Skip to main content
    • Aa
    • Aa

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

  • Lawrence W. Lass, Timothy S. Prather (a1), Nancy F. Glenn (a2), Keith T. Weber (a3), Jacob T. Mundt (a2) and Jeffery Pettingill (a4)...

Remote sensing technology is a tool for detecting invasive species affecting forest, rangeland, and pasture environments. This article provides a review of the technology, and algorithms used to process remotely sensed data when detecting weeds and a working example of the detection of spotted knapweed and babysbreath with a hyperspectral sensor. Spotted knapweed and babysbreath frequently invade semiarid rangeland and irrigated pastures of the western United States. Ground surveys to identify the extent of invasive species infestations should be more efficient with the use of classified images from remotely sensed data because dispersal of an invasive plant may have occurred before the discovery or treatment of an infestation. Remote sensing data were classified to determine if infestations of spotted knapweed and babysbreath were detectable in Swan Valley near Idaho Falls, ID. Hyperspectral images at 2-m spatial resolution and 400- to 953-nm spectral resolution with 12-nm increments were used to identify locations of spotted knapweed and babysbreath. Images were classified using the spectral angle mapper (SAM) algorithm at 1, 2, 3, 4, 5, and 10° angles. Ground validation of the classified images established that 57% of known spotted knapweed infestations and 97% of known babysbreath infestations were identified through the use of hyperspectral imagery and the SAM algorithm.

Corresponding author
Corresponding author. Department of Plant, Soil, and Entomological Sciences, University of Idaho, Moscow, ID 83844-2339;
Linked references
Hide All

This list contains references from the content that can be linked to their source. For a full set of references and notes please see the PDF or HTML where available.

G. L. Anderson , J. H. Everitt , D. E. Escobar , N. R. Spencer , and R. J. Andrascik 1996. Mapping leafy spurge (Euphorbia esula) infestations using aerial photography and geographic information systems. Geocarto Int 11:8189.

G. W. Arnold , P. G. Ozanne , K. A. Galbraith , and F. Dandridge 1985. The capeweed content of pastures in south-west Western Australia. Aust. J. Exp. Agric 25:117123.

C. I. Chang and H. Ren 2000. An experiment-based quantitative and comparative analysis of target detection and image classification algorithms for hyperspectral imagery. IEEE Trans. Geosci. Remote Sens 38:10441063.

C. I. Chang , X. Zhao , M. L. G. Althouse , and J. J. Pan 1998. A posteriori least squares orthogonal subspace projection approach to mixed pixel classification in hyperspectral images. IEEE Trans. Geosci. Remote Sens 36:898912.

R. G. Congalton 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ 37:3546.

A. L. Darant 1975. The biology of Canadian weeds. Gypsophila paniculata L. Can. J. Plant Sci 55:10491058.

A. L. Darant and R. T. Coupland 1966. Life history of Gypsophila paniculata . Weeds 14:313318.

J. H. Everitt , D. E. Escobar , M. A. Alaniz , and M. R. Davis 1987. Using airborne middle-infrared (1.45–2.0 μm) video imagery for distinguishing plant species and soil conditions. Remote Sens. Environ 22:423428.

J. H. Everitt , D. E. Escobar , M. A. Alaniz , and M. R. Davis 1991. Light reflectance characteristics and video remote-sensing of prickly pear. J. Range Manage 44:587592.

J. H. Everitt and P. R. Nixon 1985. Video imagery: a new remote sensing tool for range management. J. Range Manage 38:421424.

B. C. Forster 1984. Derivation of atmospheric correction procedures for LANDSAT MSS with particular reference to urban data. Int. J. Remote Sens 5:799817.

D. J. King 1995. Airborne multi-spectral digital camera and video sensors: a critical review of system designs and applications. Can. J. Remote Sens 21:245273.

F. A. Kruse , A. B. Lefkoff , J. W. Boardman , K. B. Hiebedrecht , A. T. Shapiro , P. J. Barloom , and A. F. H. Goetz 1993. The spatial image processing system (SIPS)—interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ 44:145163.

L. W. Lass and T. S. Prather 2004. Detecting the locations of Brazilian pepper trees in the Everglades with a hyperspectral sensor. Weed Technol 18:437442.

L. W. Lass , B. Shafii , W. J. Price , and D. C. Thill 2000. Assessing agreement in multispectral images of yellow starthistle (Centaurea solstitialis) with ground truth data using a Bayesian methodology. Weed Technol 14:539544.

L. W. Lass , D. C. Thill , B. Shafii , and T. S. Prather 2002. Detecting spotted knapweed (Centaurea maculosa) with hyperspectral remote sensing technology. Weed Technol 16:426432.

F. E. Manzer and G. R. Cooper 1982. Use of portable video taping for aerial infrared imaging of potato disease. Plant Dis 66:665667.

A. L. O'Neill 1996. Satellite-derived vegetation indices applied to semi-arid shrublands in Australia. Aust. Geogr 27:185199.

A. Parker-Williams and E. R. Hunt 2002. Estimation of leafy spurge cover from hyperspectral imagery using mixture tuned matched filtering. Remote Sens. Environ 82:446456.

A. E. Parker-Williams and E. R. Hunt 2004. Accuracy assessment for detection of leafy spurge with hyperspectral imagery. J. Range Manage 57:106112.

S. Ringrose and W. Matheson 1987. Spectral assessment of indicators of range degradation in the Botswana Hardveld Environment. Remote Sens. Environ 23:379396.

J. J. Settle and N. A. Drake 1993. Linear mixing and the estimation of ground proportions. Int. J. Remote Sens 14:11591177.

Y. E. Shimabukuro and J. A. Smith 1991. The least-squares mixing models to generate fraction images derived from remote sensing multispectral data. IEEE Trans. Geosci. Remote Sens 29:1620.

E. Underwood , S. L. Ustin , and D. DiPietro 2003. Mapping nonnative plants using hyperspectral imagery. Remote Sens. Environ 86:150161.

Recommend this journal

Email your librarian or administrator to recommend adding this journal to your organisation's collection.

Weed Science
  • ISSN: 0043-1745
  • EISSN: 1550-2759
  • URL: /core/journals/weed-science
Please enter your name
Please enter a valid email address
Who would you like to send this to? *



Full text views

Total number of HTML views: 0
Total number of PDF views: 1 *
Loading metrics...

Abstract views

Total abstract views: 9 *
Loading metrics...

* Views captured on Cambridge Core between 20th January 2017 - 24th March 2017. This data will be updated every 24 hours.