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Detecting an Invasive Shrub in Deciduous Forest Understories Using Remote Sensing

Published online by Cambridge University Press:  20 January 2017

Bryan N. Wilfong ,
David L. Gorchov  and
Mary C. Henry
Bryan N. Wilfong
Affiliation:
Institute of Environmental Sciences and Department of Botany, Miami University, Oxford, OH 45056
David L. Gorchov*
Affiliation:
Department of Botany, Miami University, Oxford, OH 45056
Mary C. Henry
Affiliation:
Department of Geography, Miami University, Oxford, OH 45056
*
Corresponding author's E-mail: GorchoDL@muohio.edu
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Abstract

Remote sensing has been used to directly detect and map invasive plants, but has not been used for forest understory invaders because they are obscured by a canopy. However, if the invasive species has a leaf phenology distinct from native forest species, then temporal opportunities exist to detect the invasive. Amur honeysuckle, an Asian shrub that invades North American forests, expands leaves earlier and retains leaves later than native woody species. This research project explored whether Landsat 5 TM and Landsat 7 ETM+ imagery could predict Amur honeysuckle cover in woodlots across Darke and Preble Counties in southwestern Ohio and Wayne County in adjacent eastern Indiana. The predictive abilities of six spectral vegetation indices and six reflectance bands were evaluated to determine the best predictor or predictors of Amur honeysuckle cover. The use of image differencing in which a January 2001 image was subtracted from a November 2005 image provided better prediction of Amur honeysuckle cover than the use of the single November 2005 image. The Normalized Difference Vegetation Index (NDVI) was the best-performing predictor variable, compared to other spectral indices, with a quadratic function providing a better fit (R2 = 0.75) than a linear function (R2 = 0.65). This predictive model was verified with 15 other woodlots (R2 = 0.77). With refinement, this approach could map current and past understory invasion by Amur honeysuckle.

Keywords

Amur honeysuckle, Lonicera maackii (Rupr.) Herder.LandsatTMETM+leaf phenologyNormalized Difference Vegetation IndexNDVI
Type
Weed Biology and Ecology
Information
Weed Science , Volume 57 , Issue 5 , October 2009 , pp. 512 - 520
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Andersen, M. A., Adams, H., Hope, B., and Powell, M. 2004. Risk analysis for invasive species: general framework and research needs. Risk Analysis. 24:893–900.Google Scholar
Anderson, G. L., Delfosse, E. S., Spencer, N. R., Prosser, C. W., and Richard, R. D. 2003. Lessons in developing successful invasive weed control programs. J. Range Manag. 56:2–12.Google Scholar
Bartuszevige, A. M. and Gorchov, D. L. 2006. Avian seed dispersal of an invasive shrub. Biol. Invasions. 8:1013–1022.Google Scholar
Bartuszevige, A. M., Gorchov, D. L., and Raab, L. 2006. The relative importance of landscape and community features in the invasion of an exotic shrub in a fragmented landscape. Ecography. 29:213–222.Google Scholar
Birth, G. S. and McVey, G. R. 1968. Measuring the color of growing turf with a reflectance spectrophotometer. Agron. J. 60:640–643.Google Scholar
Bradley, B. A. and Mustard, J. F. 2005. Identifying land cover variability distinct from land cover change: cheatgrass in the Great Basin. Remote Sens. Environ. 94:204–213.Google Scholar
Bradley, B. A. and Mustard, J. F. 2006. Characterizing the landscape dynamics of an invasive plant and risk of invasion using remote sensing. Ecol. Appl. 16:1132–1147.Google Scholar
Byers, J. E., Reichard, S., Randall, J. M., Parker, I. M., Smith, C. S., Lonsdale, W. M., Atkinson, I. A. E., Seastedt, T. R., Williamson, M., Chornesky, E., and Hayes, D. 2001. Directing research to reduce the impacts of exotic species. Conserv. Biol. 16:630–640.Google Scholar
Caughlan, L. and Oakley, K. L. 2002. Cost considerations for long-term ecological monitoring. Ecol. Indicators. 1:123–134.Google Scholar
