Yeah. Our next speaker is Dr Matthew Sumnall,
who's a postdoctoral associate in Forest Resources and Environmental Conservation.
Matt has a Masters in remote sensing and spatial
analysis from the University of Southampton and a doctorate in
remote sensing from Bournemouth University in the UK. His
research deals with use of lidar to separate over
story trees from understory vegetation, primarily in loblolly pine
plantations. The goal is to develop more accurate methods
to determine leaf area of both the over story ponds
and the understory vegetation. Thanks very much how to
work this thing. Give them a show. Mhm
. Good afternoon everybody. Let's give you a quick
rundown on some of the methods have been working on
over the past few months um sort of been tasked
with developing methods to aid in forest inventory. And
again following on from what Randy said, looking at
sort of identifying features beneath the top of the canopy
, so canopy depth, the presence of understory.
And then there's other conditions find many cannot be in
terms of how much light there is available and you
speak up a tiny bit oh sorry. And it
was sort of a brief overview of what we're doing
. I am I'm pretty sure most of you are
aware of what lighter is. And then we'll go
through sort of creation of how we go about sort
of extracting those metrics beneath the top of the canopy
. And then we'll go into what metrics we actually
have and the sort of prediction of the various other
ones um sort of a sort of brief overview of
what binder is. It's uh what we're talking about
here is the capture of and data from typical airport
platforms that can either be planes or helicopters. And
it generates a vast quantities of three dimensional points from
the ground surface below and in the case of forest
you can see from the pretty picture there and it
penetrates through the canopy. You can get pretty good
measurements of various features which would otherwise be obscured.
And other techniques such as aerial photographs or hyper spectral
imagery and so later is typically processed in the following
way. You use a technique to classify the ground
returns. So features which are the lowest elevation usually
. And then you can trade surface from that and
normalize the whites to remove the effects of terrain and
produce an above ground hide map. Um In the
in the case of some of the examples were used
, we identify features which are sort of plaintive services
which we attributed to being and buildings or my main
structures. And then finally we can extract very statistics
and then perform various analyses on them. And within
the context of this project, we didn't typically use
the standard, basic little metrics such as um sort
of the average height of all returns. So we
produced um a vertical profile and in order to do
that we stratified both, stratified the point cloud both
horizontally and vertically. Um With the example here,
it's it essentially summed up all the leader returns within
each one by one by one m three D objects
um typically typically referred to either as a box along
with volumetric pixel or height bent. Um So so
really it ends up being so each sort of vertical
elevation chunk can be attributed to abandon a hyper spectral
multispectral image. Yeah, for this first example we
used Are kind of 2008 data for three sites in
both Virginia and North Carolina. Um 11 particular example
here which have all run through and we generated a
a vertical profile which is essentially a number of returns
by height to sort of a summary of what the
what the what the well the graphic summary of what
the previous graphic was all about. And as you
can see from this, it's there are two main
features you can see, does this point to.
Um Anyway, the main feature here, you can
see that is um well it's obviously that the primary
company will make a canopy. And the the other
feature is the understory and well, there's the main
focus of this particular method was to develop an automated
technique to classify the canopy. Another story components.
We did this first by fitting a curve function to
the data and then identifying maximum minimum rights. And
from that we could estimates from from from the maximum
the presence of a canopy layer for the plot level
or the president to another story away. Mhm.
And this could be replicated for the entire study side
of whatever sort of field plot scale we decided to
implement. Um in terms of a couple of results
for three of our study sites with this Anderson,
which is in North Carolina region wide, 18 Virginia
and um see trees. This is the sort of
validation for the for the primary canopy. So the
top of the canopy height and the height of the
living crown, which is what can be very site
As just as you can see from the vertical profile
, we usually get an accuracy of around one m
for the very top plots height and within about two
m for the base height. Mhm. Um Well
just going a bit more into detail about the detection
of the story. As you can see, we
can detect very much from the profile of the presence
or absence. Um That's where the top of the
canopy can make estimates on the height and depth of
the understory as well. We can also use this
as a sort of an aging management tool. Um
This particular example has four different side treatment techniques.
Well, initial planting techniques where the whole stamp was
removed or the whole tree was removed, including roots
and then, Yeah, so you get sort of
very different sort of initial conditions for these plots and
the vertical profiles are well. Well, for the
service site treatments, where the only story is completely
clear from the start are all very similar. Also
when the one The story is left, you can
see that and some of them is very sort of
minute changes. But for the some removal of aliens
because in but you can see that the primary cannot
be is very much very different because might be able
to infer that because of the presence of the story
. But we don't know ourselves yet. Mhm.
