Information extraction of coastal aquaculture ponds based on spectral features and spatial convolution

doi:10.6046/zrzyyg.2022037

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Abstract

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

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TP751

Issue Date: 27 December 2022

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	Yefan LI
	Lin WANG
	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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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022037 OR https://www.gtzyyg.com/EN/Y2022/V34/I4/42

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

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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