In Journal of environmental management
Coastal ecosystems offer substantial support and space for the sustainable development of human society, and hence the ecological risk evaluation of coastal ecosystems is of great significance. In this article, we propose an innovative framework for evaluating coastal ecological risk by considering oil spill risk information and environmental vulnerability information. Specifically, a deep learning based marine oil spill monitoring method is presented to obtain the oil spill risk information from Sentinel-1 polarimetric synthetic aperture radar (PolSAR) images. The environmental vulnerability information is then obtained from biological sample data and habitat information. Finally, a weighted probability model is introduced to utilize the oil spill risk and environmental vulnerability information, to evaluate the coastal ecological risk. In the experimental part, the proposed oil spill monitoring method shows its reliability in global ocean areas, and the proposed model is adopted to evaluate the ecological risk in Jiaozhou Bay, China. The results show that the ecological situation of more than half of the areas in Jiaozhou Bay is unstable, and the areas with high risk are mainly concentrated in the ports, shipping channels, and those areas with high biodiversity. This study provides some new perspectives on ecological risk assessment for coastal ecosystems, facilitating the planning process and the actions to be taken in response to the accidents that occur in the ocean, especially oil spill accidents.
Ma Xiaoshuang, Xu Jiangong, Pan Jun, Yang Jie, Wu Penghai, Meng Xiangchao
2023-Jan-01
Deep learning, Ecological risk assessment, Marine oil spill detection, Synthetic aperture radar