Um Not that potential tool is high scale cannot be
an openness index which is essentially for mapping vertical gaps
. It's another sort of aid in detecting the presence
or absence from the story. This particular technique was
developed in Queensland Australia for um well I think I
guess typically for the detection of suppressed trees or trees
that will not be represented in the cabinet behind model
. Mhm. This is one particular example we produced
where, well each of these figures plots were well
well her but I would use for each of these
field plots to remove any any understory vegetation at all
for the for the plots where you can obviously see
the ground and have been thinned. It's pretty obvious
but closed canopy conditions. Um If you look at
the hcl I you can see that there is very
much in the absence of the story that Yeah,
another technique we've been, I've been trying to develop
is an automated individual treatment technique which will sort of
identify the position and various other attributes of the individual
trees. Um This time it typically starts with utilizing
a high resolution coming behind model and identifying the heights
of maximum elevation using a global search window and each
of the each of the points in the graphic that
represents the the absolute maximum of each of the kind
of people each of the individual trees. Um Once
we know that that location will be used in iterative
technique to grow a region around each point, expanding
into areas of lower elevation from the maximum in order
to generate an estimate of primary radius and area.
You can see this is a sort of a blow
up of the previous graphic. You can see it
corresponds pretty closely to what you can complete by i
in terms of where the canopy is, it's not
perfect. Unfortunately, He said this one. This
particular one, for example, is bit of an
anomaly. It's still still very much work in progress
. Yes, in terms of accuracy is in terms
of the population of plots here varies by underestimating between
five and centuries, but otherwise is is usually pretty
good. We can also generate vertical profiles for the
spatial extent of each individual tree crown. And obviously
we can we can infer sort of and top flight
and base height metrics as well. You can see
from the graphic vertical profile graphic the The top two
, sorry that the two maximal red fights on the
right hand side of the rough indicative of the canopy
itself. And it corresponds pretty well with field data
at this point. Unfortunately, I'm not sure why
it generates to maximum for each of the three drums
have locked down so far, but it's still like
I said work in progress and the lower points to
the left of the main ones are either indicative of
understory vegetation or potentially that branches. And likewise,
once we have estimates of trial radius and various elevations
, we can make pretty reasonable estimates of mechanical crown
volume. Mm. Yeah. Um The last sort
of lightning tour method I'm going to take you through
is sort of estimation of leaf error index that a
number of vertical levels within the within the within the
field. What extent to do this? We visited
two particular sites in Focus trapped into forest in North
Carolina so separated by probably about 200 miles. Um
Data set was provided very gracefully by Nasa G.
Like team. Um In order to estimate, oh
, I should probably mention a bit, but leaf
area index says, I'm sure most of you are
aware but it's essentially can be defined as the amount
of leaf matter above ground. It's going to have
various various influences on growth models and the things that
may be quite useful in a forest in for treatment
sense. In order to measure leaf area index at
this scale, we used The Guy 20 200 which
is this Magic one here. Yeah. And then
We used to 50 m. Transact with one with
one measurement every meter. Um three vertical levels of
0m ground level 1.5 m on 2.5 m. So
this is this is order to test the hypothesis that
lie measurement under on the story of, Sorry and
I measurement of understory 0m may not be indicative of
the the leaf area conditions of the main canopy.
And obviously this is very important for growth models.
Um for this, for this particular project we had
to to field sites were only able to do 18
parkas tracks due to relatively small size of the acquisition
and well relatively hi difficulty actually getting around the site
because of high levels of understory and a lot of
pain. And with the Duke forrest We were able
to install 20 plots at a far greater spatial extent
and we're able to example various um stand ages and
cannot be structural types. Um In terms of wider
processing, it continue very much as it has been
the previous steps And we extracted metrics and produce vertical
profiles for 20 by 20 metre areas. Um We're
also able to estimate so various various metrics based on
the stratification of the point cloud based on those layers
. Um So we're able to for example, estimate
the total proportion of points within the main canopy itself
. We're also able to modify an existing technique first
employed by uh Alicia producing a couple of years ago
, developing gave certainly called canopy Yeah, kind of
authenticity slices. So it's essentially focuses on finding the
The point within three canopy way, you'll find the
most light or returns, identifying that point and developing
metrics, sort of both above and below that five
m meter increments. Uh For each of these these
slices you can have estimate metrics such as the mean
standard deviation various. And I'm coefficient two variants.
Yeah. And we're able to produce a couple of
days ago actually a number of models for the estimation
of a at these three particular levels. Um Used
standard linear regression technique, story stepwise linear progression technique
, Our party's 100 30 metrics. And eventually we
came to these three models. You got like that's
the vast majority of the main predictors of these models
where the kind of identity slices, the proportion of
ground returns and a number of metrics related to the
stratification of the point. Cloud yourself. Yeah.
As Vincent greek. Um So the vertical profiles can
be a very powerful tool to estimate directly and statistically
elements within the within the forest canopy and underneath itself
. And yeah, so automated techniques for developing individual
tree methods, R. And D. Possible and
fairly accurate and Yeah, that's pretty much it does
. Does anybody have any questions? Yes. 1st
, Great Presentation. Thank you. two I guess
to me some idea of the process and kind of
uh kind of machines and people used to use in
the process. The vast majority of processing was done
on my my work laptop, which is probably about
five years old and pretty rubbish. Um But in
terms of processing time it probably took probably about the
day her study site. So what about 24 hours
per study site? So on a more modern machine
of people fossil period? Yeah. Mhm. Mhm
. Well, thank you one.