Characterization and Classification of Vegetation Canopy Structure And

Home , Abrams Creek (Tennessee), Benton MacKaye Trail, Cane Creek Mountains, Great Smoky Mountains, Little Chestnut Mountain, Mount Cammerer

Characterization and Classiﬁcation of Vegetation Canopy Structure and Distribution within the Great Smoky Mountains National Park using LiDAR

Jitendra Kumar1, Jon Weiner2, William W. Hargrove3, Steven Norman3, Forrest M. Hoﬀman1, Doug Newcomb4

1Oak Ridge National Laboratory, 2University of California Berkeley, 3USDA Forest Service, 4U.S. Fish and Wildlife Service

10th International Workshop on Spatial and Spatiotemporal Data Mining Atlantic City, New Jersey, USA November 14, 2015 Introduction

I Forests form a complex mosaic of diverse tree and coexisting plant and animal species.

I The structure of vegetation reveals information about stand age and height; forest composition, health, and disturbance; and suitability as habitat for birds and other animal species.

I Airborne Light Detection and Ranging (LiDAR) enables large scale remote sensing of topography, built infrastructure, and vegetation structure.

I Multiple laser “returns” produce “point clouds” used to map the ground surface, buildings, roads, and utility infrastructure, and to reconstruct the structure of vegetation canopies.

I Large data volumes pose signiﬁcant computational challenges to employing LiDAR to monitor and manage forests and animal habitats. Great Smoky Mountains National Park (GSMNP)

I The GSMNP is the most visited national park in the U.S., and it hosts a rich ecosystem of plants and wildlife.

I The Park encompasses 816 sq. miles in Tennessee and North Carolina and ranges in elevation from 876 to 6,643 feet above mean sea level.

1 2 3 4 5 6 7 8 9 10 11 12

T E To Knoxville To Knoxville To Newport To Newport N N E SEVIERVILLE S 321 S E E 40 411

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V 441 E R

r T 411 r Stream Crossings e r CHEROKEE NATIONAL FOREST e y r m Exit Litt T a A l n m ) e i a w er 443 Nearly all park trails cross small streams—making very wet crossings ta 1.0 C rk t P n in 129 ig u t a w n P during flooding. The following trails that cross streams with no bridges e o 0.3 n i o M u r s d n ve e o e can be difficult and dangerous at flood stage. (Asterisks ** indicate the Ri ab os G M cl 0.4 r ( McGhee-Tyson L most difficult and potentially dangerous.) This list is not all-inclusive. e s ittl 441 ll Airport e w i n o Cosby h o 0.3 L ot e Beard Cane Trail near campsite #3 Fo Pig R R ive iv r Beech Gap Trail on Straight Fork Road er Cold Spring Gap Trail at Hazel Creek 0.2 W Eagle Creek Trail** 15 crossings e 0.3 0.4 SNOWBIRD s e Tr t Ridg L Fork Ridge Trail crossing of Deep Creek at junction with Deep Creek Trail en 0.4 o P 416 D w IN r e o k Forney Creek Trail** seven crossings G TENNESSEE TA n a nWEB a N g B p Gunter Fork Trail** five crossingsU S OUNTAIN 0.1 Exit 451 O M 32 Hannah Mountain Trail** justM before Abrams Falls Trail L i NORTH CAROLINA tt Jonas Creek Trail near Forney Creek le Little River Trail near campsite #30 PIGEON FORGE C 7.4 Long Hungry Ridge Trail both sides of campsite #92 Pig o 35 Davenport eo s MOUNTAIN n b mere MARYVILLE Lost Cove Trail near Lakeshore Trail junction y Cam r Trail Gap nt Waterville R Pittman u C 1.9

Meigs Creek Trail 18 crossings k i o h E v Big Creek E e M 1.0 e B W e Mt HO e Center 73 Mount s L Noland Creek Trail** both sides of campsite #62 r r 321 Hen Wallow Falls t 2.1 HI C r Cammerer n C Cammerer C r e u 321 1.2 e Panther Creek Trail at Middle Prong Trail junction 0.6 t e w Trail Br Tr k o

