Hostname: page-component-7dd5485656-7jgsp Total loading time: 0 Render date: 2025-10-31T08:34:25.732Z Has data issue: false hasContentIssue false
  • English
  • Français

The Effect of Phenological Stage on Detectability of Yellow Hawkweed (Hieracium pratense) and Oxeye Daisy (Chrysanthemum leucanthemum) with Remote Multispectral Digital Imagery

Published online by Cambridge University Press:  12 June 2017

Lawrence W. Lass  and
Robert H. Callihan
Lawrence W. Lass
Affiliation:
Department of Plant, Soil, and Entomological Sciences, University of Idaho, Moscow, ID 83844-2339
Robert H. Callihan
Affiliation:
Department of Plant, Soil, and Entomological Sciences, University of Idaho, Moscow, ID 83844-2339
Get access Rights & Permissions [Opens in a new window]

Abstract

Many upland pastures and forest meadows in the western United States contain significant infestations of yellow hawkweed and oxeye daisy. Documentation of infestations is necessary in order to plan and assess control tactics. Previous work with an airborne charge coupled device (CCD) with spectral filters indicated that flowering yellow hawkweed with at least 30% cover was detectable at 1 m resolution. A single image of a large area may not capture all plants in the flowering phase and multiple images are costly. The objective of this paper was to assess the accuracy of images recorded at different phenological stages. We compared three methods of classification: unsupervised classification of a three principal component analysis image, supervised classification of a three principal component analysis image, and supervised classification of a composited image consisting of four bands and normalized difference near infrared (NIR)/red band. Regardless of the classification method, images of yellow hawkweed and oxeye daisy in full bloom had lower classification error than at early bloom or post bloom. The percent error for yellow hawkweed classification was about twice as high at post bloom as at full bloom, but varied slightly depending on the method of classification and cover class. The ability to detect discrete colonies of yellow hawkweed was not affected by phenological stage, but the ability to measure the area of each cluster differed among stages. Less than one-third fo the pixels classified as yellow hawkweed or oxeye daisy in the early bloom image remained in the same class in the full bloom image. About half the pixels in the full bloom image remained in the 90 to 100% cover class at the post bloom image. Seasonal growth of the grasses masked some yellow hawkweed and oxeye daisy plants, and accounted for differences in classification among phenological stages.

Keywords

Oxeye daisy, Chrysanthemum leucanthemum L. # CHYLEyellow hawkweed, Hieracium pratense Tausch # HIECAAirborne charge coupled devicesremote sensingweed detectionCCD, Charge Coupled DeviceGIS, Geographic Information SystemGPS, Global Positioning SystemKIA, Kappa Index of AgreementNDVI, normalized difference vegetation indexNIR, Near InfraredOIF, Optimum Index FactorsPCA, Principal Components AnalysisTIGER, Topologically Integrated Geographic Encoding and Reference SystemUTM, Universal Transverse Mercator

Information

Type
Research
Information
Weed Technology , Volume 11 , Issue 2 , June 1997 , pp. 248 - 256
Copyright
Copyright © 1997 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

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
Callihan, R. H., Thill, D. C., and Wattenbarger, D. W. 1989. Hawkweeds. University of Idaho. Current Information Series No. 633. 4 p.Google Scholar
Campbell, J. B. 1987. Remote sensing applications in the plant sciences. In Campbell, J. B., ed. Introduction to Remote Sensing. New York: Guilford Press. pp. 384–386.Google Scholar
Carson, H. W., Lass, L. W., and Callihan, R. H. 1995. Detection of yellow hawkweed with high resolution digital images. Weed Technol. 9:477–483.Google Scholar
Congalton, R. G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37:35–46.Google Scholar
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
Everitt, J. H., Escobar, D. E., Alaniz, M. A., and Davis, M. R. 1991. Light reflectance characteristics and video remote sensing of pricklypear. J. Range Manage. 44:587–592.CrossRefGoogle Scholar
Everitt, J. H., Alaniz, M. A., Escobar, D. E., and Davis, M. R. 1992. Using remote sensing to distinguish common (Isocoma coronopifolia) and Drummond goldenweed (Isocoma drummondii). Weed Sci. 40:621–628.Google Scholar
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.CrossRefGoogle Scholar
Jensen, J. R. 1986. Image enhancement. In Jensen, J. R., ed. Introductory Digital Image Processing. Englewood Cliffs, NJ: Prentice-Hall. pp. 175–186.Google Scholar
Lass, L. W. and Callihan, R. H. 1993. GIS and GPS for weed surveys and management. Weed Technol. 7:249–254.Google Scholar
Lass, L. W., Carson, H. W., and Callihan, R. H. 1996. Detection of yellow starthistle with high resolution multispectral digital images. Weed Technol. 10:466–474.Google Scholar