The world’s natural systems are at a tipping point and many
biologists believe that we are facing a massive ecological extinction
event as a result of anthropogenic impacts on the planet
(Pimm et al. 1995, Wilson 2002). Globally coral reefs face an
unprecedented convergence of stressors including overfishing,
pollution, diseases, destructive fishing practices, sedimentation,
and coastal development that have led to led to massive global declines
(Kline et al 2005, Jackson et al 2001, Hughes et al., 2003 ;Pandolfi
et al 2005; Hoegh-Guldberg, 2007).
Traditional coral reef monitoring has required trained experts scuba diving on reefs to specify coverage and health of the key ecological groups (corals, algae, other invertebrates, fish, bare substrate, etc.). Recently, photographs or videos now routinely complement such surveys by experts but there is lacking a rapid, objective, quantitative, and automated classification of digital imagery. New technologies are needed to improve the efficiency and objectivity of surveys to assess the health of global coral reef communities on appropriate temporal and spatial scales. Analyses of these growing digital image archives remain extremely constrained by the extensive effort required by coral experts.
A convergence of several rapidly advancing technologies, including digital imaging, computational mass storage and processing speed, integrated with computer vision image analysis, now makes it feasible to acquire, archive, and digitally classify important aspects of coral reef community ecology and physiology. Computer vision technology has considerable potential to address these problems for coral reef ecosystems, but additional innovations by an interdisciplinary team are required to overcome challenges before a robust, automated cyber-enabled image analysis system can be confidently used for objective coral reef monitoring.