Spatial Tools for Managing Hemlock Woolly Adelgid in the
Southern Appalachians (EM Project #03-DG-11083150-620)
Frank Koch1, Department of Forestry and Environmental Resources, North Carolina State University (NCSU)
Heather Cheshire, Department of Forestry and Environmental Resources, NCSU
Hugh Devine, Department of Parks, Recreation, and Tourism Management, NCSU
Introduction
Component Two: Predicting Areas at Risk of Imminent Infestation2
After causing substantial mortality in the northeastern U.S., the hemlock woolly adelgid (Adelges tsugae)
has spread into the southern Appalachian region. This non-native insect attacks all age classes of the
region’s two hemlock species, eastern (Tsuga canadensis) and Carolina hemlock (T. caroliniana), and
can kill trees within a few years. While the adelgid has no natural enemies in eastern North America,
biological control through introduced predator beetles shows some promise for combating the pest.
Unfortunately, counter-measures are impeded because both adelgid and hemlock distribution patterns in
the southern Appalachians have been poorly detailed. We developed a spatial management system to
better target control efforts in the region, with two components: (1) a protocol for mapping hemlock
stands and (2) a technique to map areas at risk of imminent infestation.
• Started with “first-year” hemlock woolly adelgid infestation locations from GSMNP (first detected
2002) and along the Blue Ridge Parkway in NC (first detected 2003)
Component One: Technique for Mapping Hemlock Stands
• For 84 infested and 67 non-infested sites, calculated values for suite of variables in GIS:
o Topographic: elevation, aspect, curvature, TRMI, landform index
o Environmental: geology, vegetation type, disturbance history
o Proximity: to roads, to trails, to streams
• Applied four techniques to predict infestation/non-infestation based on input sample: 1) (Quadratic)
discriminant analysis; 2) k-nearest neighbor analysis; 3) logistic regression; 4) decision tree
• Randomly partitioned data (2/3 training, 1/3 validation) into five different sets
• Variable selection: stepwise discriminant analysis, Akaike’s Information Criterion (AIC) for logistic
regression, validation-based tree pruning
• Calculated mean error rates (ER) for models derived from the five sets (Table 1)
• Also used resulting models to generate GIS maps of infestation risk in GSMNP
• For each map, determined the following (Table 2):
o Percentage of GSMNP total area predicted as infested
o Percentage of GSMNP hemlock stands predicted as infested
o Percentage of infestation points surveyed after first year (i.e., 2003-2005) captured by model
• Multispectral images from two areas of Great Smoky Mountains National Park (GSMNP)
1. East: winter 2001 Landsat 7 image (leaf-off), summer 2000 ASTER image (leaf-on)
2. West: winter 2003 ASTER image (leaf-off), summer 2000 ASTER image (leaf-on)
Great Smoky Mountains National Park
winter
winter
summer
summer
First-year infestation areas
along the Blue Ridge
Parkway
First-year infestation
locations in GSMNP
• ASTER images already 15-m resolution, Landsat multispectral fused with panchromatic to create
15-m image
• Images geometrically corrected, then topographically normalized via C-correction
• Evergreen/non-evergreen maps created from winter images via several iterations of unsupervised
classification (“cluster busting”)
• Used these maps to mask out non-evergreen areas from the summer images
• Collected ~14,500 stratified random sample points from hemlock and non-hemlock pixels of the
masked summer images. Hemlock presence (in two classes, “dominant/co-dominant” and
“secondary component/inclusion”) based on air-photo-derived vegetation map of GSMNP
• Sample points used to create training data set for decision tree classifier. Each sample point included
values from image data and environmental variables
• Expert classifier used to create output hemlock maps for the western and eastern study areas
Table 1. Mean Error Rates (ER)
Training Data ER
Validation Data ER
Discriminant Analysis Distance to road, distance to stream, distance to trail, elevation
0.16
0.14
k-Nearest Neighbor
Distance to road, distance to stream, distance to trail, elevation
Variables Included in Model
0.23
0.22
