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Mapping Forest Biomass Carbon by Fusing Terrestrial and Airborne LiDAR Datasets

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The storage and flux of terrestrial carbon (C) is one of the most uncertain components of the global C budget and detailed quantification of forest C remains difficult to measure on a large scale. Remote sensing of forests with airborne LiDAR has proven to be an effective method of bridging the gap between data from plot-level mensuration and landscape-scale C storage estimates, but the standard method of assessing forest C is typically based on national or regional-scale allometric equations that are often not representative on the local-scale. The goals of this study were to determine if terrestrial LiDAR (TLS) can be used to improve local allometric equations and what are the implications of fusing these equations to airborne LiDAR data sources for biomass estimation. Our non-destructively determined allometric relationships had lower error than national-scale equations (R 2 = 0.98, RMSE = 125 kg). National allometric equations overestimated biomass in the species studied and total site-wide biomass significantly decreased by over 35 Mg/ha when our equation was used. The proposed pipeline has the potential to significantly reduce labor costs associated with destructive sampling and simplify the process of detailed C monitoring, while improving the quality and quantity of data retrieved.
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Atticus Stovall 1
MAPPING FOREST BIOMASS CARBON BY FUSING TERRESTRIAL AND
AIRBORNE LIDAR DATASETS
Author: Atticus E. L. Stovall, University of Virginia
Advisor: Herman H. Shugart, University of Virginia
1.0: Abstract
The storage and flux of terrestrial carbon (C)
is one of the most uncertain components of the
global C budget and detailed quantification of
forest C remains difficult to measure on a
large scale. Remote sensing of forests with
airborne LiDAR has proven to be an effective
method of bridging the gap between data from
plot-level mensuration and landscape-scale C
storage estimates, but the standard method of
assessing forest C is typically based on
national or regional-scale allometric equations
that are often not representative on the local-
scale. The goals of this study were to
determine if terrestrial LiDAR (TLS) can be
used to improve local allometric equations and
what are the implications of fusing these
equations to airborne LiDAR data sources for
biomass estimation. Our non-destructively
determined allometric relationships had lower
error than national-scale equations (R2= 0.98,
RMSE = 125 kg). National allometric
equations overestimated biomass in the
species studied and total site-wide biomass
significantly decreased by over 35 Mg/ha
when our equation was used. The proposed
pipeline has the potential to significantly
reduce labor costs associated with destructive
sampling and simplify the process of detailed
C monitoring, while improving the quality and
quantity of data retrieved.
2.0: Introduction
Current rising atmospheric CO2
concentrations are a major concern with
significant global ramifications and the
quantification of terrestrial carbon (C) storage
lies on the forefront of scientific
understanding of the global C cycle. Of the C
fluxes that are known to occur on Earth, the
terrestrial sink has the greatest amount of
uncertainty and human-induced change of
landscape-scale ecosystems has complicated
the C cycle, creating a necessity for
monitoring the dynamics of these global C
stocks. Existing mature and secondary
successional forests act as an extremely
effective means of active C sequestration,
potentially mitigating the predicted future
changes in climate.
Actions have been taken by the United
Nations Framework Convention on Climate
Change (UNFCCC) to improve monitoring of
forest cover for Reduced Deforestation and
Degradation (REDD). The three tier system
implemented in these assessments of C
storage are categorized specifically according
to the level of uncertainty and level of detail
within the estimates, with Tier 3 being the
most accurate (IPCC 2006). Forest C storage
is typically based on national- or regional-
scale allometric equations using traditional
methods of determining aboveground
biomass. These estimates vary significantly
depending on region and environmental
factors (Jenkins et al. 2003; Chojnacky, Heath,
and Jenkins 2014). Furthermore, current
methods for developing these relationships
require destruction of the sample and trees that
are located in protected areas often go
unmeasured due to their ecological and
cultural significance (Picard, Saint-Andre, and
Henry 2012). Improvement of these
measurements is necessary in order for
collaborative international carbon monitoring
programs to be successful in areas with tree
Atticus Stovall 2
species that have insufficiently documented
allometric relationships (Asner 2009).
Strengthening confidence in these estimates
will aid in development of landscape-scale
modeling projections of carbon storage.
Furthermore, the study of the structural
allocation of this carbon is needed in order to
fully understand the role of forests in the
carbon cycle (Shugart, Saatchi, and Hall
2010).
Remote sensing has become the primary
method of monitoring C storage and fluxes on
a global scale, offering increasingly higher
spatial resolutions as technology improves.
