Hostname: page-component-8448b6f56d-jr42d Total loading time: 0 Render date: 2024-04-25T04:14:22.241Z Has data issue: false hasContentIssue false

Image Analysis of Leafy Spurge (Euphorbia esula) Cover

Published online by Cambridge University Press:  12 June 2017

Jennifer L. Birdsall
Affiliation:
USDA-FS Forestry Sciences Laboratory, Bozeman, MT 59717
Paul C. Quimby Jr.
Affiliation:
USDA-ARS, Sidney, MT 59270
Norman E. Rees
Affiliation:
USDA-ARS, Sidney, MT 59270
Tony J. Svejcar
Affiliation:
USDA-ARS Eastern Oregon Agricultural Research Center, Burns, OR 97720
Bok F. Sowell
Affiliation:
Department of Animal and Range Sciences, Montana State University, Bozeman, MT 59717

Abstract

We examined whether image analysis could separate leafy spurge from other plant species and objects by comparing image analysis to the ocular method of estimating cover. Image analysis was acceptably precise at low and medium cover levels. Image analysis was as repeatable as the ocular method at all sites and cover levels and acceptably reliable at low and medium cover levels but estimated cover lower by 12 to 22% than the ocular method at high cover levels. The average error levels of image analysis and the ocular method did not differ. Estimating leafy spurge cover with a 10% error required only 20 quadrats when image analysis was used, while twice as many quadrats were needed when cover was measured ocularly. Image analysis was recommended as a measurement tool because quantification was efficient, the equipment is inexpensive, and the color prints provide a permanent photo record of the study.

Type
Research
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.)

References

Literature Cited

Bonham, C. D. 1989. Measurements for Terrestrial Vegetation. New York: J. Wiley. 338 p.Google Scholar
Brown, D. 1954. Methods of Surveying and Measuring Vegetation. Commonwealth Agricultural Bureau Pastures and Field Crops Bull. 42. Reading, England: Bradley and Sons. 223 p.Google Scholar
Daniel, W. W. 1990. Applied Nonparametric Statistics. 2nd ed. Boston, MA: PWS-Kent Publishing. 635 p.Google Scholar
Daubenmire, R. 1959. A canopy-coverage method of vegetational analysis. Northwest Sci. 33:4364.Google Scholar
Everitt, J. H., Anderson, G. L., Escobar, D. E., Davis, M. R., Spencer, N. R., and Andrascik, R. J. 1995. Use of remote sensing for detecting and mapping leafy spurge (Euphorbia esula). Weed Technol. 9:599609.Google Scholar
Fisser, H. G. and Van Dyne, G. M. 1966. Influence of number and spacing of points on accuracy and precision of basal cover estimates. J. Range Manage. 19:205211.Google Scholar
Gerten, D. M. and Wiese, M. V. 1987. Microcomputer-assisted video image analysis of lodging in winter wheat. Photogrammetric Eng. Remote Sens. 53:8388.Google Scholar
Hutchings, S. S. and Pase, C. P. 1963. Measurement of plant cover—basal, crown, leaf area. In Blaisdell, J. P. and Parker, K. W., eds. Range Research Methods. Forestry Service Miscellaneous Publ. 940. Washington, DC: U.S. Department of Agriculture. pp. 2230.Google Scholar
Kinsinger, F. E., Eckert, R. E., and Currie, P. O. 1960. A comparison of the line interception variable plot and loop methods as used to measure shrub crown cover. J. Range Manage. 13:1721.Google Scholar
Knipling, E. B. 1970. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens. Environ. 1:155159.Google Scholar
McMillan, M. S. and Schwartz, H. F. 1993. Use of image analysis to quantify bean leaf damage. Annu. Rep. Bean Improv. Coop. 36:168169.Google Scholar
Molloy, J. M. and Moran, C. J. 1991. Compiling a field manual from overhead photographs for estimating crop residue cover. Soil Use Manage. 7:177183.Google Scholar
Neter, J. and Wasserman, W. 1974. Applied Linear Statistical Models. Homewood, IL: R. D. Irwin. 842 p.Google Scholar
Nutter, F. W. Jr., Gleason, M. L., Jenco, J. H., and Christians, N. C. 1993. Assessing the accuracy, intra-rater repeatability, and inter-rater reliability of disease assessment systems. Phytopathology 83:806812.CrossRefGoogle Scholar
Pieper, R. D. 1978. Measurement Techniques for Herbaceous and Shrubby Vegetation. Department of Animal and Range Science. Las Cruces, NM: New Mexico State University. 148 p.Google Scholar
[SAS] Statistical Analysis Systems. 1987. Statistical Analysis System. Release 6.04. Cary, NC: Statistical Analysis Systems Institute.Google Scholar
Schultz, A. M., Gibbons, R. D., and DeBano, L. 1961. Artificial populations for teaching and testing range techniques. J. Range Manage. 14:236242.CrossRefGoogle Scholar
Smith, A. D. 1944. A study of the reliability of range vegetation estimates. Ecology 25:441448.CrossRefGoogle Scholar
Stone, D. A., Lancashire, B., Sutherland, R. A., Niendorf, K. B., and Sampson, R. B. 1988. A low cost microcomputer-based image analysis system for the measurement of percent ground cover. Res. Dev. Agric. 5:6570.Google Scholar
Stutte, G. W. 1990. Analysis of video images using an interactive image capture and analysis system. HortScience 25:695697.Google Scholar
Thomas, D. L., da Silva, F. J., and Cromer, W. A. 1988. Image processing techniques for plant canopy cover evaluation. Trans. ASAE 31:428434.Google Scholar