Cite this paper:
LIU Yueming, YANG Xiaomei, WANG Zhihua, LU Chen, LI Zhi, YANG Fengshuo. Aquaculture area extraction and vulnerability assessment in Sanduao based on richer convolutional features network model[J]. HaiyangYuHuZhao, 2019, 37(6): 1941-1954

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

LIU Yueming1,3, YANG Xiaomei1,3,4, WANG Zhihua1, LU Chen1,3, LI Zhi2,3, YANG Fengshuo1,3
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
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.
Key words:    aquaculture area|vulnerability assessment|Richer Convolutional Features (RCF) network model|deep learning|high-resolution remote sensing   
Received: 2018-09-25   Revised: 2019-03-18
Tools
PDF (5241 KB) Free
Print this page
Add to favorites
Email this article to others
Authors
Articles by LIU Yueming
Articles by YANG Xiaomei
Articles by WANG Zhihua
Articles by LU Chen
Articles by LI Zhi
Articles by YANG Fengshuo
References:
Blaschke T, Hay G J, Kelly M, Lang S, Hofmann P, Addink E, Feitosa R Q, van der Meer F, van der Werff H, van Coillie F, Tiede D. 2014. Geographic object-based image analysis-towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing, 87:180-191, https://doi.org/10.1016/j.isprsjprs.2013.09.014.
Chen G, Weng Q H, Hay G J, He Y N. 2018. Geographic Object-Based Image Analysis (GEOBIA):emerging trends and future opportunities. GIScience & Remote Sensing, 55(2):159-182, https://doi.org/10.1080/15481603.2018.1426092.
Cheng T F, Zhou W F, Fan W. 2012. Progress in the methods for extracting aquaculture areas from remote sensing data.Remote Sensing for Land & Resources, 24(3):1-5. (in Chinese with English abstract)
Chu J L, Zhao D Z, Zhang F S. 2012. Wakame raft interpretation method of remote sensing based on association rules.Remote Sensing Technology and Application, 27(6):941-946. (in Chinese with English abstract)
Fan J Y, Huang H J, Fan H, Gao A. 2005. Extracting aquaculture area with RADASAT-1. Marine Sciences, 29(10):44-47.(in Chinese with English abstract)
Guan C T, Wang Q Y. 2005. Development and prospect of marine net cage technology in China. Fishery Modernization, 3:5-7. (in Chinese)
He K M, Zhang X Y, Ren S Q, Su J. 2015. Delving deep into rectifiers:surpassing human-level performance on imagenet classification. arXiv:1502.01852.
Huang B, Qian L M, Liu J F. 2002. Nutrient salts content and eutrophication assessment For Sanduao sea area, Fujian.Journal of Oceanography in Taiwan Strait, 21(4):411-415. (in Chinese with English abstract)
Ji W W, Zhou J. 2012. Community structure of macrobenthos in response to mariculture practices in Sandu Bay. Journal of Fishery Sciences of China, 19(3):491-499. (in Chinese with English abstract)
Lahsen M, Sanchez-Rodriguez R, Lankao P R, Dube P, Leemans R, Gaffney O, Mirza M, Pinho P, Osman-Elasha B, Smith M S. 2010.Impacts, adaptation and vulnerability to global environmental change:challenges and pathways for an action-oriented research agenda for middle-income and low-income countries. Current Opinion in Environmental Sustainability, 2(5-6):364-374, https://doi.org/10.1016/j.cosust.2010.10.009.
Li X G, Jiang N, Yang Y B, Yin L Q. 2006. Remote sensing investigation and survey of lake reclamation and enclosure aquaculture in Lake Taihu. Transactions of Oceanology and Limnology, (1):93-99. (in Chinese with English abstract)
Liu P, Du Y Y. 2012. A CBR approach for extracting coastal aquaculture area. Remote Sensing Technology and Application, 27(6):857-864. (in Chinese with English abstract)
Liu Y, Cheng M M, Hu X W, Wang K, Bai X. 2017. Richer convolutional features for edge detection. In:Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Honolulu, HI, https://doi.org/10.1109/CVPR.2017.622.
Lu Y W, Li Q Z, Du X, Wang H Y, Liu J L. 2015. A Method of coastal aquaculture area Automatic Extraction with high spatial resolution images. Remote Sensing Technology and Application, 30(3):486-494. (in Chinese with English abstract)
Romieu E, Welle T, Schneiderbauer S, Pelling M, Vinchon C. 2010.Vulnerability assessment within climate change and natural hazard contexts:revealing gaps and synergies through coastal applications. Sustainability Science, 5(2):159-170, https://doi.org/10.1007/s11625-010-0112-2.
Shelhamer E, Long J, Darrell T. 2017. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4):640-651, https://doi.org/10.1109/TPAMI.2016.2572683.
Simonyan K, Zisserman Z. 2014. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556.
Wang J, Gao J F. 2008. Extraction of enclosure culture in Gehu Lake based on correspondence analysis. Journal of Remote Sensing, 12(5):716-723. (in Chinese with English abstract)
Wang M, Cui Q, Wang J, Ming D P, Lv G N. 2017. Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features. ISPRS Journal of Photogrammetry and Remote Sensing, 123:104-113, https://doi.org/10.1016/j.isprsjprs.2016.10.008.
Wang Z H, Meng F, Yang X M, Yang F S, Fang Y. 2016. Study on the automatic selection of segmentation scale parameters for high spatial resolution remote sensing images. Journal of Geo-information Science, 18(5):639-648, https://doi.org/10.3724/SP.J.1047.2016.00639.
Wang Z H, Yang X M, Liu Y M, Lu C. 2018. Extraction of coastal raft cultivation area with heterogeneous water background by thresholding object-based visually salient NDVI from high spatial resolution imagery. Remote Sensing Letters, 9(9):839-846, https://doi.org/10.1080/2150704X.2018.1468103.
Xie Y L, Wang M, Zhang X Y. 2009. An object-oriented approach for extracting farm waters within coastal belts.Remote Sensing Technology and Application, 24(1):68-72. (in Chinese with English abstract)
Zhang T, Yang X M, Tong L Q, He P. 2016. Selection of bestfitting scale parameters in image segmentation based on multiscale segmentation image database. Remote Sensing for Land & Resources, 28(4):59-63, https://doi.org/10.6046/gtzyyg.2016.04.09.
Zhou X C, Wang X Q, Xiang T L, Jiang H. 2006. Method of automatic extracting seaside aquaculture land based on ASTER remote sensing image. Wetland Science, 4(1):64-68. (in Chinese with English abstract)
Zhu C M, Luo J C, Shen Z F, Li J L, Hu X D. 2011. Extract enclosure culture in coastal waters based on high spatial resolution remote sensing image. Journal of Dalian Maritime University, 37(3):66-69. (in Chinese with English abstract)