Cubesat-derived Detection of Seagrasses using Planet Imagery following unmixing-based Denoising: Is small the next big?

Seagrasses are one of the most productive and widespread yet threatened coastal ecosystems on Earth. Despite their importance, they are declining due to various threats, which are mainly anthropogenic. Lack of data on their distribution hinders any effort to rectify this decline through effective detection, mapping and monitoring. Remote sensing can mitigate this data gap by allowing retrospective quantitative assessment of seagrass beds over large and remote areas. In this paper, we evaluate the quantitative application of Planet high resolution imagery for the detection of seagrasses in the Thermaikos Gulf, NW Aegean Sea, Greece. The low Signal-to-noise Ratio (SNR), which characterizes spectral bands at shorter wavelengths, prompts the application of the Unmixing-based denoising (UBD) as a pre-processing step for seagrass detection. A total of 15 spectral-temporal patterns is extracted from a Planet image time series to restore the corrupted blue and green band in the processed Planet image. Subsequently, we implement Lyzenga’s empirical water column correction and Support Vector Machines (SVM) to evaluate quantitative benefits of denoising. Denoising aids detection of Posidonia oceanica seagrass species by increasing its producer and user accuracy by 31.7% and 10.4%, correspondingly, with a respective increase in its Kappa value from 0.3 to 0.48. In the near future, our objective is to improve accuracies in seagrass detection by applying more sophisticated, analytical water column correction algorithms to Planet imagery, developing time- and cost-effective monitoring of seagrass distribution that will enable in turn the effective management and conservation of these highly valuable and productive ecosystems