Change detection is an essential and widely used approach for investigating ecosystem dynamics. Multi-temporal 3D models increasingly underpin photogrammetry-based analyses of change for many ecologically relevant attributes. To detect change, it is necessary to accurately align 3D models collected at different times using a process referred to as co-registration. However, achieving precise co-registration is difficult in underwater habitats due to practical challenges intrinsic to surveying them. These include a lack of accurate georeferencing information, variable light, turbidity and weather conditions, and diving restrictions dictated by the diver's pressure exposure over time. Here we present an efficient co-registration workflow for 3D models that directly addresses these challenges, derived from underwater structure-from-motion methods. To test our approach, we used 3D models from across a wide range of coral reef habitats covering all those that one may encounter in shallow reefs (15m depth and above). We then identified and empirically estimated four key sources of error: co-registration, 3D processing, image acquisition, and reference and scaling features (RSF) placement, and quantified their relative contributions to the overall error. Our proposed co-registration workflow had a mean precision of 1.37±16.55mm. Image acquisition and RSF placement errors contributed the most to the total workflow error (37% and 53%, respectively), while the contribution of co-registration and 3D processing errors was minimal (3% and 7%, respectively). As a result of our analysis, we provide ‘good practice’ guidelines to reduce errors associated with photogrammetric workflows and to facilitate efficient and reliable detection of 3D change in complex underwater ecosystems.
This study was conducted as part of the Ecological Intelligence for Reef Restoration and Adaptation Program (EcoRRAP) (https://gbrrestoration.org/program/ecorrap/). Underwater imagery was collected using EcoRRAP 3D photogrammetry techniques described by Gordon et al. (2023).
