Premium
Application of multiplatform, multispectral remote sensors for mapping intertidal macroalgae: A comparative approach
Author(s) -
Rossiter Thomas,
Furey Thomas,
McCarthy Timothy,
Stengel Dagmar B.
Publication year - 2020
Publication title -
aquatic conservation: marine and freshwater ecosystems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.95
H-Index - 77
eISSN - 1099-0755
pISSN - 1052-7613
DOI - 10.1002/aqc.3357
Subject(s) - intertidal zone , satellite imagery , multispectral image , ascophyllum , remote sensing , habitat , environmental science , hyperspectral imaging , fucales , aerial imagery , ecology , geography , biology , algae
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. 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. 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). 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 canopy‐forming species.