基于遥感监测的黑泥湾筏式海带养殖区时空变化研究

doi:10.3976/j.issn.1002-4026.2025036

Abstract

Abstract:

Accurate assessment of the scale and distribution of offshore marine aquaculture is critical for effective management, spatial planning, and ecological protection. This study employed high-resolution Sentinel-2A/2B satellite imagery, a U-Net deep learning model for automatic feature extraction, and human-computer interactive correction to map the spatial extent of raft-based kelp farming in Heiniwan Bay from 2016 to 2024. The analysis revealed a three-phase development trajectory in the aquaculture area over the nine-year period. Spatial distribution exhibited a stable “north-south agglomeration with central sparsity” pattern. The observed spatiotemporal dynamics reflect the combined influence of technological advancements, policy interventions, and natural environmental conditions. These findings offer a robust scientific basis for optimizing aquaculture zoning, adaptive management strategies, and ecological governance in coastal regions with comparable aquaculture practices and environmental settings.

Key words: Sentinel-2A/2B, remote sensing, raft-based kelp aquaculture, deep learning, Heiniwan Bay, spatiotemporal evolution

CLC Number:

TP79

LI Xin, HAO Zengzhou, LI Yunzhou, HUANG Haiqing, PAN Delu. Spatiotemporal variation in raft-based kelp aquaculture in Heiniwan Bay using a remote sensing and monitoring technique[J].Shandong Science, 2025, 38(3): 25-33.

