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Potential for Remote Sensing to Detect and Predict Herbicide Injury on Waterhyacinth (Eichhornia crassipes)

Published online by Cambridge University Press:  20 January 2017

Wilfredo Robles ,
John D. Madsen  and
Ryan M. Wersal
Wilfredo Robles*
Affiliation:
Geosystems Research Institute, Box 9652, Mississippi State, MS 39762
John D. Madsen
Affiliation:
Geosystems Research Institute, Box 9652, Mississippi State, MS 39762
Ryan M. Wersal
Affiliation:
Geosystems Research Institute, Box 9652, Mississippi State, MS 39762
*
Corresponding author's E-mail: wilfredo.robles2@upr.edu
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Abstract

Many large-scale management programs directed toward the control of waterhyacinth rely on maintenance management with herbicides. Improving the implementation of these programs could be achieved through accurately detecting herbicide injury in order to evaluate efficacy. Mesocosm studies were conducted in the fall and summer of 2006 and 2007 at the R. R. Foil Plant Science Research Center, Mississippi State University, to detect and predict herbicide injury on waterhyacinth treated with four different rates of imazapyr and glyphosate. Herbicide rates corresponded to maximum recommended rates of 0.6 and 3.4 kg ae ha−1 (0.5 and 3 lb ac−1) for imazapyr and glyphosate, respectively, and three rates lower than recommended maximum. Injury was visually estimated using a phytotoxicity rating scale, and reflectance measurements were collected using a handheld hyperspectral sensor. Reflectance measurements were then transformed into a Landsat 5 Thematic Mapper (TM) simulated data set to obtain pixel values for each spectral band. Statistical analyses were performed to determine if a correlation existed between bands 1, 2, 3, 4, 5, and 7 and phytotoxicity ratings. Simulated data from Landsat 5 TM indicated that band 4 was the most useful band to detect and predict herbicide injury of waterhyacinth by glyphosate and imazapyr. The relationship was negative because pixel values of band 4 decreased when herbicide injury increased. At 2 wk after treatment, the relationship between band 4 and phytotoxicity was best (r 2 of 0.75 and 0.90 for glyphosate and imazapyr, respectively), which served to predict herbicide injury in the following weeks.

Keywords

Glyphosateimazapyrwaterhyacinth, Eichhornia crassipes (Mart.) Solms EICCRLandsat 5 TMreflectancespectral bandsphytotoxicityinvasive plantaquatic plant management
Type
Research
Information
Invasive Plant Science and Management , Volume 3 , Issue 4 , December 2010 , pp. 440 - 450
Copyright
Copyright © Weed Science Society of America 

