3D Fine-scale Terrain Variables from Underwater Photogrammetry: A New Approach to Benthic Microhabitat Modeling in a Circalittoral Rocky Shelf

Abstract

The relationship between 3D terrain complexity and fine-scale localization and distribution of species is poorly understood. Here we present a very fine-scale 3D reconstruction model of three zones of circalittoral rocky shelf in the Bay of Biscay. Detailed terrain variables are extracted from 3D models using a structure-from-motion (SfM) approach applied to ROTV images. Significant terrain variables that explain species location were selected using general additive models (GAMs) and micro-distribution of the species were predicted. Two models combining BPI, curvature and rugosity can explain 55% and 77% of the Ophiuroidea and Crinoidea distribution, respectively. The third model contributes to explaining the terrain variables that induce the localization of Dendrophyllia cornigera. GAM univariate models detect the terrain variables for each structural species in this third zone (Artemisina transiens, D. cornigera and Phakellia ventilabrum). To avoid the time-consuming task of manual annotation of presence, a deep-learning algorithm (YOLO v4) is proposed. This approach achieves very high reliability and low uncertainty in automatic object detection, identification and location. These new advances applied to underwater imagery (SfM and deep-learning) can resolve the very-high resolution information needed for predictive microhabitat modeling in a very complex zone.

Modelling of the reef benthic habitat distribution within the Cabrera National Park (Western Mediterranean Sea)

“…Despite increasing awareness, advances in techniques that characterize habitat complexity in the marine environment are limited compared to terrestrial ecology (Lucieer and Pederson 2008). Habitat maps also help design environmental measures for specific vulnerable habitats in the establishment and management of marine reserves (Prado et al 2020). If we want highlight the importance of marine benthic habitat mapping with GIS semi-automated classification, we could say that the results obtained in this survey have contributed in the creation of a homogeneous fine-scale habitat map, as part of a relevant marine habitat mapping model for the Western Mediterranean zones.…”

“…The bathymetry final was gridded to 1 metre (Figure 6), obtaining a continuous high resolution bathymetric GRID for the finer visualization, interpretation, and analysis of the spatial data. Seafloor topography is a good approximation to define habitat structural complexity (Prado et al 2020). Furthermore, the homogeneous mosaic covers 100% of the surface, representing the depth values on a continuous raster across the whole study site.…”

Digitizing the coral reef: machine learning of underwater spectral images enables dense taxonomic mapping of benthic habitats

“…Newer in-situ monitoring techniques are providing ways to densely describe benthic reef habitats. Recent studies have used photogrammetry to stitch a large number of images to produce 3D models, orthomosaics and digital elevation models of the reef benthos (33)(34)(35). The acquisition of large number of images has been enabled by the advancements in instrumentation (36)(37)(38) and robotic platforms, both aerial (39,40) and underwater (41)(42)(43).…”

Fine-Scale Heterogeneity of a Cold-Water Coral Reef and Its Influence on the Distribution of Associated Taxa

“…Each of those studies revealed species density changes and broad aggregation within the study sites which were attributed to local environmental factors such as mechanical disturbances, physical gradients, and geological processes. Recently, the application of SfM has enabled the quantification of spatial patterns in deep-sea sponges, ophiuroids, and gorgonians, utilising PPA (Prado et al, 2019(Prado et al, , 2020Mitchell and Harris, 2020).…”