Multi-Source Evidence Data Fusion Approach to Detect Daily Distribution and Coverage of Ulva Prolifera in the Yellow Sea, China

Abstract

Ulva prolifera (U. prolifera), a seaweed species in the family Ulvaceae, has been causing green tides in the Yellow Sea, China, every year since 2008. This has attracted the attention of the government and the scientific community because of its influence on the region's marine economy and coastal zone environment. Remote sensing, surveillance ships, and airplanes are three common surveillance approaches used by the State Oceanic Administration (SOA) for U. prolifera detection in the Yellow Sea. However, improving the accuracy and convenience of daily U. prolifera detection by fusing the data from these sources, and obtaining the coverage area and distribution of U. prolifera without human intervention, are two challenges that need to be addressed. To this end, this study proposes an improved multi-source evidence fusion method based on Dempster-Shafer evidence theory to detect the distribution and coverage area of U. prolifera using data from multiple sources (MODIS images, Sentinel-2A images, and surveillance ship data). This improved approach can handle the conflicts caused by differences in image characteristics and spatial resolutions as well as differences between surveillance ship data and remote sensing data. The capability of the improved method to extract the distribution and coverage area of U. prolifera was tested experimentally. The results agree with the reference data supplied by SOA in terms of spatial position and geometry. The fusion of multi-source data demonstrates better results in the detection of U. prolifera coverage area and spatial distribution compared with those from individual data sources. The improved approach can be applied to daily and emergent monitoring tasks for U. prolifera detection.