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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (4) : 53-59     DOI: 10.6046/zrzyyg.2022186
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Remote sensing monitoring of the spatio-temporal changes in pond aquaculture based on mixed pixel decomposition
SHENG Dezhi1,2,3(), XING Qianguo1,2,3(), LIU Hailong1,2,3, ZHENG Xiangyang1,2,3
1. Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
2. Shandong Key Laboratory of Coastal Environmental Processes, Yantai 264003, China
3. University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract  

Aquaculture is an important way for humans to obtain food, and aquaculture ponds are a major production mode of aquaculture. The Pearl River Delta, as an important aquaculture base in southern China, has undergone great changes in its spatial distribution in the past 30 years. This study investigated Zhongshan City and its adjacent areas. First, the mixed pixels of Landsat and Sentinel-2 remote sensing data were decomposed using the linear mixed pixel decomposition method. Then, the NDWI threshold range corresponding to the water abundance of 70% and above was selected through visual comparison and analysis. Finally, the spatio-temporal distribution of typical aquaculture ponds from 1990 to 2021 was obtained. The study results show that the aquaculture ponds in Zhongshan City and its adjacent areas have experienced a process of first increasing and then decreasing since 1990. Specifically, the area of aquaculture ponds nearly doubled from 1990 to 2000, tended to be stable from 2000 to 2010, but decreased by nearly 50% from 2010 to 2021. This study can reduce the impact of mixed pixels on the monitoring of aquaculture ponds and support the scientific aquaculture and sustainable development of fisheries in the Greater Bay Area.
Keywords NDWI      mixed pixel decomposition      aquaculture pond      Zhongshan City      Pearl River Delta     
ZTFLH:  TP79  
Issue Date: 27 December 2022
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Dezhi SHENG
Qianguo XING
Hailong LIU
Xiangyang ZHENG
Cite this article:   
Dezhi SHENG,Qianguo XING,Hailong LIU, et al. Remote sensing monitoring of the spatio-temporal changes in pond aquaculture based on mixed pixel decomposition[J]. Remote Sensing for Natural Resources, 2022, 34(4): 53-59.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022186     OR     https://www.gtzyyg.com/EN/Y2022/V34/I4/53
Fig.1  Location of study area
日期 卫星影像 空间分辨率/m
1990-10-13 Landsat5 TM 30
2000-09-06 Landsat5 TM 30
2010-03-26 Landsat5 TM 30
2020-02-08 Landsat8 OLI 30
2021-02-20 Landsat8 OLI 30
2020-10-26 Sentinel-2 MSI 10
2021-02-23 Sentinel-2 MSI 10
Tab.1  Information of satellite images
年份 数据源 NDWI阈值
1990年 Landsat5 [-0.15,1]
2000年 Landsat5 [-0.12,1]
2010年 Landsat5 [-0.03,1]
2020年 Landsat8 [-0.09,1]
Sentinel-2 [-0.11,1]
2021年 Landsat8 [-0.10,1]
Sentinel-2 [-0.07,1]
Tab.2  NDWI thresholds in different years
Fig.2  Spectral curves of mixing water and vegetation in different proportions
Fig.3  Reflectance of water and vegetation in the Landsat8 image in 2020
Fig.4  Abundance of aquaculture pond water based on remote sensing image inversion on February 8, 2020
年份/卫星 池塘面积/km2 面积占
比/%		 NDWI阈值
2020年/Landsat8 357 73 [-0.09,0.69]
2020年/Sentine-2 426 99 [-0.10,0.90]
2021年/Landsat8 303 68 [-0.10,0.69]
2021年/Sentinel-2 360 77 [-0.08,0.80]
Tab.3  Statistics of aquaculture ponds with abundance of water bodies above 0.7
年份 线性关系 调整前阈值 调整后阈值
1990年 y = 0.66 x + 0.01 [-0.15,1] [-0.06,0.47]
2000年 y = 0.78 x - 0.03 [-0.12,1] [-0.10,0.51]
2010年 y = 0.39 x - 0.05 [-0.03,1] [-0.01,0.22]
Tab.4  NDWI threshold adjustment
Fig.5  Distribution of aquaculture ponds from 1990 to 2020
Fig.6  Interannual variation of aquaculture ponds in the study area
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