Coastal Oyster Aquaculture Area Extraction and Nutrient Loading Estimation Using a GF-2 Satellite Image

Abstract

The accurate extraction of an aquaculture area is significant in aquaculture management, post-disaster evaluation, and aquatic environment protection. However, little attention has been paid to the aquaculture area extraction in coastal water with high turbidity. In this study, based on the spectral and geospatial features of aquaculture cages in complex coastal water with varying turbidity, we proposed a new aquaculture area extraction method using a Gaofen-2 (GF-2) satellite image with 0.8-m spatial resolution. The water was classified into clear, medium, and high turbidity categories according to the suspended sediment concentration derived from the inversion of the GF-2 image. Different rules of extraction were developed with respect to those three categories of water body: First, the normalized difference water index threshold was set for the clear water, second, a ratio index (R = Green/NIR) was established for the medium turbid water body, and third, for the turbid water body, feature analysis with a specified classification rule was established. The experimental results demonstrated that our proposed method worked well, with the high accuracies of 87.3300% for the overall accuracy, even for the high turbidity water. The kappa coefficient was 0.7375, which was much better than the kappa coefficient values of the three conventional classification methods represented in this article. This study provides effective information support and auxiliary decision analysis for management departments to scientifically plan and environmentally manage coastal aquaculture areas.