Integrating Sentinel-2 and PlanetScope Image with Drone-based Seagrass Data for Seagrass Percent Cover Mapping

Abstract

Seagrass field data collection activities to train remote sensing images for seagrass percent cover mapping and assess its accuracy can be laborious, costly, and time-consuming, especially for vast seagrass meadows with high density variations. There is also a potential discrepancy in information between seagrass data collected in the field, which usually covers 0.25m2 or 1m2 ground area, and the spatial resolution of remote sensing image used. PlanetScope at 3m and Sentinel-2 at 10m are the currently frequently remote sensing images used to map seagrass. There is a considerable information gap between seagrass data collected in the field and their spatial resolution. The use of seagrass field data thus involves a generalization process and a set of assumptions to justify its integration with remote sensing image. An alternative is to use the drone-based aerial image (hereafter drone data), which captures seagrass meadows at very high spatial resolution, to interpret seagrass percent cover at a level of precision similar to the remote sensing data used. This research assessed the integration of drone-based seagrass data with PlanetScope and Sentinel-2 images to map seagrass percent cover. Seagrass percent cover was interpreted from drone data for each 9m2 and 100m2 ground size following the PlanetScope and Sentinel-2 grids, respectively. Stepwise, random forest, and support vector regression were employed to develop the seagrass’s percent cover mapping model. The accuracy assessment of the resulting seagrass percent cover map involves the calculation of RMS error and plot 1:1 and its derivative analyses. Our results showed that an unparalleled benefit of using drone data is the possibility to obtain SPC information that matches the spatial resolution of satellite imagery, where techniques such as photo-quadrat and photo-transect cannot match. Drone data is successfully integrated with PlanetScope and Sentinel-2 images to produce a high accuracy SPC map effectively and efficiently. Indeed, there are challenges in using drone data, mainly related to oceanographic and weather conditions, and the difficulties in interpreting SPC at the species level.