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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (4) : 42-52     DOI: 10.6046/zrzyyg.2022037
Information extraction of coastal aquaculture ponds based on spectral features and spatial convolution
LI Yefan1,2,3(), WANG Lin1,2,3(), ZHANG Dongzhu1,2,3
1. College of Environmental and Safety Engineering, Fuzhou University, Fuzhou 350108, China
2. Academy of Geography and Ecological Environment, Fuzhou University, Fuzhou 350108, China
3. Fujian Provincial Key Laboratory of Remote Sensing of Soil Erosion and Disaster Prevention, Fuzhou 350108, China
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To control the negative effects of the disorderly development of aquaculture ponds and promote the further development of the aquaculture industry, the top priority is to realize rapid and accurate identification and extraction of information on aquaculture ponds. Aquaculture ponds are special net-like water bodies divided by complex roads and dikes. Simple spectral features or spatial texture features are not enough for accurate information extraction. Moreover, the mixed feature rule set gets more demanding on computer performance. Therefore, based on the Landsat image sequence and the Google Earth Engine (GEE) platform, this study proposed an automatic extraction method for coastal aquaculture ponds, which combined the image spectral information, spatial features, and morphological operation. In this method, dual characteristic water spectral indices, that is, the modified combined index for water identification (MCIWI) and the modified normalized difference water index (MNDWI), were employed to highlight the grid characteristics of large water bodies and aquaculture ponds. Then, the low-frequency filtering spatial convolution operation was used to stretch the differences between aquaculture and non-aquaculture water bodies. Finally, the information on aquaculture pond areas as a whole was identified and extracted accurately and quickly. The results are as follows. ① This method has overall precision of 93% and a Kappa coefficient of 0.86. According to the test process verification of typical area superposition comparison, the overlapping proportions between the extraction results and the actual results were all more than 90%, averaging 92.5%, reflecting the high precision and reliability of this extraction method. ② In 2020, the coastal aquaculture ponds in Fujian Province had a total area of 511.73 km2and were mainly distributed in Zhangzhou, Fuzhou, and Ningde cities. ③ The kernel density analysis suggested that Zhangzhou had a high concentration of aquaculture ponds and thus had high pressure in the management of aquaculture ponds. This method can realize automatic information extraction of coastal aquaculture ponds. Thus, it is of great significance to promote the orderly management and scientific development of fishery aquaculture.

Keywords aquaculture pond area      threshold segmentation      spatial convolution      GEE platform     
ZTFLH:  TP751  
Issue Date: 27 December 2022
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Yefan LI
Dongzhu ZHANG
Cite this article:   
Yefan LI,Lin WANG,Dongzhu ZHANG. Information extraction of coastal aquaculture ponds based on spectral features and spatial convolution[J]. Remote Sensing for Natural Resources, 2022, 34(4): 42-52.
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Fig.1  Location of study area
Fig.2  Objects of aquaculture pond in remote sensing images and in situ photos
Fig.3  Flowchart of the aquaculture pond region extraction procedure
Fig.4  Delineation of potential aquaculture covered areas in test area
Fig.5  Comparison of MCIWI values of water body/non-water body in the study area
Fig.6  Elimination of non-waterbody information in test area
Fig.7  Comparison of MNDWI values between aquaculture/ non-aquaculture water body in the study area
Fig.8  Comparison of MNDWI convolution values of aquaculture/non-aquaculture water body in the study area
Fig.9  Extraction of aquaculture water in test area
Fig.10  Post-processing of aquaculture water identification results in the test area
Fig.11  Spatial distribution of aquaculture ponds in coastal waters of Fujian Province
Fig.12  Spatial distribution of validation sample points
属性数据 验证数据 用户精度/%
养殖塘 非养殖塘
养殖塘 205 27 88
非养殖塘 8 260 97
生产者精度/% 96 91
Tab.1  Confusion matrix of extraction results
Fig.13  Overlapping proportions of results between automated extraction and visual interpretation
Fig.14  Statistical charts and kernel density analysis of the aquaculture pond region in coastal waters of Fujian Province
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