Where's woolly? An integrative use of remote sensing to improve predictions of the spatial distribution of an invasive forest pest the Hemlock Woolly Adelgid.

Abstract Non-native pests and pathogens present serious challenges to the management of forested ecosystems around the world. Early detection of pest and pathogen invasions may allow timely control and prevention methods to be implemented. Species distribution models (SDMs) and remote sensing (RS) methods have both been used effectively to determine locations of pest and pathogen damage. However, previous work integrating these two methods has rarely used RS metrics that have biological meaning. We use RS difference indices that show changes in forest cover from defoliation in order to map Hemlock Woolly Adelgid (HWA), Adelges tsugae, locations using MaxEnt in the Delaware Water Gap National Recreation Area (DWGNRA). Brightness, greenness, wetness, and Normalized Difference Vegetation Index (NDVI) were calculated from Landsat Thematic Mapper (TM) images for December 1982 and 2010. A difference for each index was created by subtracting the 1982 value from the 2010 value. We compared two models, one using difference indices and the other using 2010 indices along with other ancillary data layers, to determine if the more complicated but more biologically relevant difference indices were necessary for improved model performance. Variables with low importance were removed from both models, leaving NDVI, Wetness, soil, and elevation in the two final models. The difference model had an improvement in accuracy of three percent, across a number of threshold values. Despite this small difference in accuracy, however, the infected area predicted by the difference model (5.1% of total area) was approximately ½ of that predicted by the single year model (9.6% of total area). These results suggest that using remote sensing difference indices improves model accuracy slightly in terms of errors of omission, but also decreases predicted area of forest infestation by about 50%, suggesting that errors of commission would be substantially reduced using the difference approach. This method can provide forest managers more accurate information on the best locations to sample and treat.

[1]  Steven E. Franklin,et al.  Forest Structure Classification in the North Columbia Mountains Using the Landsat TM Tasseled Cap Wetness Component , 2001 .

[2]  D. Leopold,et al.  Effects of Tsuga canadensis mortality on soil water chemistry and understory vegetation: possible consequences of an invasive insect herbivore , 2003 .

[3]  Andrew M. Liebhold,et al.  Historical Accumulation of Nonindigenous Forest Pests in the Continental United States , 2010 .

[4]  Hinrich Schütze,et al.  Book Reviews: Foundations of Statistical Natural Language Processing , 1999, CL.

[5]  S. Fei,et al.  Mapping eastern hemlock: Comparing classification techniques to evaluate susceptibility of a fragmented and valued resource to an exotic invader, the hemlock woolly adelgid , 2012 .

[6]  S. Bentz,et al.  Evaluation of Hemlock (Tsuga) Species and Hybrids for Resistance to Adelges tsugae (Hemiptera: Adelgidae) Using Artificial Infestation , 2009, Journal of economic entomology.

[7]  M. McClure,et al.  Reshaping the Ecology of Invading Populations of Hemlock Woolly Adelgid, Adelges tsugae (Homoptera: Adelgidae), in Eastern North America , 1999, Biological Invasions.

[8]  John A. Young,et al.  Influence of eastern hemlock (Tsuga canadensis L.) on fish community structure and function in headwater streams of the Delaware River basin , 2003 .

[9]  Q. Guo,et al.  A comparison of standard and hybrid classifier methods for mapping hardwood mortality in areas affected by sudden oak death , 2004 .

[10]  Robert P. Anderson,et al.  Maximum entropy modeling of species geographic distributions , 2006 .

[11]  David R. Foster,et al.  Multi-year ecosystem response to hemlock woolly adelgid infestation in southern New England forests , 2008 .

[12]  Pamela L. Nagler,et al.  Remote monitoring of tamarisk defoliation and evapotranspiration following saltcedar leaf beetle attack , 2009 .

[13]  T. Gregoire,et al.  A geographically variable model of hemlock woolly adelgid spread , 2007, Biological Invasions.

[14]  George L. Farnsworth,et al.  Factors Affecting Nesting Success of Wood Thrushes in Great Smoky Mountains National Park , 1999 .

[15]  S. Salom,et al.  Biology of Adelges tsugae and its potential for spread in the northeastern United States. , 1996 .

[16]  J. S. Ward,et al.  Eastern Hemlock Forests: Guidelines to Minimize the Impacts of Hemlock Woolly Adelgid , 2004 .

[17]  J. Aber,et al.  Hemlock woolly adelgid impacts on community structure and N cycling rates in eastern hemlock forests , 1999 .

[18]  D. Orwig,et al.  Hemlock Woolly Adelgid , 2019 .

[19]  R. Kauth,et al.  The tasselled cap - A graphic description of the spectral-temporal development of agricultural crops as seen by Landsat , 1976 .

[20]  K. Shields,et al.  Overview of hemlock health , 2000 .

[21]  David M. Rizzo,et al.  Sudden Oak Death , 2003 .

[22]  P. J. Boer,et al.  Spreading of risk and stabilization of animal numbers , 1968 .

