Hello.
My name is Brandon Puckett, and on behalf of my coauthors,
excited to share with you some of our research
assessing the utility of drones and drone
derived products to estimate key indicators of intertidal
oyster reef resilience, persistence and function.
And this comes at the request of managers
who have identified as a need novel tools
and approaches to improve their ability to monitor
and assess the resource at useful spatial and temporal scales.
Because oysters present some pretty unique
management challenges as
as a foundation species that is its own
habitat, yet is often managed to support
commercial and recreational fisheries.
So to do
so, we conducted a coordinated, regionally coordinated effort
from North Carolina to North Florida, working with
and within five National Estuarine Research reserves
highlighted in the map on the left, their respective logos
and really key members of the project team at each of those reserves
are also highlighted here.
Importantly, we also worked with representatives
from each of the state resource agencies responsible
for managing oysters in their respective states.
And from them we heard the need for
tools, approaches that are affordable,
nondestructive
methods that are capable of rapidly and quantitatively in a standardizable,
fashion of monitoring and assessing
intertidal oyster reefs.
A critical part of that is the ability
to develop tools and workflows to really quantify
key indicators of intertidal reef resilience,
persistence and function.
And so conceptually, on this picture of an intertidal Crassostrea virginica
Oyster Reef, which is the focal species of this work,
I just want to highlight five key indicators that that managers prioritized,
the first of which is Reef height and elevation.
Like most organisms in intertidal settings, their abundance and distribution
is really driven by where they sit in the tidal frame.
Extent of the habitat managers really want to know how much of the resource
is out there and how footprints of reefs may be changing over time.
Whether that's expanding or contracting
structural complexity, the 3D matrix of the reef.
We know those interstitial spaces are super critical habitat,
so this gets a habitat function of the reef
shell budget.
Shell is the currency of the reef.
There are removal terms
through dissolution of shell, burial of shell
and sediment and removal via recreational commercial harvest.
There are also addition terms
recruitment of new oysters to the reef, growth of oysters, and then addition
of reef through excuse me, addition of of shell through restoration
and lastly, abundance.
How many oysters are on the reef
and how many of those are of legal harvestable size?
Those are questions that managers are often asking.
And so these
metrics indicators have generally been estimated in situ with approaches
like using quadrats or in some cases such as extent using remotely
sensed satellite derived imagery and both of those approaches
have their strengths, albeit some severe weaknesses as well.
There's a clear need, I think, to
sort of bridge that gap between quadrats and satellites.
And drones really offer a platform that could potentially do so.
With that said, we set out as part of this work
to really accomplish two objectives, the first of which is to use
drone derived products.
So point clouds, digital surface models and orthomosaics
to quantify reef extent
Elevation, rugosity, which is going to be our proxy
for structural complexity, shell volume, which is our proxy for shell budget
and oyster density and size structure, which is our proxy for abundance.
And then secondly, to compare those drone derived estimates of those indicators
with the same measurements conducted on the ground.
So in situ measurements to assess
essentially the accuracy of drone derived estimates.
And both
of these objectives are sort of a necessary precursor
before operationalizing drone derived products
in sort of a monitoring and assessment framework.
So briefly,
for methods for in-situ sampling and lab processing.
I'm going to focus today on the work we did in
Central North Carolina, the coast of North Carolina.
But this was a coordinated
experiment done across North Carolina to North Florida.
But in North Carolina, we sampled 13 reef sites
during the summer of 2023 and more or less a two by two factorial design
whereby we had replicate patch reefs that were open to harvest and replicate
patch reefs that were closed to harvest as well as replicate patch reefs or
fringing reefs excuse me, open to harvest and fringing reefs closed to harvest.
And that was just so we'd get a pretty wide span
of sort of reef types as well as management regimes.
At each of these reefs, we took 4 to 6 rugosity measurements,
as shown in the second picture from the left,
using the traditional sort of chain and tape approach
where you can see the chain where we tried
to lay that throughout the interstitial spacing on the reef,
we excavated six quadrats
at each reef and then took those samples
back to the lab and measured and counted oysters to estimate density.
And then estimated volume of material.
Shell excavated via water displacement.
So for drone based sampling, our methods were as such.
We used the DJI Phantom four Pro
with a 20 megapixel red green blue sensor.
So pretty consumer grade level airframe and sensor.
We flew at low tide when when reefs were exposed
at approximately solar noon to minimize shadowing on the reef,
we set front and side overlap at 75%.
And generally speaking, the reefs that we were flying
for the study were on the scale of 1 to 5 hectares in size.
Our ground sampling
distance was one centimeter per pixel, albeit at a subset of sites.
We did fly at lower altitudes to
achieve a ground sampling distance of half a centimeter per pixel.
To look at the sensitivity of some of the metrics to ground sampling distance.
And I'll highlight that on the next slide.
When talking about rugosity,
at each site we deployed and surveyed with RTK GPS,
12 ground control points as well as six checkpoints to
to assess the accuracy of our our elevation models.
We flew before and after our in-situ quadrat excavation.
So flights before and after were staggered by about an hour
and used structure for motion photogrammetry software.
In this case Agisoft Metashpe to generate orthomosaics
point clouds and digital elevation models and analyzed those products in ArcGIS Pro.
So again using consumer grade airframes and sensors
and software that is very readily available to the management community,
The picture here on the bottom just shows
sort of a typical field set up with our ground control points in yellow
quadrat locations in red, rugosity measurements in blue dots
and then elevation along axial profiles in purple.
