Airborne LiDAR for
evaluating the impacts of
development on urban
forest: A case study in the City of
Charlotte
Chris Godwin1
Gang Chen1
Kunwar K. Singh2
1 Dept. of Geography and Earth Sciences
University of North Carolina at Charlotte
2 Department of Forestry and Environmental resources
North Carolina State University
THE URBAN FOREST
 Forests offer
innumerable benefits to a
region and its residents
 Changes in land use when
a region becomes
developed alters the
natural carbon cycle
 Cities must provide
adequate space for trees
to mature
2
RESEARCH QUESTION
How does urban neighborhood
development affect forest carbon
density?
3
4
DATA
 Small footprint
LiDAR 2012- 1
meter point
density
 NAIP 2012 aerial
imagery- 1 meter
resolution
 75 Field plots
within the study
area
5
METHODS
Aerial
Imagery
Object Based
Classification
Field Plot
Data
Plot Level
Biomass
Estimate
LiDAR Data
LiDAR
Metric
Extraction
Above Ground
Biomass
Modeling
(Phase 1)
Landscape
Metrics
Neighborhood Carbon
Density Modeling
(Phase 2)
PHASE 1: ESTIMATION OF ABOVE
GROUND BIOMASS
 Multiple regression is used
LiDAR
Metrics
Plot Level
Biomass
Estimates
 Accuracy of multiple regression models in biomass
prediction benefit from separating deciduous,
evergreen, and mixed forest stands
7
VARIABLE SELECTION AND
TRANSFORMATIONS
LiDAR metrics based on tree height
 Literature review and
extensive testing of variable
combinations
 Logarithmic and Box cox
transformations were tested
to help establish a
relationship between nonlinear biomass and forest
structure parameters
Total Return Count
Height 25th Percentile
Minimum
Height 30th Percentile
Maximum
Height 40th Percentile
Mean
Height 50th Percentile
Mode
Height 60th Percentile
Standard Deviation
Height 70th Percentile
Variance
Height 75th Percentile
Skewness
Height 80th Percentile
Kurtosis
Height 90th Percentile
Height 1st Percentile
Height 95th Percentile
Height 5th Percentile
Height 99th Percentile
Height 10th Percentile Canopy Relief Ratio
Height 20th Percentile
8
FINAL BIOMASS PREDICTION MODELS
Species
Group
Deciduous
Evergreen
Mixed
Dependent Variable
Plot AGB-0.5
Plot AGB
ln(Plot AGB)
Independent variables
ln(Height Skewness)
ln(Height 95th Percentile)
ln(Height 5th Percentile)
ln(Total Return Count)
ln(Height Minimum)
Height 5th Percentile
Total Return Count
Height Variance
ln(Height 1st percentile)
ln(Height Skewness)
ln(Height 40th percentile)
RMSE
R
(t/ha) Squared
20.35
0.792
18.79
0.882
21.53
0.618
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Carbon storage in Charlotte forests 3.8 million tonnes
$298 million value
Average carbon density in the region- 53.6 tonnes / hectare
10
PHASE 2: CARBON DENSITY
MODELING
Using carbon quantities derived from Phase I multiple
regression is used to understand:
 What neighborhood characteristics have a positive impact on the
health of trees?
 What neighborhood characteristics have negative impact on the
health of trees?
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NEIGHBORHOOD SELECTION AND
CATEGORIZATION
464 Original NPA’s
Narrowed to 308 based on
percentage of residentially
zoned land
Neighborhood
categorization
100 neighborhoods
selected with 25
observations in each
category
Neighborhoods
broken into 4
categories based
on percentage of
Built land
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13
LANDSCAPE METRICS
Used as a means of
quantifying landscape
structure
Two types used in this
study
 Class level metrics
 Landscape level
metrics
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PHASE 2: ESTIMATION OF
NEIGHBORHOOD CARBON DENSITY
Multiple Regression is used once again
Landscape
Metrics
Neighborhood
Carbon Density
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1.
≤ 15% BUILT UP
<= 15% built land
 As diversity increases carbon density
will decrease
 As amounts of built land increases
carbon density will decrease
 As forests become more aggregated
carbon density will increase
1
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2. >15%
>15%
AND
≤ 25%
BUILT
and
<=25%
built
land UP
 As diversity increases carbon
density will decrease
 As deciduous forest patches
become more evenly
distributed carbon density will
increase
 As water becomes more
aggregated increases carbon
density will increase
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3. >25%
>25%
AND
≤ 40%
and
<=40%
builtBUILT
land UP
 As built land increases carbon
density will decrease
 As open land decreases carbon
density will decrease
 As bare soil distribution
becomes more even carbon
density will decrease
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4. >40%
> 40%
BUILT
built
land UP
 Once again As diversity
increases carbon density will
decrease
 As aggregation of deciduous
forests increase carbon density
will increase
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CONCLUSION
Aggregation of forests
Overcome problems that arise when pairing
built environments with the natural landscape
Facilitate urban forest development and
community planning techniques
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QUESTIONS OR
COMMENTS?
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