{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T14:42:52Z","timestamp":1775054572952,"version":"3.50.1"},"reference-count":92,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"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":["41961037"],"award-info":[{"award-number":["41961037"]}],"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":["AD20297066"],"award-info":[{"award-number":["AD20297066"]}],"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":["GTEU-KLOP-X1718"],"award-info":[{"award-number":["GTEU-KLOP-X1718"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangxi Science and Technology Project","award":["41961037"],"award-info":[{"award-number":["41961037"]}]},{"name":"Guangxi Science and Technology Project","award":["AD20297066"],"award-info":[{"award-number":["AD20297066"]}]},{"name":"Guangxi Science and Technology Project","award":["GTEU-KLOP-X1718"],"award-info":[{"award-number":["GTEU-KLOP-X1718"]}]},{"name":"the Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Nanning Normal University), Ministry of Education","award":["41961037"],"award-info":[{"award-number":["41961037"]}]},{"name":"the Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Nanning Normal University), Ministry of Education","award":["AD20297066"],"award-info":[{"award-number":["AD20297066"]}]},{"name":"the Key Laboratory of Environment Change and Resources Use in Beibu Gulf (Nanning Normal University), Ministry of Education","award":["GTEU-KLOP-X1718"],"award-info":[{"award-number":["GTEU-KLOP-X1718"]}]},{"name":"the BaGui Scholars Program of Guangxi Zhuang Autonomous Region","award":["41961037"],"award-info":[{"award-number":["41961037"]}]},{"name":"the BaGui Scholars Program of Guangxi Zhuang Autonomous Region","award":["AD20297066"],"award-info":[{"award-number":["AD20297066"]}]},{"name":"the BaGui Scholars Program of Guangxi Zhuang Autonomous Region","award":["GTEU-KLOP-X1718"],"award-info":[{"award-number":["GTEU-KLOP-X1718"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Mangrove ecosystems play an irreplaceable role in coastal environments by providing essential ecosystem services. Diverse mangrove species have different functions due to their morphological and physiological characteristics. A precise spatial distribution map of mangrove species is therefore crucial for biodiversity maintenance and environmental conservation of coastal ecosystems. Traditional satellite data are limited in fine-scale mangrove species classification due to low spatial resolution and less spectral information. This study employed unmanned aerial vehicle (UAV) technology to acquire high-resolution multispectral and hyperspectral mangrove forest imagery in Guangxi, China. We leveraged advanced algorithms, including RFE-RF for feature selection and machine learning models (Adaptive Boosting (AdaBoost), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM)), to achieve mangrove species mapping with high classification accuracy. The study assessed the classification performance of these four machine learning models for two types of image data (UAV multispectral and hyperspectral imagery), respectively. The results demonstrated that hyperspectral imagery had superiority over multispectral data by offering enhanced noise reduction and classification performance. Hyperspectral imagery produced mangrove species classification with overall accuracy (OA) higher than 91% across the four machine learning models. LightGBM achieved the highest OA of 97.15% and kappa coefficient (Kappa) of 0.97 based on hyperspectral imagery. Dimensionality reduction and feature extraction techniques were effectively applied to the UAV data, with vegetation indices proving to be particularly valuable for species classification. The present research underscored the effectiveness of UAV hyperspectral images using machine learning models for fine-scale mangrove species classification. This approach has the potential to significantly improve ecological management and conservation strategies, providing a robust framework for monitoring and safeguarding these essential coastal habitats.<\/jats:p>","DOI":"10.3390\/rs16163093","type":"journal-article","created":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T04:26:57Z","timestamp":1724300817000},"page":"3093","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Fine-Scale Mangrove Species Classification Based on UAV Multispectral and Hyperspectral Remote Sensing Using Machine Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Yuanzheng","family":"Yang","sequence":"first","affiliation":[{"name":"Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Nanning Normal University, Nanning 530001, China"},{"name":"Guangxi Beihai Wetland Ecosystem National Observation and Research Station, Beihai 536001, China"}]},{"given":"Zhouju","family":"Meng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Nanning Normal University, Nanning 530001, China"}]},{"given":"Jiaxing","family":"Zu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Nanning Normal University, Nanning 530001, China"}]},{"given":"Wenhua","family":"Cai","sequence":"additional","affiliation":[{"name":"Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Nanning Normal University, Nanning 530001, China"}]},{"given":"Jiali","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Nanning Normal University, Nanning 530001, China"}]},{"given":"Hongxin","family":"Su","sequence":"additional","affiliation":[{"name":"Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Nanning Normal University, Nanning 530001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2170-589X","authenticated-orcid":false,"given":"Jian","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Forestry and Natural Resources, TP Cooper Building, University of Kentucky, Lexington, KY 40546, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.isprsjprs.2022.05.002","article-title":"Deep learning-based individual tree crown delineation in mangrove forests using very-high-resolution satellite imagery","volume":"189","author":"Lassalle","year":"2022","journal-title":"ISPRS J. 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