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

Remote Sensing

Manuel González-Rivero¹  Oscar Beijbom¹  Alberto Rodriguez-Ramirez¹  Tadzio Holtrop¹  Yeray González-Marrero¹  Anjani Ganase¹  Chris Roelfsema²  Stuart Phinn²  Ove Hoegh-Guldberg¹  Xiaofeng Li² 

[1] Global Change Institute, The University of Queensland, St Lucia, QLD 4072, Australia;;Global Change Institute, The University of Queensland, St Lucia, QLD 4072, Australia

关键词: XL Catlin Seaview Survey;    coral reefs;    monitoring;    support vector machine;   

DOI  :  10.3390/rs8010030

来源: mdpi

【 摘 要 】

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).

【 授权许可】

CC BY   
© 2016 by the authors; licensee MDPI, Basel, Switzerland.