Satellites such as the newly launched Landsat
8 greatly improve the understanding of land-
cover changes; however, resolving C stocks in
areas with high vegetation density (leaf area
index (LAI) > 3) is rarely successful without
ground-based validation (Lefsky et al. 2002).
Airborne Light Detection and Ranging
(LiDAR) provides extremely high-resolution
data that is useful for quantifying biomass and
forest structure over large areas (Cook et al.
2013). Ground validation of these estimates is
essential in order for these data to be useful
for C quantification.
Terrestrial Laser Scanners (TLS) are
stationary ground-based LiDAR systems that
emit a high number of pulses. Multiple TLS
scans registered together can resolve
vegetation structure with a previously
unprecedented level of detail. These devices
are being used more frequently in the field of
precision forestry (Hopkinson et al. 2004) and
techniques have been developed that fuse ALS
and TLS to form a comprehensive view of the
forest (Popescu, Wynne, and Scrivani 2004).
Volumetric measurements of vegetation are
made with digital analysis, resulting in a fine-
scale plot level estimation of biomass with
minimal effort compared to labor intensive
field-based methods (Srinivasan et al. 2014).
The focus of this work is to determine if
terrestrial LiDAR can be used as an effective
tool to improve local allometric equations and
what might be the implications of fusing these
equations to airborne LiDAR data sources for
biomass and carbon estimation across the
landscape.
3.0: Methodology
3.1: Data Sources
The acquisition of data for this project are
from a mixture of publicly available data sets
and field-based data collection. A stem map
from a 25.6 ha Smithsonian Institute Global
Earth Observatory (SIGEO) plot in the
Smithsonian Conservation Biology Institute
near Front Royal, VA was used, containing
data on all woody stems > 1 cm DBH (Bourg
et al. 2013). The allometric relationship used
in this work is within the Hardwood -
Figure 1: Map of site and plot distribution.
Atticus Stovall 3
Fagaceae, deciduous classification, as
described by Chojnacky et al. (2014).
3.2: Field Sampling Techniques
Sampling took place in the SIGEO plot in
Front Royal, Virginia, USA. 14 1/10 ha plots
in each sampling location were chosen at
random using ArcGIS mapping software.
Individual plots were located using a handheld
Garmin eTrex unit and plot centers were
found on the marked grid intersections of the
SIGEO plot. We used the Faro Focus3D120
terrestrial laser-scanner to collect the
following data: location relative to the scanner
in xyz space, intensity values determined by
the 905 nm laser wavelength reflectance, and
RGB values. Scan resolution and incidence of
error are on the order of ±2 mm. In order to
reduce occlusion from the presence of high-
density vegetation we scanned 5-6 times in
different locations on each plot.
Time elapsed for an individual TLS scan
averages about 5.5 minutes, depending on the
specific settings selected. Prior research
suggests that the ideal scanning parameters for
resolving individual tree features is ¼
resolution (10 kpts/360°), resulting in a 2.5
minute scan time with point spacing of 0.6 cm
at a 10 m distance (Pueschel et al. 2013). The
faster scan times reduce the error induced by
movement of objects in the scan area (i.e.
wind-blown trees and leaves). The center scan
takes place at the marked plot center and all
subsequent scan locations are geo-registered
from that point.
3.3: Post Processing
Individual scans were registered using the
automatic registration algorithms included in
Faro’s proprietary software package SCENE
and overlapping redundant points were
deleted/fused together to create a seamless 3D
point cloud that was used for analysis. A “dark
scan point” filter was used to reduce noise
caused by low intensity returns and a 2 cm
“density filter” reduces the presence of uneven
distribution of points. The fully registered
scan were then exported with intensity values
and RGB values for further analysis. The
ground level was estimated using the Quick
Terrain Modeler Automatic Ground Level
(AGL) tool and a Digital Elevation Model
(DEM) was exported. The ground-level points
were then removed from the point cloud to
reduce the statistical bias from the high
density of ground points in TLS data. Once
leveled, the data was isolated between 1.3 and
1.4 m above ground level and exported for use
in automatic stem map creation and diameter
(DBH) measurement.
3.4: Individual Tree Measurements
To automate and increase the accuracy of
DBH measurements we developed an R-script
that completes this task with the least squares
circle fitting algorithm by minimizing the sum
of the squared residuals between a 2 cm DBH-
height slice in the returns and the optimally
fitting circle (Coope 1993). Preliminary
testing indicates this method to be ideal for
noisier scenes, where vegetation may be
Figure 2: Study design and workflow.
