{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T13:15:36Z","timestamp":1782825336815,"version":"3.54.5"},"reference-count":58,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:00:00Z","timestamp":1656374400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42101356"],"award-info":[{"award-number":["42101356"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42074033"],"award-info":[{"award-number":["42074033"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022JJ40473"],"award-info":[{"award-number":["2022JJ40473"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["19C0042"],"award-info":[{"award-number":["19C0042"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2017FY100706"],"award-info":[{"award-number":["2017FY100706"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Hunan Province","award":["42101356"],"award-info":[{"award-number":["42101356"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["42074033"],"award-info":[{"award-number":["42074033"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["2022JJ40473"],"award-info":[{"award-number":["2022JJ40473"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["19C0042"],"award-info":[{"award-number":["19C0042"]}]},{"name":"Natural Science Foundation of Hunan Province","award":["2017FY100706"],"award-info":[{"award-number":["2017FY100706"]}]},{"name":"Research Foundation of Education Bureau of Hunan Province, China","award":["42101356"],"award-info":[{"award-number":["42101356"]}]},{"name":"Research Foundation of Education Bureau of Hunan Province, China","award":["42074033"],"award-info":[{"award-number":["42074033"]}]},{"name":"Research Foundation of Education Bureau of Hunan Province, China","award":["2022JJ40473"],"award-info":[{"award-number":["2022JJ40473"]}]},{"name":"Research Foundation of Education Bureau of Hunan Province, China","award":["19C0042"],"award-info":[{"award-number":["19C0042"]}]},{"name":"Research Foundation of Education Bureau of Hunan Province, China","award":["2017FY100706"],"award-info":[{"award-number":["2017FY100706"]}]},{"name":"Science and Technology Basic Resources Investigation Program of China","award":["42101356"],"award-info":[{"award-number":["42101356"]}]},{"name":"Science and Technology Basic Resources Investigation Program of China","award":["42074033"],"award-info":[{"award-number":["42074033"]}]},{"name":"Science and Technology Basic Resources Investigation Program of China","award":["2022JJ40473"],"award-info":[{"award-number":["2022JJ40473"]}]},{"name":"Science and Technology Basic Resources Investigation Program of China","award":["19C0042"],"award-info":[{"award-number":["19C0042"]}]},{"name":"Science and Technology Basic Resources Investigation Program of China","award":["2017FY100706"],"award-info":[{"award-number":["2017FY100706"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Currently, it is a great challenge for remote sensing technology to accurately map mangrove forests owing to periodic inundation. A submerged mangrove recognition index (SMRI) using two high- and low-tide images was recently proposed to remove the influence of tides and identify mangrove forests. However, when the tidal height of the selected low-tide image is not at the lowest tidal level, the corresponding SMRI does not function well, which results in mangrove forests below the low tidal height being undetected. Furthermore, Spartina alterniflora Loisel (S. alterniflora) was introduced to China in 1979 and rapidly spread to become the most serious invasive plant along the Chinese coastline. The current SMRI has failed to distinguish S. alterniflora from submerged mangrove forests because of their similar spectral signatures. In this study, an SMRI-based mangrove forest mapping method was developed using the time series of Sentinel-2 images to mitigate the two aforementioned issues. In the proposed method, quantile synthesis was applied to the time series of Sentinel-2 images to generate a lowest-tide synthetic image for creating SMRI to identify submerged mangrove forests. Unsubmerged mangrove forests were classified using a support vector machine, and a preliminary mangrove forest map was created by merging them. In addition, S. alterniflora was distinguished from the mangrove forests by analyzing their phenological differences. Finally, mangrove forest mapping was performed by masking S. alterniflora. The proposed method was applied to the entire coastline of the Guangxi Province, China. The results showed that it can reliably and accurately identify submerged mangrove forests derived from SMRI by synthesizing low- and high-tide images using quantile synthesis, and the differentiation of S.\u00a0alterniflora using phenological differences results in more accurate mangrove mapping. This work helps to improve the accuracy of mangrove forest mapping using SMRI and its feasibility for coastal wetland monitoring. It also provides data for sustainable management, ecological protection, and restoration of vegetation in coastal zones.