Related Experiment Video
Updated: May 16, 2025

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
Published on: February 25, 2021
Green tide cover area monitoring and prediction based on multi-source remote sensing fusion
1College of Oceanography and Ecological Science, Shanghai Ocean University, Shanghai 201306, China; Donghai Laboratory, Zhoushan 316021, China.
Abstract:
Green tides are recurring ecological disasters in the Yellow Sea region. In this paper, a Fully Automated Green Tide Extraction Method (FAGTE) is proposed to extract Yellow Sea green tide data from multi-source satellite remote sensing (RS) images with resolutions of 16-250 m obtained during 2021-2024. The average accuracy of green tide extractions exceeds 91 %, and the extraction of small green tide patches from high-resolution satellite images was significantly superior to that from low-resolution images. Additionally, a novel method for fusing the extracted green tide cover areas from satellite images of varying resolutions is proposed. In 2023, the maximum post-fusion green tide cover area was 2262.12 km2. Gompertz and Logistic growth curve models were used to monitor and predict regional green tide growth trends. The fitted growth curves exhibited R2 values >96 %, while curves fused from multiple sources demonstrated R2 values >99 %, indicating high accuracy. The predicted green tide start and end dates roughly matched the actual dates, with the highest accuracy in 2023 (relative errors: 1.63 % and 0.81 %, respectively). Both the growth curve fitting effect and the relative errors of predicted start and end dates were related to the green tide development mode. This study provides a scientific basis for monitoring and predicting green tides in the Yellow Sea.
More Related Videos
12:26Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
Published on: October 11, 2016
08:47Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
Published on: February 9, 2024
Related Concept Videos
Light Acquisition
Applications of GIS: Disaster Management and Emergency Response