TLS Data Collection
Data Fusion
and
Model Evaluation
Plot location
Volume Algorithm
Site Stem Map Biomass
Airborne LiDAR Biomass
Convex-hullVoxel building
TLS Allometric Relationships
DBH Height Biomass
Chojnacky et al. 2014 Biomass
Post-processing
Filter Registration
Clip Tree Segmentation
5 TLS Scans
Atticus Stovall 4
occluding a portion of the trunk section. To
further validate these three-dimensional
measurements, the DBH values from the stem
map on the SIGEO plot were used as a cross-
reference to determine the accuracy of this
TLS-based measurement.
3.6: Biomass Estimations from TLS Data
In order to provide a standard estimate of
aboveground biomass for a clear comparison
to our improved measurements we calculated
plot-level biomass from measured DBH based
on the newly updated United States allometric
equations (Chojnacky, Heath, and Jenkins
2014).
For this project, volume was estimated with
TLS data using a novel combination of a
convex-hull algorithm and the voxelization
process. Several studies have produced
successful algorithms for volume estimation in
trees ranging from simple stem diameter
calculations (Bienert et al. 2006) and
quantitative surface models (Hackenberg et al.
2014; Calders et al. 2015) to voxel-based
models (Fumiki Hosoi, Nakai, and Omasa
2013; Bienert et al. 2014). We developed an
optimized voxel-convex-hull-based algorithm
that combines several of the techniques
implemented in the above work. Voxelization
of the entire point cloud is the essential
preliminary step in volume quantification.
Once completed, the bulk of the algorithm
development was focused on effective voxel
infilling of the tree point clouds. The method
developed by Hosoi et al. in 2013 uses a voxel
grid to generate surface contours before
infilling takes place, but this can lead to
overestimates of the total wood volume if
filled voxels are used instead of interpolated
original points.
In order to attempt to overcome these stated
limitations of the previous methods described
our algorithm relies on a convex-hull method
of creating a solid exterior model of the tree.
The algorithm essentially traces the outermost
points of the trunk to create a seamless outline
of the trunk, from which a volume can be
determined. Appropriate segmentation of the
tree is essential for accurate volume
estimations from the TLS data. Since the
current version of the algorithm cannot
differentiate between branching points,
branches larger than the voxel size must be
separated out individually in order for the
functions that the algorithm relies on to be
valid. Furthermore, areas where branches
become bent downward must be segmented at
the vertex of the bend. If correct segmentation
is not completed the volume will invariably be
significantly overestimated. Once all branches
are segmented the algorithm can be initiated
across both the central bole of the tree, as well
as all branches and segments larger than the
specified voxel size. The process, at this point,
is entirely automated and completed on
individual 2 cm thick slices from the point
cloud, moving from Zmin ≤ Z ≤ Zmax,
Figure 3: Example trunk section before
and after processing the TLS data with
the volume infill algorithm.
Atticus Stovall 5
producing a completely solid volumetric
model of an individual tree. The final process
can also be completed with a height buffer of
specified thickness in order to infer
information from the areas of missing data on
the trunk, reducing the likelihood of
underestimation of volume. The final step
invokes a voxelizing algorithm to estimate
volume attributed to branches smaller than the
stated voxel size. In order to complete this,
each remaining voxel is assumed to represent
a 1 cm diameter by 2 cm long cylinder and
small-branch volume is then estimated. The
combined volume estimates were then
converted to aboveground biomass by
applying species-based wood density values.
This will provide two pieces of essential
information:
[a] An individual species specific non-
destructively estimated biomass data point that
will be used to improve and create new local
allometric equations.
[b] A high resolution model of the vertical
distribution of biomass and carbon on a per-
plot level, information of which will be
essential in development of predictive models
of these values using the point cloud
structure.
3.7: Creation of TLS-based Allometric
Relationships
The process of allometric equation
development is one that is time consuming
and expensive. The methods of non-
destructive biomass estimation described
above attempt to improve allometric
equations via inclusion of previously
prohibitively complex trees that were not
ideal candidates for destructive sampling. In
order to link the TLS-based biomass estimates
to both the whole forest stem map and
airborne LiDAR dataset we created our own
allometric equation from 24 digitally sampled
trees across the forest within the Hardwood -
Fagaceae, deciduous classification, as
described by Chojnacky et al. (2014). The
final equation uses diameter as a predictor for
aboveground biomass as a log-linear
relationship.
