Predicting and Visualizing Maximum Canopy Height in the Great
Smoky Mountains National Park Using Regional Scale Lidar Data
Christopher W. StrotherMasters ThesisCenter for Geospatial
ResearchDepartment of GeographyUniversity of GeorgiaSpring 2013
Detection and analysis of extraordinary tree heights in the
Great Smoky Mountains National Park Using Regional Scale Lidar
Data
1
OutlineIntroductionThesis ObjectivesDetection of Tall TreesOLS
AnalysisConclusionsReferences
introduction
Manuscript-style thesis with 2 articles for publication -LiDAR
Detection of the Ten Tallest Trees in the Tennessee Portion of the
Great Smoky Mountains National Park Ordinary Least Squares Analysis
of a LiDAR-Derived Tree Height Database
The Importance of Tall TreesLocation of old growth
communitiesConditions favorable for growth potentialEcological
importance as biomass and carbon sinksHabitat for other species of
plants and animalsEvidence that tall trees on the decline due to
the warming associated with climate change (Laurance, 2012)
American Recovery and Reinvestment Act of 2009U.S. Geological
Survey (USGS)Center for Remote Sensing and Mapping Science at
UGAInstitute for Environmental and Spatial Analysis at GSCPhoto
Science, Inc.Eastern Native Tree SocietyNational Park Service
Great Smoky Mountains National Park
The Park was established in 1934 to mitigate erosion and fire
damage caused by logging (Houk, 2000).
The GRSM has approximately 209,000 ha of forest cover most
expansive virgin forest land on East Coast (NPS, 1981).
The Park receives up to 10 million visitors a year and has been
designated an International Biosphere Reserve and a U.N. World
Heritage Site (Welch et al., 2002).
http://forsys.cfr.washington.edu/JFSP06/lidar_technology.htmLiDAR
Principles - acquisition
LiDAR Principles the point cloud
LiDAR Principles multiple returns
LiDAR Principles - classificationClassified according to ASPRS
standards (LAS 1.2)4 categories: 1 = Nonground 2 = Ground 7 =
Noise12 = Overlap
LiDAR Principles digital elevation model (DEM)
LiDAR Use In ForestryAirborne LIDAR has been used extensively in
the last twenty years to obtain accurate measurements of forested
areas (Nilsson, 1996; Maune, 2001; Andersen et al., 2006; Jensen,
2007).Maximum tree height is an indicator of ecological and
environmental quantities in tree communities regarding biomass and
resource use (Kempes et al., 2011).Errors inherent in LIDAR data
include post spacing issues (Fig. 2), which create
misrepresentation of crown structure (Zimble et al., 2003).
Zimble et al., 2003
Ground Based Tree Height Measurement TechniquesAccurate direct
measurements of trees in the field are difficult (Andersen et al.,
2006).The USFS indicates that the best measurements are made using
a laser rangefinder with a built-in clinometer like the Impulse100
(USFS, 2005).
h = hd (tan + tan )
Thesis objectives
Primary Goal To investigate LiDAR as a remote sensing tool for
assessing vegetation structure and providing resource managers with
detailed information on canopy height.
Detection of maximum tree heights in the GRSM by creating a
methodology for processing a large dataset (724 tiles each
representing 225 ha in area and around 200 300 Mb file size) of
recently acquired (2011) LiDAR data to identify potential trees of
extraordinary height and to assess the environmental conditions at
the top ten sites (Chapter 2).
Assessment of LiDAR-derived tree height databases to predict
tree heights in a highly variable forested environment using
multivariate regression (Chapter 3).
Strother, C.W., M. Madden, T. Jordan, and A. Presotto. To be
submitted to Photogrammatic Engineering & Remote Sensing.
