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.

Keywords: 3D habitat mapping; coral ecology; photogrammetry; structural complexity; structure‐from‐motion.

Figure 1

(a) Artificial reef used to evaluate reconstruction accuracy and repeatability, (b) underwater photomosaic of artificial reef, (c) corresponding digital surface model colored by relief height. (d) Comparison of rugosity measurements derived from 3D models reconstructed using diver‐rig underwater stereo imagery and rugosity measurements from an in‐air reference model, reconstructed using in‐air high‐resolution images

Figure 2

(a) Diver‐operated stereo camera rig (diver‐rig) containing stereo camera pair, depth pressure sensor, tilt sensors, magnetic compass and GPS receiver, (b) diver‐rig in operation during surveys (photograph by Chris Roelfsema)

Figure 3

(a) Orthographic imagery mosaic and (b) topographic surface model at Horseshoe Reef survey site, Lizard Island using “Reef Record” survey method. (c) Orthographic imagery mosaic and (d) topographic surface model at Blue Pools survey site, Heron Island using “Mow‐the‐Lawn” survey method

Figure 4

Imagery mosaics from multiday surveys at Horseshoe Reef, Lizard Island (a) Day 1, (b) Day 2, (c) Day 3, (d) Day 4

Figure 5

Surface models from multiday surveys at Horseshoe Reef, Lizard Island (a) Day 1, (b) Day 2, (c) Day 3, (d) Day 4

Figure 6

Distribution of average rugosity for 2 × 2 m virtual quadrats (μ_SR) at each of the four reefs surveyed in this study (resolution 2.5 cm)

Figure 7

2 × 2 m quadrat rugosity measurement errors at each site: (a) 2 × 2 m quadrat standard deviation from quadrat average σ_SR, averaged for all quadrats at each site. (b) Root mean square (RMS) of average 2 × 2 m quadrat rugosity difference from quadrat average per survey (b), a measure of the per‐survey bias induced by conditions for a particular survey

Figure 8

Boxplot distributions of 2 × 2 m quadrat rugosity measurement differences δSR _j,i per survey (i = 1 to 4) from quadrat average across all surveys, for surveys performed on the same day. The distribution of differences from average for a given survey may be greater or less than zero, indicating that quadrats are being measured consistently higher or lower for the conditions in which the survey was performed

Figure 9

Boxplot distributions of 2 × 2 m quadrat rugosity measurement differences δSR _j,i per survey (i = 1 to 4) from quadrat average across all surveys, for surveys across multiple days. The distribution of differences from average for a given survey may be greater or less than zero, indicating that quadrats are being measured consistently higher or lower for the conditions in which the survey was performed

Figure 10

2 × 2 m quadrat rugosity errors (variation) versus coverage variation during image collection (number of images covering quadrat (average coverage) and standard deviation of number of images covering any part of the quadrat (coverage variation), per survey) for all sites (a) and using data selected from the Blue pools site only (b)

Figure 11

Relationships between 2 × 2 m quadrat rugosity errors and quadrat rugosity modeled using OLS

Figure 12

Relationships between 2 × 2 m quadrat rugosity errors and quadrat rugosity categorized by dominant coral morphotype coverage class modeled using OLS (adj. R ² = .473). Model predicted rugosity error versus rugosity is shown for each class (solid line) and compared to the model predicted relationship for “mixed” type quadrats (dashed line)