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Spectral characteristics of leafy spurge (Euphorbia esula) leaves and flower bracts

Published online by Cambridge University Press:  20 January 2017

E. Raymond Hunt Jr. ,
James E. McMurtrey III ,
Amy E. Parker Williams  and
Lawrence A. Corp
James E. McMurtrey III
Affiliation:
USDA ARS Hydrology and Remote Sensing Laboratory, Building 007, Room 104, 10300 Baltimore Avenue, Beltsville, MD 20705-2350
Amy E. Parker Williams
Affiliation:
Department of Botany, University of Wyoming, P.O. Box 3165, Laramie, WY 82071-3165
Lawrence A. Corp
Affiliation:
Science Systems and Applications Inc., 10210 Greenbelt Road, Suite 600, Lanham, MD 20706
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Abstract

Leafy spurge can be detected during flowering with either aerial photography or hyperspectral remote sensing because of the distinctive yellow-green color of the flower bracts. The spectral characteristics of flower bracts and leaves were compared with pigment concentrations to determine the physiological basis of the remote sensing signature. Compared with leaves of leafy spurge, flower bracts had lower reflectance at blue wavelengths (400 to 500 nm), greater reflectance at green, yellow, and orange wavelengths (525 to 650 nm), and approximately equal reflectances at 680 nm (red) and at near-infrared wavelengths (725 to 850 nm). Pigments from leaves and flower bracts were extracted in dimethyl sulfoxide, and the pigment concentrations were determined spectrophotometrically. Carotenoid pigments were identified using high-performance liquid chromatography. Flower bracts had 84% less chlorophyll a, 82% less chlorophyll b, and 44% less total carotenoids than leaves, thus absorptance by the flower bracts should be less and the reflectance should be greater at blue and red wavelengths. The carotenoid to chlorophyll ratio of the flower bracts was approximately 1:1, explaining the hue of the flower bracts but not the value of reflectance. The primary carotenoids were lutein, β-carotene, and β-cryptoxanthin in a 3.7:1.5:1 ratio for flower bracts and in a 4.8:1.3:1 ratio for leaves, respectively. There was 10.2 μg g−1 fresh weight of colorless phytofluene present in the flower bracts and none in the leaves. The fluorescence spectrum indicated high blue, red, and far-red emission for leaves compared with flower bracts. Fluorescent emissions from leaves may contribute to the higher apparent leaf reflectance in the blue and red wavelength regions. The spectral characteristics of leafy spurge are important for constructing a well-documented spectral library that could be used with hyperspectral remote sensing.

