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Using Satellite Data to Map False Broomweed (Ericameria austrotexana) Infestations on South Texas Rangelands

Published online by Cambridge University Press:  12 June 2017

Gerald L. Anderson ,
James H. Everitt ,
Arthur J. Richardson  and
David E. Escobar
Gerald L. Anderson
Affiliation:
Remote Sensing Res. Unit, Agric. Res. Serv., U.S. Dep. Agric., 2413 e. Hwy. 83, Weslaco, TX 78596
James H. Everitt
Affiliation:
Remote Sensing Res. Unit, Agric. Res. Serv., U.S. Dep. Agric., 2413 e. Hwy. 83, Weslaco, TX 78596
Arthur J. Richardson
Affiliation:
Remote Sensing Res. Unit, Agric. Res. Serv., U.S. Dep. Agric., 2413 e. Hwy. 83, Weslaco, TX 78596
David E. Escobar
Affiliation:
Remote Sensing Res. Unit, Agric. Res. Serv., U.S. Dep. Agric., 2413 e. Hwy. 83, Weslaco, TX 78596
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Abstract

False broomweed is a troublesome weed on south Texas rangelands. The plant suppresses the growth of desirable herbaceous plant species and is unpalatable to livestock and wildlife. The objectives of this study were to evaluate multispectral satellite data for automated detection, classification, and mapping of false broomweed infestations. Determining the optimum phenological conditions for false broomweed detection was a major goal. Results indicate that satellite data can be used to detect major stands of this shrub and map the relative extent of infested areas. The best classification was obtained when the foliage of the shrub was fully developed and during periods of low herbaceous biomass production. Limited ground or aerial surveying will be needed to produce more exact estimates of the extent of false broomweed stands; however, these efforts can be focused on areas identified by satellite classification.

Keywords

False broomweed, Ericameria austrotexana M. Johnst., # EIMAUKappaclassificationremote sensing
Type
Research
Information
Weed Technology , Volume 7 , Issue 4 , December 1993 , pp. 865 - 871
Copyright
Copyright © 1994 by the Weed Science Society of America 

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References

Literature Cited

1. Bishop, Y. S., Fienberg, S., and Holland, P. 1975. Discrete Multivariate Analysis: Theory and Practice. MIT Press. Cambridge, MA. p. 374–400.Google Scholar
2. Cohen, J. 1960. A coefficient of agreement for nominal scales. Educ. Psychol. Measure. 20:37–40.Google Scholar
3. Congalton, R. G. and Mead, R. A. 1983. A quantitative method to test for consistency and correctness in photointerpretation. Photogram. Eng. Remote Sensing 49:69–74.Google Scholar
4. Correll, D. S. and Johnston, M. C. 1970. Manual of the Vascular Plants of Texas. Texas Research Foundation, Renner, TX. p. 1577–1578.Google Scholar
5. Driscoll, R. S. and Coleman, M. D. 1974. Color for shrubs. Photogram. Eng. Remote Sensing 40:451–460.Google Scholar
6. Eastman, J. R. 1992. IDRISI Technical Reference Manual. Clark Univ. Graduate School of Geography, Worcester, MA. p. 16–17.Google Scholar
7. Everitt, J. H., Escobar, D. E., and Noreaga, J. 1991. A high resolution multispectral video system. Geocarto Int. 4:45–51.Google Scholar
8. Everitt, J. H., Gerbermann, A. H., Alaniz, M. A., and Bowen, R. L. 1980. Using 70-mm aerial photography to identify rangeland sites. Photogram. Eng. Remote Sensing 46:1339–1348.Google Scholar
9. Everitt, J. H., Ingle, S. J., Gausman, H. W., and Mayeux, H. S. Jr. 1984. Detection of false broomweed (Ericameria austrotexana) by aerial photography. Weed Sci. 32:621–624.CrossRefGoogle Scholar
10. Gausman, H. W., Menges, R. M., Escobar, D. E., Everitt, J. H., and Bowen, R. L. 1977. Pubescence affects spectra and imagery of silverleaf sunflower (Helianthus argophyllus). Weed Sci. 25:437–440.Google Scholar
11. Gould, F. W. 1975. Texas plants—A checklist and ecological summary. Texas Agric. Exp. Stn., Texas A&M Univ., College Station, TX. MP-585. 121 p.Google Scholar
12. Hudson, W. D. and Ramm, C. W. 1987. Correct formulation of the Kappa Coefficient of Agreement. Photogram. Eng. Remote Sensing 53: 421–422.Google Scholar
13. Jensen, J. R. 1986. Introductory Digital Image Processing. A Remote Sensing Perspective. Prentice-Hall, Englewood Cliffs, NJ. p. 177–233.Google Scholar
14. Mayeux, H. S. Jr. and Chamrad, A. D. 1982. Response of false broomweed (Ericameria austrotexana) and associated herbaceous vegetation to pelleted herbicides. Weed Sci. 30:668–671.Google Scholar
15. Mayeux, H. S. Jr. and Hamilton, W. T. 1988. Response of false broomweed and associated herbaceous species to prescribed fire. J. Range Manage. 41:2–6.Google Scholar
16. Mayeux, H. S. Jr., Scifres, C. J., and Crane, R. A. 1980. Ericameria austrotexana and associated range forage responses to herbicides. Weed Sci. 28:602–606.Google Scholar
17. Rosenfield, G. H. and Fitzpatrick-Lins, K. 1986. A coefficient of agreement as a measure of thematic classification accuracy. Photogram. Eng. Remote Sensing 52:223–227.Google Scholar
18. Sabins, F. F. 1986. Remote Sensing Principles and Interpretation. Second Edition. H. W. Freeman and Co., New York. p. 117–123.Google Scholar
19. Wilson, J. D. 1992. A comparison of procedures for classifying remotely-sensed data using simulated data sets. Int. J. Remote Sensing 13:265–386.Google Scholar
20. Wiegand, C. L. and Richardson, A. J. 1982. Comparisons Among a New Soil Index and Other Two- and Four-dimensional Vegetation Indices. Tech. Papers Am. Cong. Survey. Map.—Am. Soc. Phtogramm. Annu. Conv. (Denver, CO., Mar. 14–20, 1982) p. 210–227.Google Scholar