Logistic Regression
Distance to road, distance to stream, distance to trail, elevation, percent slope
0.20
0.19
Decision Tree
Distance to road, distance to stream, distance to trail
0.18
0.24
Discriminant Analysis
k-Nearest Neighbor
Logistic Regression
Decision Tree
GIS Variables:
•
+
Image Data
Elevation
•
Slope
•
Aspect
•
Curvature
•
Topographic
Relative Moisture
Index (TRMI)
•
Landform Index
•
Distance to closest
stream
Table 2. Efficiency and shortterm relevance measures
Elevat ion
Decision Tree
Classifier
•
•
27 terminal nodes
after automated
pruning
Elevation, image
values, TRMI, stream
distance most
important variables
(in terms of # of
nodes, depth in tree)
< 976.6
>= 976.6
Distance
Elevat ion
< 49.4345
>= 49.4345
< 1489.9
B3/B1 Rat io
T RMI
B4/B9 Rat io
< 0.554116
>= 0.554116
< 27.5
Elevat ion
< 412.25
>= 412.25
Elevat ion
< 801.9
P ct . Slope
< 49.0247
>= 801.9
>= 27.5
< 0.813516
Dist ance
Elevat ion
>= 94.6041
< 94.6041
B4/B9 Rat io
>= 49.0247
< 0.79721
< 1236.45
< 893.3
>= 1489.9
>= 0.813516
< 0.820715
>= 0.820715
T RMI
>= 893.3
< 12.5
T RMI
>= 12.5
< 21.5
Elevat ion
< 594.8
B1/B2 Rat io
< 0.577497
Hemlock Dominant /
Co-Dominant
>= 0.577497
>= 21.5
B4/B9 Ratio
>= 594.8
< 0.836017
B4/B5 Rat io
< 0.684353
>= 0.836017
B3/B1 Rat io
>= 0.684353
< 0.52983
18.5
% Surveyed Points
93.2
k-Nearest Neighbor
29.6
23.9
87.5
Logistic Regression
22.8
20.1
85.4
Decision Tree
30.7
34.6
93.5
• All techniques performed well; discriminant analysis was most accurate, but logistic regression best
balanced accuracy, efficiency, and map usability (distinct risk zones vs. diffuse pattern)
• All included >85% of subsequent (2003-2005) infestation points in areas mapped as high risk
• Results suggest that roads, major trails, and streams provide connectivity enabling long-distance
dispersal of hemlock woolly adelgid, perhaps by humans or birds
Elevat ion
>= 23.5896
< 1096.55
>= 1096.55
Curvature
B1/B2 Ratio
< 0.536382
% Hemlock Area
19.1
>= 0.52983
P ct . Slope
< 23.5896
% Total Area
B4/B9 Rat io
>= 1236.45
Elevat ion
>= 0.79721
Discriminant Analysis
>= 0.536382
< 0.25
>= 0.25
Hemlock Secondary
Component / Inclusion
Discussion
Elevation
Non-Hemlock
Evergreen
Output
Map
< 1414.05
>= 1414.05
Output
Map
• Three-class output map (hemlock, non-hemlock evergreen, non-evergreen) for eastern study area
assessed for accuracy based on 206 reference points classified through field survey, aerial photos
• Limited assessment for the western area map based on 61 hemlock survey points
• Judged map accuracy by occurrence of reference point’s class within a 22.5-m radius window
• Overall accuracy of eastern area map greater than 90%, with all producer’s and user’s accuracies
greater than 81%
• Accurately captured greater than 75% of hemlock survey points in western area
• Lower accuracy in western area perhaps due to limited sample, limitations of vegetation map used to
construct decision tree, or because elevation and other variables less distinguishing in gentler terrain
of this area
• Both components seem general enough for application throughout southern Appalachian region:
1. Because it is based on readily available or calculable GIS layers and inexpensive satellite
imagery, decision tree classifier represents a straightforward way to map hemlock distribution
2. Any of the developed prediction models can be used to generate risk maps for areas not yet or
recently infested by hemlock woolly adelgid
• Together, these tools allow managers to identify and prioritize areas for HWA control measures
• Both components may be refined by further application, validation
• Acknowledgements: Many thanks to Kris Johnson and Tom Remaley from GSMNP, as well as
Chris Ulrey from the Blue Ridge Parkway, for providing key hemlock and HWA data
• NC State University is an equal opportunity and affirmative action employer
1 Current
address: USDA-FS Forest Health Monitoring, 3041 Cornwallis Road, Research Triangle
Park, NC 27709; Phone: 919-549-4006; Fax: 919-549-4047; E-mail: fkoch@fs.fed.us
2 For
further details, see article: Koch, FH; Cheshire, HM; Devine, HA. 2006. Landscape-scale
prediction of hemlock woolly adelgid, Adelges tsugae (Homoptera: Adelgidae), infestation in the
southern Appalachian Mountains. Environmental Entomology 35(5): 1313-1323.