Keywords

Leafy spurge, Euphorbia esula L. EPHESCarotenoidschlorophyllfluorescencereflectancespectral libraries
Type
Physiology, Chemistry, and Biochemistry
Information
Weed Science , Volume 52 , Issue 4 , August 2004 , pp. 492 - 497
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Anderson, G. L., Delfosse, E. S., Spencer, N. R., Prosser, C. W., and Richard, R. D. 2003. Lessons in developing successful invasive weed control programs. J. Range Manag 56:2–12.CrossRefGoogle Scholar
Anderson, G. L., Everitt, J. H., Escobar, D. E., Spencer, N. R., and Andrascik, R. J. 1996. Mapping leafy spurge (Euphorbia esula) infestations using aerial photography and geographic information systems. Geocarto Int 11:81–89.CrossRefGoogle Scholar
Anderson, G. L., Prosser, C. W., Hagger, S., and Foster, B. 1999. Change detection of leafy spurge infestations using aerial photography and geographic information systems. Pages 223–230 in Tueller, P. T. ed. Proceedings of the 17th Biennial Workshop on Color Aerial Photography and Videography in Resource Assessment. Bethesda, MD: American Society for Photogrammetry and Remote Sensing.Google Scholar
Bangsund, D. A., Leistritz, F. L., and Leitch, J. A. 1999. Assessing economic impacts of biological control of weeds: the case of leafy spurge in the northern Great Plains of the United States. J. Environ. Manag 56:35–43.CrossRefGoogle Scholar
Bartley, G. E. and Scolnik, P. A. 1995. Plant carotenoids: pigments for photoprotection, visual attraction, and human health. Plant Cell 7:1027–1038.Google ScholarPubMed
Bieri, J. G., Tolliver, T. J., and Catignani, G. L. 1979. Simultaneous determination of α-tocopherol and retinal in plasma or red cell by high pressure liquid chromatography. Am. J. Clin. Nutr 32:2143–2149.CrossRefGoogle ScholarPubMed
Bramley, P. M. 2002. Regulation of carotenoid formation during tomato fruit ripening and development. J. Exp. Bot 53:2107–2113.Google Scholar
Britton, G. 1995. Structure and properties of carotenoids in relation to function. FASEB J 9:1551–1558.Google Scholar
Buschmann, C., Langsdorf, G., and Lichtenthaller, H. K. 2000. Imaging of the blue, green, and red fluorescence emission of plants: an overview. Photosynthetica 38:483–491.CrossRefGoogle Scholar
Buschmann, C. and Lichtenthaller, H. K. 1998. Principles and characteristics of multi-colour fluorescence imaging of plants. J. Plant Physiol 152:297–314.Google Scholar
Carter, G. A. and Knapp, A. K. 2001. Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentrations. Am. J. Bot 88:677–684.Google Scholar
Chappelle, E. W., Kim, M. S., Mulchi, C. L., Daughtry, C. S. T., McMurtrey, J., and Corp, L. 1999. Laser induced fluorescence (LIF) as a remote sensing tool: a review. IEEE Geosci. Remote Sens. Soc. Newslett 110:6–15.Google Scholar
Corp, L. A., McMurtrey, J. E., Middleton, E. M., Mulchi, C. L., Chappelle, E. M., and Daughtry, C. S. T. 2003. Fluorescence sensing systems: in vivo detection of biophysical variations in field corn due to nitrogen supply. Remote Sens. Environ 86:470–479.Google Scholar
Daughtry, C. S. T., Biehl, L. L., and Ransom, K. J. 1989. A new technique to measure the spectral properties of conifer needles. Remote Sens. Environ 27:81–91.CrossRefGoogle Scholar
Deming-Adams, B. and Adams, W. W. III. 1996. The role of xanthophyll cycle carotenoids in the protection of photosynthesis. Trends Plant Sci 1:21–26.CrossRefGoogle Scholar
Deming-Adams, B., Gilmore, A. M., and Adams, W. W. III. 1996. In vivo functions of carotenoids in higher plants. FASEB J 10:403–412.CrossRefGoogle Scholar
DiTomaso, J. M. 2000. Invasive weeds in rangelands: species, impacts, and management. Weed Sci 48:255–265.Google Scholar
Edwards, A. J., Vinyard, B. T., Wiley, E. R., Brown, E. D., Collins, J. K., Perkins Veazie, P., Baker, R. A., and Clevidence, B. A. 1999. Consumption of watermelon juice increases plasma concentrations of lycopene and beta-carotene in human. J. Nutr 133:1043–1050.CrossRefGoogle Scholar
Entcheva-Campbell, P. K., Middleton, E. M., Corp, L. A., McMurtrey, J. E., Kim, M. S., Chappelle, E. W., and Butcher, L. M. 2002. Contribution of chlorophyll fluorescence to the reflectance of corn foliage. Pages 5–8 in Proceedings of the International Geoscience and Remote Sensing Symposium IGARSS 2002, Volume 1 on CD-ROM. Piscataway, NJ: IEEE.Google Scholar
[EPA-ERT] Environmental Protection Agency, Environmental Response Team. 1994. Chlorophyll Determination, Standard Operating Procedure #2030. Edison, NJ: Environmental Protection Agency.Google 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., 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 9:599–609.Google Scholar
Fordham, I. M., Clevidence, B. A., Wiley, E. R., and Zimmerman, R. H. 2001. Fruit of autumn olive: a rich source of lycopene. Hortic. Sci 36:1136–1137.Google Scholar
Franklin, S. E. 2001. Remote Sensing for Sustainable Forest Management. Boca Raton, FL: Lewis. Pp. 85–120, 205–249.Google Scholar
Gates, D. M. 1980. Biophysical Ecology. New York, NY: Springer. Pp. 75–95.Google Scholar