Chavez, P. S. Jr. 1996. Image-based atmospheric corrections: revisited and improved. Photgram. Eng. Remote Sens. 62:1025–1036.Google Scholar
Chen, J. M. and Cihlar, J. 1996. Retrieving leaf area index of boreal conifer forests using Landsat TM images. Remote Sens. Environ. 55:153–162.Google Scholar
Chornesky, E. A., Bartuska, A. M., Aplet, G. H., et al. 2005. Science priorities for reducing the threat of invasive species to sustainable forestry. Bioscience. 55:335–348.Google Scholar
Cipollini, D., Stevenson, R., Enright, S., and Cipollini, K. 2008. Contrasting effects of two invasive plants on the performance of a nonmycorrhizal plant. Int. J. Plant Sci. 169:371–375.Google Scholar
Cohen, W. B. 1991. Response of vegetation indices to changes in three measures of leaf water stress. Photogram. Eng. Remote Sens. 57:195–202.Google Scholar
Cohen, W. B. and Goward, S. N. 2004. Landsat's role in ecological applications of remote sensing. Bioscience. 54:535–545.Google Scholar
Collier, M. H., Vankat, J. L., and Hughes, M. R. 2002. Diminished plant richness and abundance below (Lonicera maackii), an invasive shrub. Am. Midl. Nat. 147:60–71.Google Scholar
Cooksey, D. and Sheley, R. 1997. Noxious weed survey and mapping system. Rangelands. 19:20–23.Google Scholar
Crist, E. P. and Cicone, R. J. 1984. Application of the Tasseled Cap concept to simulated Thematic Mapper data. Photogram. Eng. Remote Sens. 50:343–352.Google Scholar
Deckers, B., Verheyen, K., Hermy, M., and Muys, B. 2005. Effects of landscape structure on the spread of black cherry (Prunus serotina) in an agricultural landscape in Flanders, Belgium. Ecography. 28:99–109.Google Scholar
Di Bella, C. M., Paruelos, J. M., Becerra, J. E., Bacour, C., and Baret, F. 2004. Effect of senescent leaves on NDVI-based estimates of fAPAR: experimental and modeling evidences. Int. J. Remote Sens. 25:5415–5427.Google Scholar
Dorning, M. and Cipollini, D. 2006. Leaf and root extracts of the invasive shrub, Lonicera maackii, inhibit seed germination of three herbs with no autotoxic effects. Plant Ecol. 184:287–296.Google Scholar
Dymond, C. C., Mladenoff, D. J., and Radeloff, V. C. 2002. Phenological differences in tasseled cap indices improve deciduous forest classification. Remote Sens. Environ. 80:460–472.Google Scholar
Eiswerth, M. and Johnson, W. 2002. Managing nonindigenous invasive species: insights from dynamic analysis. Environ. Resource Econ. 23:319–342.Google Scholar
Fisher, J. I., Mustard, J. F., and Vadeboncoeur, M. A. 2006. Green leaf phenology at Landsat resolution: scaling from the field to the satellite. Remote Sens. Environ. 100:265–279.Google Scholar
Gitelson, A. A., Kaufman, Y. J., Stark, R., and Rundquist, D. 2002. Novel algorithms for remote estimation of vegetation fraction. Remote Sens. Environ. 108:76–87.Google Scholar
Gorchov, D. L. and Trisel, D. E. 2003. Competitive effects of the invasive shrub Lonicera maackii (Rupr.) Herder (Caprifoliaceae) on growth and survival of native tree seedlings. Plant Ecol. 166:13–24.Google Scholar
Gould, A. M. A. and Gorchov, D. L. 2000. Effects of the exotic invasive shrub (Lonicera maackii) on the survival and fecundity of three species of native annuals. Amer. Midl. Nat. 144:36–50.Google Scholar
Hamada, Y., Stow, D. A., Coulter, L. L., Jafolla, J. C., and Hendricks, L. W. 2007. Detecting Tamarisk species (Tamarix spp.) in riparian habitats of southern California using high spatial resolution hyperspectral imagery. Remote Sens. Environ. 109:237–248.Google Scholar
Hardisky, M. A., Klemas, V., and Smart, R. M. 1983. The influence of soil salinity, growth form, and leaf moisture on the spectral radiance of (Spartina alterniflora) canopies. Photogram. Eng. Remote Sens. 49:47–83.Google Scholar
Hartman, K. M. and McCarthy, B. C. 2004. Restoration of a forest understory after the removal of an invasive shrub, Amur Honeysuckle (Lonicera maackii). Restoration Ecol. 12:154–165.Google Scholar
Hartman, K. M. and McCarthy, B. C. 2007. A dendro-ecological study of forest overstory productivity following the invasion of the non-indigenous shrub (Lonicera maackii). Appl. Veg. Sci. 10:3–14.Google Scholar