<\/jats:p>","DOI":"10.3390\/rs14133112","type":"journal-article","created":{"date-parts":[[2022,6,29]],"date-time":"2022-06-29T01:48:38Z","timestamp":1656467318000},"page":"3112","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["An Improved Submerged Mangrove Recognition Index-Based Method for Mapping Mangrove Forests by Removing the Disturbance of Tidal Dynamics and S. alterniflora"],"prefix":"10.3390","volume":"14","author":[{"given":"Qing","family":"Xia","sequence":"first","affiliation":[{"name":"Engineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province, Changsha University of Science and Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6889-3333","authenticated-orcid":false,"given":"Ting-Ting","family":"He","sequence":"additional","affiliation":[{"name":"Department of Land Management, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5910-9807","authenticated-orcid":false,"given":"Cheng-Zhi","family":"Qin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7741-4899","authenticated-orcid":false,"given":"Xue-Min","family":"Xing","sequence":"additional","affiliation":[{"name":"Engineering Laboratory of Spatial Information Technology of Highway Geological Disaster Early Warning in Hunan Province, Changsha University of Science and Technology, Changsha 410114, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wu","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Land Management, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1111\/j.1365-2699.2007.01806.x","article-title":"Mangrove forest distributions and dynamics (1975\u20132005) of the tsunami-affected region of Asia","volume":"35","author":"Giri","year":"2008","journal-title":"J. Biogeogr."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1177\/0309133310385371","article-title":"Satellite remote sensing of mangrove forests: Recent advances and future opportunities","volume":"35","author":"Heumann","year":"2011","journal-title":"Prog. Phys. Geogr."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"878","DOI":"10.3390\/rs3050878","article-title":"Remote Sensing of mangrove Ecosystems: A review","volume":"3","author":"Kuenzer","year":"2011","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1582","DOI":"10.2112\/07-0987.1","article-title":"Using high resolution satellite imagery to map black mangrove on the Texas Gulf Coast","volume":"246","author":"Everitt","year":"2008","journal-title":"J. Coastal Res."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2972","DOI":"10.3390\/s110302972","article-title":"Mapping the Philippines\u2019 mangrove forests using Landsat imagery","volume":"11","author":"Long","year":"2011","journal-title":"Sensors"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"111185","DOI":"10.1016\/j.rse.2019.05.004","article-title":"Mapping the multi-decadal mangrove dynamics of the Australian coastline","volume":"238","author":"Lymburner","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"111223","DOI":"10.1016\/j.rse.2019.111223","article-title":"A review of remote sensing for mangrove forests: 1956\u20132018","volume":"231","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, C., and Qin, C. (2021). A detailed mangrove map of China for 2019 derived from Sentinel-1 and -2 images and Google Earth images. Geosci. Data J.","DOI":"10.1002\/gdj3.119"},{"key":"ref_9","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":"Coastal Conserv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Jia, M., Wang, Z., Wang, C., Mao, D., and Zhang, Y. (2019). A new vegetation index to detect periodically submerged mangrove forest using single-tide Sentinel-2 imagery. Remote Sens., 11.","DOI":"10.3390\/rs11172043"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.isprsjprs.2020.06.001","article-title":"Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping","volume":"166","author":"Baloloy","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","first-page":"535","article-title":"Monitoring loss and recovery of mangrove forests during 42 years: The achievements of mangrove conservation in China","volume":"73","author":"Jia","year":"2018","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.isprsjprs.2020.10.001","article-title":"10-m-resolution mangrove maps of China derived from multi-source and multi-temporal satellite observations","volume":"169","author":"Zhao","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1016\/j.isprsjprs.2008.04.002","article-title":"An object-based method for mapping and change analysis in mangrove ecosystems","volume":"63","author":"Conchedda","year":"2008","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","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_16","doi-asserted-by":"crossref","unstructured":"Zhang, X. (2011, January 25\u201327). Identification of Mangrove Using Decision Tree Method. Proceedings of the 2011 Fourth International Conference on Information and Computing, Phuket, Thailand.","DOI":"10.1109\/ICIC.2011.70"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.ecss.2013.03.023","article-title":"Change and fragmentation trends of Zhanjiang mangrove forests in southern China using multi-temporal Landsat imagery (1977\u20132010)","volume":"130","author":"Li","year":"2013","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_18","first-page":"64","article-title":"Integration of remote sensing and spatial information technologies for mapping black mangrove on the Texas Gulf Coast","volume":"12","author":"Everitt","year":"2012","journal-title":"J. Coast. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1016\/j.ecolind.2019.03.047","article-title":"Using multi-indices approach to quantify mangrove changes over the Western Arabian Gulf along Saudi Arabia coast","volume":"102","author":"Li","year":"2019","journal-title":"Ecol. Indic."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.isprsjprs.2017.07.011","article-title":"A mangrove forest map of China in 2015: Analysis of time series Landsat 7\/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform","volume":"131","author":"Chen","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","first-page":"1","article-title":"Monitoring mangrove forests: Are we taking full advantage of technology?","volume":"63","author":"Joyce","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","first-page":"201","article-title":"Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure","volume":"62","author":"Zhang","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.isprsjprs.2022.05.003","article-title":"Enhanced mangrove vegetation index based on hyperspectral images for mapping mangrove","volume":"189","author":"Yang","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","unstructured":"Winarso, G., Purwanto, A., and Yuwono, D. (2014, January 4\u20137). New mangrove index as degradation\/health indicator using remote sensing data: Segara Anakan and Alas Purwo case study. Proceedings of the 12th Biennial Conference of Pan Ocean Remote Sensing Conference, Bali, Indonesia."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1129","DOI":"10.1016\/j.mex.2018.09.011","article-title":"An index for discrimination of mangroves from non-mangroves using LANDSAT 8 OLI imagery","volume":"5","author":"Gupta","year":"2018","journal-title":"MethodsX"},{"key":"ref_26","first-page":"1149","article-title":"A mangrove recognition index for remote sensing of mangrove forest from space","volume":"105","author":"Zhang","year":"2013","journal-title":"Curr. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xia, Q., Qin, C., Li, H., and Su, F. (2018). Mapping mangrove forests based on multi-tidal high-resolution satellite imagery. Remote Sens., 10.","DOI":"10.3390\/rs10091343"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1080\/2150704X.2016.1195935","article-title":"New spectral metrics for mangrove forest identification","volume":"7","author":"Shi","year":"2016","journal-title":"Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1080\/10106049.2017.1408699","article-title":"Discrimination and classification of mangrove forests using EO-1 Hyperion data: A case study of Indian Sundarbans","volume":"34","author":"Kumar","year":"2017","journal-title":"Geocarto Int."},{"key":"ref_30","first-page":"49","article-title":"Extraction method of intertidal mangrove by using Sentinel-2 images","volume":"2","author":"Xu","year":"2020","journal-title":"Bull. Surv. Mapp."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"100040","DOI":"10.1016\/j.srs.2022.100040","article-title":"Optical and SAR images Combined Mangrove Index based on multi-feature fusion","volume":"5","author":"Huang","year":"2022","journal-title":"Sci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.ecoleng.2007.08.005","article-title":"An experimental study on physical controls of an exotic plant Spartina alterniflora in Shanghai, China","volume":"32","author":"Li","year":"2008","journal-title":"Ecol. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.ecoleng.2008.05.020","article-title":"The positive and negative effects of exotic Spartina alterniflora in China","volume":"35","author":"Wan","year":"2009","journal-title":"Ecol. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1933","DOI":"10.3390\/rs10121933","article-title":"Rapid Invasion of Spartina alterniflora in the Coastal Zone of Mainland China: New Observations from Landsat OLI Images","volume":"10","author":"Mao","year":"2018","journal-title":"Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"111745","DOI":"10.1016\/j.rse.2020.111745","article-title":"Development of spectral-phenological features for deep learning to understand Spartina alterniflora invasion","volume":"242","author":"Tian","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"15698","DOI":"10.1038\/ncomms15698","article-title":"Tidal dynamics and mangrove carbon sequestration during the Oligo-Miocene in the South China Sea","volume":"8","author":"Collins","year":"2017","journal-title":"Nat. Commun."