Gates, D. M., Keegan, H. J., Schleter, J. C., and Weidner, V. R. 1965. Spectral properties of plants. Appl. Opt 4:11–20.Google Scholar
Gitelson, A. A. and Merzlyak, M. N. 1996. Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. J. Plant Physiol 148:494–500.Google Scholar
Goodwin, T. W. 1980. The Biochemistry of the Carotenoids, Volume 1 Plants. 2nd ed. New York, NY: Chapman and Hall. Pp. 33–68.Google Scholar
Hall, J. C., Van Eerd, L. L., Miller, S. D., Owen, M. D. K., Prather, T. S., Shaner, D. L., Singh, M., Vaughn, K. C., and Weller, S. C. 2000. Future research directions for weed science. Weed Technol 14:647–658.Google Scholar
Hendry, G. A. F., Houghton, J. D., and Brown, S. B. 1987. The degradation of chlorophyll—a biological enigma. New Phytol 107:255–302.Google Scholar
Hunt, E. R. Jr., Everitt, J. H., Ritchie, J. C., Moran, M. S., Booth, D. T., Anderson, G. L., Clark, P. E., and Seyfried, M. S. 2003. Applications and research using remote sensing for rangeland management. Photogramm. Eng. Remote Sens 69:675–693.Google Scholar
Kim, M. S., Chappelle, E. W., Corp, L., and McMurtrey, J. E. III. 1993. The contribution of chlorophyll fluorescence to the reflectance spectra of green vegetation. Pages 1321–1324 in Proceedings of the International Geoscience and Remote Sensing Symposium, IGARSS'93. Volume 3. Piscataway, NJ: IEEE.Google Scholar
Knipling, E. B. 1970. Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sens. Environ 1:155–159.Google Scholar
Kokaly, R. F., Despain, D. G., Clark, Roger N., and Livo, K. E. 2003. Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data. Remote Sens. Environ 84:437–456.Google Scholar
Lajeunesse, S., Sheley, R., Duncan, C., and Lym, R. 1999. Leafy spurge. Pages 249–260 in Sheley, R. L. and Petroff, J. K. eds. Biology and Management of Noxious Rangeland Weeds. Corvallis, OR: Oregon State University Press.Google Scholar
Lass, L. W., Carson, H. W., and Callihan, R. H. 1996. Detection of yellow starthistle (Centaurea solstitialis) and common St. Johnswort (Hypericum perforatum) with multispectral digital imagery. Weed Technol 10:466–474.Google Scholar
Leitch, J. A., Leistritz, F. L., and Bangsund, D. A. 1996. Economic effect of leafy spurge in the upper Great Plains: methods, models, and results. Impact Assess 14:419–433.Google Scholar
Lichtenthaler, H. K. 1987. Chlorophyll and carotenoids: pigments of photosynthetic membranes. Methods Enzymol 148:350–382.CrossRefGoogle Scholar
Maas, S. J. and Dunlap, J. R. 1989. Reflectance, transmittance, and absorptance of light by normal, etiolated, and albino corn leaves. Agron. J 81:105–110.Google Scholar
McMurtrey, J. E., Chappelle, E. W., Kim, M., Mesinger, J., and Corp, L. 1994. Development of algorithms for detecting N fertilization levels in field corn (Zea mays L.) with laser induced fluorescence. Remote Sens. Environ 47:36–44.CrossRefGoogle Scholar
Parker Williams, A. E. 2001. Biological Control and Hyperspectral Remote Sensing of Leafy Spurge (Euphorbia esula L.), an Exotic Plant Species in North America. Ph.D. dissertation. University of Wyoming, Laramie, WY.Google Scholar
Parker Williams, A. and Hunt, E. R. Jr. 2002. Estimation of leafy spurge cover from hyperspectral imagery using mixture tuned matched filtering. Remote Sens. Environ 82:446–456.Google Scholar
Parker Williams, A. E. and Hunt, E. R. Jr. 2004. Accuracy assessment for detection of leafy spurge with hyperspectral imagery. J. Range Manag 57:106–112.Google Scholar
Price, J. C. 1994. How unique are spectral signatures? Remote Sens. Environ 49:181–186.Google Scholar
Price, J. C. 1998. An approach for analysis of reflectance spectra. Remote Sens. Environ 64:316–330.Google Scholar
Radhakrishnan, J., Teasdale, J. R., Liang, S., and Shuey, C. J. 2002. Remote sensing of weed canopies. Pages 175–202 in Muttiah, R. S. ed. From Laboratory Spectroscopy to Remotely Sensed Spectra of Terrestrial Ecosystems. Dordrecht, The Netherlands: Kluwer Academic.Google Scholar
Slaton, M. R., Hunt, E. R. Jr., and Smith, W. K. 2001. Estimating near-infrared leaf reflectance from leaf structural characteristics. Am. J. Bot 88:278–284.Google Scholar
Verhoef, W. 1984. Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sens. Environ 16:125–141.Google Scholar
Wellburn, A. R. 1994. The spectral determination of chlorophylls a and b, as well as total carotenoids, using various solvents with spectrometers of different resolution. J. Plant Physiol 144:307–313.CrossRefGoogle Scholar
Zarco-Tejada, P. J., Miller, J. R., Mohammed, G. H., and Noland, T. L. 2000. Chlorophyll fluorescence effects on vegetation apparent reflectance: I. Leaf-level measurements and model simulation. Remote Sens. Environ 74:582–595.Google Scholar
Zarco-Tejada, P. J., Pushnik, J. C., Debrowski, S., and Ustin, S. L. 2003. Steady-state chlorophyll a fluorescence detection from canopy derivative reflectance and double-peak red-edge effects. Remote Sens. Environ 84:283–294.Google Scholar