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

LI Yefan; WANG Lin; ZHANG Dongzhu

doi:10.6046/zrzyyg.2022037

2022 Vol. 34, No. 4

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LI Yefan, WANG Lin, ZHANG Dongzhu. 2022. Information extraction of coastal aquaculture ponds based on spectral features and spatial convolution. Remote Sensing for Natural Resources, 34(4): 42-52. doi: 10.6046/zrzyyg.2022037

Citation:

LI Yefan, WANG Lin, ZHANG Dongzhu. 2022. Information extraction of coastal aquaculture ponds based on spectral features and spatial convolution. Remote Sensing for Natural Resources, 34(4): 42-52. doi: 10.6046/zrzyyg.2022037

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

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

Received Date: 11 February 2022

Revised Date: 15 December 2022

Publish Date: 27 December 2022

Abstract

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.
- aquaculture pond area /
- threshold segmentation /
- spatial convolution /
- GEE platform

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