Using object‐based image analysis to determine seafloor fine‐scale features and complexity

Autonomous and remotely operated underwater vehicles equipped with high‐definition video and photographic cameras are used to perform benthic surveys. These devices record fine‐scale (< 1 m) seafloor features (seafloor complexity) and their local (10–100s m) variability (seafloor heterogeneity). Here, we introduce a methodology to efficiently process this optical imagery using object‐based image analysis, which reduces the pixels in high‐resolution digital images into a collection of “image‐objects” of homogeneous color and/or luminosity. This approach uses intuitive user‐defined parameters and reproducible computer code, which aims to facilitate comparisons between habitats and geographic regions. We test this methodology with 511 images taken on the seafloor of a glaciated continental shelf (Gulf of Maine, northwest Atlantic), and describe three applications: (1) estimating percent cover of conspicuous epibenthic fauna by building a Random Forest binary classifier assigning an identity to image‐objects; (2) correlating image complexity (number of image‐objects) with mean particle grain size; and (3) estimating seafloor heterogeneity from local variability in image complexity within and between two physiographic regions. Percent cover of epibenthic fauna estimated by the Random Forest binary classifier was in close agreement with the human visual assessment. Mean particle grain size (ϕ scale) was inversely correlated with image complexity (maximum Spearman's ρ = −0.89, p < 0.01) with images dominated by pebbles, cobbles, boulders (low on ϕ scale) yielding high image complexity. Predictive relationships of sediment composition were established using polynomial regression. Lastly, our approach could differentiate habitats within and between physiographic regions by using mean seafloor complexity and local variability along transects.