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

Authors:
Kwanmok Kim at University of Florida
Kwanmok Kim
Vincent Lecours at Université du Québec à Chicoutimi
Vincent Lecours
Peter Frederick at University of Florida
Peter Frederick
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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.
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Geomorphometry.org/2018 Kim et al.
1
Using 3D Micro-Geomorphometry to Quantify
Interstitial Spaces of an Oyster Cluster
Kwanmok Kim
Dept. of Wildlife Ecology & Conservation
School of Nat. Resources & Environment
University of Florida
Gainesville, FL, USA
kwanmok.kim@ufl.edu
Vincent Lecours
Geomatics Program
Fisheries & Aquatic Sciences Program
University of Florida
Gainesville, FL, USA
vlecours@ufl.edu
Peter C. Frederick
Dept. of Wildlife Ecology & Conservation
University of Florida
Gainesville, FL, USA
pfred@ufl.edu
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.
I. INTRODUCTION
In ecology, the structural complexity of habitats is known
to play a key role, for instance by affecting predator-prey
interactions, local species composition, and resilience to
environmental change [1]. Structural complexity describes both
qualitative (e.g., composition, spatial arrangement) and
quantitative (number, size, and density) traits of habitat
requirements for animals [2,3]. For example, habitats with
diverse elements of varying sizes and abundance are considered
to have higher structural complexity. One of the most widely
accepted assumptions about the causal link between structural
complexity and species interactions and composition is that an
increase in structural complexity generates more abundant and
diverse interstitial spaces. Interstitial spaces are volumetric
gaps between elements such as crevices under boulders or
spaces between biological elements like seagrasses, corals, and
oysters. Those interstitial spaces work as refuges for prey
species and hence reduce predation by hindering the visibility
of prey and accessibility of the predators [4].
Based on those assumptions, it is generally acknowledged
that an increase in structural complexity leads to an increase in
biodiversity. In other words, it is assumed that the size range of
species and individuals using the refuge is limited by the size
and shape of that refuge. However, there has been very little
empirical work examining the link between the morphology of
refuges and species interactions. This is largely because of the
difficulties inherent to quantifying the morphology of refuges,
particularly at fine spatial scales.
Recent advances in photogrammetry now make it possible
to model, in three dimensions, fine-scale structures like oyster
clusters. This study aims to characterize the morphology of an
oyster cluster from a 3D model produced using Structure-from-
Motion (SfM) photogrammetry. Our specific objective is to
link quantitative measurements of interstitial spaces (i.e. the
empty space between individual oysters in a cluster) to the
potential use of those interstitial spaces as a refuge for crabs.
II. METHODS
A. 3D Model of the Oyster Cluster
Among diverse photogrammetry techniques available for
applications in fields like ecology and geology, SfM was
deemed the most appropriate for this study considering the size
of the oyster clusters in the study area (about 30 X 30 cm), their
complex structures, and their accessibility (above water,
intertidal environments).
A total of 149 photos were taken with a Canon 7d Mark II
camera equipped with 18-55mm lens, fixed at 18mm. Photos
were taken from both right and oblique angles (from nearly 5
Geomorphometry.org/2018 Kim et al.
2
(A)
(B)
(C)
(D)
Figure 1. Texturized mesh of an oyster cluster and examples of interstitial spaces produced with the Agisoft Photoscan software.
(A) View of the oyster cluster from the front. (B to D) Examples of interstitial spaces segmented using a 5x5x5 cm box.
cm distance) to fully capture the oyster cluster and its interstitial
spaces. The images were then imported into the Agisoft
Photoscan Pro software v.1.4.0 to produce a point cloud, a
mesh, and a texturized mesh.
The accuracy of the 3D mesh was verified by measuring
distances between defined features (edges of oysters and
barnacles) and comparing them to the distances in the 3D mesh
(n = 6). Later, the point cloud was scaled based on known
distances between markers.
B. 3D Geomorphometric Analysis
The mesh was then imported into CloudCompare v.2.9.1 for
further surface analyses. In CloudCompare, measures of
roughness and curvature were computed, in addition to
interstitial space volume (using the ‘compute 2.5D volume’
option). Each interstitial space was individually extracted from
the mesh using a fixed size box (5 X 5 X 5 cm square; outer
box) and later, subsampled points (8,000 points/cm2) for the
geomorphometric analyses. The size of the box was determined
by considering the maximum of the size range of the studied
crab species – and thus the size needed for an interstitial space
to have the potential to serve as a refuge for the crabs – and the
extent needed to fully capture the morphology of their
immediate surroundings. Specifically, to measure the volume
of the interstitial spaces, a 2 X 2 X 2 cm box (inner box) was
used, as this is the average size of the crabs that use interstitial
spaces. For more accurate results, spurious points (i.e. noise in
10 cm
1.5 cm
2 cm
Geomorphometry.org/2018 Kim et al.
