Since 2007, the continuous large-scale outbreaks of green tides in the Yellow Sea have caused severe impacts on the marine ecological environment and marine economy. Satellite remote sensing monitoring information provides crucial support for emergency decision-making in green tide disasters. The existing monitoring envelope for green tide distribution is drawn using buffer analysis, but the excessive number of vertices poses a significant computational burden for drift prediction. Therefore, this paper developed a method for green tide distribution envelope mapping based on remote sensing images. Firstly, the green tide coverage information was extracted using the NDVI threshold method. Then, the green tide distribution envelope was mapped based on coverage using buffer analysis. Finally, the envelope simplification algorithm based on edge length and vertex angle characteristics (SM-SAC) proposed in this paper was applied to simplify the distribution envelope. This model (SM-SAC) can effectively simplify the vertices, thereby improving the efficiency of green tide distribution drift prediction and providing technical support for developing emergency decision-making plans for green tide disasters.
The Yellow Sea green tide has occurred every year on a large scale since 2007, causing harm to marine ecology, fisheries, tourism, and so on. Affected areas usually formulate green tide containment and removal programs based on satellite images green tide monitoring information. However, due to the influence of clouds and fog and the width of satellite images, it is difficult for one satellite image to monitor the green tide comprehensively, so it is necessary to fuse green tide monitoring information from multi-source satellite images to obtain comprehensive green tide information. However, we face the problems of different scope, time, and accuracy when fusing green tide multi-source monitoring information. Therefore, this paper developed a fusion module based on resampling technology, area refinement methods, green tide drift/tracking technique, and spatial overlay analysis and integration technique. A case study found that the fusion module generated a spatially and temporally uniform green tide fusion product with comprehensive coverage, and the product greatly reduced the number of green tide points, which effectively reduced the amount of drift prediction calculations, improving the prediction efficiency and thematic mapping fluency. It can be concluded that, comparing the single satellite monitoring information, the green tide multi-source fusion product can better provide technical support for Green Tide disaster prevention and mitigation emergency decision-making.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.