author":"Li","year":"2016","journal-title":"Ecol. Indic."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1111\/j.1466-8238.2010.00584.x","article-title":"Status and distribution of mangrove forests of the world using earth observation satellite data","volume":"20","author":"Giri","year":"2011","journal-title":"Glob. Ecol. Biogeogr."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1080\/014311698215801","article-title":"Remote sensing techniques for mangrove mapping","volume":"19","author":"Green","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2739","DOI":"10.1080\/0143116031000066323","article-title":"High resolution mapping of tropical mangrove ecosystems using hyperspectral and radar remote sensing","volume":"24","author":"Held","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s11852-014-0322-3","article-title":"A study on abundance and distribution of mangrove species in Indian Sundarban using remote sensing technique","volume":"18","author":"Giri","year":"2014","journal-title":"J. Coast. Conserv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.rse.2014.04.019","article-title":"Exploring the effects of biophysical parameters on the spatial pattern of rare cold damage to mangrove forests","volume":"150","author":"Liu","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"921","DOI":"10.14358\/PERS.74.7.921","article-title":"Neural network classification of mangrove species from multi-seasonal IKONOS imagery","volume":"74","author":"Wang","year":"2008","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1080\/14498596.2008.9635137","article-title":"Mangrove species and stand mapping in gazi bay (Kenya) using quickbird satellite imagery","volume":"53","author":"Neukermans","year":"2008","journal-title":"J. Spat. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.rse.2004.04.005","article-title":"Comparison of IKONOS and quickbird images for mapping mangrove species on the caribbean coast of panama","volume":"91","author":"Wang","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"12192","DOI":"10.3390\/rs70912192","article-title":"Retrieval of mangrove aboveground biomass at the individual species level with worldview-2 images","volume":"7","author":"Zhu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_14","first-page":"102","article-title":"Mangrove community classification based on worldview-2 image and SVM method","volume":"54","author":"Tang","year":"2015","journal-title":"Acta Sci. Nat. Univ. Sunyatseni"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1007\/s10661-010-1327-5","article-title":"Mapping urban forest tree species using IKONOS imagery: Preliminary results","volume":"172","author":"Pu","year":"2011","journal-title":"Environ. Monit. Assess."},{"key":"ref_16","unstructured":"Tong, Q., Zhang, B., and Zheng, L. (2006). Hyperspectral Remote Sensing: Principles, Techniques and Applications, Higher Education Press."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Guo, M., Li, J., Sheng, C., Xu, J., and Wu, L. (2017). A review of wetland remote sensing. Sensors, 17.","DOI":"10.3390\/s17040777"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1672\/18-20","article-title":"Hyperspectral image data for mapping wetland vegetation","volume":"23","author":"Hirano","year":"2003","journal-title":"Wetlands"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3562","DOI":"10.3390\/rs5073562","article-title":"Discrimination of tropical mangroves at the species level with EO-1 hyperion data","volume":"5","author":"Koedsin","year":"2013","journal-title":"Remote Sens."},{"key":"ref_20","first-page":"226","article-title":"Mapping the distribution of mangrove species in the Core Zone of Mai Po Marshes Nature Reserve, Hong Kong, using hyperspectral data and high-resolution data","volume":"33","author":"Jia","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.3390\/rs3102222","article-title":"Hyperspectral data for mangrove species mapping: A comparison of pixel-based and object-based approach","volume":"3","author":"Kamal","year":"2011","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1109\/JPROC.2012.2197589","article-title":"Advances in spectral-spatial classification of hyperspectral images","volume":"101","author":"Fauvel","year":"2013","journal-title":"Proc. IEEE"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2177","DOI":"10.1109\/JSTARS.2015.2417859","article-title":"Hyperspectral tree species classification of Japanese complex mixed forest with the aid of LiDAR data","volume":"8","author":"Matsuki","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","unstructured":"Boas, D.A., Pitris, C., and Ramanujam, N. (2011). Multi\/Hyper-Spectral Imaging. Handbook of Biomedical Optics, CRC Press."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.isprsjprs.2014.02.013","article-title":"Unmanned aerial systems for photogrammetry and remote sensing: A review","volume":"92","author":"Colomina","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.isprsjprs.2015.08.002","article-title":"Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance","volume":"108","author":"Aasen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1127\/pfg\/2015\/0256","article-title":"Low-weight and UAV-based hyperspectral full-frame cameras for monitoring crops: Spectral comparison with portable spectroradiometer measurements","volume":"2015","author":"Bareth","year":"2015","journal-title":"Photogramm. Fernerkund. Geoinf."