Artificial Mangrove Species Mapping Using Pleiades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms
文献类型：期刊论文
| 作者 | Wang, Dezhi2; Wan, Bo2; Qiu, Penghua3; Su, Yanjun4; Guo, Qinghua4 ; Wu, Xincai2
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| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2018 |
| 卷号 | 10期号:2 |
| 关键词 | artificial mangrove object-based pixel-based decision tree random forest support vector machine |
| DOI | 10.3390/rs10020294 |
| 文献子类 | Article |
| 英文摘要 | 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 Pleiades-1 in classifying artificial mangrove species using both pixel-based and object-based classification schemes. For comparison, three machine learning algorithmsdecision tree (DT), support vector machine (SVM), and random forest (RF)were 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's 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. |
| 学科主题 | Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
| 出版地 | BASEL |
| 电子版国际标准刊号 | 2072-4292 |
| WOS关键词 | RANDOM FOREST ; LAND-COVER ; IMAGE CLASSIFICATION ; ACCURACY ASSESSMENT ; IKONOS ; WORLDVIEW-2 ; INTEGRATION ; ECOSYSTEMS |
| 语种 | 英语 |
| WOS记录号 | WOS:000427542100138 |
| 出版者 | MDPI |
| 资助机构 | National Key Research & Development (R&D) Plan of China [2016YFB0502304] ; National Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [41361090] |
| 源URL | [http://ir.ibcas.ac.cn/handle/2S10CLM1/20378]  |
| 专题 | 植被与环境变化国家重点实验室 |
| 作者单位 | 1.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 2.China Univ Geosci Wuhan, Fac Informat Engn, Wuhan 430074, Hubei, Peoples R China 3.Natl Engn Res Ctr Geog Informat Syst, Wuhan 430074, Peoples R China 4.Hainan Normal Univ, Coll Geog & Environm Sci, Haikou 571158, Hainan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Dezhi,Wan, Bo,Qiu, Penghua,et al. Artificial Mangrove Species Mapping Using Pleiades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms[J]. REMOTE SENSING,2018,10(2). |
| APA | Wang, Dezhi,Wan, Bo,Qiu, Penghua,Su, Yanjun,Guo, Qinghua,&Wu, Xincai.(2018).Artificial Mangrove Species Mapping Using Pleiades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms.REMOTE SENSING,10(2). |
| MLA | Wang, Dezhi,et al."Artificial Mangrove Species Mapping Using Pleiades-1: An Evaluation of Pixel-Based and Object-Based Classifications with Selected Machine Learning Algorithms".REMOTE SENSING 10.2(2018). |
入库方式： OAI收割
来源：植物研究所
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