3.8: Fusion of TLS and Airborne LiDAR
The final step in our analysis was the
assessment of how feasible our TLS-based
allometric equation would be when included
in a conventional airborne LiDAR processing
pipeline. Following standard procedure for
airborne LiDAR analysis, we used the
CloudMetrics algorithm included within the
Fusion LiDAR Data Toolkit to produce 94
different point cloud metrics for each of the 14
TLS sample plots. A subset of these metrics
was used to find the most logical combination
of variables that produce the strongest
relationship to the plot-level biomass
measurements through stepwise regression.
Between 1 and 4 metrics were used in order to
form a meaningful model for the relationship
Figure 4: DBH is reliably measured using a Least
Squares circle fit algorithm (see inset). Model fit
versus field based measured values gives a high R2
and low RMSE (R2 = 0.96, RMSE = 2.40 cm)
Atticus Stovall 6
between the LiDAR returns and aboveground
biomass. The final predictive model includes a
combination of height metrics, such as mean
canopy height and percentiles, percent cover
estimates, and structural metrics, such as
skewness and kurtosis of the point cloud
distribution at each plot location. The final
model was applied across the landscape and a
map of LiDAR-derived biomass created for
assessment.
4.0: Results and Discussion
4.1: Individual Tree Measurements
TLS is an effective tool for automatically
recovering DBH measurements with minimal
effort. By using the Least Squares circle fit
algorithm DBH was reliably measured (R2 =
0.96, RMSE = 2.40 cm) with a slight
underestimation at larger diameters (Figure
4). The likely cause of this underestimation is
the non-circular (or non-cylindrical) nature of
large diameter trees. Many of the individuals
sampled in larger DBH size classes had
multiple trunks, resulting in a much more
oblong or ellipsoidal shape than circular.
Moreover, in our study site we have found it
common that larger trees generally exhibit
novel trunk shapes, likely as a result of
variations in growing conditions over the
lifetime of the individual. We anticipate that
improvements to this method of automatic
DBH recovery can be made with the
application of the above described volume
algorithm. Since the very nature of our
algorithm allows for a wide range of shapes
that can occur in trees, the retrieval of
diameter would be an ideal application. We
have experimented with trunk diameter
retrieval by calculating the area at a specific
vertical interval of the point cloud and back-
calculating an estimated equivalent circle, in
turn allowing for DBH measurement.
Interestingly, we can use this same process to
estimate diameter at a fine vertical resolution
moving up the tree, allowing for detailed
observations of stem taper, where occlusion of
the base points is not significant.
4.2: Creation of TLS-based Allometric
Relationships
We were able to successfully create a TLS-
based local allometric equation from the 24
individual trees that fell within the Hardwood
- Fagaceae, deciduous classification, as
described by Chojnacky et al. (2014). With a
significantly smaller sample size (n = 24) our
equation for the same class of trees displayed
a much higher R2, describing 98% of the
variation in our data, as opposed to 84%, as
seen in the Chojnacky et al. equation (Figure
5a). The explanation for this observation can
primarily be attributed to the increased
amount of variation that will inherently be
found in larger sample sizes, even without the
consideration that the Chojnacky et al.
equations are of a national rather than a local
scale. RMSE, although not reported in the
Chojnacky et al. paper, is higher than the
RMSE found in a previous and frequently
applied set of allometric equations (Jenkins et
al. 2003) at 0.29 log(Biomass (kg)) versus
0.24 log(Biomass (kg)). Our equation may
have higher RMSE because of the small
sample size, making our dataset more
susceptible to outliers. This finding will be
investigated in future work and likely
improved with the addition of a larger number
of data points.
When comparing the two equations, the most
notable difference in the two equations is a
significant decrease in the biomass for large
trees as seen in Figure 5b and 5c. The
implications of such a large change in biomass
are of great interest. Currently, the most
Atticus Stovall 7
commonly applied allometric equations are of
a national rather than a local scale. There is
evidence that suggests application of local as
opposed to national scale biomass equations
can lead to significant differences, especially
in the context of LiDAR applications (Zhao,
Guo, and Kelly 2012). In our case, since this
study focused primarily on a single species
group, we anticipated only a slight change in
the site level biomass observed. However,
Figure 5: a) Final log-linear biomass equation produced from the three-dimensionally
modeled TLS dataset. b) Chojnacky et al. 2014 Hardwood - Fagaceae, deciduous equation
(red) compared to the TLS-derived allometric equation (RMSE = 125.33 kg). c) Comparison
of Chojnacky et al. 2014 Hardwood - Fagaceae, deciduous equation to the TLS-derived
allometric equation across all modeled trees (grey dotted-line is the 1:1 line). d) Site-wide
extrapolation of biomass density using the two equations, resulting in over 35 Mg/ha decrease
(error bars indicate 95% confidence).