},{"key":"ref_28","first-page":"159","article-title":"Determining surface magnetic susceptibility of loess-paleosol sections based on spectral features: Application to a UHD 185 hyperspectral image","volume":"50","author":"Cui","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2736","DOI":"10.3390\/rs4092736","article-title":"Radiometric and geometric analysis of hyperspectral imagery acquired from an unmanned aerial vehicle","volume":"4","author":"Hruska","year":"2012","journal-title":"Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mitchell, J.J., Glenn, N.F., Anderson, M.O., Hruska, R.C., Halford, A., Baun, C., and Nydegger, N. (2012, January 4\u20137). Unmanned aerial vehicle (UAV) hyperspectral remote sensing for dryland vegetation monitoring. Proceedings of the Workshop on Hyperspectral Image & Signal Processing: Evolution in Remote Sensing, Shanghai, China.","DOI":"10.1109\/WHISPERS.2012.6874315"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.rse.2011.10.007","article-title":"Fluorescence, temperature and narrow-band indices acquired from a UAV platform for water stress detection using a micro-hyperspectral imager and a thermal camera","volume":"117","author":"Berni","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.rse.2013.05.011","article-title":"Relationships between net photosynthesis and steady-state chlorophyll fluorescence retrieved from airborne hyperspectral imagery","volume":"136","author":"Catalina","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.agrformet.2012.12.013","article-title":"Estimating leaf carotenoid content in vineyards using high resolution hyperspectral imagery acquired from an unmanned aerial vehicle (UAV)","volume":"171","author":"Zarcotejada","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.rse.2013.02.003","article-title":"Spatio-temporal patterns of chlorophyll fluorescence and physiological and structural indices acquired from hyperspectral imagery as compared with carbon fluxes measured with eddy covariance","volume":"133","author":"Morales","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1016\/j.rse.2013.07.031","article-title":"High-resolution airborne hyperspectral and thermal imagery for early detection of verticillium wilt of olive using fluorescence, temperature and narrow-band spectral indices","volume":"139","author":"Lucena","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yuan, H., Yang, G., Li, C., Wang, Y., Liu, J., Yu, H., Feng, H., Xu, B., Zhao, X., and Yang, X. (2017). Retrieving soybean leaf area index from unmanned aerial vehicle hyperspectral remote sensing: Analysis of RF, ANN, and SVM regression models. Remote Sens., 9.","DOI":"10.3390\/rs9040309"},{"key":"ref_37","first-page":"41","article-title":"Inversion of soybean fresh biomass based on multi-payload unmanned aerial vehicles (UAVs)","volume":"36","author":"Lu","year":"2017","journal-title":"Soybean Sci."},{"key":"ref_38","first-page":"110","article-title":"Estimation of soybean breeding yield based on optimization of spatial scale of UAV hyperspectral image","volume":"33","author":"Zhao","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_39","first-page":"113","article-title":"Retrieving winter wheat leaf area index based on unmanned aerial vehicle hyperspectral remote sensing","volume":"32","author":"Gao","year":"2016","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_40","first-page":"77","article-title":"Rice leaf nitrogen content estimation based on hysperspectral imagery of UAV in yellow river diversion irrigation district","volume":"32","author":"Qin","year":"2016","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_41","first-page":"102","article-title":"Use of hyperspectral images from UAV-based imaging spectroradiometer to estimate cotton leaf area index","volume":"32","author":"Tian","year":"2016","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.rse.2017.04.007","article-title":"UAV LiDAR and hyperspectral fusion for forest monitoring in the southwestern USA","volume":"195","author":"Sankey","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_44","first-page":"209","article-title":"Assessing the utility of UAV-borne hyperspectral image and photogrammetry derived 3D data for wetland species distribution quick mapping","volume":"XLII-2\/W6","author":"Li","year":"2017","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1080\/01490410701296663","article-title":"Optimizing remote sensing and GIS tools for mapping and managing the distribution of an invasive mangrove (Rhizophora mangle) on South Molokai, Hawaii","volume":"30","author":"Jupiter","year":"2007","journal-title":"Mar. Geodesy"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"425","DOI":"10.14358\/PERS.75.4.425","article-title":"Evaluating aisa + hyperspectral imagery for mapping black mangrove along the south texas gulf coast","volume":"75","author":"Yang","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chakravortty, S. (2013). Analysis of end member detection and subpixel classification algorithms on hyperspectral imagery for tropical mangrove species discrimination in the Sunderbans Delta, India. J. Appl. Remote Sens., 7.","DOI":"10.1117\/1.JRS.7.073523"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chakravortty, S., Shah, E., and