L Pole Road Creek Trail near Deep Creek Trail M

6.6 2.3 321 a 34

321 il Rabbit Creek Trail at the Abrams Falls Trailhead d G ra

Gatlinburg Welcome Center 5.8 d ab T National Service Park U.S. Department the of Interior 1 e Roundtop Trail** crossing of Little River at Little River Road r s Mountain e Tr Cosby o g id 4.6 2.5 National Park Information Center n Trillium Gap Trail at Grotto Falls (icy in winter) e R v 2.1 il en Low k o a e r D G T e C T ra Upper Hazel Creek Trail hiker-only portion IN L B e a Low Gap ek il r it s a p re C TA tl er Albright Grove ld k C Wet Bottom Trail follow signs for Elijah Oliver Trail as alternate route N e tl a T 5.1 g 73 P t 33 T n r i U i e a B B O g S Albright Grove r 29 S i 2.5 Walland e 0.7 l ig a M o d Loop Trail 2.4 B m x 129 321 n l o te GATLINBURG R O 2.3 t 6.1 r i 6.1 t v Inadu Knob 37 o C

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r o o O e C D i n R y i n l a l y c u F a w M L B n s e y Mount Collins ( n T y o d U C l n Pr d e G w r l 1.4 l r c l 16 o r T d u l C t Greenbrier n a Ga N e a K 2.3 Appalachian Highlands 5.1 a i a r g p t E i C n 41 15 e o R T s n e V k e M T o d k A r R a F t r e h e k Ridge Trail t b e H 2.5 4.4 a d A a e Science Learning Center

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i d 2.1 l n 4.4 4.5 n Tunnel n Trail 2.6 e l r Creek 1.0 I CHEROKEE 3.7 e k 1.1 z o e 93 w Ha H Bypass e Falls Deals Gap F T 88 LC r 0.9 5.2 E Trail 65 C k 0.5 W 0.6 2.0 Cree NATIONAL 1.0 1.3 d oco WAYNESVILLE 86 n S 0.7 0.4 a T l 1.1 Stone Pile r o Waterrock Knob Twentymile e a N Gap Trail FOREST il 74 0.4 La Deep Creek L y ke A EOA a 1.2 vi Horse Trail K CH H 1.0 e E K w Juney Whank 19 c 72 67 Falls Trail 87 Lake a sh M Ela o nton 98 Goldmine re e 76 2.0 Drive Fontana 81 Trai 77 B Loop 66 Deep Creek S l Trail M Dam 8.7 441 SA AL FONTAN 12.3 B ue A Bl 73 BRYSON CITY idge C 78 R OLINA h Fontana Whittier e Marina L o YEL A ckas a LOW KE Tu eg TENNESSEECAR h ee FONTANA 28 R i P 129 CRE ve 74 a EK VILLAGE r r TH k M w G OU T

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Wesser L 0 1 10 Kilometers kywa itt ic S y le en Sc 0 1 10 Miles rohala he T C ROBBINSVILLE o w

e Cheoah Bald r T 1.0 h e e s T 28 N Tr t v n ew ld i ton Ba r i n Unpaved road Appalachian Trail Ranger station R n

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19 e r Horse and hiking Developed campground e

74 2.2 R Hiking trail Mountains-To-Sea iv Trail Auto-access horse camp e r la a h Observation tower ta an Horse and hiking Benton MacKaye N trail Trail Backcountry shelter (by reservation) I 4 Backcountry campsite (by reservation) NANTA Roads in park are closed Trail distances are M HALA 1.3 OUN MOUNT to commercial vehicles. shown in miles be- 2 Backcountry campsite (non-reservation) IRD TA 129 B INS AINS tween intersections OW SN 6 Boat-in only campsite (non-reservation) Great Smoky Mountains Trail Map To Atlanta 2011 1 2 3 4 5 6 7 8 9 10 11 12 LiDAR Tiles for Tennessee side of GSMNP

I LiDAR data for 540 sq. miles of the Tennessee portion of the GSMNP and the Foothills Parkway from 1,658 ﬂight miles were collected during February–April 2011 by the U. of Georgia and Photo Science, Inc.

I Four multiple discrete returns per pulse were collected at a rate of 20.2 Hz from a nominal ﬂying height of 1,981 m above ground level.

I Overlapping data were split into 724 non-overlapping 1,500 × 1,500 m tiles, which we obtained from the National Park Service.

