期刊
REMOTE SENSING
卷 14, 期 21, 页码 -出版社
MDPI
DOI: 10.3390/rs14215372
关键词
aquaculture ponds; spatial distribution; Sentinel-1 SAR images; Google Earth Engine; coastal area of China
类别
资金
- National Natural Science Funded project [41976209, 42206236]
- College Students' Science and Technology Innovation Activity Plan and Xinmiao Talent Plan of Zhejiang Province [2022R405B086]
- Postgraduate Research and Innovation Fund of Ningbo University [IF2022021]
By utilizing GEE and ArcGIS platforms, this study developed a map of coastal aquaculture ponds in China, revealing distribution characteristics and providing important references for the management and decision-making in the aquaculture industry.
Aquaculture has enormous potential for ensuring global food security and has experienced rapid growth globally. Thus, the accurate monitoring and mapping of coastal aquaculture ponds is necessary for the sustainable development and efficient management of the aquaculture industry. Here, we developed a map of coastal aquaculture ponds in China using Google Earth Engine (GEE) and the ArcGIS platform, Sentinel-1 SAR image data for 2020, the Sentinel-1 Dual-Polarized Water Index (SDWI), and water frequency obtained by identifying the special object features of aquaculture ponds and postprocessing interpretation. Our map had an overall accuracy of 93%, and we found that the coastal aquaculture pond area in China reached 6937 km(2) in 2020. The aquaculture pond area was highest in Shandong, Guangdong, and Jiangsu Provinces, and at the city level, Dongying, Binzhou, Tangshan, and Dalian had the most aquaculture pond area. Aquaculture ponds had spatial heterogeneity; the aquaculture pond area in north China was larger than in south China and seaside areas had more pond area than inland regions. In addition, aquaculture ponds were concentrated near river estuaries, coastal plains, and gulfs, and were most dense in the Huang-Huai-Hai Plain and Pearl River Delta. We showed that GEE cloud processing and ArcGIS local processing could facilitate the classification of coastal aquaculture ponds, which can be used to inform and improve decision-making for the spatial optimization and intelligent monitoring of coastal aquaculture, with certain potential for spatial migration.
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