Figures/Tables 8

Fig.1

Table 1

Table 2

Fig.2

Fig.3

Fig.4

Table 3

Fig.5

References 26

[1]	刘方旭. 海水养殖的污染现状和优化策略[J]. 水上安全, 2025(2): 91-93.
[2]	Food and Agriculture Organization of the United Nations. Fishery and aquaculture statistics:Global aquaculture production 1950—2020[R]. Rome:FAO, 2022.
[3]	姜珊珊. 黑泥湾海带养殖活动对水体光学特性及颗粒有机碳的影响[D]. 青岛: 中国科学院大学(中国科学院海洋研究所), 2023.DOI:10.27551/d.cnki.gzkhs.2023.000050.
[4]	吴岩峻, 张京红, 田光辉, 等. 利用遥感技术进行海南省水产养殖调查[J]. 热带作物学报, 2006(2):110-113.
[5]	曹利, 顾文俊, 李小恕, 等. 基于WorldView数据的浙江省铁港牡蛎养殖分布遥感调查[J]. 渔业信息与战略, 2016, 31(4): 286-292. DOI: 10.13233/j.cnki.fishis.2016.04.007.
[6]	许海蓬, 张彦彦, 陈志远, 等. 近10年连云港海域紫菜养殖区遥感监测与分析[J]. 现代测绘, 2019, 42(3): 10-14. DOI: 10.3969/j.issn.1672-4097.2019.03.003.
[7]	XU Y, HU Z W, ZHANG Y H, et al. Mapping aquaculture areas with multi-source spectral and texture features: A case study in the Pearl River Basin (Guangdong), China[J]. Remote Sensing, 2021, 13(21): 4320. DOI: 10.3390/rs13214320.
[8]	陈翱. 基于多特征和对象分类耦合的近海水产养殖区信息提取研究:以福建省三沙湾为例[D]. 上海: 上海海洋大学, 2023.DOI:10.27314/d.cnki.gsscu.2023.000504.
[9]	KUREKIN A A, MILLER P I, AVILLANOSA A L, et al. Monitoring of coastal aquaculture sites in the Philippines through automated time series analysis of sentinel-1 SAR images[J]. Remote Sensing, 2022, 14(12): 2862. DOI: 10.3390/rs14122862.
[10]	胡姣婵, 黄梦迪, 于浩洋, 等. 基于哨兵二号遥感影像的近海养殖区提取方法研究[J]. 海洋环境科学, 2022, 41(4): 619-627. DOI: 10.13634/j.cnki.mes.2022.04.016.
[11]	LECUN Y, BENGIO Y, HINTON G. Deep learning[J]. Nature, 2015, 521(7553): 436-444. DOI: 10.1038/nature14539.
[12]	SHELHAMER E, LONG J, DARRELL T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640-651. DOI: 10.1109/TPAMI.2016.2572683.
[13]	CHEN L C, PAPANDREOU G, KOKKINOS I, et al. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848. DOI: 10.1109/TPAMI.2017.2699184.
[14]	RONNEBERGER O, FISCHER P, BROX T. U-Net: Convolutional networks for biomedical image segmentation[M]// Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015. Cham: Springer International Publishing, 2015: 234-241. DOI: 10.1007/978-3-319-24574-4_28.
[15]	由金浩, 刘威, 王权明. 基于U-Net神经网络的浮筏养殖信息提取:以长海县为例[J]. 绿色科技, 2024, 26(2):261-265.DOI:10.16663/j.cnki.lskj.2024.02.003.
[16]	CUI B G, FEI D, SHAO G H, et al. Extracting raft aquaculture areas from remote sensing images via an improved U-Net with a PSE structure[J]. Remote Sensing, 2019, 11(17): 2053. DOI: 10.3390/rs11172053.
[17]	刘继鹏, 王常颖, 初佳兰. 基于U-Net的国产高分卫星影像海水养殖区分类提取方法[J]. 海洋环境科学, 2023, 42(3):471-482.DOI:10.13634/j.cnki.mes.2023.03.013.
[18]	凡仁福, 魏皓, 赵亮, 等. 黄海黑泥湾近岸水体动能变化分析[J]. 海洋科学进展, 2025, 43(1):106-117. DOI:10.12362/j.issn.1671-6647.20231026001.
[19]	张泽华, 黄海军, 刘艳霞, 等. 浅海筏式养殖对周边海域潮流和悬浮体特征影响研究[J]. 海洋科学进展, 2016, 34(1): 37-49. DOI: 10.3969/j.issn.1671-6647.2016.01.004.
[20]	YU H M, HOU Y Z, WANG F X, et al. MSSFNet: A multiscale spatial-spectral fusion network for extracting offshore floating raft aquaculture areas in multispectral remote sensing images[J]. Sensors, 2024, 24(16): 5220. DOI: 10.3390/s24165220.
[21]	威海市发展和改革委员会. 威海市海洋与渔业发展“十三五”规划:2018[EB/OL]. [2025-03-29]. https://fgw.weihai.gov.cn/attach/0/1801231006382823570.pdf.
[22]	威海市人民政府. 威海市人民政府关于印发威海市养殖水域滩涂规划(2018—2030年)的通知:威政字〔2020〕11号[EB/OL].[2025-03-29]. https://www.weihai.gov.cn/art/2020/2/25/art_103284_15916.html.
[23]	威海市统计局. 2019年威海市国民经济和社会发展统计公报[N]. 威海日报,2020-03-25(003).DOI:10.28823/n.cnki.nwhrb.2020.000246.
[24]	威海市人民代表大会常务委员会. 威海市海洋牧场管理条例[N]. 威海日报,2022-01-22(003).DOI:10.28823/n.cnki.nwhrb.2022.000148.
[25]	威海市人民政府办公室. 威海市“十四五”海洋经济发展规划:威政办发〔2021〕21号[EB/OL].[2025-03-29]. https://www.weihai.gov.cn/col/col127396/index.html.
[26]	威海市海洋发展局. 关于《威海市海水养殖污染控制方案》的政策解读[EB/OL].[2025-03-29]. https://www.weihai.gov.cn/art/2020/8/6/art_51913_2519180.html.

波段	波段描述	中心波长/μm	空间分辨率/m
B₂	蓝波段	0.490	10
B₃	绿波段	0.560	10
B₄	红波段	0.665	10

年份	卫星影像日期	影像数量
2016	2016-03-25(S2A);2016-04-24(S2A)	2
2017	2017-01-19(S2A);2017-04-09(S2A);2017-04-29(S2A)	3
2018	2018-01-09(S2B);2018-02-13(S2A);2018-02-23(S2A)	3
2019	2019-01-24(S2B);2019-02-23(S2B);2019-03-10(S2A)	3
2020	2020-02-23(S2A);2020-03-04(S2A);2020-04-03(S2A)	3
2021	2020-12-24(S2B);2021-02-07(S2B);2021-02-27(S2A)	3
2022	2022-01-03(S2A);2022-01-18(S2B);2022-01-28(S2B)	3
2023	2023-02-27(S2A);2023-03-04(S2B);2023-03-19(S2A)	3
2024	2024-03-18(S2B);2024-04-07(S2B);2024-04-17(S2B)	3

