Hostname: page-component-7dd5485656-g8tfn Total loading time: 0 Render date: 2025-10-31T20:44:56.574Z Has data issue: false hasContentIssue false
  • English
  • Français

Detection of Yellow Hawkweed (Hieracium pratense) with High Resolution Multispectral Digital Imagery

Published online by Cambridge University Press:  12 June 2017

Hubert W. Carson ,
Lawrence W. Lass  and
Robert H. Callihan
Hubert W. Carson
Affiliation:
Dep. of Plant, Soil, and Entomol. Sci., Univ. of Idaho, Moscow, ID 83844-2339
Lawrence W. Lass
Affiliation:
Dep. of Plant, Soil, and Entomol. Sci., Univ. of Idaho, Moscow, ID 83844-2339
Robert H. Callihan
Affiliation:
Dep. of Plant, Soil, and Entomol. Sci., Univ. of Idaho, Moscow, ID 83844-2339
Get access Rights & Permissions [Opens in a new window]

Abstract

Yellow hawkweed infests permanent upland pastures and forest meadows in northern Idaho. Conventional surveys to determine infestations of this weed are not practical. A charge coupled device with spectral filters mounted in an airplane was used to obtain digital images (1 m resolution) of flowering yellow hawkweed. Supervised classification of the digital images predicted more area infested by yellow hawkweed than did unsupervised classification. Where yellow hawkweed was the dominant ground cover species, infestations were detectable with high accuracy from digital images. Moderate yellow hawkweed infestation detection was unreliable, and areas having less than 20% yellow hawkweed cover were not detected.

Keywords

Yellow hawkweed, Hieracium pratense Tausch # HIECAAirborne charge coupled devicesremote sensingweed detection

Information

Type
Research
Information
Weed Technology , Volume 9 , Issue 3 , September 1995 , pp. 477 - 483
Copyright
Copyright © 1995 by the 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.)

Article purchase

Temporarily unavailable

References

Literature Cited

1. Anderson, G. L., Everitt, J. H., Richardson, A. J., and Escobar, D. E. 1993. Using satellite data to map false broomweed (Ericameria austrotexana) infestations on south Texas rangelands. Weed Technol. 7:865–871.Google Scholar
2. Brannon, D. P., Hill, C. L., Davis, B. D., and Birk, R. J. 1994. Commercial Remote Sensing Program. Photogramm. Eng. Remote Sens. 60:317–330.Google Scholar
3. Callihan, R. H., Hunt, C. W., and Marsh, B. B. 1995. Yellow hawkweed (Hieraceum pratense L.) forage quality. Weed Sci. Soc. Am. Abstr. 35:27.Google Scholar
4. Callihan, R. H., Thill, D. C., and Wattenbarger, D. W. 1989. Hawkweeds. Coop. Ext. Serv., Idaho Current Info. Ser. No. 633, Univ. of Idaho, Moscow. p. 4.Google Scholar
5. Campbell, J. B. 1987. Introduction to Remote Sensing. Guilford Press, New York. p. 551.Google Scholar
6. Congalton, R. G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37:35–46.Google Scholar
7. Everitt, J. H., Escobar, D. E., Villarreal, R., Alaniz, M. A., and Davis, M. R. 1993. Integration of airborne video, global positioning system and geographic information system technologies for detecting and mapping two woody legumes on rangelands. Weed Technol. 7:981–987.Google Scholar
8. Everitt, J. H., Alaniz, M. A., and Escobar, D. E. 1992. Using remote sensing to distinguish common (Isocoma coronopifolia and Drummond goldenweed (Isocoma drummondii). Weed Sci. 40:621–628.Google Scholar
9. Everitt, J. H. and Deloach, C. J. 1990. Remote Sensing of Chinese tamarisk (Tamarix chinensis) and associated vegetation. Weed Sci. 38:273–278.Google Scholar
10. Everitt, J. H., Escobar, D. E., and Alaniz, M. A. 1991. Light reflectance characteristics and video remote sensing of pricklypear. J. Range Manage. 44:587–592.Google Scholar
11. Jensen, J. R. 1986. Introductory Digital Image Processing. Prentice-Hall, Englewood Cliffs, NJ. p. 379.Google Scholar
12. Leckie, D. G. and Ostaff, D. P. 1988. Classification of airborne multispectral scanner data for mapping current defoliation caused by the spruce budworm. Forest Sci. 34:259–275.Google Scholar
13. Story, M. and Congalton, R. G. 1986. Accuracy assessment: a user's perspective. Photogramm. Eng. Remote Sens. 52:397–399.Google Scholar