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Remote Sens. 2016, 8(1), 30; doi:10.3390/rs8010030

Scaling up Ecological Measurements of Coral Reefs Using Semi-Automated Field Image Collection and Analysis

1
Global Change Institute, The University of Queensland, St Lucia, QLD 4072, Australia
2
Australian Research Council Centre of Excellence for Coral Reef Studies, St Lucia, QLD 4072, Australia
3
Department of Electrical Engineering & Computer Sciences, University of California, Berkeley, CA 94709, USA
4
Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, P.O. Box 94248, Amsterdam 1090 GE, The Netherlands
5
Department of Biology, Ghent University, Ghent 9000, Belgium
6
School of Biological Sciences, The University of Queensland, St Lucia, QLD 4072, Australia
7
Remote Sensing Research Centre, School of Geography, Planning and Environmental Management, The University of Queensland, St Lucia, QLD 4072, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: Stuart Phinn, Chris Roelfsema, Xiaofeng Li and Prasad S. Thenkabail
Received: 26 September 2015 / Revised: 8 December 2015 / Accepted: 15 December 2015 / Published: 5 January 2016
(This article belongs to the Special Issue Remote Sensing for Coral Reef Monitoring)
View Full-Text   |   Download PDF [2011 KB, uploaded 5 January 2016]   |  

Abstract

Ecological measurements in marine settings are often constrained in space and time, with spatial heterogeneity obscuring broader generalisations. While advances in remote sensing, integrative modelling and meta-analysis enable generalisations from field observations, there is an underlying need for high-resolution, standardised and geo-referenced field data. Here, we evaluate a new approach aimed at optimising data collection and analysis to assess broad-scale patterns of coral reef community composition using automatically annotated underwater imagery, captured along 2 km transects. We validate this approach by investigating its ability to detect spatial (e.g., across regions) and temporal (e.g., over years) change, and by comparing automated annotation errors to those of multiple human annotators. Our results indicate that change of coral reef benthos can be captured at high resolution both spatially and temporally, with an average error below 5%, among key benthic groups. Cover estimation errors using automated annotation varied between 2% and 12%, slightly larger than human errors (which varied between 1% and 7%), but small enough to detect significant changes among dominant groups. Overall, this approach allows a rapid collection of in-situ observations at larger spatial scales (km) than previously possible, and provides a pathway to link, calibrate, and validate broader analyses across even larger spatial scales (10–10,000 km2). View Full-Text
Keywords: XL Catlin Seaview Survey; coral reefs; monitoring; support vector machine XL Catlin Seaview Survey; coral reefs; monitoring; support vector machine
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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

González-Rivero, M.; Beijbom, O.; Rodriguez-Ramirez, A.; Holtrop, T.; González-Marrero, Y.; Ganase, A.; Roelfsema, C.; Phinn, S.; Hoegh-Guldberg, O. Scaling up Ecological Measurements of Coral Reefs Using Semi-Automated Field Image Collection and Analysis. Remote Sens. 2016, 8, 30.

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