Improving Intertidal Reef Mapping Using UAV Surface, Red Edge, and Near-Infrared Data

Abstract

Coastal living reefs provide considerable services from tropical to temperate systems. Threatened by global ocean-climate and local anthropogenic changes, reefs require spatially explicit management at the submeter scale, where socioecological processes occur. Drone surveys have adequately addressed these requirements with red-green-blue (RGB) orthomosaics and digital surface models (DSMs). The use of ancillary spectral bands has the potential to increase the mapping of all reefscapes that emerge during low tide. This research investigates the contribution of the drone-based red edge (RE), near-infrared (NIR), and DSM into the classification accuracy of five main habitats of the largest intertidal biogenic reefs in Europe, built by the honeycomb worm Sabellaria alveolata. Based on photoquadrats and the maximum likelihood algorithm, overall, producer’s and user’s accuracies were distinctly augmented. When isolated, the DSM provided the highest gain percentage (3.42%), followed by the NIR (2.58%), and RE (2.02%). When joined, the combination of the DSM with both RE and NIR was the best contributor (4.98%), followed by the DSM with RE (4.80%), DSM with NIR (3.74%), and RE with NIR (3.22%). At the class scale, all datasets increasingly advantaged sand, gravel, reef, mud and water. The rather low effect of the DSM with NIR (3.74%) was assumed to be linked with a statistical noise originated from redundant information in the intertidal area.

Hyperspectral and Lidar: Complementary Tools to Identify Benthic Features and Assess the Ecological Status of Sabellaria alveolata Reefs

“…Spatial patterns emerging from ecological status classification, also display a barrier reef on sand with a vigorous seaward front core, while the retrograded and sparse reefs are mainly located in the south-east part and next to shoreline. This seems to be consistent with previous work carried out by Collin et al (2019) that have described a strong polarization of S. alveolata reefs with high abundance on forereef and low abundance on backreef. The forereef is subject to strong hydrodynamic and potentially higher coarse sediment resuspension, leading to increased tube-building activity, while the back reef mainly expands into more sheltered and muddier environments, less favorable to S. alveolata development (Bonnot-Courtois et al, 2008).…”

“…Obtained results also reinforce the recommendations encouraging integration of direct observation field techniques with remotely sensed data to capture detailed habitat information at large scales, thus avoiding trade-offs between three types of scale: spatial, temporal and thematic (Lecours et al, 2015;Rhodes et al, 2015;D'Urban et al, 2020). The benthic habitat map of Champeaux obtained from the hyperspectral airborne imagery using Mahalanobis supervised classification allows the delineation of a higher number of discriminated habitats (ten) than what was identified using RGB (Red-Green-Blue) images and digital surface model, as shown for S. alveolata reefs growing atop of soft sediment exclusively (Collin et al, 2019). The most significant information that the pattern of class distribution reveals is the distinction between two types of S. alveolata formations; large reefs developing on sandy bottoms and smaller veneers encrusting rocky shore areas.…”

“…Acoustic tools were more specifically adopted as suitable method for Sabellaria reefs in subtidal areas (Griffin et al, 2020). The most the studies were mainly based on aerial photography (Bonnot-Courtois et al, 2005;Desroy et al, 2011) or satellite imagery (Marchand and Cazoulat, 2003;Jones, 2017) as well as UAVs (unmanned aerial vehicles) as an efficient and cost-effective tool for fine scale reef characterization (Ventura et al, 2018;Collin et al, 2019). Noernberg et al (2010) have also exploited aerial photographs and LiDAR (Light Detection and RAnging) digital elevation model to quantify intertidal complex landform volume.…”

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

“…Therefore, the central areas of forests should provide optimal material to build a global map of giant kelp. (c) The use of Unmanned Aerial Vehicles (UAV) for coastal habitat mapping is a simple, cost-effective and reliable technology [39] that has been successfully used to map and validate intertidal biogenic reefs [40], saltmarsh biomass [41], and algal blooms [42]. Recent surveys to detect macroalgae in temperate coastlines have shown that RGB (additive primary colors-red, green, and blue-model) and multispectral cameras mounted on UAVs produce accurate imagery able to detect water turbidity and a range of taxonomical groups of algae in surface or shallow water, with the exception of spectrally similar species [18,43].…”

Using Multispectral Drone Imagery for Spatially Explicit Modeling of Wave Attenuation through a Salt Marsh Meadow

“…The use of drone technology for the imagery acquisition and for the generation of digital surface models (DSMs), using structure from motion (SfM) photogrammetry [15], is of many interests, especially for the coastal monitoring [16]. It allows for creating orthophotomosaics and DSMs at VHSR and very high temporal resolution (VHTR) [17].…”