No abstract

1. Intertidal macroalgal communities are economically and ecologically important and, with a likely increase in anthropogenic pressures, there is need to evaluate and monitor these diverse habitats. Efforts to conserve and sustainably manage these habitats must be underpinned by accurate, cost-effective, and efficient data collection methods. The high spatial and temporal resolution of unmanned aerial vehicles (UAVs), compared with satellites and aircraft, combined with the development of lightweight sensors, provides researchers with a valuable set of tools to research intertidal macroalgal communities.2. The ability of multispectral sensors, mounted on a satellite, an aircraft, and a UAV, to identify and accurately map the intertidal brown fucoid Ascophyllum nodosum (Fucales, Ochrophyta) at a site with a low species diversity of macroalgae were compared.3. Visual analysis confirmed that the spatial resolution of satellite imagery was too coarse to map intertidal macroalgae as it could not capture the fine spatial patterns of the macroalgal community. High-resolution RGB (colour) imagery, taken during the aircraft and UAV surveys, was used to collect training and reference data through the visual identification and digital delineation of species. Classes were determined based on the level of taxonomic detail that could be observed, with higher levels of taxonomic detail observed in the UAV imagery over the aircraft imagery. Data from both were used to train a maximum-likelihood classifier (MLC).4. The UAV imagery was able to more accurately classify a distinct A. nodosum class, along with other macroalgal and substratum classes (overall accuracy, OA, 92%), than the aerial imagery, which could only identify a lower taxonomic resolution of mixed A. nodosum and fucoid class, achieving a lower OA (78.9%). This study has demonstrated that in a coastal site with low macroalgal species diversity, and despite the spectral similarity of macroalgal species, UAV-mounted multispectral sensors proved the most accurate for focused assessments of individual canopyforming species.

The aim of this work is to develop an innovative and multi-disciplinary approach to assess the distribution and biomass of both intertidal fucoids (Ascoplyllum nodosum) and subtidal kelps (Laminariales) which are both of commercial and ecological importance in Ireland. We look to address the current deficit of information about these Irish seaweeds, which will underpin sustainable resource development. Aerial hyperspectral imagery is used to assess A. nodosum populations and will enhance species discrimination in a spatially and spectrally heterogeneous environment like the intertidal zone. Kelp habitat assessment will be done using a multibeam system (MBES) on a small vessel. Field surveys will be used to ground-truth both A. nodosum and kelp distribution, evaluate morphology, and estimate biomass. An aerial survey (July 2016) was conducted in western Ireland (Galway Bay) to survey A. nodosum using a Cessna 172 mounted with a Multispectral AIRINOV AgroSensor and a OCITM-U-1000 Ultra Compact Hyperspectral Imager. The flight plan was designed to cover as much of the intertidal zone as possible. Field-based surveys have been conducted at multiple sites to match algal species presence, composition and biomass within the A. nodosum beds along with a field-radiometry survey, using a TriOS RAMSES radiometer, to construct a spectral library of species and substrate to classify the remote sensing data. To improve the accuracy of the A. nodosum assessment, ground-truthing data are being collected on-foot, using a hand-held GPS device (Garmin Montana 600), to support the classification process by underpinning the selection of training areas. We present a detailed methodology for assessing the spectral response of A. nodosum in relation to biological and physiological parameters in its habitat and the challenges associated with the discrimination between species of the same genus. Small drones offer very high resolution colour imagery and will support the A. nodosum assessment by collecting ground-truthing data far more efficiently than on-foot methods.