Aquaculture area extraction and vulnerability assessment in Sanduao based on richer convolutional features network model
文献类型：期刊论文
| 作者 | Liu Yueming1,2; Yang Xiaomei1,2,4 ; Wang Zhihua2; Lu Chen1,2; Li Zhi1,3; Yang Fengshuo1,2
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| 刊名 | JOURNAL OF OCEANOLOGY AND LIMNOLOGY
 |
| 出版日期 | 2019-11-01 |
| 卷号 | 37期号:6页码:1941-1954 |
| ISSN号 | 2096-5508 |
| 关键词 | aquaculture area vulnerability assessment Richer Convolutional Features (RCF) network model deep learning high-resolution remote sensing |
| DOI | 10.1007/s00343-019-8265-z |
| 通讯作者 | Yang Xiaomei(yangxm@lreis.ac.cn) |
| 英文摘要 | Sanduao is an important sea-breeding bay in Fujian, South China and holds a high economic status in aquaculture. Quickly and accurately obtaining information including the distribution area, quantity, and aquaculture area is important for breeding area planning, production value estimation, ecological survey, and storm surge prevention. However, as the aquaculture area expands, the seawater background becomes increasingly complex and spectral characteristics differ dramatically, making it difficult to determine the aquaculture area. In this study, we used a high-resolution remote-sensing satellite GF-2 image to introduce a deep-learning Richer Convolutional Features (RCF) network model to extract the aquaculture area. Then we used the density of aquaculture as an assessment index to assess the vulnerability of aquaculture areas in Sanduao. The results demonstrate that this method does not require land and water separation of the area in advance, and good extraction can be achieved in the areas with more sediment and waves, with an extraction accuracy >93%, which is suitable for large-scale aquaculture area extraction. Vulnerability assessment results indicate that the density of aquaculture in the eastern part of Sanduao is considerably high, reaching a higher vulnerability level than other parts. |
| WOS关键词 | RAFT CULTIVATION AREA ; IMAGE-ANALYSIS |
| 资助项目 | National Key Research and Development Program of China[2016YFC1402003] ; National Natural Science Foundation of China[41671436] ; Innovation Project of LREIS[O88RAA01YA] |
| WOS研究方向 | Marine & Freshwater Biology ; Oceanography |
| 语种 | 英语 |
| 出版者 | SCIENCE PRESS |
| WOS记录号 | WOS:000495243300014 |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Innovation Project of LREIS |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/131904]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Yang Xiaomei |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Chinese Acad Sci, Xinjiang Inst Ecol & Geog, State Key Lab Desert & Oasis Ecol, Urumqi 830011, Peoples R China 4.Jiangsu Ctr Collaborat Innovat Geog Informat Reso, Nanjing 210023, Jiangsu, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu Yueming,Yang Xiaomei,Wang Zhihua,et al. Aquaculture area extraction and vulnerability assessment in Sanduao based on richer convolutional features network model[J]. JOURNAL OF OCEANOLOGY AND LIMNOLOGY,2019,37(6):1941-1954. |
| APA | Liu Yueming,Yang Xiaomei,Wang Zhihua,Lu Chen,Li Zhi,&Yang Fengshuo.(2019).Aquaculture area extraction and vulnerability assessment in Sanduao based on richer convolutional features network model.JOURNAL OF OCEANOLOGY AND LIMNOLOGY,37(6),1941-1954. |
| MLA | Liu Yueming,et al."Aquaculture area extraction and vulnerability assessment in Sanduao based on richer convolutional features network model".JOURNAL OF OCEANOLOGY AND LIMNOLOGY 37.6(2019):1941-1954. |
入库方式： OAI收割
来源：地理科学与资源研究所
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