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Scaling up ecological measurements of coral reefs using semi-automated field image collection and analysis
Gonzalez-Rivero, M; Beijbom, O; Rodriguez-Ramirez, A; Holtrop, T; Gonzalez-Marrero, Y; Ganase, A; Roelfsema, C; Phinn, S; Hoegh-Guldberg, O (2016). Scaling up ecological measurements of coral reefs using semi-automated field image collection and analysis. Remote Sens. 8(1): 20 pp. dx.doi.org/10.3390/rs8010030
In: Remote Sensing. MDPI: Basel. ISSN 2072-4292; e-ISSN 2072-4292, meer
Peer reviewed article  

Beschikbaar in  Auteurs 

Trefwoord
    Marien
Author keywords
    XL Catlin Seaview Survey; coral reefs; monitoring; support vectormachine

Auteurs  Top 
  • Gonzalez-Rivero, M.
  • Beijbom, O.
  • Rodriguez-Ramirez, A.
  • Holtrop, T.
  • Gonzalez-Marrero, Y.
  • Ganase, A.
  • Roelfsema, C.
  • Phinn, S.
  • Hoegh-Guldberg, O.

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 km(2)).

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