Chowdhury, A.S. (2014, January 13\u201315). Application of spectral unmixing algorithm on hyperspectral data for mangrove species classification. Proceedings of the International Conference on Applied Algorithms, Kolkata, India.","DOI":"10.1007\/978-3-319-04126-1_19"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"559","DOI":"10.1007\/s12524-014-0437-x","article-title":"Analysis of multiple scattering of radiation amongst end members in a mixed pixel of hyperspectral data for identification of mangrove species in a mixed stand","volume":"43","author":"Chakravortty","year":"2015","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Kazakova, A., Moskal, L., and Styers, D. (2016). Object-based tree species classification in urban ecosystems using LiDAR and hyperspectral data. Forests, 7.","DOI":"10.3390\/f7060122"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/s11852-012-0223-2","article-title":"Classification of floristic composition of mangrove forests using hyperspectral data: Case study of Bhitarkanika National Park, India","volume":"17","author":"Kumar","year":"2013","journal-title":"J. Coast. Conserv."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"7828","DOI":"10.1080\/01431161.2014.978034","article-title":"Combining EO-1 hyperion and ENVISAT ASAR data for mangrove species classification in Mai Po Ramsar Site, Hong Kong","volume":"35","author":"Wong","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2011.11.020","article-title":"A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using spot-5 HRG imagery","volume":"118","author":"Duro","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_54","unstructured":"Pu, R. (2013). Tree species classification. Remote Sensing of Natural Resources, CRC Press."},{"key":"ref_55","first-page":"136","article-title":"Mangrove canopy species discrimination based on spectral features of geoeye-1 imagery","volume":"33","author":"Li","year":"2013","journal-title":"Spectrosc. Spectr. Anal."},{"key":"ref_56","first-page":"531","article-title":"Decision tree model in extraction of mangrove community information using hyperspectral image data","volume":"11","author":"Xiao","year":"2007","journal-title":"J. Remote Sens."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"922","DOI":"10.3390\/rs70100922","article-title":"Object-based crop species classification based on the combination of airborne hyperspectral images and LiDAR data","volume":"7","author":"Liu","year":"2015","journal-title":"Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"6765","DOI":"10.1080\/01431161.2010.512944","article-title":"Integrated LiDAR and IKONOS multispectral imagery for mapping mangrove distribution and physical properties","volume":"32","author":"Chadwick","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"4753","DOI":"10.3390\/rs70404753","article-title":"Object-based approach for multi-scale mangrove composition mapping using multi-resolution image datasets","volume":"7","author":"Kamal","year":"2015","journal-title":"Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.eja.2014.01.004","article-title":"Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods","volume":"55","author":"Angileri","year":"2014","journal-title":"Eur. J. Agron."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2738","DOI":"10.1109\/TGRS.2013.2265295","article-title":"Direct georeferencing of ultrahigh-resolution UAV imagery","volume":"52","author":"Turner","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","first-page":"59","article-title":"Studies on dynamic development of mangrove communities on Qi\u2019ao Island, Zhuhai","volume":"29","author":"Liao","year":"2008","journal-title":"J. South China Agric. Univ."},{"key":"ref_63","first-page":"534","article-title":"Mangrove reform-planting trial on Qi\u2019ao Island","volume":"32","author":"Liu","year":"2013","journal-title":"Ecol. Sci."},{"key":"ref_64","unstructured":"Baatz, M., and Sch\u00e4pe, A. (2017, November 21). Multiresolution Segmentation: An Optimization Approach for High Quality Multi-Scale Image Segmentation. Available online: http:\/\/www.ecognition.com\/sites\/default\/files\/405_baatz_fp_12.pdf."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"3816","DOI":"10.1080\/01431161.2014.919678","article-title":"A novel method for assessing the segmentation quality of high-spatial resolution remote-sensing images","volume":"35","author":"Cheng","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/j.isprsjprs.2003.10.002","article-title":"Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information","volume":"58","author":"Benz","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"153","DOI":"10.3390\/rs70100153","article-title":"Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery","volume":"7","author":"Qian","year":"2014","journal-title":"Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/S0034-4257(02)00018-4","article-title":"Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture","volume":"81","author":"Haboudane","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/0034-4257(95)00186-7","article-title":"Optimization of soil-adjusted vegetation indices","volume":"55","author":"Rondeaux","year":"1996","journal-title":"Remote Sens. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2005.09.002","article-title":"Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopy","volume":"99","author":"Miller","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2360","DOI":"10.1016\/j.rse.2011.04.036","article-title":"Assessing structural effects on PRI for stress detection in conifer forests","volume":"115","author":"Morales","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_72","unstructured":"Rouse, J.W., Haas, R.H., Schell, J.A., and Deering, D.W. (1974). Monitoring Vegetation Systems in the Great Plains with ERTS."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/0034-4257(94)00114-3","article-title":"Estimating par absorbed by vegetation from bidirectional reflectance measurements","volume":"51","author":"Roujean","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"6","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Tan, Y., Xia, W., Xu, B., and Bai, L. (2017). Multi-feature classification approach for high spatial resolution hyperspectral images. J. Indian Soc. Remote Sens., 1\u20139.","DOI":"10.1007\/s12524-017-0663-0"},{"key":"ref_77","unstructured":"Pu, R., and Gong, P. (2011). Hyperspectral remote sensing of vegetation bioparameters. Advances in Environmental Remote Sensing: Sensors, Algorithm, and Applications, CRC Press."},{"key":"ref_78","unstructured":"Breiman, L., Friedman, J., Olshen, R., and Stone, C. (1984). Classification and Regresssion Trees, Wadsworth International Group."},{"key":"ref_79","unstructured":"Gomez-Chova, L., Calpe, J., Soria, E., Camps-Valls, G., Martin, J.D., and Moreno, J. (2003, January 14\u201317). CART-based feature selection of hyperspectral images for crop cover classification. Proceedings of the International Conference on Image Processing, Barcelona, Spain."},{"key":"ref_80","first-page":"66","article-title":"Feature selection by using classification and regression trees (CART)","volume":"35","author":"Bittencourt","year":"2004","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_81","unstructured":"Hall, M.A. (July, January 29). Feature selection for discrete and numeric class machine learning. Proceedings of the Seventeenth International Conference on Machine Learning, Stanford, CA, USA."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1109\/JSTSP.2010.2057200","article-title":"Combining long short-term memory and dynamic bayesian networks for incremental emotion-sensitive artificial listening","volume":"4","author":"Wollmer","year":"2010","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1109\/TKDE.2003.1245283","article-title":"Benchmarking attribute selection techniques for discrete class data mining","volume":"15","author":"Hall","year":"2003","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"4193","DOI":"10.1109\/TGRS.2010.2050067","article-title":"Feature selection in avhrr ocean satellite images by means of filter methods","volume":"48","author":"Wang","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.apgeog.2011.10.010","article-title":"Optimizing land cover classification accuracy for change detection, a combined pixel-based and object-based approach in a mountainous area in mexico","volume":"34","author":"Seijmonsbergen","year":"2012","journal-title":"Appl. Geogr."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1109\/TIT.1968.1054155","article-title":"The condensed nearest neighbor rule","volume":"14","author":"Hart","year":"1968","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1109\/TGRS.2009.2031812","article-title":"A nonparametric feature extraction and its application to nearest neighbor classification for hyperspectral image data","volume":"48","author":"Yang","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"2457","DOI":"10.3390\/rs4082457","article-title":"Semi-supervised methods to identify individual crowns of lowland tropical canopy species using imaging spectroscopy and LiDAR","volume":"4","author":"Asner","year":"2012","journal-title":"Remote Sens."},{"key":"ref_90","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_91","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Congalton, R.G., and Green, K. (2008). Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press.","DOI":"10.1201\/9781420055139"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.isprsjprs.2014.12.026","article-title":"Training set size, scale, and features in geographic object-based image analysis of very high resolution unmanned aerial vehicle imagery","volume":"102","author":"Ma","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_94","unstructured":"Gomez-Chova, L., Calpe, J., Camps-Valls, G., Martin, J.D., Soria, E., Vila, J., Alonso-Chorda, L., and Moreno, J. (2003, January 21\u201325). Feature selection of hyperspectral data through local correlation and SFFS for crop classification. Proceedings of the 2003 IEEE International Geoscience & Remote Sensing Symposium, Toulouse, France."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/1\/89\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:50:56Z","timestamp":1760194256000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/1\/89"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,11]]},"references-count":94,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,1]]}},"alternative-id":["rs10010089"],"URL":"https:\/\/doi.org\/10.3390\/rs10010089","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,11]]}}}