iForest - Biogeosciences and Forestry


Development of monitoring methods for Hemlock Woolly Adelgid induced tree mortality within a Southern Appalachian landscape with inhibited access

Tuula Kantola (1)   , Päivi Lyytikäinen-Saarenmaa (2), Robert N Coulson (1), Markus Holopainen (2), Maria D Tchakerian (1), Douglas A Streett (3)

iForest - Biogeosciences and Forestry, Volume 9, Issue 2, Pages 178-186 (2016)
doi: https://doi.org/10.3832/ifor1712-008
Published: Jan 02, 2016 - Copyright © 2016 SISEF

Research Articles

Hemlock woolly adelgid (Adelges tsugae Annand, HWA) is an introduced invasive forest pest in eastern North America. Herbivory by this insect results in mortality to eastern hemlock (Tsuga canadensis L. Carr.) and Carolina hemlock (Tsuga caroliniana Engelm.). These species occur in landscapes where extreme topographic variation is common. The vegetation communities within these landscapes feature high diversity of tree species, including several other conifer species. Traditional forest inventory procedures and insect pest detection methods within these limited-access landscapes are impractical. However, further information is needed to evaluate the impacts of HWA-induced hemlock mortality. Accordingly, our goal was to develop a semi-automatic method for mapping patches of coniferous tree species that include the living hemlock component and tree mortality by the HWA using aerial images and LiDAR (light detection and ranging) to increase our understanding of the severity and pattern of hemlock decline. The study was conducted in the Linville River Gorge in the Southern Appalachians of western North Carolina, USA. The mapping task included a two-phase approach: decision-tree and support vector machine classifications. We found that about 2% of the forest canopy surface was covered by dead trees and 43% by coniferous tree species. A large portion of the forest canopy surface (over 55%) was covered by deciduous tree species. The resulting maps provide a means for evaluating the impact of HWA herbivory, since this insect was the only significant coniferous mortality agent present within the study site.


Decision-tree Classification, Eastern Hemlock, Hemlock Woolly Adelgid, Remote Sensing, Support Vector Machine

Authors’ address

Tuula Kantola
Robert N Coulson
Maria D Tchakerian
Knowledge Engineering Laboratory, Department of Entomology, Texas A&M University, College Station, TX 77843-2475 (USA)
Päivi Lyytikäinen-Saarenmaa
Markus Holopainen
Department of Forest Sciences, University of Helsinki, P.O. Box 27, FI-00014 Helsinki (Finland)
Douglas A Streett
USDA Forest Service, Southern Research Station, Alexandria Forestry Center, 2500 Shreveport Highway, Pineville, LA 71360 (USA)

Corresponding author



Kantola T, Lyytikäinen-Saarenmaa P, Coulson RN, Holopainen M, Tchakerian MD, Streett DA (2016). Development of monitoring methods for Hemlock Woolly Adelgid induced tree mortality within a Southern Appalachian landscape with inhibited access. iForest 9: 178-186. - doi: 10.3832/ifor1712-008

Academic Editor

Massimo Faccoli

Paper history

Received: May 14, 2015
Accepted: Nov 20, 2015

First online: Jan 02, 2016
Publication Date: Apr 26, 2016
Publication Time: 1.43 months