So the first indicator I want to speak about is structural complexity,
which we're going to rugosity as a proxy of that.
And so I've shown an elevation profile extracted
from a linear measurement from our surface model of one of our reefs.
So you have distance and meters on the x axis and an elevation on the Y axis.
And the way rugosity works is essentially it is the distance
it would take to traverse all of those 3D structure intricacies,
interstitial spaces of the reef as a function of linear distance.
So a flat table has a rugosity of 1 to 1 because those values are the same
as your 3D structure of your reef increases.
So does your rugosity.
And so how do our estimates
of rugosity measured from our surface models compare to what we measure in situ?
So here that is plotted so in situ rugosity estimates on the x axis
and rugosity estimated from digital surface models on the Y axis, the little
dashed line there is the 1 to 1 line, which would be perfect agreement.
And so a couple of take homes here.
So first of all, we are underestimating rugosity pretty considerably.
So all of those observations you see there are below that 1 to 1 line
and that underestimation tends to get worse
as our ground sampling distance gets larger.
So the light green dots are one centimeter per pixel, whereas the dark
blue dots are half a centimeter per pixel.
The third point here, though, is that the functions are linear,
and I think we need to have discussion with our management group to see if
it's important that the relationship here is 1 to 1 or if
if this linear relationship, albeit
an underestimate, is substantial for their purposes in terms of estimating
and thinking about the 3D structural complexity of a reef.
But just to highlight
sort of the differences of what those profiles look like at those two
ground sampling distances, I've shown the profiles here
and what you see is that at some of the
some of the larger peaks like here, you lose about a centimeter in elevation
as you increase your ground sampling distance and you fail to pick up
some of the the minor details and smaller peaks.
So those peaks that are here aren't as readily
picked up as our ground sampling distance increases.
The second
indicator I want to speak about is shell budget.
And so again, we're going to use volumetric change as a proxy for that.
And so what I've shown here on the left is essentially a difference map.
So we subtract our surface model before and after excavation.
So again, flights before and after staggered by about an hour.
And what you're seeing is a lot of purple.
And if you look at the color bar there, that means no difference.
Right?
Shouldn't be very much difference in this reef surface over the course of an hour,
except where we went out and excavated material.
So zoomed in here are our quadrat plots, the corners there are the dots there,
the corners of the quadrats and the teal color indicates a loss in elevation.
So where we excavated material.
So if we take and we can sum the volume, the total volume of reef material
that we removed, we can then look at how that sum total compares
with what we measured via water
displacement in the lab versus what we predict from our surface models.
And so I've shown that here with in situ volumetric change on the x axis
and surface volume, volumetric change on the Y axis and a couple of points.
So the dots there are 13 reefs, many of which fall along that dashed
1 to 1 line, albeit the best fitting linear regression suggests
that it's not completely 1 to 1, albeit the slopes there are close to one
and the Y intercepts are very small.
So very good agreement between observed and predicted
volumetric change at the reef scale.
And just for context here, we're predicting
we're picking up pretty small changes in volume.
The largest volumetric change that we observed
down here is about half a bushel.
So if we think about removal from recreational commercial fisheries,
that's a pretty minute amount.
In the third and final indicator I want to
discuss is abundance, and density
and size structure will be the proxy we'll use for that.
So here I've shown observed density on the x
axis versus predicted density on the Y axis. And the way we predicted
total density shown here is via
sort of multiple linear regression modeling whereby we use kind of structural
characteristics of the quadrats and spectral characteristics of the quadrats.
So things like the number of of light and dark pixels tend to be important
predictors, as
does the average elevation of the quadrat and deviation around that average.
And so what we see
here, again, the dots are the 13 reefs with their
associated errors around average total density.
The dash line again is the 1 to 1 line.
And what you see is that in many cases our estimates predicted values do fall
right along that one, two, one line, albeit
the best fitting linear regression would suggest that at really high
observed oyster densities, we are now underestimating total density
and really low estimates of observed density,
we're probably overestimating density.
The cool thing about the the drone derived products is we can start to project those
at sort of reef wide scales.
And so I've shown that here a map at a small patch reef
where by total density, the highest total density values are indicated
by the yellow and red colors and lowest values by the blue.
And so I think maybe with the exception of rugosity,
we've demonstrated to some extent that that drones are a tool
that can be used to quantify some key
indicators of oyster reef resilience, persistence and function.
And while I didn't have time to to talk about our results for reef extent
in aerial and reef elevation, you'll just suffice it
to say that we were able
to capture those very accurately with drone derived products.
So for next steps we're going to refine the workflows that I've sort of shown here
and apply those throughout the geography of the rest of the study.
South Carolina, Georgia and Florida,
we're going to conduct a coordinated experiment across that geography
whereby we're going to try to mimic harvest by removing shells in sort of a
more manner that more closely resembles fishery removal.
And we'll fly before and after that removal and measure
the same response variables.
And then we'll add shell to
mimic restoration and fly before and after that addition.
And then I think lastly, and something that could be pretty cool
is sort of a first of its kind with our ability to project these
indicators across the reef is to develop a multi metric index of reef resilience
that can really inform, I think, oyster restoration and management.
And with that, I would like to thank you for your time
and I'd like to thank NOAA's National Center for Coastal Ocean Science
and the NERRS Science Collaborative for funding this work.
And my contact information is provided here.