724 LiDAR tiles projected onto a 1.5 m resolution digital elevation model (DEM) derived from the LiDAR point cloud. Computational Workﬂow and Data Processing

I We employed a process-parallel Start

approach to extract and analyze P0 P1 PN LiDAR point cloud data using get subset “las” file list get subset “las” file list get subset “las” file list

check geographical area python. covered by the “las” file

To estimate vegetation heights above open DEM file and read I elevation value for the region ground level, elevations from the

check ground elevation at S

LiDAR point and detrend a

m 1.5 m DEM were subtracted from m

apply noise and k

point cloud data. k

ground filtering o

P I The resulting points were grouped assign point to appropriate P

0 into 1 m vertical bins, up to 75 m, at 3-D bin 0

a horizontal resolution of 30 × 30 m. are there more points in the file? I Anomalous high points (aerosols,

birds) and low points (steep slopes, are there more files surface litter) were ﬁltered out. to process? I Corrections were made for low height write data to file write data to file write data to file

vegetation (shrubs and grasses) and perform k-means clustering

for many returns at the same generate maps and statistics

elevation. End LiDAR Point Cloud Example

a) 3-D LiDAR point cloud extent at b) Raw LiDAR point cloud (3,985 points), 30 × 30 m (black square) shown in a showing imprints of underlying topography. typical GSMNP cove forest.

c) LiDAR point cloud after topographic d) Vertical distribution of LiDAR point detrending and ﬁltering (3,936 points). density in a cove forest dominated by tall trees and a dense understory. Multivariate Spatiotemporal Clustering (MSTC)

(Hargrove and Hoﬀman, 2004) Map of 30 Vegetation Canopy Structure Classes Vertical proﬁle distributions were input to the cluster analysis, considering all tiles simultaneously.

This map shows the 30 most-different classes of vegetation canopy structure, randomly colored, as identified by k-means clustering for the Tennessee portion of the Great Smoky Mountains National Park. 30 Vegetation Canopy Structure Prototypes Automated Supervision for Unsupervised Classification

I Clustering is an unsupervised classiﬁcation technique, so ecoregions have no descriptive labels (e.g., chestnut oak forest or spruce-ﬁr forest).

I Label stealing allows us to perform automated “supervision” by “stealing” the best corresponding human-created descriptive labels to assign to ecoregions or vegetation structure classes.

I We employed a tool called Mapcurves to select the best vegetation class labels from vegetation type maps delineated by human experts for the GSMNP (Madden, 2014).

I We considered a library of vegetation type land cover maps at 30 m resolution, and chose the label with the highest goodness-of-ﬁt (GOF) score for every ecoregion polygon. Mapcurves: A Method for Comparing Categorical Maps

I Hargrove et al. (2006) developed a method for quantitatively comparing categorical maps that is

I independent of diﬀerences in resolution, I independent of the number of categories in maps, and I independent of the directionality of comparison.

Polygon from Map 2 B Goodness of Fit (GOF) is a unitless measure of (Reference Map) spatial overlap between map categories: C X C C Polygon from GOF = × Map 1 A B + C A + C polygons Being Compared I GOF provides “credit” for the area of overlap, but also “debit” for the area of non-overlap. I Mapcurves comparisons allow us to reclassify any map in terms of any other map (i.e., color Map 2 like Map 1). I A greyscale GOF map shows the degree of correspondence between two maps based on the highest GOF score. Overstory Vegetation Cover Map for GSMNP Translating Cluster Numbers to Vegetation Types

Dominant Vegetation Type Dominant Vegetation Type 0 Successional or modiﬁed vegetation 16 Chestnut Oak Forest 1 Chestnut Oak Forest 17 Montane Cove Forest 2 Chestnut Oak Forest 18 Montane Oak-Hickory Forest 3 Successional or Modiﬁed Vegetation 19 Chestnut Oak Forest 4 Chestnut Oak Forest 20 Montane Oak-Hickory Forest 5 Northern Hardwood/acid Hardwood Forest 21 Spruce-Fir Forest 6 Chestnut Oak Forest 22 Northern Hardwood/Acid Hardwood Forest 7 Yellow Pine Forest 23 Chestnut Oak Forest 8 Northern Hardwood/acid Hardwood Forest 24 Yellow Pine Forest 9 Chestnut Oak Forest 25 Montane Oak-Hickory Forest 10 Montane Cove Forest 26 Chestnut Oak Forest 11 Chestnut Oak Forest 27 Ericaceous Shrubs (Heath Bald Type) 12 Northern Hardwood/acid Hardwood Forest 28 Chestnut Oak Forest 13 Montane Oak-hickory Forest 29 Yellow Pine Forest 14 Northern Hardwood/acid Hardwood Forest 30 Chestnut Oak Forest 15 Yellow Pine Forest

