Aquaculture area extraction and vulnerability assessment in Sanduao based on richer convolutional features network model

Yueming LIU; Xiaomei YANG; Zhihua WANG; Chen LU; Zhi LI; Fengshuo YANG

doi:10.1007/s00343-019-8265-z

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Aquaculture area extraction and vulnerability assessment in Sanduao based on richer convolutional features network model

Aquaculture and Fisheries | Updated：2023-05-20

- Aquaculture area extraction and vulnerability assessment in Sanduao based on richer convolutional features network model
- Journal of Oceanology and Limnology Vol. 37, Issue 6, Pages: 1941-1954(2019)
- Affiliations：
  
  1.State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
  2.State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
  3.University of Chinese Academy of Sciences, Beijing 100049, China
  4.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
- Author bio：
  
  YANG Xiaomei, E-mail:yangxm@lreis.ac.cn
- Funds：
- DOI：10.1007/s00343-019-8265-z
  CLC：
- Received：25 September 2018，
  
  Accepted：11 January 2019，
  
  Online First：18 March 2019，
  
  Published：2019-11
- Accepted：
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Yueming LIU, Xiaomei YANG, Zhihua WANG, 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.
DOI：

Yueming LIU, Xiaomei YANG, Zhihua WANG, 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. DOI： 10.1007/s00343-019-8265-z.

摘要

Abstract

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.

关键词

Keywords

references

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