年份	面积/km²	变化量/km²
2016	19.81	—
2017	24.56	+4.75
2018	28.31	+3.75
2019	24.17	-4.14
2020	26.04	+1.87
2021	24.51	-1.53
2022	27.66	+3.15
2023	26.11	-1.55
2024	26.71	+0.60

Spatiotemporal variation in raft-based kelp aquaculture in Heiniwan Bay using a remote sensing and monitoring technique

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 26

Related Articles 12

Metrics

Comments

Recommended 0

[1]	LI Yunzhou, ZHOU Maosheng, ZHU Lin, YU Dingfeng, HAO Zengzhou, LI Min, WANG Juncheng, PAN Delu. Validation of satellite scatterometer-derived sea-surface wind fields based on ocean buoy data [J]. Shandong Science, 2025, 38(3): 1-13.
[2]	CHEN Peng, LI Yunzhou, ZHANG Siqi, ZHANG Zhenhua, PAN Delu. LiDAR-based monitoring of diurnal-nocturnal oceanic carbon flux [J]. Shandong Science, 2025, 38(3): 14-24.
[3]	GU Ping, WANG Pengjie, WANG Guoliang, XU Shengrong, ZHAO Ran, ZHANG Rui, ZHANG Xiaolei, YAN Xincheng, GAO Yunfeng, WANG Na. A review on water quality remote sensing technology based on domestic Gaofen series satellites [J]. Shandong Science, 2025, 38(3): 40-50.
[4]	FAN Qianyi, LIU Fangyuan, JI Zelu, BIAN Xiaodong, YU Dingfeng, ZHAO Xinqi. Spatiotemporal evolution and trend analysis of suspended sediment mass concentration in the Yellow River Estuary and adjacent sea areas using Google Earth Engine [J]. Shandong Science, 2025, 38(3): 51-63.
[5]	SHI Zhenjia, HAO Zengzhou, LI Yunzhou, YE Feng, HUANG Haiqing, PAN Delu. Fusion of mesoscale eddy data for the South China Sea [J]. Shandong Science, 2025, 38(3): 99-108.
[6]	LIU Yu, GAO Shangbing, ZHANG Qintao, ZHANG Yingying. Object detection model YOLO-T for complex traffic scenarios [J]. Shandong Science, 2024, 37(6): 104-115.
[7]	YU Yu, GUO Baoqi, CHU Shibo, LI Heng, YANG Pengru. Survey of underwater biological object detection methods based on deep learning [J]. Shandong Science, 2023, 36(6): 1-7.
[8]	LI Lian-wei,ZHANG Yuan-yu,YUE Zeng-you,XUE Cun-jin,FU Yu-xuan,XU Yang-feng. Extracting inland cage aquacultural areas from high-resolution remote sensing images using fully convolutional networks model [J]. Shandong Science, 2022, 35(2): 1-10.
[9]	WU Yun-qing, TAO Yu-ting, ZHANG Yun-peng, MA Jing. Spatial-Temporal evolution of national air pollutants based on DMSP/OLS data [J]. Shandong Science, 2020, 33(2): 97-105.
[10]	ZHANG Zhenzhen,LIU Tongyu,NAN Gangyang,ZHAO Yang,WANG Qiwu, JU Yang. Research advances of the application of laser radar in atmospheric detection [J]. SHANDONG SCIENCE, 2015, 28(6): 73-80.
[11]	QI Suiping，LIU Tao，HU Tong，ZOU Jing. Ground cloud observation technology and its instrument research advances [J]. SHANDONG SCIENCE, 2014, 27(6): 1-9.
[12]	ZHOU Bin, LIU En-Xiao, YANG Lei, LIANG Zhi-Heng, TAO Qing-Chang. Physical model based electromagnetic simulation methodology for dynamic ocean surface [J]. J4, 2014, 27(1): 9-15.