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Habitat classification of temperate marine macroalgal communities using bathymetric LiDAR

Zavalas, Richard, Ierodiaconou, Daniel, Ryan, David, Rattray, Alex and Monk, Jacquomo 2014, Habitat classification of temperate marine macroalgal communities using bathymetric LiDAR, Remote sensing, vol. 6, no. 3, pp. 2154-2175, doi: 10.3390/rs6032154.

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Title Habitat classification of temperate marine macroalgal communities using bathymetric LiDAR
Author(s) Zavalas, Richard
Ierodiaconou, DanielORCID iD for Ierodiaconou, Daniel orcid.org/0000-0002-7832-4801
Ryan, David
Rattray, Alex
Monk, JacquomoORCID iD for Monk, Jacquomo orcid.org/0000-0002-1874-0619
Journal name Remote sensing
Volume number 6
Issue number 3
Start page 2154
End page 2175
Total pages 22
Publisher MDPI AG
Place of publication Basel, Switzerland
Publication date 2014
ISSN 2072-4292
Keyword(s) Bathymetry
Coastal
Exposed coast
Groundtruth video
Habitat mapping
LiDAR
reflectance
Subtidal macroalgae
Science & Technology
Technology
Remote Sensing
ECOSYSTEM-BASED MANAGEMENT
MULTIBEAM BATHYMETRY
CONTINENTAL-SHELF
PROTECTED AREA
AIRBORNE LIDAR
COASTAL ZONE
AUSTRALIA
WATER
MODELS
VIDEO
Summary 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.
Language eng
DOI 10.3390/rs6032154
Field of Research 070402 Aquatic Ecosystem Studies and Stock Assessment
Socio Economic Objective 961302 Protected Conservation Areas in Fresh
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30071700

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.