Abstract
Ecosystem monitoring is central to effective management, where rapid reporting is essential to provide timely advice. While digital imagery has greatly improved the speed of underwater data collection for monitoring benthic communities, image analysis remains a bottleneck in reporting observations. In recent years, a rapid evolution of artificial intelligence in image recognition has been evident in its broad applications in modern society, offering new opportunities for increasing the capabilities of coral reef monitoring. Here, we evaluated the performance of Deep Learning Convolutional Neural Networks for automated image analysis, using a global coral reef monitoring dataset. The study demonstrates the advantages of automated image analysis for coral reef monitoring in terms of error and repeatability of benthic abundance estimations, as well as cost and benefit. We found unbiased and high agreement between expert and automated observations (97%). Repeated surveys and comparisons against existing monitoring programs also show that automated estimation of benthic composition is equally robust in detecting change and ensuring the continuity of existing monitoring data. Using this automated approach, data analysis and reporting can be accelerated by at least 200x and at a fraction of the cost (1%). Combining commonly used underwater imagery in monitoring with automated image annotation can dramatically improve how we measure and monitor coral reefs worldwide, particularly in terms of allocating limited resources, rapid reporting and data integration within and across management areas.
Highlights
In a rapidly changing world, robust and accurate ecological information is essential for plausible management responses to the potential collapse of many ecosystems [1,2]
Long-term monitoring of coral reef ecosystems influences the implementation of successful policies and management actions [4,5]
In order to be a useful and reliable tool for monitoring, automated image annotation should be capable of: (1) reproducing expert estimates of abundance by ensuring minimal estimation errors, (2) detecting change over time with the same statistical power than traditional methods, (3) preserving long-term integrity of data by being comparable to other monitoring programs, and (4) ensuring cost-effectiveness. To assess these key points, this study evaluated the automation of image analysis for monitoring across a global dataset within five bioregions (Western Atlantic Ocean, Central Indian Ocean, Southeast Asia, Eastern Australia and Central Pacific Ocean)
Summary
In a rapidly changing world, robust and accurate ecological information is essential for plausible management responses to the potential collapse of many ecosystems [1,2]. Given the remoteness of coral reefs and the need for scuba diving, monitoring often results in scattered or spatially constrained long-term datasets [6]. Analysing the digital information within each image (e.g., RGB intensity, texture) to provide ecologically relevant metrics (e.g., benthic composition) often requires a substantial amount of time from experts (e.g., ecologists and taxonomists) before the information is ready to inform conservation decisions. This delaying effect creates a substantial bottleneck in the flow of information from monitoring programs to conservation practitioners
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