Chapter 2 - LIDAR DETECTION OF THE TEN TALLEST TREES IN THE
TENNESSEE PORTION OF THE GREAT SMOKY MOUNTAINS NATIONAL PARK
Introduction
June 2011 Correspondence between Michael Davie of the Eastern
Native Tree Society (ENTS) and Dr. Marguerite Madden began
August 2011 Intrepid and youthful new graduate student became
interested in the search
Breckheimer (2011) work led to the discovery of a tulip tree
58.0 meters tall in NC portion of the GRSM
Tallest tree in TN portion of the GRSM listed as a tulip tree
52.7 meters tall
data724 tiles of LIDAR data, CIR imagery, and DEMs
methodology
Convert .las point cloud data to multipoint shapefiles for
ArcGIS processing
Create Digital Surface Models (DSMs)
Create normalized DSMs (nDSMs)
Classify nDSM rasters for values of >51.8 m (170 ft) and
mosaic
Convert raster values to points and query for height values 52
59 m in the park
Manual removal of noise, man-made objects, and points outside of
park boundary
List the top ten height clusters with coordinates for field
verification
Results
SiteLidar Height (m)Field Height (m)% Error vs. FieldElevation
(m)Degree SlopeAspectOverstoryTree
Type159.0UnknownUnknown376.335.1SWPIs-TUnknown259.0UnknownUnknown358.651.9NOmH/TUnknown355.972.8-30.2494.210.3ECHxA-TPine457.0UnknownUnknown477.939.4NWPIs-TUnknown557.056.60.7394.054.7NWPI
Pine657.061.5-7.9367.480.5NWPIs White
oak755.058.4-6.2785.328.6NECHx Tulip poplar856.056.9-1.6765.026.8E
CHx Tulip poplar956.051.67.9761.119.0NECHx Tulip
poplar1055.056.4-2.5761.431.7NCHx Tulip poplar
Conclusions and recommendations
All ten sites are taller than the current height record holder
in the Tennessee portion of the GRSM
Field measurement in rugged terrain is difficult
More rigorous examination of the environmental and ecological
conditions at these sites is needed
Strother, C.W., M. Madden, T. Jordan, and S. Holloway. To be
submitted to The Professional Geographer.
Chapter 3 ordinary least squares analysis of a lidar-derived
tree height database
introduction
LiDAR data format provides large numbers of possible
observations for statistical analysis
Ordinary Least Squares (OLS) analysis is a linear, unbiased
estimator that is useful in multivariate regression
With a wealth of canopy height observations, it should be
possible to model optimal conditions for growth that can be used to
predict recoverable carbon stock after destructive events
False Gap
methodology
LiDAR Analyst was used to extract 22,187 tree points from the
LiDAR point cloud
DEM of the study area was used to create slope and aspect
rasters
Overstory vegetation, soil, and stream layers were added
All layers joined in ArcGIS to create database of 22,187 trees
with elevation, slope, aspect, soil type, vegetation community,
distance to stream, and tree height attributesResults imported to
STATA IC 10 statistical analysis software for modeling
Results and discussion
treeheight = b0 + b1*elevation + b2*near_dist +b3*slope + b4*s1
+ b5*s2 + b6*a1 + b7*a2 + b8*a4 + b9*a5 + b10*a6 + b11*a7 + b12*a8
+ b13*n1 + b14*n2 + b15*n3 + b16*n4 + b17*n5 + b18*n7 + b19*n8
R = 0.2390 Model 1
treeheight = b0 + b1*elevation + b2*near_dist + b4*s1 + b5*s2 +
b6*a1 + b7*a2 + b8*a4 + b9*a5 + b10*a6 + b11*a7 + b12*a8
R = 0.2057Model 2
Conclusions and recommendations
LiDAR data format = large number of observations (n)
Environmental factors such as elevation, distance to water,
aspect, and soil type significantly affect tree heights in this
highly variable environment
Model only accounted for 20% of variability more work is needed
to identify other variables that may contribute
Complex natural environments are difficult to model
effectively
Thesis conclusions
LiDAR data collected in forested environments provide an
embarrassment of riches for researchersConsideration of data
processing workflows and computational limitations should be
addressedNew LiDAR technologies such as terrestrial and flash LiDAR
should be examined and fused with current airborne collections to
provide even more rigorous datasets
Thank you!