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Birt AG, Zeng Y, Tchakerian MD, Coulson RN, Lafon DM, Cairns DM, Waldron J, Xi W, Chen S-H, Street DA (2014)
Evaluating Southern Appalachian forest dynamics without eastern hemlock: consequence of herbivory by the hemlock woolly adelgid. Open Journal of Forestry 4 (2): 91-98.
CrossRef | Gscholar
Bonneau LR, Shields KS, Civco DL (1999)
Using satellite images to classify and analyze the health of hemlock forests infested by the hemlock woolly adelgid. Biological Invasions 1 (2-3): 255-267.
CrossRef | Gscholar
Boser BE, Guyon IM, Vapnik VN (1992)
A training algorithm for optimal margin classifiers. In: Proceedings of the “5th annual workshop on computational learning theory” (Haussler D ed). Pittsburgh (PA, USA) 27-29 July 1992. ACM Press, Pittsburgh, PA, USA, pp. 144-152.
CrossRef | Gscholar
Clark JT, Fei S, Liang L, Rieske LK (2012)
Mapping eastern hemlock: comparing classification techniques to evaluate susceptibility of a fragmented and valued resource to an exotic invader, the hemlock woolly adelgid. Forest Ecology and Management 266: 216-222.
CrossRef | Gscholar
Cobb RC (2010)
Species shift drives decomposition rates following invasion by hemlock woolly adelgid. Oikos 119 (8): 1291-1298.
CrossRef | Gscholar
Coggins S, Coops NC, Wulder MA (2008)
Initialization of an insect infestation spread model using tree structure and spatial characteristics derived from high spatial resolution digital aerial imagery. Canadian Journal of Remote Sensing 34 (6): 485-502.
CrossRef | Gscholar
Cohen JA (1960)
Coefficient of agreement for nominal scales. Educational and Psychological Measurement 20: 37-46.
CrossRef | Gscholar
Cortes C, Vapnik V (1995)
Support-vector networks. Machine Learning 20 (3): 273-297.
Coulson RN, Tchakerian MD (2010)
Basic landscape ecology. KEL Partners Incorporated, Boston, USA, pp. 300.
Online | Gscholar
Daley MJ, Phillips NG, Pettijohn C, Hadley JL (2007)
Water use by eastern hemlock (Tsuga canadensis) and black birch (Betula lenta): implications of effects of the hemlock woolly adelgid. Canadian Journal of Forest Research 37 (10): 2031-2040.
CrossRef | Gscholar
Elliott KJ, Vose JM (2011)
The contribution of the Coweeta Hydrologic Laboratory to developing an understanding of long-term (1934-2008) changes in managed and unmanaged forests. Forest Ecology and Management 261: 900-910.
CrossRef | Gscholar
Elliot KJ, Knoepp JD, Vose JM, Jackson WA (2013)
Interacting effects of wildfire severity and liming on nutrient cycling in a Southern Appalachian wilderness area. Plant and Soil 366 (1-2): 165-183.
CrossRef | Gscholar
Foody GM (2002)
Status of land cover classification accuracy assessment. Remote Sensing of Environment 80 (1): 185-201.
CrossRef | Gscholar
Ford CR, Elliott KJ, Clinton BD, Kloeppel BD, Vose JM (2012)
Forest dynamics following eastern hemlock mortality in the Southern Appalachians. Oikos 121 (4): 523-536.
CrossRef | Gscholar
Ford CR, Vose JM (2007)
Tsuga canadensis (L.) Carr. mortality will impact hydrologic processes in Southern Appalachian forest ecosystems. Ecological Applications 17 (4): 1156-1167.
CrossRef | Gscholar
Friedl MA, Brodley CE (1997)
Decision tree classification of land cover from remotely sensed data. Remote Sensing of Environment 61 (3): 399-409.
CrossRef | Gscholar
Gualtieri JA, Cromp RF (1999)
Support vector machines for hyperspectral remote sensing classification. In: Proceedings of the “27th AIPR Workshop: Advances in Computer Assisted Recognition” (Merisko RJ ed). Washington (DC, USA) 27 Oct 1999. SPIE, Washington, DC, USA, pp. 221-232.
CrossRef | Gscholar
Havill NP, Montgomery ME, Yu G, Shiyake S, Caccone A (2006)
Mitochondrial DNA from hemlock woolly adelgid (Hemiptera: Adelgidae) suggests cryptic speciation and pinpoints the source of the introduction to eastern North America. Annals of the Entomological Society of America 99 (2): 195-203.
CrossRef | Gscholar
Heikkinen V, Tokola T, Parkkinen J, Korpela I, Jääskelainen T (2010)
