Mapping Aquaculture Areas with Multi-Source Spectral and Texture Features: A Case Study in the Pearl River Basin (Guangdong), China

Abstract

Aquaculture has grown rapidly in the field of food industry in recent years; however, it brought many environmental problems, such as water pollution and reclamations of lakes and coastal wetland areas. Thus, the evaluation and management of aquaculture industry are needed, in which accurate aquaculture mapping is an essential prerequisite. Due to the difference between inland and marine aquaculture areas and the difficulty in processing large amounts of remote sensing images, the accurate mapping of different aquaculture types is still challenging. In this study, a novel approach based on multi-source spectral and texture features was proposed to map simultaneously inland and marine aquaculture areas. Time series optical Sentinel-2 images were first employed to derive spectral indices for obtaining texture features. The backscattering and texture features derived from the synthetic aperture radar (SAR) images of Sentinel-1A were then used to distinguish aquaculture areas from other geographical entities. Finally, a supervised Random Forest classifier was applied for large scale aquaculture area mapping. To address the low efficiency in processing large amounts of remote sensing images, the proposed approach was implemented on the Google Earth Engine (GEE) platform. A case study in the Pearl River Basin (Guangdong Province) of China showed that the proposed approach obtained aquaculture map with an overall accuracy of 89.5%, and the implementation of proposed approach on GEE platform greatly improved the efficiency for large scale aquaculture area mapping. The derived aquaculture map may support decision-making services for the sustainable development of aquaculture areas and ecological protection in the study area, and the proposed approach holds great potential for mapping aquacultures on both national and global scales.

A review of Google Earth Engine application in mapping aquaculture ponds

“…By comparing with high-resolution Google Earth data, their approach able to detect spatiotemporal changes of aquaculture ponds with 89% overall accuracy. Xu et al [25] used time series Sentinel-2 images to derive spectral indices to obtain texture features, then used the backscattering and texture features obtained from Sentinel-1 images to differentiate the aquaculture region from other land features. Next, Random Forest Classifiers were used for aquaculture area mapping in the Pearl River Basin, with an overall accuracy of 89.5%.…”

Dynamic Mapping of Inland Freshwater Aquaculture Areas in Jianghan Plain, China

“…Dynamic mapping and area change detection have great potential for better understanding the development of the aquaculture industry. In recent years, remote sensing (RS) was found at low cost and highly efficient in achieving such a goal and it has been implemented in many related worldwide studies [2]. In China, many lakes have been This work was supported in part by the Joint Funds of the National Natural Science Foundation of China under grant U22A20567; in part by a grant from State Key Laboratory of Resources and Environmental Information System; in part by a grant from State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences; in part by a grant from Hubei Provincial Natural Science Foundation for Innovation Groups under grant 2019CFA019; and in part by the National Natural Science Foundation of China under grant 62071457.…”

The Assessment of More Suitable Image Spatial Resolutions for Offshore Aquaculture Areas Automatic Monitoring Based on Coupled NDWI and Mask R-CNN

“…However, the resolution of remote sensing images greatly influences the recognition effect. At present, the primary remote sensing data sources used to identify the mariculture area are multispectral satellite remote sensing images and microwave remote sensing images [7]. The multispectral satellite remote sensing images mainly include Spot, GF-1, GF-2, and Landsat [4,[8][9][10][11]; while microwave remote sensing images mainly include Radarsat-2, GF-3, Sentinel-1, and Sentinel-2 [12][13][14][15][16][17].…”