Combining unmanned aerial vehicles and satellite imagery to quantify areal extent of intertidal brown canopy‐forming macroalgae

Abstract

AbstractBrown macroalgae habitats provide a range of ecosystem services, offering coastal protection, supporting and increasing biodiversity, and more recently have been recognized for their potential role as blue carbon habitats. Consequently, accurate areal estimates of these habitats are vitally important. Satellite imagery is often utilized for areal estimates of vegetated habitats due to their ability to capture vast areas but are disadvantaged by their lower resolution. In contrast, imagery collected by unmanned aerial vehicles (UAV) provide high‐resolution datasets but are unable to cover the necessary spatial scale required for calculating areal estimates at regional, national or international scales. This study successfully and accurately corrects the outputs from low‐resolution Sentinel 2 imagery to the standard of high‐resolution UAV imagery by using a novel brown algae index and a simple regression model to provide accurate spatial estimates. This model was applied to rocky shores across Wales, UK to predict a spatial extent of 6.2 km2 for three fucoid macroalgae species; Ascophyllum nodosum, Fucus vesiculosus and F. serratus. The regression model was validated in two ways. First, the data used to create the regression model was split to train and test (50:50) the model, with a root mean square error of ~8%–14%. Secondly, spatial estimates of fucoids in independent aerial imagery were assessed using aerial photography interpretation and compared to that of the regression model (7% difference). The carbon standing stock of fucoids calculated from the spatial estimate (6.2 km2) was found to be significantly lower than that of other marine carbon stores, indicating that fucoids do not significantly contribute as a blue carbon habitat based on biomass alone. This study produces a robust and accurate remote sensing technique to estimate spatial extent of macroalgae at large spatial scales, with possible worldwide applicability.