Remote Sensing Mapping of Cage and Floating-raft Aquaculture in China's Offshore Waters Using Machine Learning Methods and Google Earth Engine

Abstract

Cage and floating-raft aquaculture (CFRA) in China’s offshore waters has been rapidly expanding. Mapping the spatial distribution of such large-scale CFRA is the basis for studying and controlling impacts of aquaculture on the environment, and further optimizing CFRA spatial arrangement. Supporting by Google Earth Engine (GEE) platform, this paper took China’s coastal areas as study region, and applied machine learning methods to extract CFRA from Sentine1-2 remote sensing big data. Firstly, according to the characteristics of CFRA, images with cloud-free in April, 2020 were selected and the sea area within 30 kilometers away from the coastline was determined as study area. Secondly, the random forest (RF) and support vector machine (SVM) classification algorithms were utilized to extract CFRA with sample set. Finally, on the basis of accuracy test, spatial distribution characteristics of CFRA were analyzed in ArcMap. The results show: (1) Compared with SVM, RF can obtain higher classification accuracies with 0.945 of overall accuracy and 0.904 of Kappa coefficient; (2) Large-scale CFRA are distributed in the bays of Liaoning, Shandong and Fujian Province, as well as around the islands of Zhejiang, Fujian Province; (3) Large-scale CFRA are often distributed around small islands in the south while around ports in the north; (4) The CFRA areas in Fujian and Shandong rank first and second, occupying 29% and 23%, respectively, of the total CFRA area.