A High-Resolution Global Map of Giant Kelp (Macrocystis pyrifera) Forests and Intertidal Green Algae (Ulvophyceae) with Sentinel-2 Imagery

Abstract

Giant kelp (Macrocystis pyrifera) is the most widely distributed kelp species on the planet, constituting one of the richest and most productive ecosystems on Earth, but detailed information on its distribution is entirely missing in some marine ecoregions, especially in the high latitudes of the Southern Hemisphere. Here, we present an algorithm based on a series of filter thresholds to detect giant kelp employing Sentinel-2 imagery. Given the overlap between the reflectances of giant kelp and intertidal green algae (Ulvophyceae), the latter are also detected on shallow rocky intertidal areas. The kelp filter algorithm was applied separately to vegetation indices, the Floating Algae Index (FAI), the Normalised Difference Vegetation Index (NDVI), and a novel formula (the Kelp Difference, KD). Training data from previously surveyed kelp forests and other coastal and ocean features were used to identify reflectance threshold values. This procedure was validated with independent field data collected with UAV imagery at a high spatial resolution and point-georeferenced sites at a low spatial resolution. When comparing UAV with Sentinel data (high-resolution validation), an average overall accuracy ≥ 0.88 and Cohen’s kappa ≥ 0.64 coefficients were found in all three indices for canopies reaching the surface with extensions greater than 1 hectare, with the KD showing the highest average kappa score (0.66). Measurements between previously surveyed georeferenced points and remotely-sensed kelp grid cells (low-resolution validation) showed that 66% of the georeferenced points had grid cells indicating kelp presence within a linear distance of 300 m. We employed the KD in our kelp filter algorithm to estimate the global extent of giant kelp and intertidal green algae per marine ecoregion and province, producing a high-resolution global map of giant kelp and intertidal green algae, powered by Google Earth Engine.

Application of UAV Imagery to Detect and Quantify Submerged Filamentous Algae and Rooted Macrophytes in a Non-Wadeable River

“…Given that classification accuracy was highest in regions with reduced water surface tension, such as in a pool or a slow-moving river run, techniques from this study would also be well-suited for lake environments [19]. Additionally, the Random Trees classifier most easily identified large, homogenous patches of algae and macrophytes, and would similarly perform well with other large groups of macroalgae and submerged aquatic vegetation, such as kelp or seagrass [96][97][98]. Despite these applications, this method is not optimal for distinguishing multiple aquatic species, as RGB imagery is spectrally sparse.…”

Branching Algorithm to Identify Bottom Habitat in the Optically Complex Coastal Waters of Atlantic Canada Using Sentinel-2 Satellite Imagery

“…Our stepwise approach to image classification is a unique hybrid of other methods of image classification, which generally focus on simple band ratios (e.g., Dierssen et al, 2019;Mora-Soto et al, 2020) or a supervised classifier to quantify bottom habitat (e.g., Traganos and Reinartz, 2018b;Poursanidis et al, 2019), but not both. The ratio approach was applied first to all pixels, and when the threshold was met, pixels were classified with no assumptions made on the other pixels (i.e., the one that did not meet the threshold criteria).…”

“…The band ratios include vegetation indices based on red-edge indices such as NDVI and the Red-Green ratio, which can be effective tools for quantifying intertidal vegetation at low tide or vegetation that floats at, or near, the surface (e.g., Barillé et al, 2010;Dierssen et al, 2019;Mora-Soto et al, 2020). Following Dierssen et al (2019) we found that the Red-Green ratio could successfully classify bare sand at shallow to moderate depths, albeit at a slightly lower threshold value (<0.30 this study, <0.35 Dierssen et al, 2019).…”

“…Satellite remote sensing of seagrass and coral habitat is widely used in tropical clear waters, where bottom habitat is readily detectable to great depths (<40 m) (Hossain et al, 2015;Kovacs et al, 2018;Wicaksono et al, 2019). Satellite remote sensing is also widely used in a range of water types for certain seaweed habitats, when the vegetation canopy reaches the surface, as the measured signal comes from the sea surface, opposed to the seafloor, and there is negligible interaction of the water-leaving signal with the water column (e.g., Schroeder et al, 2019;Bell et al, 2020;Mora-Soto et al, 2020). A more complicated classification question arises for submerged macrophytes in optically complex temperate waters, where high CDOM, suspended particulate matter, and phytoplankton concentration reduce the maximum depth at which the seafloor is visible compared to tropical habitats (3-10 m vs. <40 m).…”

A review of subtidal kelp forests in Ireland: From first descriptions to new habitat monitoring techniques

“…Although more typically used to monitor blooms in estuarine and coastal habitats ( Ulva spp. : Mora‐soto et al., 2020), satellite data are also useful in mapping kelp species that span the water column and were pioneered in the east Pacific ( M. pyrifera: Mora‐soto et al., 2020; Cavanaugh, Siegel, Kinlan, & Reed, 2010). Simms and Dubois (2010) created a method for submerged kelp beds in the northwestern Atlantic, which could potentially be used on the subtidal L. hyperborea forests in Ireland.…”