3D Photogrammetry and deep-learning deliver accurate estimates of epibenthic biomass

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Abstract

Accurate biomass estimates are key to understanding a wide variety of ecological functions. In marine systems, epibenthic biomass estimates have traditionally relied on either destructive/extractive methods which are limited to horizontal soft-sediment environments, or simplistic geometry-based biomass conversions which are unsuitable for more complex morphologies. Consequently, there is a requirement for non-destructive, higher-accuracy methods that can be used in an array of environments, targeting more morphologically diverse taxa, and at ecological relevant scales. We used a combination of 3D photogrammetry, convolutional-neural-network (CNN) automated taxonomic identification, and taxa-specific biovolume:biomass calibrations to test the viability of estimating biomass of three species of morphologically-complex epibenthic taxa from in situ stereo 2D source imagery. Our trained CNN produced accurate and reliable annotations of our target taxa across a wide range of conditions. When incorporated into photogrammetric 3D models of underwater surveys, we were able to automatically isolate our three target taxa from their environment, producing biovolume measurements that had respective mean similarities of 99, 102, and 120% of those obtained from human annotators. When combined with taxa-specific biovolume:biomass calibrations values, we produced biomass estimates of 88, 125, and 133% mean similarity to that of the “true” biomass of the respective taxa. Our methodology provides a highly reliable and efficient method for estimating the epibenthic biomass of morphologically complex taxa using non-destructive 2D imagery. This approach can be applied to a variety of environments and photo/video survey approaches (e.g. SCUBA, ROV, AUV) and is especially valuable in spatially extensive surveys where manual approaches are prohibitively time-consuming.
Original languageEnglish
TypeDataset
DOIs
Publication statusPublished - 6 Mar 2024

Keywords

  • Machine learning
  • Natural sciences
  • 3D
  • CNN

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