Scaling up ecological measurements of coral reefs using semi-automated field image collection and analysis

Gonzalez-Rivero, Manuel, Beijbom, Oscar, Rodriguez-Ramirez, Alberto, Holtrop, Tadzio, Gonzalez-Marrero, Yeray, Ganase, Anjani, Roelfsema, Chris, Phinn, Stuart and Hoegh-Guldberg, Ove (2016) Scaling up ecological measurements of coral reefs using semi-automated field image collection and analysis. Remote Sensing, 8 1: . doi:10.3390/rs8010030

Author Gonzalez-Rivero, Manuel
Beijbom, Oscar
Rodriguez-Ramirez, Alberto
Holtrop, Tadzio
Gonzalez-Marrero, Yeray
Ganase, Anjani
Roelfsema, Chris
Phinn, Stuart
Hoegh-Guldberg, Ove
Title Scaling up ecological measurements of coral reefs using semi-automated field image collection and analysis
Journal name Remote Sensing   Check publisher's open access policy
ISSN 2072-4292
Publication date 2016
Sub-type Article (original research)
DOI 10.3390/rs8010030
Open Access Status DOI
Volume 8
Issue 1
Total pages 20
Place of publication Basel, Switzerland
Publisher MDPI
Collection year 2017
Language eng
Formatted 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).
Keyword XL Catlin Seaview Survey
Coral reefs
Support vector machine
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Geography, Planning and Environmental Management Publications
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School of Biological Sciences Publications
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Created: Mon, 22 Feb 2016, 09:02:34 EST by Manuel Gonzalez Rivero on behalf of Global Change Institute