Hobbs, R. J. and Humphries, S. E. 1995. An integrated approach to the ecology and management of plant invasions. Conserv. Biol. 4:761–770.Google Scholar
Hooper, D. U., Chapin, F. S. III, Ewel, J. J., et al. 2005. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecol. Monogr. 75:3–35.Google Scholar
Huang, C., Wylie, B., Yang, L., Homer, C., and Zylstra, G. 2002. Derivation of a Tasseled Cap transformation based on Landsat 7 at-satellite reflectance. Int. J. Remote Sens. 23:1741–1748.Google Scholar
Huete, A. R. 1988. A soil-adjusted vegetation index (SAVI). Remote Sens. Environ. 25:295–309.Google Scholar
Huete, A. R., Jackson, R. D., and Post, D. F. 1985. Spectral response of a plant canopy with different soil backgrounds. Remote Sens. Environ. 17:37–53.Google Scholar
Huete, A. R., Liu, H. Q., Batchily, K., and van Leeuwen, W. 1997. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens. Environ. 59:440–451.Google Scholar
Hutchinson, T. F. and Vankat, J. L. 1997. Invasibility and effects of Amur Honeysuckle in southwestern Ohio forests. Conserv. Biol. 11:1117–1124.Google Scholar
Kauth, R. J. and Thomas, G. S. 1976. The Tasseled Cap—a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. Pages 41–51. in. Proceedings of the Symposium on Machine Processing of Remotely Sensed Data. West Lafayette, IN Purdue University.Google Scholar
Kerr, J. T. and Ostrovsky, M. 2003. From space to species: ecological applications for remote sensing. Trends Ecol. Evol. 18:299–305.Google Scholar
Laba, M., Downs, R., Smith, S., Welsh, S., Neider, C., White, S., Richmond, M., Philpot, W., and Baveye, P. 2008. Mapping invasive wetland plants in the Hudson River National Estuarine Research Reserve using Quickbird satellite imagery. Remote Sens. Environ. 112:286–300.Google Scholar
Lass, L. W., Prather, T. S., Glenn, N. F., Weber, K. T., Mundt, J. T., and Pettinghill, J. 2005. A review of remote sensing of invasive weeds and example of the early detection of spotted knapweed (Centuarea maculosa) and babybreath (Gypsophila paniculata) with hyperspectral sensor. Weed Sci. 53:242–251.Google Scholar
Lawrence, R. L., Wood, S. D., and Sheley, R. L. 2006. Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest). Remote Sens. Environ. 100:356–362.Google Scholar
Lee, D. W., O'Keefe, J., Holbrook, N. M., and Field, T. S. 2003. Pigment dynamics and autumn leaf senescence in a New England deciduous forest, eastern USA. Ecol. Res. 18:677–694.Google Scholar
Leung, B., Finnhoff, D., Shogren, J. F., and Lodge, D. 2005. Managing invasive species: rules of thumb for rapid assessment. Ecol. Econ. 55:24–36.Google Scholar
Levine, J. M., Vila, M., D'Antonio, C. M., Dukes, J. S., Grigulis, K., and Lavorel, S. 2003. Mechanisms underlying the impacts of exotic plant invasions. Proc. R. Soc. Lond. B Biol. Sci. 270:775–781.Google Scholar
Lu, D. 2006. The potential and challenge of remote sensed-based biomass estimation. Int. J. Remote Sens. 27:1297–1328.Google Scholar
Luken, J. O. and Thieret, J. W. 1995. Amur honeysuckle (Lonicera maackii, Caprifoliaceae): its ascent, decline, and fall. Sida. 16:479–503.Google Scholar
MacDonald, A. J., Gemmel, F. M., and Lewis, P. E. 1998. Investigation of the utility of spectral vegetation indices for determining information on coniferous forests. Remote Sens. Environ. 66:250–272.Google Scholar
Madden, M. 2004. Remote sensing and geographic information system operations for vegetation mapping of invasive exotics. Weed Tech. 18:1457–1463.Google Scholar
Miller, K. and Gorchov, D. L. 2004. The invasive shrub (Lonicera maackii), reduced growth and fecundity of perennial forest herbs. Oecologia. 139:359–375.Google Scholar
Nemani, R., Pierce, L., Running, S., and Band, L. 1993. Forest ecosystem processes at the watershed scale: sensitivity to remotely-sensed leaf area index estimates. Int. J. Remote Sens. 14:2519–2534.Google Scholar
