Green tide cover area monitoring and prediction based on multi-source remote sensing fusion
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View abstract on PubMed
Summary
This summary is machine-generated.A new Fully Automated Green Tide Extraction Method (FAGTE) accurately monitors Yellow Sea green tides using satellite data. This method enhances prediction accuracy for ecological disaster management.
Area Of Science
- Marine ecology
- Remote sensing science
- Ecological modeling
Background
- Green tides pose recurring ecological threats in the Yellow Sea.
- Accurate monitoring and prediction are crucial for managing these events.
Purpose Of The Study
- To develop and validate a Fully Automated Green Tide Extraction Method (FAGTE) for Yellow Sea green tide data.
- To propose a novel fusion method for multi-resolution satellite imagery.
- To monitor and predict green tide growth trends using mathematical models.
Main Methods
- Utilized multi-source satellite remote sensing (RS) images (16-250m resolution) from 2021-2024.
- Developed FAGTE for automated green tide extraction and a fusion method for varying resolutions.
- Applied Gompertz and Logistic growth curve models for trend analysis and prediction.
Main Results
- FAGTE achieved average extraction accuracy exceeding 91%, with superior performance on high-resolution images.
- The maximum post-fusion green tide cover in 2023 reached 2262.12 km².
- Growth curve models showed high accuracy (R² >96% for single source, >99% for fused data), with low prediction errors for start/end dates.
Conclusions
- The FAGTE method provides a robust and accurate approach for extracting Yellow Sea green tide data.
- Data fusion techniques enhance the comprehensive assessment of green tide extent.
- Accurate prediction models offer a scientific basis for managing and mitigating green tide impacts.
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