Comparing the Use of Red-Edge and Near-Infrared Wavelength Ranges for Detecting Submerged Kelp Canopy

Abstract

Kelp forests are commonly classified within remote sensing imagery by contrasting the high reflectance in the near-infrared spectral region of kelp canopy floating at the surface with the low reflectance in the same spectral region of water. However, kelp canopy is often submerged below the surface of the water, making it important to understand the effects of kelp submersion on the above-water reflectance of kelp, and the depth to which kelp can be detected, in order to reduce uncertainties around the kelp canopy area when mapping kelp. Here, we characterized changes to the above-water spectra of Nereocystis luetkeana (Bull kelp) as different canopy structures (bulb and blades) were submerged in water from the surface to 100 cm in 10 cm increments, while collecting above-water hyperspectral measurements with a spectroradiometer (325–1075 nm). The hyperspectral data were simulated into the multispectral bandwidths of the WorldView-3 satellite and the Micasense RedEdge-MX unoccupied aerial vehicle sensors and vegetation indices were calculated to compare detection limits of kelp with a focus on differences between red edge and near infrared indices. For kelp on the surface, near-infrared reflectance was higher than red-edge reflectance. Once submerged, the kelp spectra showed two narrow reflectance peaks in the red-edge and near-infrared wavelength ranges, and the red-edge peak was consistently higher than the near-infrared peak. As a result, kelp was detected deeper with vegetation indices calculated with a red-edge band versus those calculated with a near infrared band. Our results show that using red-edge bands increased detection of submerged kelp canopy, which may be beneficial for estimating kelp surface-canopy area and biomass.

Temperature and food chain length, but not latitude, explain region-specific kelp forest responses to an unprecedented heatwave

“…It is worth noting that because Cowichan Bay data were derived from high resolution satellite rather than oblique aerial imagery, small fringing beds may have been classified as kelp absence points (see discussion of accuracy in Schroeder et al 2019). Further, this region is characterized by high currents which can easily submerge fringing kelp and reduce the ability to detect it at the surface (Britton-Simmons, Eckman & Duggins, 2008; Timmer et al, 2022). Thus, false negatives are probably more likely in this one region than in other regions analyzed using two time points.…”

“…A summary of data sources for each region is provided in Table S1 For the ten regions that involved oblique aerial imagery (including Barkley Sound), we created shoreline segments to classify stretches of shoreline that could be identified in both pre-and post-MHW imagery, and where either one or both of the images contained kelp canopy that was clearly visible in oblique images. Oblique imagery was taken at low tidal heights when most kelp canopy can be expected to be floating at the surface (Schroeder et al, 2019;Timmer et al, 2022), but since the imagery was collected at an oblique angle, we were unable to accurately assess changes in the area of kelp canopy over time and restricted these analyses to presenceabsence. Therefore, kelp canopy was determined to be either present or absent within each image for each segment, and the segment was accordingly classified as either a 'gain' (colonisation; absent in pre-MHW imagery but present in post-MHW imagery), a 'loss' (extirpation; present pre-MHW but absent after), or as 'stable' (kelp remained present at both time points) for each segment between the two time periods.…”

Capturing accurate kelp canopy extent: integrating tides, currents, and species-level morphology in kelp remote sensing

“…Remote sensing is an effective technology used to monitor floating kelp at both local and regional scales (e.g., Schroeder et al, 2019a;Cavanaugh et al, 2021a;Gendall et al, 2023). The remote sensing of floating kelp canopy relies on the kelp's high reflectance in the near-infrared (NIR) (700-1,000 nm) in contrast with the surrounding water's low NIR reflectance (Jensen, 1980;Schroeder et al, 2019b;Timmer et al, 2022). However, in situ oceanographic and biological conditions during remote sensing imagery acquisition can introduce uncertainties when measuring kelp extent.…”

“…For example, portions of the kelp canopy may be submerged by changing tides and associated tidal currents (Britton-Simmons et al, 2008;Cavanaugh et al, 2021b), or simply due to the morphological differences in the canopies and their position at the water's surface (Schroeder et al, 2019b). Further, different parts of the kelp canopy (e.g., Nereocystis pneumatocyst vs blades) are either positively or negatively buoyant, which can lead to a spectral signal containing both submerged kelp (low NIR reflectance) and floating kelp (high NIR reflectance) with the surrounding water (Schroeder et al, 2019b;Timmer et al, 2022). The above-water reflectance of both floating and submerged kelp detected with remote sensing is a combination of the kelp and water's spectral signals, which are further influenced by the presence of optically active water components (e.g., chlorophyll or suspended sediments) (Mobley, 1994).…”

“…Specifically, when kelp is submerged, the typically high NIR signal is dampened by the water molecules' strong absorption, potentially reducing the detectability of submerged canopy with an above-water sensor (Augenstein et al, 1991;Schroeder et al, 2019b;Cavanaugh et al, 2021b;Timmer et al, 2022). Generally, this problem can be minimized by collecting remote sensing imagery at relatively low tidal heights and during times when current speeds are relatively low, when most kelp canopy is expected to be floating at or near the water's surface (Pfister et al, 2017;Schroeder et al, 2019a;Nijland et al, 2019;Hamilton et al, 2020).…”