Hostname: page-component-cd9895bd7-7cvxr Total loading time: 0 Render date: 2024-12-28T01:19:59.341Z Has data issue: false hasContentIssue false
  • English
  • Français

Detecting and Mapping Four Invasive Species along the Floodplain of North Platte River, Nebraska

Published online by Cambridge University Press:  20 January 2017

Sunil Narumalani ,
Deepak R. Mishra ,
Robert Wilson ,
Patrick Reece  and
Ann Kohler
Sunil Narumalani*
Affiliation:
School of Natural Resources, 302 Hardin Hall, University of Nebraska, Lincoln, NE 68583
Deepak R. Mishra
Affiliation:
Pontchartrain Institute for Environmental Sciences, Earth and Environmental Sciences, 1065 Geology and Psychology, University of New Orleans, New Orleans, LA 70148
Robert Wilson
Affiliation:
UNL Panhandle Research and Extension Center, 4502 Avenue I, Scottsbluff, NE, 69361
Patrick Reece
Affiliation:
UNL Panhandle Research and Extension Center, 4502 Avenue I, Scottsbluff, NE, 69361
Ann Kohler
Affiliation:
UNL Panhandle Research and Extension Center, 4502 Avenue I, Scottsbluff, NE, 69361
*
Corresponding author's E-mail: snarumalani1@unl.edu.
Get access Rights & Permissions [Opens in a new window]

Abstract

Geospatial technologies are increasingly important tools used to assess the spatial distributions and predict the spread of invasive species. The objective of our research was to quantify and map four dominant invasive plant species, including saltcedar, Russian olive, Canada thistle, and musk thistle, along the flood plain of the North Platte River corridor within a 1-mile (1.6-km) buffer. Using the Airborne Imaging Spectroradiometer for Applications (AISA) hyperspectral imager (from visible to near infrared), we evaluated an image processing technique known as spectral angle mapping for mapping the invasive species distribution. A minimum noise fraction algorithm was used to remove the inherent noise and redundancy within the dataset during the classification. The classification algorithm applied on the AISA image revealed five categories of invasive species distribution including (1) saltcedar; (2) Russian olive; and a mix of (3) Canada and musk thistle, (4) Canada/musk thistle and reed canary grass, or (5) Canada/musk thistle, saltcedar, and reed canary grass. Validation procedures confirmed an overall map accuracy of 74%. Saltcedar and Russian olive classes showed producer and user accuracies of greater than 90%, whereas the mixed categories revealed accuracy values of between 35 and 74%. The immediate benefit of this research has been to provide information on the spatial distribution of invasive species to land managers for implementation of management programs. In addition, these data can be used to establish a baseline of the species distributions for future monitoring and control efforts.

Keywords

Canada thistle, Cirsium arvense L. Scop.musk thistle, Carduus nutans L.reed canary grass, Phalaris arundinacea L.Russian olive, Elaeagnus angustifolia L.saltcedar, Tamarix sp. LourHyperspectralminimum noise fractionremote sensingspectral angle mapping
Type
Weed Management—Techniques
Information
Weed Technology , Volume 23 , Issue 1 , March 2009 , pp. 99 - 107
Copyright
Copyright © Weed Science Society of America 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Literature Cited