Simulated multispectral imagery for tree species classification using support vector machines. IEEE Transactions on Geoscience and Remote Sensing 48 (3): 1355-1364.
CrossRef | Gscholar
Hodkinson ID (2005)
Terrestrial insects along elevation gradients: species and community responses to altitude. Biological Reviews 80 (3): 489-513.
CrossRef | Gscholar
Holmgren J, Persson A (2004)
Identifying species of individual trees using airborne laser scanner. Remote Sensing of Environment 90 (4): 415-423.
CrossRef | Gscholar
Holopainen M, Vastaranta M, Liang X, Hyyppä J, Jaakkola A, Kankare V (2014)
Estimation of forest stock and yield using Lidar data. In: “Remote Sensing of Natural Resources” (Wang G, Weng Q eds). CRC Press, Taylor and Francis Group, Boca Raton, FL, USA, pp. 259-290.
Online | Gscholar
Huang H, Gong P, Clinton N, Hui F (2008)
Reduction of atmospheric and topographic effect on Landsat TM data for forest classification. International Journal of Remote Sensing 29 (19): 5623-5642.
CrossRef | Gscholar
Jetton RM, Dvorak WS, Whittier WA (2008)
Ecological and genetic factors that define the natural distribution of Carolina hemlock in the southeastern United States and their role in ex situ conservation. Forest Ecology and Management 255: 3212-3221.
CrossRef | Gscholar
Jordan JS, Sharp WM (1967)
Seeding and planting hemlock for ruffed grouse cover. Research paper NE-83, USDA Forest Service, Upper Darby, PA, USA, pp. 17.
Online | Gscholar
Kantola T, Vastaranta M, Yu X, Lyytikäinen-Saarenmaa P, Holopainen M, Talvitie M, Kaasalainen S, Solberg S, Hyyppä J (2010)
Classification of defoliated trees using tree-level airborne laser scanning data combined with aerial images. Remote Sensing 2: 2665-2679.
CrossRef | Gscholar
Kantola T, Vastaranta M, Lyytikäinen-Saarenmaa P, Holopainen M, Kankare V, Talvitie M, Hyyppä J (2013)
Classification of needle loss of individual Scots pine trees by means of airborne laser scanning. Forests 4 (2): 386-403.
CrossRef | Gscholar
Kantola T, Lyytikäinen-Saarenmaa P, Coulson RN, Strauch S, Tchakerian MD, Holopainen M, Saareenmaa H, Streett DA (2014)
Spatial distribution of hemlock woolly adelgid induced hemlock mortality in the Southern Appalachians. Open Journal of Forestry 4 (05): 492-506.
CrossRef | Gscholar
Kavzoglu T, Colkesen I (2009)
A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation 11 (5): 352-359.
CrossRef | Gscholar
Knebel L, Wentworth TR (2007)
Influence of fire and southern pine beetle on pine-dominated forests in the Linville Gorge Wilderness, North Carolina. Castanea 72 (4): 214-225.
CrossRef | Gscholar
Koch FH, Cheshire HM, Devine HA (2005)
Mapping hemlocks via tree-based classification of satellite imagery and environmental data. Forest Health Technology Enterprise Team 2005-01, USDA Forest Service, Asheville, NC, USA, pp. 109-115. -
Online | Gscholar
Koch FH, Chesire 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.
CrossRef | Gscholar
Kong N, Fei S, Rieske-Kinney L, Obrichy J (2008)
Mapping hemlock forest in Harlan County, Kentucky. In: Proceedings of the “6th Southern Forestry and Natural Resources GIS Conference” (Bettinger P, Merry P, Fei K, Drake S, Nibbelink J, Hepinstall N, Athens J eds). Orlando (FL, USA) 24-26 Mar 2008. Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA, USA, pp. 107-117.
Online | Gscholar
Krapfl KJ, Holzmueller EJ, Jenkins MA (2011)
Early impacts of hemlock woolly adelgid in Tsuga canadensis forest communities of the southern Appalachian Mountains. Journal of the Torrey Botanical Society 138: 93-106.
CrossRef | Gscholar
Kraus K, Pfeifer N (1998)
Determination of terrain models in wooded areas with airborne laser scanner data. ISPRS Journal of Photogrammetry and Remote Sensing 53 (4): 193-203.
CrossRef | Gscholar
Lapin B (1994)
The impact of hemlock woolly adelgid on resources in the Lower Connecticut River Valley. Report for the NE Center for Forest Health Research, USDA Forest Service, Hamden, CT, USA, pp. 43.
Lardeux C, Frison PL, Tison C, Souyris JC, Stoll B, Fruneau B, Rudant JP (2009)
Support vector machine for multifrequency SAR polarimetric data classification. IEEE Transactions on Geoscience and Remote Sensing 47 (12): 4143-4152.
