Satellite-based tool monitors coastal seagrass ecosystems

Seagrass meadows are critical marine ecosystems, acting as carbon sinks and providing habitats for diverse marine species. However, they face increasing threats from climate change and human activities. Monitoring these ecosystems has proven difficult due to their fluctuating nature and the challenge of differentiating them from similar coastal vegetation like mangroves and salt marshes. These challenges underscore the urgent need for more precise and scalable monitoring technologies.

In a study (DOI: 10.34133/remotesensing.0506) published on March 19, 2025, in Journal of Remote Sensing, researchers from Xiamen University and Tulane University unveiled the Automatic Mapping through integrating Optical and SAR images for intertidal Seagrass meadows (AMOSS) algorithm. Unlike traditional methods that struggle with spectral similarities among vegetation types and tidal variations, AMOSS employed Sentinel-1 SAR and Sentinel-2 optical imagery to assess the distinct biophysical, tidal, and spectral characteristics of seagrass and other intertidal land cover types (including mangroves, salt marshes, tidal flats, and seawater). Compared with mangroves and salt marshes, seagrasses lack upright structures, like upright stems or trunks. Based on this difference, researchers developed a model that identifies seagrass by its lower backscatter coefficient in VH polarization, differentiating it from other salt marshes and mangroves. Using the Otsu algorithm, the team automatically extracted low-tide zones containing intertidal seagrass, while multi-binary classification helped refine intertidal seagrass mapping. To track ecosystem changes, the Spectral Angle Mapper (SAM) method was applied, identifying seagrass loss and recovery trends over time. Even in complex coastal landscapes, AMOSS successfully mapped intertidal seagrass meadows with high precision.

AMOSS achieved an impressive 84% overall accuracy across 15 global study sites, spanning tropical to sub-polar regions. Unlike supervised classification methods, AMOSS avoided the need for manual sample selection, making it highly scalable and efficient for large-scale ecological monitoring. A key feature of AMOSS is its change detection capability, which enables scientists to track seagrass dynamics over time, offering valuable insights into ecosystem resilience.

"Our AMOSS algorithm marks an advancement in seagrass monitoring. By automating the mapping process, we can now track seagrass changes globally with high accuracy and efficiency, which is crucial for protecting coastal ecosystems in the face of climate change." the research team stated.

Looking ahead, AMOSS could transform global seagrass monitoring, offering a scalable and automated tool for tracking seagrass meadows. This technology has the potential to be integrated into national and international conservation programs, helping safeguard coastal biodiversity and contributing to climate change mitigation. By addressing the limitations of traditional monitoring methods, AMOSS paves the way for more effective conservation strategies and a deeper understanding of coastal ecosystems amid accelerating global environmental change. Future applications may include real-time intertidal seagrass health monitoring and the development of early warning systems for ecosystem degradation.

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References

DOI

10.34133/remotesensing.0506

Original Source URL

https://doi.org/10.34133/remotesensing.0506

Funding information

This research was supported by the National Natural Science Foundation of China (NSFC)Grant (No. 42276232).

About Journal of Remote Sensing

The Journal of Remote Sensing, an online-only Open Access journal published in association with AIR-CAS, promotes the theory, science, and technology of remote sensing, as well as interdisciplinary research within earth and information science.