Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2025-01-03T16:31:02.025Z Has data issue: false hasContentIssue false
  • English
  • Français

Canopy Light Reflectance and Remote Sensing of Shin Oak (Quercus havardii) and Associated Vegetation

Published online by Cambridge University Press:  12 June 2017

James H. Everitt ,
David E. Escobar ,
Ricardo Villarreal ,
Mario A. Alaniz  and
Michael R. Davis
James H. Everitt
Affiliation:
Remote Sensing Res. Unit, Agric. Res. Serv., U.S. Dep. Agric., 2413 E. Hwy. 83, Weslaco, TX 78596-8344
David E. Escobar
Affiliation:
Remote Sensing Res. Unit, Agric. Res. Serv., U.S. Dep. Agric., 2413 E. Hwy. 83, Weslaco, TX 78596-8344
Ricardo Villarreal
Affiliation:
Remote Sensing Res. Unit, Agric. Res. Serv., U.S. Dep. Agric., 2413 E. Hwy. 83, Weslaco, TX 78596-8344
Mario A. Alaniz
Affiliation:
Remote Sensing Res. Unit, Agric. Res. Serv., U.S. Dep. Agric., 2413 E. Hwy. 83, Weslaco, TX 78596-8344
Michael R. Davis
Affiliation:
Remote Sensing Res. Unit, Agric. Res. Serv., U.S. Dep. Agric., 2413 E. Hwy. 83, Weslaco, TX 78596-8344
Get access Rights & Permissions [Opens in a new window]

Abstract

Shin oak is a deciduous shrub that forms dense stands of brush on sandy soils in rangeland areas of the Rolling and High Plains of Texas. Plant canopy reflectance measurements made on shin oak showed that it had both low visible (0.63- to 0.69-μm waveband) and nearinfrared (0.76- to 0.90-μm waveband) reflectance values, a characteristic generally not shared by associated plant species or mixtures of species. The low reflectance values of shin oak caused it to have dark-red, reddish-brown, or brown image tones on color-infrared photographic, videographic, and SPOT satellite images that made it distinguishable from associated vegetation and other land use features. The optimum time to remotely distinguish this noxious shrub is during the mature phenological stage from June to September. Computer-based image analyses of video and satellite images showed that shin oak populations could be quantified. This technique can permit “percent land area” estimates of shin oak on rangelands. The aerial imagery is useful for detecting shin oak on smaller rangeland areas, whereas the satellite imagery is applicable in mapping large areas of shin oak distribution.

Keywords

Shin oak, Quercus havardii Rydb., # QUEHVhoney mesquite, Prosopis glandulosa Torr., #PRCJGsand sagebrush Artemisia filifolia Torr., #ARTFIReflectancecolor-infrared photographycolor-infrared video imagerySPOT satellite imageryrangelandsQUEHVPRCJGARTFI
Type
Soil, Air, and Water
Information
Weed Science , Volume 41 , Issue 2 , June 1993 , pp. 291 - 297
Copyright
Copyright © 1993 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

