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Habitat classification of temperate marine macroalgal communities using bathymetric LiDAR

Citation

Zavalas, R and Ierodiaconou, D and Ryan, D and Rattray, A and Monk, J, Habitat classification of temperate marine macroalgal communities using bathymetric LiDAR, Remote Sensing, 6, (3) pp. 2154-2175. ISSN 2072-4292 (2014) [Refereed Article]


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DOI: doi:10.3390/rs6032154

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.

Item Details

Item Type:Refereed Article
Keywords:marine habitat mapping, LiDAR
Research Division:Agricultural and Veterinary Sciences
Research Group:Fisheries Sciences
Research Field:Aquatic Ecosystem Studies and Stock Assessment
Objective Division:Environment
Objective Group:Flora, Fauna and Biodiversity
Objective Field:Marine Flora, Fauna and Biodiversity
UTAS Author:Monk, J (Dr Jacquomo Monk)
ID Code:99550
Year Published:2014
Web of Science® Times Cited:23
Deposited By:IMAS Research and Education Centre
Deposited On:2015-03-27
Last Modified:2017-11-04
Downloads:307 View Download Statistics

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