{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T16:40:05Z","timestamp":1787244005715,"version":"3.56.0"},"reference-count":32,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T00:00:00Z","timestamp":1711065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Jiangsu Marine Science and Technology Innovation Project","award":["JSZRHYKJ202202"],"award-info":[{"award-number":["JSZRHYKJ202202"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The identification of wetland vegetation is essential for environmental protection and management as well as for monitoring wetlands\u2019 health and assessing ecosystem services. However, some limitations on vegetation classification may be related to remote sensing technology, confusion between plant species, and challenges related to inadequate data accuracy. In this paper, vegetation classification in the Yancheng Coastal Wetlands is studied and evaluated from Sentinel-2 images based on a random forest algorithm. Based on consistent time series from remote sensing observations, the characteristic patterns of the Yancheng Coastal Wetlands were better captured. Firstly, the spectral features, vegetation indices, and phenological characteristics were extracted from remote sensing images, and classification products were obtained by constructing a dense time series using a dataset based on Sentinel-2 images in Google Earth Engine (GEE). Then, remote sensing classification products based on the random forest machine learning algorithm were obtained, with an overall accuracy of 95.64% and kappa coefficient of 0.94. Four indicators (POP, SOS, NDVIre, and B12) were the main contributors to the importance of the weight analysis for all features. Comparative experiments were conducted with different classification features. The results show that the method proposed in this paper has better classification.<\/jats:p>","DOI":"10.3390\/rs16071124","type":"journal-article","created":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T12:28:06Z","timestamp":1711369686000},"page":"1124","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Vegetation Classification and Evaluation of Yancheng Coastal Wetlands Based on Random Forest Algorithm from Sentinel-2 Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Yongjun","family":"Wang","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5108-4828","authenticated-orcid":false,"given":"Shuanggen","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China"},{"name":"Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8458-0676","authenticated-orcid":false,"given":"Gino","family":"Dardanelli","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Palermo, Viale delle Scienze, 90128 Palermo, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"key":"ref_1","first-page":"6354","article-title":"Research on Dynamic Changes of Endangered Waterbird Habitats in the Yellow and Bohai Seas","volume":"43","author":"Duan","year":"2023","journal-title":"Acta Ecol. Sin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3495","DOI":"10.3390\/rs15143495","article-title":"Wetland mapping in Great Lakes using Sentinel-1\/2 time-series imagery and DEM data in Google Earth Engin","volume":"15","author":"Mohseni","year":"2023","journal-title":"Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1038\/nclimate1970","article-title":"The role of coastal plant communities for climate change mitigation and adaptation","volume":"3","author":"Duarte","year":"2013","journal-title":"Nat. Clim. Change"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.isprsjprs.2018.04.015","article-title":"Dynamics of the wetland vegetation in large lakes of the Yangtze Plain in response to both fertilizer consumption and climatic changes","volume":"141","author":"Hou","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","first-page":"386","article-title":"Wetland Information Extraction in the Heilongjiang River Basin Using Google Earth Engine and Multi-source Remote Sensing Data","volume":"26","author":"Ning","year":"2022","journal-title":"J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.isprsjprs.2018.02.021","article-title":"Dynamic monitoring of the Poyang Lake wetland by integrating Landsat and MODIS observations","volume":"139","author":"Chen","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_7","first-page":"304","article-title":"Review on Factors Affecting the Accuracy of Plant Phenology Remote Sensing Monitoring","volume":"35","author":"Fan","year":"2016","journal-title":"Prog. Geogr."},{"key":"ref_8","unstructured":"Liu, R.Q. (2022). Coastal Wetland Classification Based on Time Series Remote Sensing Images and Vegetation Phenological Characteristics. [Master\u2019s Thesis, Ningbo University]."},{"key":"ref_9","first-page":"313","article-title":"Wetland mapping of Yellow River Delta wetlands based on multi-feature optimization of Sentinel-2 images","volume":"23","author":"Zhang","year":"2019","journal-title":"J. Remote Sens."},{"key":"ref_10","first-page":"26","article-title":"Research on the Extraction Method of Spartina alterniflora Information in Coastal Wetlands Based on Google Earth Engine (GEE)","volume":"38","author":"Zheng","year":"2022","journal-title":"J. Chifeng Univ. Nat. Sci. Ed."