Allen, C. R., Epperson, D., and Garmestani, A. 2004. The impacts of fire ants on wildlife: a decade of research. Am. Midl. Nat 152:88–103.CrossRefGoogle Scholar
Allen, C. R., Johnson, A. R., and Parris, L. 2006. A framework for spatial risk assessments: Potential impacts on nonindigenous invasive species on native species. Ecol. Soc 11/1:39–41.Google Scholar
Anderson, G. L., Everitt, J. H., Escobar, D. E., Spencer, N. R., and Andrascik, R. J. 1996. Mapping leafy spurge (Euphorbia esula) infestations using aerial photography and geographic information systems. Geocarto Int 11:81–89.Google Scholar
Anderson, J. R., Hardy, E. E., Roach, T. T., and Witmer, R. E. 1976. A Land Use and Land Cover Classification System for Use with Remote Sensing Data. Washington, DC: U.S. Gov. Printing Office, U.S. Geological Survey Profession Paper 964.Google Scholar
Aspinall, R. J., Marcus, W. A., and Boardman, J. W. 2002. Considerations in collecting, processing, and analyzing high spatial resolution, hyperspectral data for environmental investigations. J. Geogr. Sys 4:15–29.Google Scholar
Boardman, J. W. and Kruse, F. A. 1994. Automated spectral analysis: a geological example using AVIRIS data, north Grapevine Mountains, Nevada. Pages I-407–I-418. in. Proceedings of the ERIM Tenth Thematic Conference on Geologic Remote Sensing. Ann Arbor, MI Environmental Research Institute of Michigan.Google Scholar
Brooks, M. L., D'Antonio, C. M., Richardson, D. M., Grace, J. B., and Keeley, J. E. 2004. Effects of invasive alien plants on fire regimes. BioScience 54/7:677–688.Google Scholar
Carson, H. W., Lass, L. W., and Callihan, R. H. 1995. Detection of yellow hawkweed (Hieracium pratense) with high resolution multispectral digital imagery. Weed Technol 9:477–483.CrossRefGoogle Scholar
Cochrane, M. A. 2000. Using vegetation reflectance variability for species level classification of hyperspectral data. Int. J. Remote Sens 21:2075–2087.CrossRefGoogle Scholar
Congalton, R. G. and Mead, R. A. 1983. A quantitative method to test for consistency and correctness in photointerpretation. Photogramm. Eng. Remote Sens 49/1:69–74.Google Scholar
Dewey, S. A., Price, K. P., and Ramsey, D. 1991. Satellite remote sensing to predict potential distribution of dyers woad (Isatis tinctoria). Weed Technol 5:479–484.Google Scholar
Driscoll, R. S. and Coleman, M. D. 1974. Color for shrubs. Photogramm. Eng. Remote Sens 40:451–459.Google Scholar
Everitt, J. H. and Escobar, D. E. 1996. Use of spatial information technologies for noxious plant detection and distribution on rangelands. Geocarto Int 11:63–80.Google Scholar
Everitt, J. H., Escobar, D. E., Alaniz, M. A., Davis, M. R., and Richerson, J. V. 1996. Using spatial information technologies to map Chinese tamarisk (Tamarix chinensis) infestations. Weed Sci 44:194–201.Google Scholar
Farrar, J. 1983. Nebraska Rivers. Pages 1–146. in. Nebraskaland Magazine. Lincoln, NE: Nebraska Game and Parks Commission.Google Scholar
Friederici, P. 1995. The alien saltcedar. Am. For 101:45–47.Google Scholar
Glenn, N. F., Mundt, J. T., Weber, K. T., Prather, T. S., Lass, L. W., and Pettingill, J. 2005. Hyperspectral data processing for repeat detection of small infestations of leafy spurge. Remote Sens Environ 95:395–412.CrossRefGoogle Scholar
Jensen, J. R. 2005. Introductory Digital Image Processing: A Remote Sensing Perspective. 3rd ed. Upper Saddle River, NJ: Prentice Hall. 526.Google Scholar
Jensen, J. R. 2007. Remote Sensing of the Environment: An Earth Resource Perspective. 2nd ed. Upper Saddle River, NJ: Prentice Hall. 592.Google Scholar
Joshi, C., de Leeuw, J., and van Duren, I. C. 2004. Remote sensing and GIS applications for mapping and spatial modeling of invasive species. Proceedings of the International Society for Photogrammetry and Remote Sensing. Istanbul. CD-ROM, unpaginated.Google Scholar
Kruse, F. A., Lefkoff, A. B., Boardman, J. B., Heidebrecht, K. B., Shapiro, A. T., Barloon, P. J., and Goetz, A. F. H. 1993. The Spectral Image Processing System (SIPS)—interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ 44:145–163.Google Scholar
Kuzelka, R. D., Flowerday, C. A., Manley, R. N., Rundquist, B. C., and Herrin, S. J. 1993. Flat Water: A History of Nebraska and its Water University of Nebraska Resource Report. 291.Google Scholar
Landis, J. R. and Koch, G. G. 1977. The measurement of observer agreement for categorical data. Biometrics 76:378–382.Google Scholar
Lass, L. W. and Callihan, R. H. 1997. The effect of phenological stage on detectibility of yellow hawkweed (Hieracium pratense) and oxeye daisy (Chrysanthemum leucanthemum) with remote multispectral digital imagery. Weed Technol 11:248–256.Google Scholar
Lass, L. W., Carson, H. W., and Callihan, R. H. 1996. Detection of yellow starthistle (Centaurea solstitialis) and common St. Johnswort (Hypericum perforatum) with multispectral digital imagery. Weed Technol 10:466–474.Google Scholar
Lass, L. W., Prather, T. S., Glenn, N. F., Weber, K. T., Mundt, J. T., and Pettingill, J. 2005. A review of remote sensing of invasive weeds and example of the early detection of spotted knapweed (Centaurea maculosa) and babysbreath (Gypsophila paniculata) with a hyperspectral sensor. Weed Sci 53:242–251.Google Scholar
