Capturing complexity: field-testing the use of ‘structure from motion’ derived virtual models to replicate standard measures of reef physical structure

TLDR

Reef structural complexity is a key habitat indicator, and although Structure from Motion photogrammetry has recently been adopted to capture 3D reef structure, its comparability to established analogue methods such as tape‑and‑chain rugosity index and visual grading remains largely untested. Our study demonstrates that Structure from Motion rugosity index can be reliably converted to tape‑and‑chain values up to an RI of 2.0, correlates strongly with visual assessment grades, and offers an affordable, non‑destructive, archival, and less observer‑biased alternative that facilitates seamless transition from analogue to digital reef monitoring.

Abstract

Reef structural complexity provides important refuge habitat for a range of marine organisms, and is a useful indicator of the health and resilience of reefs as a whole. Marine scientists have recently begun to use ‘Structure from Motion’ (SfM) photogrammetry in order to accurately and repeatably capture the 3D structure of physical objects underwater, including reefs. There has however been limited research on the comparability of this new method with existing analogue methods already used widely for measuring and monitoring 3D structure, such as ‘tape and chain rugosity index (RI)’ and graded visual assessments. Our findings show that analogue and SfM RI can be reliably converted over a standard 10-m reef section (SfM RI = 1.348 × chain RI—0.359, r 2 = 0.82; and Chain RI = 0.606 × SfM RI + 0.465) for RI values up to 2.0; however, SfM RI values above this number become increasingly divergent from traditional tape and chain measurements. Additionally, we found SfM RI correlates well with visual assessment grades of coral reefs over a 10 × 10 m area (SfM RI = 0.1461 × visual grade + 1.117; r 2 = 0.83). The SfM method is shown to be affordable and non-destructive whilst also allowing the data collected to be archival, less biased by the observer, and broader in its scope of applications than standard methods. This work allows researchers to easily transition from analogue to digital structural assessment techniques, facilitating continued long-term monitoring, whilst also improving the quality and additional research value of the data collected.