a) b)
c) d)
Atticus Stovall 8
when applied across the entirety of the 25.6 ha
area we observed a 35 Mg/ha decrease in
biomass when applying the local equation to
the stem map. The large difference in biomass
density can be attributed to the prevalence of
the species group observed. Within our study
forest the dominant species (as defined by
largest basal area per plot) are oak, hickory,
and other hardwood species, so, we would
infer that small differences in the allometric
equation used will result in large differences
in biomass density across the plot.
Furthermore, this species group is especially
biomass dense, in that the wood density is
quite high compared to other dominant species
that are also large in size (i.e. yellow tulip
poplar).
4.3: Fusion of TLS and Airborne LiDAR
Although this study included a relatively small
number of sample plots, the creation of
predictive multiple linear regression model for
biomass was possible, describing around 71%
if the biomass density variation across the
landscape. The sampling plan for this study
allowed us to include 5.47% (1.4 ha) of the
total plot area, which is larger than many
studies that utilize airborne LiDAR for
biomass predictive models. This relatively
high percentage of sampled area is helpful in
Figure 5: a) Comparison between plot level biomass estimates (per 1/10 ha) from the two
predictive multiple linear regression models (inset is a table of the four predictive point cloud
statistics used to create the final model). b) National scale biomass prediction using the
multiple linear regression model. c) TLS-based biomass prediction using the multiple linear
regression model.
Variable Definition
H_Skew Skewness of point cloud returns
30_P 30th percentile of returns
CCE_1m Canopy cover estimate using 1 m cutoff
CCE_Mean Canopy cover estimate using mean height cutoff
Atticus Stovall 9
creating a final predictive model, however, it
should be noted that the LiDAR dataset used
was not of the appropriate resolution to predict
biomass with a high level of confidence (point
spacing average was 1.7 m), which leads to
higher uncertainty in our final estimates. The
major findings from this portion of our study
are as follows: 1) Biomass was predicted
using the TLS-derived equations as well as the
national scale equations, with little influence
on the percentage of variation described in the
final model, and 2) biomass decreased across
all LiDAR plots with the implementation of
our TLS-based equation. If we take into
account the higher confidence due to the
higher amount of variation described in the
allometric equation development (Section
4.2), the TLS-based local allometric equation
would be the ideal selection when applying a
biomass relationship, although the R2 values
using the LiDAR data are comparable.
5.0: Conclusions and Future Work
This work has shown a potential application
and pipeline for analysis of terrestrial LiDAR
data in the context of biomass estimation in
forests. The algorithm developed for this work
performs well when applied within a complex
temperate deciduous forest during the leaf-off
season. Automatic DBH measurement was
shown to be a realistic alternative to manual
measurement with relatively low error and a
slight bias at large diameters, however we
anticipate improvements to this methodology
with the application of the described
algorithm. Our local allometric equation
derived from the TLS point cloud was low in
RMSE and described 98% of the data using a
log-linear relationship. The application of our
biomass relationship to the site-wide data
resulted in over 35 Mg/ha difference in
biomass density. When applied to the airborne
LiDAR dataset we found that our equation
performed as well as the national scale
equation, but resulted in significant site-wide
decreases in biomass density.
We are in the process of improving the
volume algorithm by comparing destructively
harvested tree samples to the TLS data. The
major current limitation is the presence of
noise in the data that will invariably lead to
over estimation in tree-level biomass. Some
exploratory methods will be used to reduce the
impact of noise when using the convex-hull
portion of our algorithm. Acquisition of high
density airborne LiDAR data and application
of the pipeline will be necessary in the future
to determine the efficacy of this process.
6.0: Acknowledgments
Thank you to my advisor Dr. Hank Shugart
for planting the seed that became this project,
Dr. Robert Washington-Allen for giving me a
foundation of TLS methods for which to build
this work, Dr. Howie Epstein, and Dr. Todd
Scanlon. Many thanks to all of those who
helped with field work including Adrienne
and Moses Stovall, Adrianna Foster, and Bella
Reyes.
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