CrossRef | Gscholar
Lausch A, Heurich M, Gordalla D, Dobner HJ, Gwillym-Margianto S, Salbach C (2013)
Forecasting potential bark beetle outbreaks based on spruce forest vitality using hyperspectral remote-sensing techniques at different scales. Forest Ecology and Management 308: 76-89.
CrossRef | Gscholar
Leckie DG, Cloney E, Joyce SP (2005)
Automated detection and mapping of crown discolouration caused by jack pine budworm with 2.5 m resolution multispectral imagery. International Journal of Applied Earth Observation and Geoinformation 7 (1): 61-77.
CrossRef | Gscholar
Mahesh P, Mather PM (2003)
An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment 86 (4): 554-565.
CrossRef | Gscholar
Mantero P, Moser G, Serpico SB (2005)
Partially supervised classification of remote sensing images through SVM-based probability density estimation. IEEE Transactions on Geoscience and Remote Sensing 43 (3): 559-570.
CrossRef | Gscholar
McGaughey RJ (2009)
FUSION/LDV: software for LiDAR data analysis and visualization. USDA Forest Service, Pacific Northwest Research Station, Seattle, WA, USA, pp. 123.
Means JE, Acker SA, Fitt BJ, Renslow M, Emerson L, Hendrix CJ (2000)
Predicting forest stand characteristics with airborne scanning LiDAR. Photogrammetric Engineering and Remote Sensing 66 (11): 1367-1372.
Online | Gscholar
Meddens AJ, Hicke JA, Vierling LA (2011)
Evaluating the potential of multispectral imagery to map multiple stages of tree mortality. Remote Sensing of Environment 115 (7): 1632-1642.
CrossRef | Gscholar
Mountrakis G, Im J, Ogole C (2011)
Support vector machines in remote sensing: a review. ISPRS Journal of Photogrammetry and Remote Sensing 66 (3): 247-259.
CrossRef | Gscholar
Myneni RB, Hall FG, Sellers PJ, Marshak AL (1995)
The interpretation of spectral vegetation indexes. IEEE Transactions on Geoscience and Remote Sensing 33 (2): 481-486.
CrossRef | Gscholar
Narayanaraj G, Bolstad PV, Elliott KJ, Vose JM (2010)
Terrain and landform influence on Tsuga canadensis (L.) Carrière (eastern hemlock) distribution in the southern Appalachian Mountains. Castanea 75: 1-18.
CrossRef | Gscholar
Newell CL, Peet RK (1998)
Vegetation of Linville Gorge Wilderness, North Carolina. Castanea 63 (3): 275-322.
Online | Gscholar
Nuckolls AE, Wurzenburger N, Ford CR, Hendrick RL, Vose JM, Kloeppel BD (2009)
Hemlock declines rapidly with hemlock woolly adelgid infestation: impacts on the carbon cycle of Southern Appalachian forests. Ecosystems 12: 179-190.
CrossRef | Gscholar
Orwig DA, Foster DR (1998)
Forest response to the introduced hemlock woolly adelgid in southern New England, USA. Journal of the Torrey Botanical Society 125: 60-73.
CrossRef | Gscholar
Orwig DA, Foster DR, Mausel DL (2002)
Landscape patterns of hemlock decline in New England due to the introduced hemlock woolly adelgid. Journal of Biogeography 29 (10- 11): 1475-1487.
CrossRef | Gscholar
Pal M, Mather PM (2005)
Support vector machines for classification in remote sensing. International Journal of Remote Sensing 26 (5): 1007-1011.
Online | Gscholar
Peet RK, Wentworth TR, White PS (1998)
A flexible, multipurpose method for recording vegetation composition and structure. Castanea 63 (3): 262-274.
Online | Gscholar
Pettorelli N, Vik JO, Mysterud A, Gaillard JM, Tucker CJ, Stenseth NC (2005)
Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology and Evolution 20 (9): 503-510.
CrossRef | Gscholar
Pontius J, Hallett R, Martin M (2005)
Using AVIRIS to assess hemlock abundance and early decline in the Catskills, New York. Remote Sensing of Environment 97 (2): 163-173.
CrossRef | Gscholar
Quimby JW (1995)
Value and importance of hemlock ecosystems in the eastern United States. In: Proceedings of the “First Hemlock Woolly Adelgid Review” (Salom S, Tigner T, Reardon RC eds). Forest Health Technology Enterprise Team 96-10, USDA Forest Service, Morgantown, WV, USA, pp. 1-8.
Online | Gscholar
Royle DD, Lathrop RG (1997)
