Abstract
Here, we evaluated the potential of using bathymetric Light Detection and Ranging (LiDAR) to characterise shallow water (<30 m) benthic habitats of high energy subtidal coastal environments. Habitat classification, quantifying benthic substrata and macroalgal communities, was achieved in this study with the application of LiDAR and underwater video groundtruth data using automated classification techniques. Bathymetry and reflectance datasets were used to produce secondary terrain derivative surfaces (e.g., rugosity, aspect) that were assumed to influence benthic patterns observed. An automated decision tree classification approach using the Quick Unbiased Efficient Statistical Tree (QUEST) was applied to produce substrata, biological and canopy structure habitat maps of the study area. Error assessment indicated that habitat maps produced were primarily accurate (>70%), with varying results for the classification of individual habitat classes; for instance, producer accuracy for mixed brown algae and sediment substrata, was 74% and 93%, respectively. LiDAR was also successful for differentiating canopy structure of macroalgae communities (i.e., canopy structure classification), such as canopy forming kelp versus erect fine branching algae. In conclusion, habitat characterisation using bathymetric LiDAR provides a unique potential to collect baseline information about biological assemblages and, hence, potential reef connectivity over large areas beyond the range of direct observation. This research contributes a new perspective for assessing the structure of subtidal coastal ecosystems, providing a novel tool for the research and management of such highly dynamic marine environments.
Highlights
Shallow marine environments are vulnerable to a range of anthropogenic threats, including nutrient inputs, invasive marine pests, fisheries over-exploitation, and climate change [1]
By validating the utility of Light Detection and Ranging (LiDAR) to differentiate habitat and macroalgal canopy types in high energy marine systems, we demonstrate the potential versatility of this technique for use in other coastal environments around the world
The predictive classification method adopted in this study presents bathymetric LiDAR as a viable mechanism for mapping macroalgal assemblages in exposed marine environments
Summary
Shallow marine environments are vulnerable to a range of anthropogenic threats, including nutrient inputs, invasive marine pests, fisheries over-exploitation, and climate change [1]. It is important to quantify, understand, and manage a representative suite of habitats in the coastal marine environment [2]. The production of benthic habitat maps using remotely-sensed information offers a practical means to define potential community distributions in the marine environment, and facilitate ecosystem scale management [3,4]. Habitat classification of these environments presents significant obstacles including logistical access restricting data collection and fluctuating water clarity [5]. A number of techniques are used for the ecological habitat mapping of marine environments. Multibeam echosounders (MBES) accurately define potential seabed habitat [6,7,8,9,10]
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