Remote Sensing Monitoring and Environmental Pollution Load Assessment of Coastal Aquaculture Area Based on GF-2

Abstract

Coastal aquaculture surveys play an important role in the marine economic development, coastal resources utilization and marine environmental protection. With the development of satellite remote sensing technology, investigation and analysis of coastal aquaculture with high resolution satellite images has been a hot topic. Based on the analysis of spectral and geospatial features of coastal cage aquaculture areas, this study proposes an object-based classification method with GF-2 image. First, the NDWI threshold was used to achieve land-sea separation. Secondly, rules designed according to the spectral feature for cage aquaculture detection in high turbidity water bodies were established considering that same spectrum with different objects and other phenomena may easily affect the extraction accuracy due to the turbidity of the water in the study area. Results show that the object-based method can quickly and accurately monitor the distribution of different types of aquaculture areas, and the overall detection accuracy can reach over 93%, which is much better than the pixel based method of Maximum Likelihood Method. This objet-based method then was used to calculate the nutrients loading of the cage aquaculture areas, which can provide effective information support and auxiliary decision analysis for management departments to scientifically plan and environmental manage coastal aquaculture areas.