Monitoring hemlock forest health in New Jersey using Landsat TM data and change detection techniques. Forest Science 43 (3): 327-335.
Online | Gscholar
Royle DD, Lathrop RG (2002)
Discriminating Tsuga canadensis hemlock forest defoliation using remotely sensed change detection. Journal of Nematology 34 (3): 213-221.
Online | Gscholar
Schafale MP, Weakly AS (1990)
Classification of the natural communities of North Carolina: third approximation. NC Natural Heritage program, Division of Parks and Recreation, Raleigh, NC, USA, pp. 321.
Online | Gscholar
Simon SA, Collins TK, Kauffman GL, McNab WH, Ulrey CJ (2005)
Ecological zones in the Southern Appalachians: first approximation. Research paper SRS-41, Southern Research Station, USDA Forest Service, Asheville, NC, USA, pp. 41.
Online | Gscholar
Spaulding HL, Rieske LK (2010)
The aftermath of an invasion: structure and composition of Central Appalachian hemlock forests following establishment of hemlock woolly adelgid, Adelges tsugae. Biological Invasions 12: 3135-3143.
CrossRef | Gscholar
Stadler B, Müller T, Orwig D (2006)
The ecology of energy and nutrient fluxes in hemlock forests invaded by hemlock woolly adelgid. Ecology 87 (7): 1792-1804.
CrossRef | Gscholar
Thomlinson JR, Bolstad PV, Cohen WB (1999)
Coordinating methodologies for scaling land cover classifications from site-specific to global: steps toward validating global map products. Remote Sensing of Environment 70 (1): 16-28.
CrossRef | Gscholar
Tooke TR, Coops NC, Goodwin NR, Voogt JA (2009)
Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications. Remote Sensing of Environment 113 (2): 398-407.
CrossRef | Gscholar
Trotter RT, Morin RS, Oswalt SN, Liebhold A (2013)
Changes in the regional abundance of hemlock associated with the invasion of hemlock woolly adelgid (Adelges tsugae Annand). Biological invasions 15 (12): 2667-2679.
CrossRef | Gscholar
USGS (2012)
EarthExplorer. Web site.
Online | Gscholar
Vapnik V (1995)
The nature of statistical learning theory. Springer-Verlag, New York, NY, USA, pp. 189.
Vastaranta M, Kantola T, Lyytikäinen-Saarenmaa P, Holopainen M, Kankare V, Wulder MA, Hyyppä J, Hyyppä H (2013)
Area-based mapping of defoliation of Scots pine stands using airborne scanning LiDAR. Remote Sensing 5 (3): 1220-1234.
CrossRef | Gscholar
Ward JS, Montgomery ME, Cheah CJ, Onken BP, Cowles RS (2004)
Eastern hemlock forests: guidelines to minimize the impacts of hemlock woolly adelgid. Northeastern Area State and Private Forestry, USDA Forest Service, Morgantown, WV, USA, pp. 1-27.
Webster JR, Morkeski K, Wojculewski CA, Niederlehner BR, Benfield EF, Elliott KJ (2012)
Effects of hemlock mortality on streams in the southern Appalachian Mountains. The American Midland Naturalist 168 (1): 112-131.
CrossRef | Gscholar
Wimberly MC, Reilly MJ (2007)
Assessment of fire severity and species diversity in the Southern Appalachians using Landsat TM and ETM+ imagery. Remote Sensing of Environment 108 (2): 189-197.
CrossRef | Gscholar
Wulder MA, Dymond CC, White JC, Leckie DG, Carroll AL (2006)
Surveying mountain pine beetle damage of forests: a review of remote sensing opportunities. Forest Ecology and Management 221 (1): 27-41.
CrossRef | Gscholar
Wulder MA, White JC, Coggins S, Ortlepp SM, Coops NC, Heath J, Mora B (2012)
Digital high spatial resolution aerial imagery to support forest health monitoring: the mountain pine beetle context. Journal of Applied Remote Sensing 6 (1): 062527-1.
CrossRef | Gscholar
Young RF, Shields KS, Berlyn GP (1995)
Hemlock woolly adelgid (Homoptera: Adelgidae): stylet bundle insertion and feeding sites. Annals of the Entomological Society of America 88 (6): 827-835.
CrossRef | Gscholar
Young JA, Morton DD (2002)
Modeling landscape-level impacts of HWA in Shenandoah National Park. In: Proceedings of the “Hemlock Woolly Adelgid in the Eastern United States Symposium” (Onken B, Reardon R, Lashomb J eds). East Brunswick (NJ, USA) 5-7 Feb 2002. Agricultural Experiment Station, Rutgers University, New Brunswick, NJ, USA, pp. 73-85. -
Online | Gscholar

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