浒苔绿潮遥感监测技术研究进展

doi:10.3976/j.issn.1002-4026.2025150

Abstract

Abstract: The Ulva prolifera green tides in the Yellow Sea are the largest-scale green tide disasters in the world. Since 2007, they have occurred continuously for 19 years. These events disrupt the marine ecological balance, hinder fisheries and maritime transportation, and affect the development of coastal tourism, thereby causing substantial economic losses. Satellite remote sensing technology, with its advantages of large spatial coverage, long time series, and retrospective capability, has become an important tool for monitoring Ulva prolifera green tides. The advancement of remote sensing monitoring technology for Ulva prolifera green tides has been primarily driven by algorithmic models, evolving from simple linear models (spectral indices) to complex nonlinear models (machine learning), and further to deep feature representation models (deep learning). Spectral index methods rely on predefined thresholds and are susceptible to interference from environmental factors such as clouds and sun glint. Machine learning methods require manual feature selection, resulting in limited accuracy and efficiency when processing complex remote sensing images. In contrast, deep learning models demonstrate remarkable advantages. However, current research on deep learning in the field of Ulva prolifera monitoring is still in its early stages, facing challenges such as the extremely sparse distribution and low recognition accuracy in transitional boundary regions. Future efforts should focus on multisource remote sensing data fusion, enhancing model generalization capability, and constructing standardized sample databases to overcome current technical bottlenecks. Ultimately, this will enable high-precision, real-time, and intelligent monitoring and early warning of Ulva prolifera green tides, providing technical support for marine ecological protection and sustainable regional development.

Key words: Ulva prolifera green tides, spectral index, satellite remote sensing, deep learning, data fusion

CLC Number:

TP79

LI Huiping, YU Dingfeng, JI Zelu, YANG Lei. Research progress on remote sensing monitoring technology for Ulva Prolifera green tides[J].Shandong Science, 0, (): 1-.

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Research progress on remote sensing monitoring technology for Ulva Prolifera green tides

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