3
Interstitial Space 1
Interstitial Space 2
Interstitial Space 3
Curvature
30.128% - 30.547%
Range: 0.001 - 0.278 cm
46.178% - 46.634 %
Range: 0.001 - 0.310 cm
51.086% - 51.700%
Range: 0.000 - 0.313 cm
Roughness
28.817 - 29.489%
Range: 0 - 0.101 cm
32.748% - 33.549 %
Range: 0 - 0.122 cm
42.920% - 43.794%
Range: 0 - 0.161 cm
Interstitial
spaces
(volume
and
surface
area)
Volume = 2.693
Surface Area = 8.976
Openness = 0.300
Volume = 3.021
Surface Area = 10.536
Openness = 0.287
Volume = 3.351
Surface Area = 12.587
Openness = 0.266
Figure 2. Results for all metrics for the three interstitial spaces (Figures 1B to 1D). Scale bars apply to rows.
the data) and hidden points were removed. The volume per unit
area was estimated by dividing the interstitial volumes by the
3D surface areas. Curvatures and roughness were measured
using kernels of 0.5 and 0.2, respectively, which best described
the fine-scale topography. Later, we calculated the proportion
of points that had either higher roughness (higher than 0.083
cm from the fitted plane) or higher curvature values (higher
than 0.020 cm from the fitted plane). Those cutoff points were
determined by visually examining the changes in difference.
While there were 256 total bins for the results of curvature and
roughness, the range distance slightly varies among interstitial
spaces resulting in a range of percentages above cutoff points.
III. RESULTS AND DISCUSSION
A. 3D Model and Quantitative Measurements
The combination of the 149 images in Agisoft Photoscan
produced a dense point cloud (83,713 points), a mesh
(4,872,998 faces) and a texturized mesh (Figure 1A). The
accuracy measurement showed that the point cloud (and mesh)
0.000cm
0.313cm
2.5 cm
0cm
0.161cm
2.5 cm
0.000cm
2.019cm
1.5 cm
Geomorphometry.org/2018 Kim et al.
4
of the oyster cluster derived from SfM photogrammetry was
relatively accurate, with a mean error of 0.654 mm.
The mesh enabled the extraction of interstitial space units
from the oyster cluster. Here we present three examples of 3D
interstitial spaces that were extracted from the point cloud and
mesh (Figures 1B to 1D).
The quantitative characteristics of the interstitial spaces that
were measured in CloudCompare (i.e. curvature, roughness,
volume and surface area, openness) are presented in Figure 2
for the three examples. The interstitial space number 1 had the
lowest values of curvature, roughness, and volume out of the
three interstitial spaces whereas interstitial space number 3 had
the highest of each metric among those. All the interstitial
spaces had volumes smaller than 3.351 cm3, which could offer
shelter for small crabs while protecting them from bigger
predators. Interstitial space number 1 had the largest volume
per unit surface area followed by number 2 and the 3.
B. Interpretation of Results in an Ecological Context
The quantitative characterization of the morphology of the
three interstitial spaces provides some insights on their
potential to serve as efficient shelter against predators. For
example, a higher surface curvature at the interstitial space
scale may result in a more difficult access to crabs for a
predator. A higher curvature may make the prey less visible to
the predator and make it more difficult for the predator to drag
the prey out of the refuge. Surface roughness may be related to
the ability of crabs to survive water turbulence (drag
coefficient), and withstand predators’ pulling force by holding
on to rough surface. Interstitial space volume and surface area
are associated with the size limitation of both individuals from
the prey species and the predator species. For example, spaces
with higher volume per surface unit, and thus a greater
openness, have a wider entrance or cavity that expose the prey
species to predators.
C. Limitations and Future Work
The results presented in this paper are those of a feasibility
study. While results show that fine-scale 3D models of
biological structures can be produced by using SfM
photogrammetry for ecological studies, it is improper at this
point to infer direct ecological relationships between refuge
geometry and crab species or sizes due to the limited sample
size and the lack of species morphology information. However,
the approach tested in this study can now be applied to bigger
datasets, which will enable to statistically test this kind of
relationships. Moreover, for accurate validations of 3D models,
further studies should include direct volumetric measurements
of the interstitial spaces to compare to the spaces from the 3D
modeled representations. Measuring the geometries of
interstitial spaces will be the stepping stones of multiscale
structural complexity studies, hence providing the applicability
to test whether interstitial spaces of oyster reefs affect species
composition. In addition, the techniques we described could be
applied to the burgeoning field of ecological restoration where
design and materials could be evaluated in a way that
maximizes species diversity.
IV. CONCLUSIONS
In this application, we demonstrated the feasibility and
adequacy of using SfM and geomorphometry to quantify the
morphology of interstitial spaces of an oyster cluster. The
millimeter-scale accuracy of the 3D model was achieved by
taking a large number of images during low tide when the
oysters were above the water, thus avoiding image distortion
from the water. Although this study was limited to a few
samples, it shows the possibility of using photogrammetry
methods (especially SfM) for a fine-scale ecological study. The
ability to accurately quantify the morphology of interstitial
spaces at fine scales is an important advancement for studies of
the role of structural complexity in community composition, a
current frontier in the field of ecology [5,6].
ACKNOWLEDGMENTS
This study was funded by the International Fulbright program, the
University of Florida, and a grant from the U.S. National Fish and
Wildlife Foundation to PCF.
ORCID
Kwanmok Kim https://orcid.org/0000-0002-9520-8191
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... At finer scales, geomorphometry was applied to underwater cryospheric environments in Antarctica where ice surface topography was mapped using multibeam acoustics at very high resolution (1 cm) to understand microstructural habitats for sea ice algae [28]. Another example is the use of fine-scale 3D geomorphometry to quantify habitat use and availability on small structures like oyster clusters on intertidal reefs [29]. ...
... As more tools and methods are developed and adopted by the community, marine scientists will be better equipped to address ongoing challenges, e.g. in relation to scale or data quality. In addition, those methods will give rise to innovative applications, for example in applying geomorphometry to 3D data (as opposed to 2.5D data) [29]. ...
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