Noonan, M. and Chafer, C. 2007. A method for mapping the distribution of willow at a catchment scale using bi-seasonal SPOT5 imagery. Weed Res. 47:173–181.Google Scholar
Parker, I. M., Simberloff, D., Lonsdale, W. M., et al. 1999. Impact: toward a framework for understanding the ecological effects of invaders. Biol. Invasions. 1:3–19.Google Scholar
Patil, G. P., Brooks, R. P., Myers, W. L., Rapport, D. J., and Taillie, C. 2001. Ecosystem health and its measurement at landscape scale: towards the next generation of quantitative assessments. Ecosystem Health. 7:307–315.Google Scholar
Pengra, B. W., Johnston, C. A., and Loveland, T. R. 2007. Mapping an invasive plant, Phragmites australis, in coastal wetlands using the EO-1 Hyperion hyperspectral sensor. Remote Sens. Environ. 108:74–81.Google Scholar
Peterson, E. B. 2005. Estimating cover of an invasive grass (Bromus tectorum) using tobit regression and phenology derived from two dates of Landsat ETM+ data. Int. J. Remote Sens. 26:2491–2507.Google Scholar
Pimentel, D., Lach, L., Zuniga, R., and Morrison, D. 2000. Environmental and economic costs of exotic species in the United States. BioScience. 50:53–65.Google Scholar
Resasco, J., Hale, A. N., Henry, M. C., and Gorchov, D. L. 2007. Detecting an invasive shrub in a deciduous forest understory using late-fall LANDSAT sensor imagery. Int. J. Remote Sens. 28:3739–3745.Google Scholar
Rew, L. J., Maxwell, B. D., and Aspinall, R. 2005. Predicting the occurrence of nonindigenous species using environmental and remotely sensed data. Weed Sci. 53:236–241.Google Scholar
Richardson, A. D., Bailey, A. S., Denny, E. G., Martin, C. W., and O'Keefe, J. 2006. Phenology of a northern hardwood forest canopy. Global Climate Change Biol. 12:1174–1188.Google Scholar
Rouse, J. W. Jr., Haas, R. H., Schell, J. A., and Deering, D. W. 1974. Monitoring vegetation systems in the Great Plains with ERTS. Pages 3010–3017. in. Proceedings of the Third Earth Resources Technology Satellite 1 Symposium. Greenbelt, MD NASA SP-351.Google Scholar
Schmidt, K. A. and Whelan, C. J. 1999. Effects of exotic Lonicera and Rhamnus on songbird nest predation. Conserv. Biol. 13:1502–1506.Google Scholar
Shaw, D. R. 2005. Symposium: Translation of remote sensing data into weed management decisions. Weed Sci. 53:264–73.Google Scholar
Smith, R. L. and Smith, T. M. 2001. Ecology and Field Biology. 6th ed. San Francisco, CA Benjamin Cummins. 720.Google Scholar
Stumpf, K. A. 1993. The estimation of forest vegetation cover descriptions using a vertical densitometer. Geographic Resource Solutions. Arcata, CA. http://www.grsgis.com/publications/saf_93.pdf. Accessed: October 3, 2005.Google Scholar
Trisel, D. E. 1997. The invasive shrub Lonicera maackii (Rupr.) Herder (Caprifoliaceae): factors contributing to its success and its effect on native species. . Oxford, OH Miami University. 200.Google Scholar
Trisel, D. E. and Gorchov, D. L. 1994. Regional distribution, leaf phenology, and herbivory of the invasive shrub, (Lonicera maackii). Bull. Ecol. Soc. Am. 75:231–232.Google Scholar
[USGS] U.S. Geological Survey 2001. MLRC Image Preprocessing Procedure, U.S. Department of Interior. Pages 10.Google Scholar
Van Leeuwen, W. J. D. and Huete, A. R. 1996. Effects of standing litter on the biophysical interpretation of plant canopies with spectral indices. Remote Sens. Environ. 55:123–138.Google Scholar
Vitousek, P. M. 1990. Biological invasions and ecosystem processes: toward an integration of population biology and ecosystem studies. Oikos. 57:7–13.Google Scholar
Wilcove, D. S., Rothstein, D., Dubow, J., Phillips, A., and Losos, E. 1998. Quantifying threats to imperiled species in the United States. Bioscience. 48:607–615.Google Scholar
Wilfong, B. N. 2008. Detecting an invasive shrub in deciduous forest understories using remote sensing. . Oxford, OH Miami University. 40.Google Scholar
Zeng, D., Rademacher, J., Chena, J., Crow, T., Bresee, M., Le Moine, J., and Ryu, S. 2004. Estimating aboveground biomass using Landsat 7 ETM+ data across a managed landscape in northern Wisconsin, USA. Remote Sens. Environ. 93:402–411.Google Scholar