Hostname: page-component-cd9895bd7-jkksz Total loading time: 0 Render date: 2024-12-22T22:35:04.189Z Has data issue: false hasContentIssue false
  • English
  • Français

Detecting Cutleaf Teasel (Dipsacus laciniatus) along a Missouri Highway with Hyperspectral Imagery

Published online by Cambridge University Press:  20 January 2017

Diego J. Bentivegna ,
Reid J. Smeda  and
Cuizhen Wang
Diego J. Bentivegna*
Affiliation:
Division of Plant Science, University of Missouri, Columbia, MO 65211
Reid J. Smeda
Affiliation:
Division of Plant Science, University of Missouri, Columbia, MO 65211
Cuizhen Wang
Affiliation:
Department of Geography, University of Missouri, Columbia, MO 65211
*
Corresponding author's E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Cutleaf teasel is an invasive, biennial plant that poses a significant threat to native species along roadsides in Missouri. Flowering plants, together with understory rosettes, often grow in dense patches. Detection of cutleaf teasel patches and accurate assessment of the infested area can enable targeted management along highways. Few studies have been conducted to identify specific species among a complex of vegetation composition along roadsides. In this study, hyperspectral images (63 bands in visible to near-infrared spectral region) with high spatial resolution (1 m) were analyzed to detect cutleaf teasel in two areas along a 6.44-km (4-mi) section of Interstate I-70 in mid Missouri. The identified classes included cutleaf teasel, bare soil, tree/shrub, grass/other broadleaf plants, and water. Classification of cutleaf teasel reached a user's accuracy of 82 to 84% and a producer's accuracy of 89% in the two sites. The conditional κ value was around 0.9 in both sites. The image-classified cutleaf teasel map provides a practical mechanism for identifying locations and extents of cutleaf teasel infestation so that specific cutleaf teasel management techniques can be implemented.

Cutleaf teasel is an exotic weed that infests roadside environments in Missouri. As a growing biennial, the plant develops as a rosette during the first year and bolts during the second. Dense patches contain flowering plants with understory rosettes. The objective of this work was to develop approaches for detecting cutleaf teasel patches with accurate assessment in a complex of species along a roadside. Thus, management of cutleaf teasel could be located at specific sites. Two hyperspectral images (63 bands with 1-m spatial resolution) were analyzed to detect cutleaf teasel along the Interstate Highway I-70 in mid Missouri. Classification of cutleaf teasel reached a user's accuracy of 82 to 84% and a producer's accuracy of 89% at the two sites. The image-classified teasel map provides a practical mechanism for identifying the locations and extents of cutleaf teasel infestation so that specific management techniques can be implemented.

Keywords

Cutleaf teasel, Dipsacus laciniatus L.Roadsidehyperspectral remote sensingweed detection
Type
Research Article
Information
Invasive Plant Science and Management , Volume 5 , Issue 2 , June 2012 , pp. 155 - 163
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.)

Footnotes

Current address: Research Associate, CERZOS-Center for Renewable Natural Resources of the Semiarid Region (CONICET), Camino La Carrindanga km 7, B8000FWB, Bahía Blanca, Argentina

