Consistent Seagrass Mapping Across Various Tide Levels From Planet SuperDove Images

Seagrass meadows are vital blue carbon ecosystems, and remote sensing provides a cost-effective means of monitoring their changes at high spatiotemporal resolution. While existing algorithms excel in low-tide mapping, accurately and consistently identifying seagrass at mid-to-high tide levels remains challenging. This study presents a Support Vector Machine (SVM)-based Substrate Classification Model (SCM_SVM) for automated seagrass identification across various tidal conditions using Planet SuperDove imagery, with a demonstration provided for the Li’An Lagoon. By training with ~1.8 million matched ground-truth substrate data and Rayleigh scattering-corrected top-of-atmosphere reflectance (ρ<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rc</i>), SCM_SVM could robustly identify seagrass from SuperDove ρ<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rc</i> measurements across low to high tide levels. Validation against independent field measurements indicated a detection accuracy of seagrass exceeding 85%. Notably, SCM_SVM provided consistent seagrass distributions in spatial patterns and extents for images acquired under different tidal levels. Time-series analysis from 2021 to 2023 revealed a significant decline of -0.15 km2/yr in the area. These results underscore the potential of SuperDove for high spatiotemporal resolution monitoring of seagrass dynamics. Future work will focus on enhancing the global applicability of SCM_SVM and extending it to detect other submerged vegetation.