},{"key":"ref_11","first-page":"1281","article-title":"Progress and Prospects of Global Wetland Hyperspectral Remote Sensing Research from 2010 to 2022","volume":"27","author":"Sun","year":"2023","journal-title":"J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"5175","DOI":"10.1007\/s11042-018-6314-9","article-title":"Tracking the phenology and expansion of Spartina alterniflora coastal wetland by time series MODIS and Landsat images","volume":"79","author":"Wu","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"265","DOI":"10.5194\/essd-15-265-2023","article-title":"GWL_FCS30: Global 30 m wetland map with fine classification system using multi-sourced and time-series remote sensing imagery in 2020","volume":"15","author":"Zhang","year":"2022","journal-title":"Earth Syst. Sci. Data Discuss."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"112320","DOI":"10.1016\/j.rse.2021.112320","article-title":"Plant species classification in salt marshes using phenological parameters derived from Sentinel-2 pixel-differential time-series","volume":"256","author":"Chao","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_15","first-page":"1680","article-title":"Vegetation Classification of Yancheng Coastal Wetlands Based on Sentinel-2 Remote Sensing Time Series Phenological Features","volume":"76","author":"Liu","year":"2021","journal-title":"Acta Geogr. Sin."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tassi, A., and Vizzari, M. (2020). Object-oriented LULC classification in Google earth engine combining SNIC, GLCM, and machine learning algorithms. Remote Sens., 12.","DOI":"10.3390\/rs12223776"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111467","DOI":"10.1016\/j.rse.2019.111467","article-title":"Spectral vegetation indices of wetland greenness: Responses to vegetation structure, composition, and spatial distribution","volume":"234","author":"Taddeo","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_18","first-page":"207","article-title":"Extraction of Winter Wheat Distribution Information Based on Multi-Phenological Feature Indices from Sentinel-2 Data","volume":"54","author":"Wu","year":"2023","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.ecolind.2012.12.026","article-title":"Deriving land surface phenology indicators from CO2 eddy covariance measurements","volume":"29","author":"Gonsamo","year":"2013","journal-title":"Ecol. Indic."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108407","DOI":"10.1016\/j.agrformet.2021.108407","article-title":"Daily leaf area index from photosynthetically active radiation for long term records of canopy structure and leaf phenology","volume":"304","author":"Rogers","year":"2021","journal-title":"Agric. For. Meteorol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.agrformet.2017.10.026","article-title":"Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate change (1982\u20132013)","volume":"248","author":"Zhang","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.agrformet.2016.11.193","article-title":"Land surface phenology derived from normalized difference vegetation index (NDVI) at global FLUXNET sites","volume":"233","author":"Wu","year":"2017","journal-title":"Agric. For. Meteorol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"102273","DOI":"10.1016\/j.ecoinf.2023.102273","article-title":"Probabilistic coastal wetland mapping with integration of optical, SAR and hydro-geomorphic data through stacking ensemble machine learning model","volume":"77","author":"Prasad","year":"2023","journal-title":"Ecol. Inform."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"167212","DOI":"10.1016\/j.scitotenv.2023.167212","article-title":"Monitoring long-term vegetation condition dynamics in persistent semi-arid wetland communities using time series of Landsat data","volume":"905","author":"Wen","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_25","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_26","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_27","first-page":"677","article-title":"Remote sensing and geographic information system data integration: Error sources and research issues","volume":"57","author":"Congalton","year":"1991","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"627","DOI":"10.14358\/PERS.70.5.627","article-title":"Thematic map comparison","volume":"70","author":"Foody","year":"2004","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_29","unstructured":"Agresti, A. (1996). An Introduction to Categorical Data Analysis, Wiley."},{"key":"ref_30","first-page":"1671","article-title":"Assessing Landsat classification accuracy using discrete multivariate statistical techniques","volume":"49","author":"Congalton","year":"1983","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"106897","DOI":"10.1016\/j.catena.2022.106897","article-title":"A coastal wetlands mapping approach of Yellow River Delta with a hierarchical classification and optimal feature selection framework","volume":"223","author":"Xing","year":"2023","journal-title":"Catena"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"138869","DOI":"10.1016\/j.scitotenv.2020.138869","article-title":"Transfer Learning for Crop classification with Cropland Data Layer data (CDL) as training samples","volume":"733","author":"Hao","year":"2020","journal-title":"Sci. Total Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/7\/1124\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:18:18Z","timestamp":1760105898000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/7\/1124"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,22]]},"references-count":32,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["rs16071124"],"URL":"https:\/\/doi.org\/10.3390\/rs16071124","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,22]]}}}