1. Correll, D. S. and Johnston, M. C. 1970. Manual of the Vascular Plants of Texas. Texas Res. Foundation, Renner, TX. 1881 pp.Google Scholar
2. Driscoll, R. S. and Coleman, M. D. 1974. Color for shrubs. Photogramm. Eng. 40:451–459.Google Scholar
3. 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
4. Everitt, J. H., Escobar, D. E., Gerbermann, A. H., and Alaniz, M. A. 1988. Detecting saline soils with video imagery. Photogramm. Eng. and Remote Sens. 54:1283–1287.Google Scholar
5. Everitt, J. H., Escobar, D. E., and Judd, F. W. 1991. Evaluation of airborne video imagery for distinguishing black mangrove (Avicennia germinans) on the lower Texas gulf coast. J. Coastal Res. 7:1169–1173.Google Scholar
6. Everitt, J. H., Escobar, D. E., Villarreal, R., Noriega, J. R., and Davis, M. R. 1991. Airborne video systems for agricultural assessment. Remote Sens. Environ. 35:231–242.Google Scholar
7. Everitt, J. H. and Nixon, P R. 1985. Video imagery: a new remote sensing tool for range management. J. Range Manage. 38:421–424.Google Scholar
8. Everitt, J. H., Richardson, A. J., Escobar, D. E., Villarreal, R., and Drawe, D. L. 1990. Mapping native plant communities with color-infrared video imagery. J. Imaging Technol. 16:96–100.Google Scholar
9. Everitt, J. H., Richardson, A. J., and Nixon, P. R. 1986. Canopy reflectance characteristics of succulent and nonsucculent rangeland plant species. Photogramm. Eng. and Remote Sens. 52:1891–1897.Google Scholar
10. Everitt, J. H. and Villarreal, R. 1987. Detecting huisache (Acacia farnesiana) and Mexican palo-verde (Parkinsonia aculeata) by aerial photography. Weed Sci. 35:427–432.Google Scholar
11. 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.CrossRefGoogle Scholar
12. Gould, F. W. 1975. Texas plants—a checklist and ecological summary. Tex. Agric. Exp. Stn., Texas A&M Univ., College Station, TX. MP-585. 121 pp.Google Scholar
13. King, D., Vlcek, J., and Yuan, X. 1987. A four-camera video sensor: its performance and applications with special regards to forestry. Pages 286–294, 339 in Proc. 11th Biennial Workshop on Color Aerial Photography and Videography in the Plant Sciences. Am. Soc. Photogramm. and Remote Sens., Falls Church, VA.Google Scholar
14. Kingsbury, J. M. 1964. Poisonous Plants of the United States and Canada. Prentice-Hall, Inc., Englewood Cliffs, NJ. 626 p.Google Scholar
15. Myers, V. I. and Allen, W. A. 1968. Electrooptical remote sensing methods as nondestructive testing and measuring techniques in agriculture. Appl. Optics 7:1818–1838.Google Scholar
16. Myers, V. I., Bauer, M. E., Gausman, H. W., Hart, W. G., Heilman, J. L., McDonald, R. B., Park, A. B., Ryerson, R. A., Schmugge, T. J., and Westin, F. C. 1983. Remote sensing in agriculture. Pages 2111–2228 in Colwell, Robert N., ed. Manual of Remote Sensing. Am. Soc. Photogramm., Falls Church, VA.Google Scholar
17. Myhre, R. J. 1987. Applications of aerial photography to several new and unusual vegetation pest problems. Pages 49–53 in Proc. 10th Biennial Workshop on Color Aerial Photography in the Plant Sciences. Am. Soc. Photogramm. and Remote Sens., Falls Church, VA.Google Scholar
18. Richardson, A. J. 1981. Measurement of reflectance factors under daily and intermittent irradiance variations. Appl. Optics 20:1336–1340.Google Scholar
19. Richardson, A. J., Escobar, D. E., Gausman, H. W., and Everitt, J. H. 1981. Use of LANDSAT-2 data technique to estimate silverleaf sunflower infestation. Pages 676–683 in Proc. 7th Symp. Machine Processing of Remotely Sensed Data. Purdue Univ., West Lafayette, IN.Google Scholar
20. Richardson, A. J., Wiegand, C. L., Gausman, H. W., Cuellar, J. A., and Gerbermann, A. H. 1975. Plant, soil, and shadow reflectance components of row crops. Photogramm. Eng. and Remote Sens. 41:1401–1407.Google Scholar
21. Scifres, C. J. 1980. Brush Management. Texas A&M Univ. Press, College Station, TX. 360 pp.Google Scholar
22. Smith, H. N. and Rechenthin, C. A. 1964. Grassland restoration. Part I. The Texas brush problem. U.S. Dep. Agric., Soil Conserv. Serv. 4–19114. 49 pp.Google Scholar
23. Sperry, O. E., Dollahite, J. W., Hoffman, G. O., and Camp, B. J. 1965. Texas Plants Poisonous to Livestock. Texas Agric. Exp. Stn. Bull. 1028. 57 pp.Google Scholar
24. Steel, R.G.D. and Torrie, J. H. 1980. Principles and Procedures of Statistics. McGraw-Hill Book Co., New York. 481 pp.Google Scholar
25. Tucker, C. J., Jones, W. H., Kley, W. A., and Sunstorm, C. J. 1980. The GSFC MARK-II three band hand-held radiometer. NASA Tech. Memo 80641. 8 pp.Google Scholar
26. Tueller, P. T. 1982. Remote sensing for range management. Pages 125–140 in Johannsen, C. J. and Sanders, J. L., eds. Remote Sensing for Resource Management. Soil Conserv. Soc. Am., Ankeny, IO.Google Scholar
27. Vines, R. A. 1960. Trees, Shrubs, and Woody Vines of the Southwest. Univ. of Texas Press, Austin, TX. 1104 pp.Google Scholar