References

Literature Cited

Bagan, H., Yasuoka, Y., Endo, T., Wang, X., and Feng, Z. 2008. Classification of airborne hyperspectral data based on the average learning subspace method. Geoscience and Remote Sens. Lett. 5:368–372.Google Scholar
Bajcsy, P. and Groves, P. 2004. Methodology for hyperspectral band selection. Photogramm. Eng. Remote Sens. 70:793–802.Google Scholar
Becker, B. L., Lusch, D. P., and Qi, J. 2005. Identifying optimal spectral bands from in situ measurements of Great Lakes coastal wetlands using second-derivative analysis. Remote Sens. Environ. 97:238–248.Google Scholar
Bentivegna, D. J. 2006. Biology and Management of Cutleaf Teasel (Dipsacus laciniatus L.) in Central Missouri. M.S. thesis. Columbia, MO: University of Missouri. 67 p.Google Scholar
Bentivegna, D. J. 2008. Integrated Management of the Invasive Weed, Cutleaf Teasel (Dipsacus laciniatus L.) along a Missouri Highway. Ph.D Dissertation. Columbia, MO: University of Missouri. 132 p.Google Scholar
Cheesman, O. D. 1998. The impact of some field boundary management practices on the development of Dipsacus fullonum L. flowering stems, and implication for conservation. Agric. Ecosyst. Environ. 68:41–49.Google Scholar
Congalton, R. G. and Green, K. 1999. Assessing the Accuracy of Remote Sensed Data: Principles and Practices. Boca Raton, FL Lewis. 137 p.Google Scholar
Czarapata, E. J. 2005. Invasive Plants of the Upper Midwest: An Illustrated guide to their identification and control. Madison, WI University of Wisconsin Press. 215 p.Google Scholar
DiPietro, D., Ustin, S. L., and Underwood, E. 2002. Mapping the invasive plant Arundo donax and associated riparian vegetation using AVIRIS. In: Proceedings 11th Airborne visible/infrared image spectrometer (AVIRIS)Workshop: Jet Propulsion Laboratory, Pasadena, CA CD-ROM.Google Scholar
[ENVI] Environment for Visualizing Images. 1999. The Environment for Visualizing Images (ENVI): User's Guide. Boulder, CO Exelis Visual Information Solutions.Google Scholar
ERDAS Imagine, . 2005. ERDAS Field Guide. Version: 9. Norcross, GA Leica Geosystems Geospatial Imaging. 705 p.Google Scholar
Glass, W. D. 1991. Vegetation management guideline: cutleaf teasel (Dipsacus laciniatus L.) and common teasel (Dipsacus sylvestris Huds.). Nat. Area J. 11:213–214.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
Hoffman, R. and Kearns, K. 1997. Wisconsin manual of control recommendation for ecologically invasive plants. Madison, WI Wisconsin Department Natural Resources. 102 p.Google Scholar
Jensen, J. R. 2005. Introductory Digital Image Processing: A Remote Sensing Perspective. Upper Saddle River, NJ Pearson Prentice Hall. 526 p.Google Scholar
Kruse, F. A., Lefkoff, A. B., Boardman, J. W., Heiedbrecht, 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
Lacey, L. W., Marlow, C. B., and Lane, J. R. 1989. Influence of spotted knapweed (Centaurea maculosa) on surface runoff and sediment yield. Weed Technol. 3:627–631.Google Scholar
Lass, L. W., Thill, D. C., Shaffii, B., and Prather, T. S. 2002. Detecting spotted knapweed (Centaurea maculosa) with hyperspectral remote sensing technology. Weed Technol. 16:426–432.Google Scholar
Lawrence, R. L., Wood, S. D., and Sheley, R. L. 2006. Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (Random Forest). Remote Sens. Environ. 100:356–362.Google Scholar
Mutanga, O. and Skidmore, A. K. 2004. Integrating imaging spectroscopy and neural networks to map grass quality in the Kruger National Park, South Africa. Remote Sens. Environ. 90:104–115.Google Scholar
[NRCS] Natural Resources Conservation Service. 2008. Official Soil Series Descriptions. http://soils.usda.gov/technical/classification/osd/index.html. Accessed February 15, 2008.Google Scholar
Okamoto, H., Murata, T., Kataoka, T., and Hata, S. 2007. Plant classification for weed detection using hyperspectral imaging with wavelength analysis. Weed Biol. Manag. 7:31–37.Google Scholar
Schowengerdt, R. A. 2007. Remote sensing: model and methods for image processing. London, UK Elsevier. 516 p.Google Scholar
Shaw, D. R. 2005a. Remote sensing and site-specific weed management. Front. Ecol. Environ. 3:526–532.Google Scholar
Shaw, D. R. 2005b. Translation of remote sensing data into weed management decisions. Weed Sci. 53:264–273.Google Scholar
Solecki, M. K. 1993. Cutleaf and common teasel (Dipsacus laciniatus L. and D. sylvestris Huds.): profile of two invasive aliens. Pages 85–92 in McKnight, B. N., ed. Biological Pollution: The Control and Impact of Invasive Exotic Species. Indianapolis, IN Indiana Academy of Science.Google Scholar
Terres, J. K. and Ratcliffe, B. 1979. Teasel, as in tease. Audubon 9:108–109.Google Scholar
Tsai, F. and Chen, C. 2004. Detecting invasive plants using hyperspectral and high resolution satellites images. Pages 14 in Proceedings of the 20th International Society for Photogrammetry and Remote Sensing Istanbul. Turkey ISPRS.Google Scholar
Underwood, E., Ustin, S., and DiPietro, D. 2003. Mapping non-native plants using hyperspectral imagery. Remote Sens. Environ. 86:150–161.Google Scholar
Ustin, S. L., DiPietro, K., Olmstead, K., Underwood, E., and Scheer, G. J. 2002. Hyperspectral remote sensing for invasive species detection and mapping. Pages 1658–1660 in the International Geoscience and Remote Sensing Symposium. Toronto, Canada IEEE.Google Scholar
Werner, P. A. 1975. A seed trap for determining patterns of seed deposition in terrestrial plant. Can. J. Bot. 53:810–813.Google Scholar
Werner, P. A. 1977. Colonization success of a “biennial” plant species: experimental field studies of species co-habitation and replacement. Ecology 58:840–849.Google Scholar
Wang, C., Bentivegna, D. J., Smeda, R. J., and Swanigan, R. E. 2010. Comparing multispectral and hyperspectral classifiers for mapping cutleaf teasel in highway environments. Photogramm. Eng. Remote Sens. 76:5:567–575.Google Scholar
Wang, C., Zhou, B., and Palm, H. L. 2008. Detecting invasive sericea lespedeza (Lespedeza cuneata) in mid-Missouri pastureland using hyperspectral imagery. Environ. Manage. 41:853–862.Google Scholar