Abstract
Abstract. Eelgrass beds are critical in coastal ecosystems and can be useful as a measure of nearshore ecosystem health. Population declines have been seen around the world, including in Atlantic Canada. Restoration has the potential to aid the eelgrass population. Traditionally, field-level protocols would be used to monitor restoration; however, using unmanned aerial vehicles (UAVs) would be faster, more cost-efficient, and produce images with higher spatial resolution. This project used RGB UAV imagery and data acquired over five sites with eelgrass beds in the northern part of the Shediac Bay (New Brunswick, Canada). The images were mosaicked using Pix4Dmapper and PCI Geomatica. Each RGB mosaic was tested for the separability of four different classes (eelgrass bed, deep water channels, sand floor, and mud floor), and training areas were created for each class. The Maximum-likelihood classifier was then applied to each mosaic for creating a map of the five sites. With an average and overall accuracy higher than 98% and a Kappa coefficient higher than 0.97, the Pix4D RGB mosaic was superior to the PCI Geomatica RGB mosaic with an average accuracy of 89%, an overall accuracy of 87%, and a Kappa coefficient of 0.83. This study indicates that mapping eelgrass beds with UAV RGB imagery is possible, but that the mosaicking step is critical. However, some factors need to be considered for creating a better map, such as acquiring the images during overcast conditions to reduce the difference in sun illumination, and the effects of glint or cloud shadow on the images.
Highlights
Eelgrass beds are critical in coastal ecosystems as they provide vital ecological functions, including stabilizing sediment, providing fish habitat, influencing current dynamics, and contributing significant amounts of biomass to food webs (Heck et al, 1995)
unmanned aerial vehicles (UAVs) can operate at much lower altitudes, which leads to images with higher spatial resolution than the ones acquired from aircraft and spacecraft platforms (Pajares, 2015)
The goal of this study is to compare the effect of two mosaicking packages (Pix4Dmapper and PCI Geomatica) on the classification accuracy obtained by applying a Maximum Likelihood classifier to RGB UAV imagery acquired over five eelgrass bed restoration sites, which are located inside a sheltered bay of Atlantic Canada
Summary
Eelgrass beds are critical in coastal ecosystems as they provide vital ecological functions, including stabilizing sediment, providing fish habitat, influencing current dynamics, and contributing significant amounts of biomass to food webs (Heck et al, 1995). While populations are stable under pristine conditions (Ward et al, 1997), eelgrasses around the world are declining at an annual rate of 7% of existing communities as a result of various types of disturbances in coastal and estuarine environments (Short, Wyllie-Echeverria, 1996). Restoration in areas with suitable habitat is a useful option to mitigate eelgrass decline and has the potential to re-establish the many essential ecosystem services eelgrass beds provide. After its development for military applications, UAV has become a popular tool for civil applications (Peasgood, Valentin, 2015). This new technology is mobile, fast, adaptable, and easy to use. While UAV technology can be advantageous, it has the drawback to require image mosaicking given the small footprint of UAV imagery
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