{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T12:54:21Z","timestamp":1771678461758,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,14]],"date-time":"2018-02-14T00:00:00Z","timestamp":1518566400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research &amp; Development (R&amp;D) Plan of China","award":["NO. 2016YFB0502304"],"award-info":[{"award-number":["NO. 2016YFB0502304"]}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["NO. 41361090"],"award-info":[{"award-number":["NO. 41361090"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In the dwindling natural mangrove today, mangrove reforestation projects are conducted worldwide to prevent further losses. Due to monoculture and the low survival rate of artificial mangroves, it is necessary to pay attention to mapping and monitoring them dynamically. Remote sensing techniques have been widely used to map mangrove forests due to their capacity for large-scale, accurate, efficient, and repetitive monitoring. This study evaluated the capability of a 0.5-m Pl\u00e9iades-1 in classifying artificial mangrove species using both pixel-based and object-based classification schemes. For comparison, three machine learning algorithms\u2014decision tree (DT), support vector machine (SVM), and random forest (RF)\u2014were used as the classifiers in the pixel-based and object-based classification procedure. The results showed that both the pixel-based and object-based approaches could recognize the major discriminations between the four major artificial mangrove species. However, the object-based method had a better overall accuracy than the pixel-based method on average. For pixel-based image analysis, SVM produced the highest overall accuracy (79.63%); for object-based image analysis, RF could achieve the highest overall accuracy (82.40%), and it was also the best machine learning algorithm for classifying artificial mangroves. The patches produced by object-based image analysis approaches presented a more generalized appearance and could contiguously depict mangrove species communities. When the same machine learning algorithms were compared by McNemar\u2019s test, a statistically significant difference in overall classification accuracy between the pixel-based and object-based classifications only existed in the RF algorithm. Regarding species, monoculture and dominant mangrove species Sonneratia apetala group 1 (SA1) as well as partly mixed and regular shape mangrove species Hibiscus tiliaceus (HT) could well be identified. However, for complex and easily-confused mangrove species Sonneratia apetala group 2 (SA2) and other occasionally presented mangroves species (OT), only major distributions could be extracted, with an accuracy of about two-thirds. This study demonstrated that more than 80% of artificial mangroves species distribution could be mapped.<\/jats:p>","DOI":"10.3390\/rs10020294","type":"journal-article","created":{"date-parts":[[2018,2,14]],"date-time":"2018-02-14T14:01:20Z","timestamp":1518616880000},"page":"294","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":85,"title":["Artificial Mangrove Species Mapping Using Pl\u00e9iades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9454-8314","authenticated-orcid":false,"given":"Dezhi","family":"Wang","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"National Engineering Research Center of Geographic Information System, Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2387-5419","authenticated-orcid":false,"given":"Bo","family":"Wan","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"National Engineering Research Center of Geographic Information System, Wuhan 430074, China"}]},{"given":"Penghua","family":"Qiu","sequence":"additional","affiliation":[{"name":"College of Geography and Environmental Science, Hainan Normal University, Haikou 571158, China"}]},{"given":"Yanjun","family":"Su","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China"}]},{"given":"Qinghua","family":"Guo","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China"}]},{"given":"Xincai","family":"Wu","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"National Engineering Research Center of Geographic Information System, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Giri, C. 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