{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T02:31:39Z","timestamp":1771036299353,"version":"3.50.1"},"reference-count":96,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T00:00:00Z","timestamp":1618444800000},"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":["42071305"],"award-info":[{"award-number":["42071305"]}],"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":["41771392"],"award-info":[{"award-number":["41771392"]}],"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":["41901364"],"award-info":[{"award-number":["41901364"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Geological Survey Project of China Geological Survey","award":["DD20208018"],"award-info":[{"award-number":["DD20208018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mangrove forests, as important ecological and economic resources, have suffered a loss in the area due to natural and human activities. Monitoring the distribution of and obtaining accurate information on mangrove species is necessary for ameliorating the damage and protecting and restoring mangrove forests. In this study, we compared the performance of UAV Rikola hyperspectral images, WorldView-2 (WV-2) satellite-based multispectral images, and a fusion of data from both in the classification of mangrove species. We first used recursive feature elimination\u2012random forest (RFE-RF) to select the vegetation\u2019s spectral and texture feature variables, and then implemented random forest (RF) and support vector machine (SVM) algorithms as classifiers. The results showed that the accuracy of the combined data was higher than that of UAV and WV-2 data; the vegetation index features of UAV hyperspectral data and texture index of WV-2 data played dominant roles; the overall accuracy of the RF algorithm was 95.89% with a Kappa coefficient of 0.95, which is more accurate and efficient than SVM. The use of combined data and RF methods for the classification of mangrove species could be useful in biomass estimation and breeding cultivation.<\/jats:p>","DOI":"10.3390\/rs13081529","type":"journal-article","created":{"date-parts":[[2021,4,15]],"date-time":"2021-04-15T21:35:13Z","timestamp":1618522513000},"page":"1529","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["High-Resolution Mangrove Forests Classification with Machine Learning Using Worldview and UAV Hyperspectral Data"],"prefix":"10.3390","volume":"13","author":[{"given":"Yufeng","family":"Jiang","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"College of Information Science and Engineering, Shandong Agricultural University, Tai\u2019an 271000, China"}]},{"given":"Li","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Earth Observation of Hainan Province, Sanya 572029, China"}]},{"given":"Min","family":"Yan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Earth Observation of Hainan Province, Sanya 572029, China"}]},{"given":"Jianguo","family":"Qi","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, Tai\u2019an 271000, China"}]},{"given":"Tianmeng","family":"Fu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Shunxiang","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6377-1094","authenticated-orcid":false,"given":"Bowei","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Earth Observation of Hainan Province, Sanya 572029, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.1126\/science.277.5333.1783a","article-title":"Nature\u2019s services: Societal dependence on natural ecosystems","volume":"277","author":"Pearce","year":"1997","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"889","DOI":"10.1016\/j.apgeog.2011.08.016","article-title":"Using the Hazus-MH flood model to evaluate community relocation as a flood mitigation response to terminal lake flooding: The case of Minnewaukan, North Dakota, USA","volume":"32","author":"Cummings","year":"2012","journal-title":"Appl. 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