Characterization of measurement errors using structure‐from‐motion and photogrammetry to measure marine habitat structural complexity

Abstract

Habitat structural complexity is one of the most important factors in determining the makeup of biological communities. Recent advances in structure‐from‐motion and photogrammetry have resulted in a proliferation of 3D digital representations of habitats from which structural complexity can be measured. Little attention has been paid to quantifying the measurement errors associated with these techniques, including the variability of results under different surveying and environmental conditions. Such errors have the potential to confound studies that compare habitat complexity over space and time. This study evaluated the accuracy, precision, and bias in measurements of marine habitat structural complexity derived from structure‐from‐motion and photogrammetric measurements using repeated surveys of artificial reefs (with known structure) as well as natural coral reefs. We quantified measurement errors as a function of survey image coverage, actual surface rugosity, and the morphological community composition of the habitat‐forming organisms (reef corals). Our results indicated that measurements could be biased by up to 7.5% of the total observed ranges of structural complexity based on the environmental conditions present during any particular survey. Positive relationships were found between measurement errors and actual complexity, and the strength of these relationships was increased when coral morphology and abundance were also used as predictors. The numerous advantages of structure‐from‐motion and photogrammetry techniques for quantifying and investigating marine habitats will mean that they are likely to replace traditional measurement techniques (e.g., chain‐and‐tape). To this end, our results have important implications for data collection and the interpretation of measurements when examining changes in habitat complexity using structure‐from‐motion and photogrammetry.

A computer vision approach for studying fossorial and cryptic crabs

“…This represented a 13-20% precision error. Precision might be affected by small differences in point clouds which can be caused due to subtle differences in the image quality during capture and/or during the image analysis (Dandois, Olano & Ellis 2015;Bryson et al 2017;Forsmoo et al 2019). In fact, small differences were observed among the pair of digital elevation models taken a same time points ( Fig 6-A, top row time before, bottom row time after).…”

“…Nonetheless, a full study of the error sources in this method and application will be worthy (see e.g. in Bryson et al 2017).…”

Photogrammetric Surveys and Geometric Processes to Analyse and Monitor Red Coral Colonies

“…See, for example, Refs. [27][28][29] or the study of structural complexity of underwater seafloor [30][31][32]. GIS software is also coupled with photogrammetry in [33] for a deep coral survey.…”

“…Some stereo tools, diver-operated or embedded in an AUV, have been developed since some years ago, in the field of underwater robotics in order to easily survey a site using photogrammetry with specific methods to manage the stereo systems and local bundle adjustment. These developments were intended to support inclusion of bathymetry in an underwater archeology context as well as in marine biology (see [32,[34][35][36]). Similarly, we have also developed our own low-cost system, which is easier to use in complex environments [37].…”

Monitoring Marine Habitats With Photogrammetry: A Cost-Effective, Accurate, Precise and High-Resolution Reconstruction Method

“…Based on this technique, recent developments in hardware and image processing have rendered possible the reconstruction of high-resolution 3D models of relatively large areas (1 ha) (Friedman et al, 2012;González-Rivero et al, 2014;Leon et al, 2015). However, the underwater environment is still challenging due to many factors: no GPS information is available to help positioning the photographs, light refraction reduces the field of view and makes necessary the use of a wide angle lens that increases image distortions (Guo et al, 2016;Menna et al, 2017), and large lighting and water clarity variations affect the image quality and consequently the calculation of the position and orientation of the photographs (i.e., bundle adjustment) (Bryson et al, 2017). Despite these environmental constraints, many studies showed relatively accurate 3D models reconstructed with photogrammetry, notably individual scleractinian corals (2-20% accuracy for volume and surface area measurements, depending on colony complexity) (Bythell et al, 2001;Cocito et al, 2003;Courtney et al, 2007;Lavy et al, 2015;Gutierrez-Heredia et al, 2016).…”