The Use of Unmanned Aerial Systems to Map Intertidal Sediment

Abstract

This paper describes a new methodology to map intertidal sediment using a commercially available unmanned aerial system (UAS). A fixed-wing UAS was flown with both thermal and multispectral cameras over three study sites comprising of sandy and muddy areas. Thermal signatures of sediment type were not observable in the recorded data and therefore only the multispectral results were used in the sediment classification. The multispectral camera consisted of a Red-Green-Blue (RGB) camera and four multispectral sensors covering the green, red, red edge and near-infrared bands. Statistically significant correlations (>99%) were noted between the multispectral reflectance and both moisture content and median grain size. The best correlation against median grain size was found with the near-infrared band. Three classification methodologies were tested to split the intertidal area into sand and mud: k-means clustering, artificial neural networks, and the random forest approach. Classification methodologies were tested with nine input subsets of the available data channels, including transforming the RGB colorspace to the Hue-Saturation-Value (HSV) colorspace. The classification approach that gave the best performance, based on the j-index, was when an artificial neural network was utilized with near-infrared reflectance and HSV color as input data. Classification performance ranged from good to excellent, with values of Youden's j-index ranging from 0.6 to 0.97 depending on flight date and site.

Spatial Variation in Coastal Dune Evolution in a High Tidal Range Environment

“…The network was trained using the scaled conjugate gradient method [72] on a subset of the data of known type (sand or vegetation). The authors have previously used a similar discrimination methodology successfully to distinguish between sand and mud [47].…”

“…Dune evolution may be effectively monitored through UAVs, commonly called drones. This technology has found a wide range of uses in coastal research: primarily photogrammetry with RGB cameras to map morphology [37][38][39][40][41]; RGB data has also been used for vegetation studies [42]; LiDAR based morphology has been recorded from UAVs [43]; and, multispectral cameras have been used for vegetation studies [44][45][46] and for sediment classification [47]. Both fixed wing, e.g., [40], and quadcopter drones, e.g., [48] have been used.…”

Supporting a Resilience Observatory to Climate Risks in French Polynesia: From Valorization of Preexisting Data to Low-Cost Data Acquisition

“…Other sensors such as LiDAR have been identified as a viable solution for penetrating areas with high vegetation and still obtaining precise digital models. Attaching these sensors to UAVs rather than piloted aircrafts increases maneuverability and access to complex terrain, as well as decreasing prices and time required for a flight mission [73,[82][83][84][85][86][87][88]. The feasibility of UAVs for coastal management is thus attested for.…”

Topographic Analysis of Intertidal Polychaete Reefs (Sabellaria alveolata) at a Very High Spatial Resolution

“…However, the costs of instrumentation and deployment may limit their use by public environmental agencies. As a result, commercially available unoccupied aerial systems or vehicles (UAVs) have been increasingly used to map intertidal habitats [30][31][32][33][34]. In intertidal soft-sedimentary environments, UAVs destined for the general public have been successfully used to map extensive reefs and associated sedimentary features that are determined using RGB [35] or multispectral cameras [36,37].…”