I Clusters 3 and 11 are anomalous and represent very small areas. I Labels are used to subsequently combine clusters or reclass regions. I Labels and vertical distribution plots indicate vegetation cover, tree height and stand age, and suggest animal habitat occurrence. Vegetation Type Label Stealing

Masked 30 m vegetation type map for Masked k = 30 LiDAR classiﬁcation Tennessee GSMNP

Reclassed k = 30 LiDAR classiﬁcation Goodness of Fit (GOF) Validation: Cades Cove Valley

I Cades Cove, now a popular tourist destination, was a 19th century agrarian settlement.

I The area consists of woodlots interspersed within old ﬁelds that are mowed and burned to “Successional or modiﬁed vegetation” mimic the original settlement from (Madden, 2014). (Thiemann et al., 2009).

I Identiﬁed as “Successional or modiﬁed vegetation” in the vegetation type map, the area corresponds well with the low height (<1 m tall) vegetation class derived from LiDAR. Low height (<1 m tall) vegetation class derived from LiDAR. Validation: Great Smoky Mountain Institute at Tremont

I The Great Smoky Mountain Institute at Tremont (GSMIT) is surrounded by “Mountain Cove” and “Hemlock” forests with tall, dense canopies and low understory vegetation.

I We found strong spatial correspondence between the “Mountain Cove” forest and the vegetation structure classes 10 and 13, which represent the tallest vegetation in the Park.

I Individual tree records from “Citizen Science” phenology plots, located at blue circles, were studied for ground truthing.

“Montain Cove” forest from (Madden, Tall canopy vegetation classes 10 and 13 2014). derived from LiDAR. Summary and Conclusions

I We developed an approach, parallel software tools, and workﬂow for analyzing large volumes of LiDAR point cloud data in a scalable fashion. I Multivariate Spatiotemporal Clustering (MSTC) provides a valuable quantitative framework for stratifying vegetation canopy structure data derived from LiDAR point clouds. I We applied these tools to LiDAR data from the GSMNP to identify vegetation classes based on overstory/understory distributions. I We used a spatial overlay method to extract descriptors for the unsupervised clustering results and to validate results in Cades Cove and GSMIT. I Label Stealing oﬀers a useful method for interpreting and understanding vegetation canopy structure delineations. I These tools and the resulting maps will inform resource management and conservation planning by forest and wildlife managers, who were not previously able to use large, complex LiDAR data sets. Acknowledgments

This research was sponsored by the U. S. Department of Agriculture Forest Service Eastern Forest Environmental Threat Assessment Center (EFETAC) and by the Climate and Environmental Sciences Division (CESD) of the Biological and Environmental Research (BER) Program in the U. S. Department of Energy Oﬃce of Science. This research used resources of the Oak Ridge Leadership Computing Facility (OLCF) at Oak Ridge National Laboratory (ORNL), which is managed by UT-Battelle, LLC, for the U. S. Department of Energy under Contract No. DE-AC05-00OR22725. References

W. W. Hargrove and F. M. Hoﬀman. Potential of multivariate quantitative methods for delineation and visualization of ecoregions. Environ. Manage., 34(Supplement 1):S39–S60, Apr. 2004. doi:10.1007/s00267-003-1084-0. W. W. Hargrove, F. M. Hoﬀman, and P. F. Hessburg. Mapcurves: A quantitative method for comparing categorical maps. J. Geograph. Syst., 8(2):187–208, July 2006. doi:10.1007/s10109-006-0025-x. M. Madden. Overstory vegetation at Great Smoky Mountains National Park, Tennessee and North Carolina (reference code: 1047498), 2014. URL https://irma.nps.gov/App/Reference/Profile/1047498. J. A. Thiemann, C. R. Webster, M. A. Jenkins, P. M. Hurley, J. H. Rock, and P. S. White. Herbaceous-layer impoverishment in a post-agricultural southern Appalachian landscape. The American Midland Naturalist, 162:148–168, 2009. doi:10.1674/0003-0031-162.1.148.