Using 3D Micro-Geomorphometry to Quantify Interstitial Spaces of an Oyster Cluster

EasyChair Preprint 137

Abstract

In ecology, it is assumed that the characteristics (e.g. shape, size) of interstitial spaces found in a variety of habitats affect the colonization of species, species interactions, and species composition. However, those characteristics have traditionally been difficult to measure due to technological limitations. In this study, we used the Structure-from-Motion (SfM) photogrammetry technique to measure the physical characteristics of interstitial spaces in a small oyster cluster. The point cloud (and mesh) of the oyster cluster derived from SfM photogrammetry was found to be accurate enough (mean error of 0.654 mm) to conduct 3D geomorphometric analyses. We present an example of measures of curvature, roughness, interstitial volume, surface area, and openness for three 3D interstitial spaces. The interpretation of those measures enabled establishing which interstitial spaces were the most likely to be used as a shelter for an average crab. Those spaces are characterized by smaller openness and higher roughness and curvature measures. This initial quantitative 3D characterization of an oyster cluster is the first step in establishing empirical relationships between structural complexity of biological structures like oyster clusters and their ecological role for instance in predator-prey interactions. Overall, this study demonstrates the feasibility of combining SfM photogrammetry with geomorphometry for fine-scale ecological studies.

Keyphrases: CloudCompare, Interstitial Spaces, Oyster cluster, Photogrammetry, Structure from Motion, fine scale, structural complexity

@booklet{EasyChair:137,
  author    = {Kwanmok Kim and Vincent Lecours and Peter C. Frederick},
  title     = {Using 3D Micro-Geomorphometry to Quantify Interstitial Spaces of an Oyster Cluster},
  doi       = {10.29007/c1pl},
  howpublished = {EasyChair Preprint 137},
  year      = {EasyChair, 2018}}