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, H., Jia, M., Zhang, R., Ren, Y., and Wen, X. (2019). Incorporating the plant phenological trajectory into mangrove species mapping with dense time series Sentinel-2 imagery and the Google Earth Engine Platform. Remote Sens., 11.","DOI":"10.3390\/rs11212479"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"106196","DOI":"10.1016\/j.ecolind.2020.106196","article-title":"Evaluation of submerged mangrove recognition index using multi-tidal remote sensing data","volume":"113","author":"Xia","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"519","DOI":"10.3390\/rs1030519","article-title":"Mapping invasive Tamarisk (Tamarix): A comparison of single-scene and time-series analyses of remotely sensed data","volume":"1","author":"Evangelista","year":"2009","journal-title":"Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1109\/JSTARS.2014.2333527","article-title":"Landsat-Based Estimation of Mangrove Forest Loss and Restoration in Guangxi Province, China, Influenced by Human and Natural Factors","volume":"8","author":"Jia","year":"2015","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_41","unstructured":"Louis, J., Debaecker, V., Pflug, B., Main-Knorn, M., Bieniarz, J., Mueller-Wilm, U., Cadau, E., and Gascon, F. (2016, January 9\u201313). Sentinel-2 Sen2Cor: L2A Processor for Users. Proceedings of the Living Planet Symposium (Spacebooks Online), Prague, Czech Republic."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3025","DOI":"10.1080\/01431160600589179","article-title":"Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery","volume":"27","author":"Xu","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"112285","DOI":"10.1016\/j.rse.2021.112285","article-title":"Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine","volume":"255","author":"Jia","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1016\/j.ecss.2008.02.003","article-title":"Coastal and estuarine habitat mapping, using LIDAR height and intensity and multi-spectral imagery","volume":"78","author":"Chust","year":"2008","journal-title":"Estuar. Coast. Shelf Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1007\/s10530-013-0578-9","article-title":"Remote detection of invasive plants: A review of spectral, textural and phenological approaches","volume":"16","author":"Bradley","year":"2013","journal-title":"Biol. Invasions"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1016\/j.rse.2013.08.014","article-title":"Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)","volume":"140","author":"Hansen","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1007\/s13157-015-0693-8","article-title":"Use of bi-seasonal Landsat-8 imagery for mapping marshland plant community combinations at the regional Scale","volume":"35","author":"Rapinel","year":"2015","journal-title":"Wetlands"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/j.rse.2018.02.036","article-title":"Landsat time series-based multiyear spectral angle clustering (MSAC) model to monitor the inter-annual leaf senescence of exotic saltcedar","volume":"209","author":"Diao","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.rse.2011.10.006","article-title":"Linking near-surface and satellite remote sensing measurements of deciduous broadleaf forest phenology","volume":"117","author":"Hufkens","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Threshold Selection Method from Gray-Level Histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_51","first-page":"11945","article-title":"Otsu Image segmentation algorithm: A Review","volume":"5","author":"Khushbu","year":"2017","journal-title":"Int. J. Inno. Res. Comp. Comm. Eng."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer. [1st ed.].","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1080\/01431160110040323","article-title":"An assessment of support vector machines for land cover classification","volume":"23","author":"Huang","year":"2002","journal-title":"Int. J. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"957","DOI":"10.1016\/j.rse.2009.01.010","article-title":"Land cover mapping of large areas using chain classification of neighboring Landsat satellite images","volume":"113","author":"Knorn","year":"2009","journal-title":"Remote. Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Shi, D., and Yang, X. (2015). Support vector machines for land cover mapping from remote sensor imagery. Monitoring and Modeling of Global Changes: A Geomatics Perspective, Springer.","DOI":"10.1007\/978-94-017-9813-6_13"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1126\/science.abm9583","article-title":"High-resolution mapping of losses and gains of Earth\u2019s tidal wetlands","volume":"376","author":"Murray","year":"2022","journal-title":"Science"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"3240","DOI":"10.1016\/j.eswa.2008.01.009","article-title":"Support vector machines combined with feature selection for breast cancer diagnosis","volume":"16","author":"Akay","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"110987","DOI":"10.1016\/j.rse.2018.11.030","article-title":"Tracking annual changes of coastal tidal flats in China during 1986\u20132016 through analyses of Landsat images with Google Earth Engine","volume":"238","author":"Wang","year":"2020","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3112\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:39:47Z","timestamp":1760139587000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/13\/3112"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,28]]},"references-count":58,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["rs14133112"],"URL":"https:\/\/doi.org\/10.3390\/rs14133112","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,28]]}}}