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Remote Sens. 2016, 8(2), 113; doi:10.3390/rs8020113

Quantifying Multiscale Habitat Structural Complexity: A Cost-Effective Framework for Underwater 3D Modelling

1
School of Biological Sciences, University of Sydney, Sydney NSW 2006, Australia
2
Sydney Institute of Marine Science, 19 Chowder Bay Road, Mosman NSW 2088, Australia
3
159 Lexington St. San Francisco, CA 94110, USA
4
College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China
5
Department of Physics and Engineering, Fort Lewis College, Durango, CO 81301, USA
6
Robotics@QUT, School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane QLD 400, Australia
7
Global Change Institute, The University of Queensland, Brisbane QLD 4072, Australia
8
ARC Centre of Excellence for Coral Reef Studies, Brisbane QLD 4072, Australia
9
Marine Spatial Ecology Lab, School of Biological Sciences, The University of Queensland, Brisbane QLD 4072, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Stuart Phinn, Chris Roelfsema, Norman Kerle and Prasad S. Thenkabail
Received: 19 September 2015 / Revised: 7 January 2016 / Accepted: 25 January 2016 / Published: 4 February 2016
(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)
View Full-Text   |   Download PDF [4189 KB, uploaded 4 February 2016]   |  

Abstract

Coral reef habitat structural complexity influences key ecological processes, ecosystem biodiversity, and resilience. Measuring structural complexity underwater is not trivial and researchers have been searching for accurate and cost-effective methods that can be applied across spatial extents for over 50 years. This study integrated a set of existing multi-view, image-processing algorithms, to accurately compute metrics of structural complexity (e.g., ratio of surface to planar area) underwater solely from images. This framework resulted in accurate, high-speed 3D habitat reconstructions at scales ranging from small corals to reef-scapes (10s km2). Structural complexity was accurately quantified from both contemporary and historical image datasets across three spatial scales: (i) branching coral colony (Acropora spp.); (ii) reef area (400 m2); and (iii) reef transect (2 km). At small scales, our method delivered models with <1 mm error over 90% of the surface area, while the accuracy at transect scale was 85.3% ± 6% (CI). Advantages are: no need for an a priori requirement for image size or resolution, no invasive techniques, cost-effectiveness, and utilization of existing imagery taken from off-the-shelf cameras (both monocular or stereo). This remote sensing method can be integrated to reef monitoring and improve our knowledge of key aspects of coral reef dynamics, from reef accretion to habitat provisioning and productivity, by measuring and up-scaling estimates of structural complexity. View Full-Text
Keywords: surface rugosity; off-the-shelf; computer vision; photogrammetry; structure from motion; coral reefs; topographic maps; habitat structural complexity; surface area; volume surface rugosity; off-the-shelf; computer vision; photogrammetry; structure from motion; coral reefs; topographic maps; habitat structural complexity; surface area; volume
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ferrari, R.; McKinnon, D.; He, H.; Smith, R.N.; Corke, P.; González-Rivero, M.; Mumby, P.J.; Upcroft, B. Quantifying Multiscale Habitat Structural Complexity: A Cost-Effective Framework for Underwater 3D Modelling. Remote Sens. 2016, 8, 113.

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