[23]  Aaron M. Ellison,et al.  Foundation species loss affects vegetation structure more than ecosystem function in a northeastern USA forest , 2013, PeerJ.

[24]  C. Woodcock,et al.  Classification and Change Detection Using Landsat TM Data: When and How to Correct Atmospheric Effects? , 2001 .

[25]  J. Vose,et al.  Forest dynamics following eastern hemlock mortality in the southern Appalachians , 2012 .

[26]  W. Cohen,et al.  Predicting temperate conifer forest successional stage distributions with multitemporal Landsat Thematic Mapper imagery , 2007 .

[27]  David R. Foster,et al.  FOREST RESPONSE TO THE INTRODUCED HEMLOCK WOOLLY ADELGID IN SOUTHERN NEW ENGLAND, USA , 1998 .

[28]  Stella W. Todd,et al.  Responses of spectral indices to variations in vegetation cover and soil background , 1998 .

[29]  C. Woodcock,et al.  An assessment of several linear change detection techniques for mapping forest mortality using multitemporal landsat TM data , 1996 .

[30]  Anna F. Cord,et al.  Remote sensing data can improve predictions of species richness by stacked species distribution models: a case study for Mexican pines , 2014 .

[31]  David Lonsdale,et al.  Tree diseases and landscape processes: the challenge of landscape pathology. , 2004, Trends in ecology & evolution.

[32]  J. Webster,et al.  Loss of foundation species: consequences for the structure and dynamics of forested ecosystems , 2005 .

[33]  M. Fiorella,et al.  Determining successional stage of temperate coniferous forests with Landsat satellite data , 1993 .

[34]  R. Cobb,et al.  Decomposition of green foliage in eastern hemlock forests of southern New England impacted by hemlock woolly adelgid infestations , 2006 .

[35]  David R. Foster,et al.  Landscape patterns of hemlock decline in New England due to the introduced hemlock woolly adelgid , 2002 .

[36]  Michael A. Wulder,et al.  Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage , 2003 .

[37]  Steven E. Franklin,et al.  Interpretation and Classification of Partially Harvested Forest Stands in the Fundy Model Forest Using Multitemporal Landsat TM Digital Data , 2000 .

[38]  S. Sader,et al.  Detection of forest harvest type using multiple dates of Landsat TM imagery , 2002 .

[39]  G. Ervin,et al.  Native ecotypic variation and the role of host identity in the spread of an invasive herbivore, Cactoblastis cactorum. , 2012, Ecology.

[40]  A. Caccone,et al.  Mitochondrial DNA from Hemlock Woolly Adelgid (Hemiptera: Adelgidae) Suggests Cryptic Speciation and Pinpoints the Source of the Introduction to Eastern North America , 2006 .

[41]  R. Lathrop,et al.  Discriminating Tsuga canadensis Hemlock Forest Defoliation Using Remotely Sensed Change Detection. , 2002, Journal of nematology.

[42]  Philip A. Townsend,et al.  Use of landsat ETM and topographic data to characterize evergreen understory communities in appalachian deciduous forests , 2007 .

[43]  Kurt W. Gottschalk,et al.  Mapping host-species abundance of three major exotic forest pests , 2005 .

[44]  Pedro J. Leitão,et al.  Modelling species distributions with remote sensing data: bridging disciplinary perspectives , 2013 .

[45]  J. A. Schell,et al.  Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation. [Great Plains Corridor] , 1973 .

[46]  R. Rogers.  HEMLOCK STANDS FROM WISCONSIN TO NOVA SCOTIA: TRANSITIONS IN UNDERSTORY COMPOSITION ALONG A FLORISTIC GRADIENT' , 1980 .

[47]  R. Meentemeyer,et al.  Common Factors Drive Disease and Coarse Woody Debris Dynamics in Forests Impacted by Sudden Oak Death , 2012, Ecosystems.

[48]  S. Fei,et al.  Spatial analysis facilitates invasive species risk assessment , 2014 .

[49]  Robert G. Haight,et al.  Economic Impacts of Non-Native Forest Insects in the Continental United States , 2011, PloS one.

[50]  Conghe Song,et al.  Monitoring forest succession with multitemporal Landsat images: factors of uncertainty , 2003, IEEE Trans. Geosci. Remote. Sens..

[51]  Maggi Kelly,et al.  Landscape Dynamics of the Spread of Sudden Oak Death , 2002 .

[52]  R. Meentemeyer,et al.  Predicting potential and actual distribution of sudden oak death in Oregon: prioritizing landscape contexts for early detection and eradication of disease outbreaks , 2010 .

[53]  R. Cobb.  Species shift drives decomposition rates following invasion by hemlock woolly adelgid , 2010 .

[54]  Miroslav Dudík,et al.  A maximum entropy approach to species distribution modeling , 2004, ICML.

[55]  Kathleen S. Shields,et al.  An exotic pest threat to eastern hemlock: an initiative for management of hemlock woolly adelgid , 2003 .

[56]  Mark Williamson,et al.  Directing Research to Reduce the Impacts of Nonindigenous Species , 2002 .