Seagrass restoration remains challenging, with success rates varying widely. While success stories exist, achieving restoration goals requires setting realistic ecological endpoints. Seagrass meadows exhibit diverse growth patterns, ranging from persistent, dense meadows to ephemeral, seasonal beds, making it crucial to align restoration targets with natural site characteristics. Misidentifying restoration goals may lead to unrealistic expectations and misinterpretations of project success.
This study leverages remote sensing technologies, including Sentinel-2 satellite imagery and Google Earth Engine (GEE), to analyse seagrass distribution and temporal trends in a focal restoration site in Western Port, Victoria. By implementing a multi-temporal classification approach, we assess seasonal and interannual variability in seagrass extent, identifying whether local meadows exhibit ephemeral or persistent characteristics. These insights provide a critical foundation for setting ecologically sound restoration targets and adaptive restoration efforts. Our findings highlight the growing role of remote sensing in seagrass research, demonstrating its potential to track restoration outcomes and detect early warning signs of decline. By developing a semi-automated, scalable classification pipeline in GEE, we enable continuous monitoring of seagrass meadows, facilitating proactive conservation strategies. This approach enhances the efficiency of restoration planning and ensures that restoration efforts are aligned with the natural dynamics of seagrass ecosystems.