Lawrence, R. L. and Ripple, W. J. 2000. Fifteen years of re-vegetation of Mount St. Helens: a landscape-scale analysis. Ecology 81:2742–2752.CrossRefGoogle Scholar
Mäkisara, K., Kärnä, J-P., and Lohi, A. 1994. Geometric correction of airborne imaging spectrometer data. Pages 1503–1505. in. Proceedings of the International Geoscience and Remote Sensing Symposium Digest. Pasadena, CA.Google Scholar
Matthew, M. W., Adler-Golden, S. M., Berk, A., Felde, G., Anderson, G. P., Gorodestzky, D., Paswaters, S., and Shippert, M. 2003. Atmospheric correction of spectral imagery: evaluation of the FLAASH algorithm with AVIRIS data. Pages 474–482. in. Proceedings of the SPIE Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery IX. Volume 5093.Google Scholar
Mundt, J. T., Glenn, N. F., Wever, K. T., Prather, T. S., Lass, L. W., and Pettingill, J. 2005. Discrimination of hoary cress and determination of its detection limits via hyperspectral image processing and accuracy assessment techniques. Remote Sens. Environ 96:509–517.Google Scholar
Narumalani, S., Mishra, D. R., Burkholder, J., Merani, P. B. T., and Wilson, G. 2006. A comparative evaluation of ISODATA and spectral angle mapping for the detection of saltcedar using airborne hyperspectral imagery. Geocarto Int 21/2:59–66.Google Scholar
Nebraska Game and Parks Commission 2001. The Nebraska Natural Legacy Project: A Blueprint for Conserving Wildlife and Their Habitats. http://www.ngpc.state.ne.us/wildlife/programs/legacy/. Accessed: August 8, 2008.Google Scholar
Okin, G. S., Roberts, D. A., Murray, B., and Okin, W. J. 2001. Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments. Remote Sens. Environ 77:212–225.CrossRefGoogle Scholar
O'Neill, M., Ustin, S. L., Hager, S., and Root, R. 2000. Mapping the distribution of leafy spurge at Theodore Roosevelt National Park using AVIRIS. in Green, R. O., editor. AVIRIS Airborne Geoscience Workshop. JPL Publication 00-18. Pasadena, CA: Jet Propulsion Laboratory, California Institute of Technology.Google Scholar
Parker Williams, A. and Hunt, E. R. 2002. Estimation of leafy spurge from hyperspectral imagery using mixture tuned matched filtering. Remote Sens. Environ 82:446–456.Google Scholar
Parker Williams, A. and Hunt, E. R. 2004. Accuracy assessment of detection of leafy spurge with hyperspectral imagery. J. Range Manage 57:106–112.Google Scholar
Pimentel, D., Lach, L., Zuniga, R., and Morrison, D. 2000. Environmental and economic costs associated with non-indigenous species in the United States. BioScience 50:53–65.Google Scholar
Schnase, J. L., Stohlgren, T. J., and Smith, J. A. 2002. The national invasive species forecasting system: a strategic NASA/USGS partnership to manage biological invasions. Earth Observ. Mag 2002/August:46–49.Google Scholar
Sohn, Y. and McCoy, R. M. 1997. Mapping desert shrub rangeland using spectral unmixing and modeling spectral mixtures with TM data. Photogramm. Eng. Remote Sens 63:707–716.Google Scholar
Story, M. and Congalton, R. 1986. Accuracy asessment: a user's perspective. Photogramm. Eng. Remote Sens 52:397–399.Google Scholar
Thenkabail, P. S., Enclona, E. A., and Ashton, M. S. 2004. Hyperion, IKONOS, ALI, and ETM+ sensors in the study of African rain forests. Remote Sens. Environ 90:23–43.Google Scholar
Treitz, P. M., Howarth, P. J., and Suffling, R. C. 1992. Application of detailed ground information to vegetation mapping with high spatial resolution imagery. Remote Sens. Environ 42:65–82.Google Scholar
Tucker, C. J., Townshend, J. R. G., and Goff, T. E. 1985. African land-cover classification using satellite data. Science 227:369–375.CrossRefGoogle ScholarPubMed
Tueller, P. T. 1982. Remote sensing for range management. Pages 125–140. in Johannsen, C. J. and Sanders, J. L., editors. Remote Sensing for Resource Management. Ankeny, IA: Soil Conservation Society of America.Google Scholar
Underwood, E., Ustin, S., and DiPietro, D. 2003. Mapping noninvasive plants using hyperspectral imgery. Remote Sens. Environ 86:150–161.Google Scholar
[USDA] United States Department of Agriculture 2007. A Weed Manager's Guide to Remote Sensing and GIS. http://www.fs.fed.us/eng/rsac/invasivespecies/. Accessed: April 12, 2007.Google Scholar
Vermote, E. F., Tanre, D., Deuze, J. L., Herman, M., and Morcrette, J. J. 1994. Second Simulation of the Satellite Signal in the Solar Spectrum (6S). 6S User Guide Version 6.0. Greenbelt, MD: NASA-GSFC. 134.Google Scholar
Vitousek, P. M., D'Antonio, C. M., Loope, L. L., and Westbrooks, R. 1996. Biological invasions as global environmental change. Am. Sci 84:468–478.Google Scholar
Woolley, J. T. 1971. Reflectance and transmittance of light by leaves. Plant Physiol 47:656–662.Google Scholar
Wylie, B. K., Meyer, D. J., Choate, M. J., Vierling, L., and Kozak, P. K. 2000. Mapping woody vegetation and eastern red cedar in the Nebraska Sand Hills using AVIRIS. in Green, R. O., editor. AVIRIS Airborne Geoscience Workshop. JPL Publication 00-18. Pasadena, CA: Jet Propulsion Laboratory, California Institute of Technology.Google Scholar
Zavaleta, E. 2000. The economic value of controlling an invasive shrub. Ambio 29/8:462–467.Google Scholar