{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T19:43:03Z","timestamp":1781811783532,"version":"3.54.5"},"reference-count":66,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,17]],"date-time":"2018-11-17T00:00:00Z","timestamp":1542412800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100008111","name":"Hainan Provincial Department of Science and Technology","doi-asserted-by":"publisher","award":["ZDKJ2016021"],"award-info":[{"award-number":["ZDKJ2016021"]}],"id":[{"id":"10.13039\/501100008111","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA19030302"],"award-info":[{"award-number":["XDA19030302"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Mangrove forests are distributed in intertidal regions that act as a \u201cnatural barrier\u201d to the coast. They have enormous ecological, economic, and social value. However, the world\u2019s mangrove forests are declining under immense pressure from anthropogenic and natural disturbances. Accurate information regarding mangrove forests is essential for their protection and restoration. The main objective of this study was to develop a method to improve the classification of mangrove forests using C-band quad-pol Synthetic Aperture Radar (SAR) data (Radarsat-2) and optical data (Landsat 8), and to analyze the spectral and backscattering signatures of mangrove forests. We used a support vector machine (SVM) classification method to classify the land use in Hainan Dongzhaigang National Nature Reserve (HDNNR). The results showed that the overall accuracy using only optical information was 83.5%. Classification accuracy was improved to a varying extent by the addition of different radar data. The highest overall accuracy was 95.0% based on a combination of SAR and optical data. The area of mangrove forest in the reserve was found to be 1981.7 ha, as determined from the group with the highest classification accuracy. Combining optical data with SAR data could improve the classification accuracy and be significant for mangrove forest conservation.<\/jats:p>","DOI":"10.3390\/s18114012","type":"journal-article","created":{"date-parts":[[2018,11,22]],"date-time":"2018-11-22T09:18:25Z","timestamp":1542878305000},"page":"4012","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Mapping Mangrove Forests of Dongzhaigang Nature Reserve in China Using Landsat 8 and Radarsat-2 Polarimetric SAR Data"],"prefix":"10.3390","volume":"18","author":[{"given":"Jianing","family":"Zhen","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Earth Observation Hainan Province, Sanya 572000, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjuan","family":"Liao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Key Laboratory of Earth Observation Hainan Province, Sanya 572000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guozhuang","family":"Shen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Giri, C. 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