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!