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References

Literature Cited

Adcock, T. E., Nutter, R. W. Jr, and Banks, P. A. 1990. Measuring herbicide injury to soybeans (Glycine max) using a radiometer. Weed Sci 38:625–627.CrossRefGoogle Scholar
Best, R. G., Wehde, M. E., and Linder, R. L. 1981. Spectral reflectance of hydrophytes. Remote Sens. Environ 11:27–35.CrossRefGoogle Scholar
Boochs, F., Kupfer, G., Dockter, K., and Kühbauch, W. 1990. Shape of the red edge as vitality indicator for plants. Int. J. Remote Sens 11:1741–1753.CrossRefGoogle Scholar
Carter, G. A. 1991. Primary and secondary effects of water content on the spectral reflectance of leaves. Am. J. Bot 78:916–924.CrossRefGoogle Scholar
Carter, G. A. 1993. Responses of leaf spectral reflectance to plant stress. Am. J. Bot 80:239–243.CrossRefGoogle Scholar
Carter, G. A. and Knapp, A. K. 2001. Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am. J. Bot 88:677–684.CrossRefGoogle ScholarPubMed
Center, T. D. and Spencer, N. R. 1981. The phenology and growth of water hyacinth in a eutrophic north-central Florida lake. Aquat. Bot 10:1–32.CrossRefGoogle Scholar
Everitt, J. H., Flores, D., Yang, C., and Davis, M. R. 2005. Assessing biological control damage of giant salvinia with field reflectance measurements and aerial photography. J. Aquat. Plant Manage 43:76–80.Google Scholar
Everitt, J. H., Yang, C., Helton, R. J., Hartmann, L. H., and Davis, M. R. 2002. Remote sensing of giant salvinia in Texas waterways. J. Aquat. Plant Manage 40:11–16.Google Scholar
Filella, I. and Peñuelas, J. 1994. The red edge position and shape as indicators of plant chlorophyll content, biomass and hydric status. Int. J. Remote Sens 15:1459–1470.CrossRefGoogle Scholar
Gates, D. M., Keegan, H. J., Schleter, J. C., and Weidner, V. R. 1965. Spectral properties of plants. Appl. Optics 4:11–20.CrossRefGoogle Scholar
Gausman, H. W. 1974. Leaf reflectance of near infrared. Photogramm. Eng. Remote Sens 40:183–191.Google Scholar
Godfrey, R. K. and Wooten, J. W. 1979. Aquatic and Wetland Plants of Southeastern United States—Monocotyledons. Athens, GA University of Georgia Press. 712 p.CrossRefGoogle Scholar
Henry, W. B., Shaw, D. R., Reddy, K. R., Bruce, L. M., and Tamhankar, H. D. 2004. Remote sensing to detect herbicide drift on crops. Weed Technol 18:358–368.CrossRefGoogle Scholar
Hickman, M. V., Everitt, J. H., Escobar, D. E., and Richardson, A. J. 1991. Aerial photography and videography for detecting and mapping dicamba injury patterns. Weed Technol 5:700–706.CrossRefGoogle Scholar
Holm, L. G., Plucknett, D. L., Pancho, J. V., and Herberger, J. P. 1991. The World's Worst Weeds; Distribution and Biology. Malabar, FL Krieger. 609 p.Google Scholar
Honnell, D. R., Madsen, J. D., and Michael Smart, R. 1993. Effects of Selected Exotic and Native Aquatic Plant Communities on Water Temperature and Dissolved Oxygen. Vicksburg, MS: U.S. Army Corps of Engineers, Environmental Laboratory, Information Bulletin A-93-2. December 1993. 8 p.Google Scholar
Jakubauskas, M. E., Peterson, D. L., Campbell, S. W., deNoyelles, F. Jr, Campbell, S. D., and Penny, D. 2002. Mapping and monitoring invasive aquatic plant obstructions in navigable waterways using satellite multispectral imagery. Pages. 105–114. in Conference Proceedings, Pecora 15/LandSatellite Information IV/ISPRS Commission I/FIEOS. Denver, CO.Google Scholar
Jensen, J. R. 2000. Remote Sensing of the Environment: An Earth Resource Perspective. Upper Saddle River, NJ Prentice Hall. 544 p.Google Scholar
Joyce, J. C. and Haller, W. T. 1984. Effect of 2, 4-D and gibberellic acid on waterhyacinths under operational conditions. J. Aquat. Plant Manag 22:75–78.Google Scholar
Langeland, K. A., Hill, O. N., Koschnick, T. J., and Haller, W. T. 2002. Evaluation of a new formulation of Reward landscape and aquatic herbicide for control of duckweed, waterhyacinth, waterlettuce, and hydrilla. J. Aquat. Plant Manag 40:51–53.Google Scholar
Lehmann, A. and Lachavanne, J. B. 1997. Geographic information systems and remote sensing in aquatic botany. Aquat. Bot 58:195–207.CrossRefGoogle Scholar
Madsen, J. D. 1993. Growth and biomass allocation patterns during waterhyacinth mat development. J. Aquat. Plant Manag 31:134–137.Google Scholar
Madsen, J. D. and Bloomfield, J. A. 1993. Aquatic vegetation quantification symposium: an overview. Lake Reserv. Manag 7:137–140.CrossRefGoogle Scholar
McVea, C. and Boyd, C. E. 1975. Effects of waterhyacinth cover on water chemistry, phytoplankton, and fish in ponds. J. Environ. Qual 4:375–378.CrossRefGoogle Scholar
Miura, T., Huete, A., Yoshioka, H., and Kim, H. 2002. An application of airborne hyperspectral and EO-1 Hyperion data for inter-sensor calibration of vegetation indices for regional-scale monitoring. Pages. 3118–3120. in Conference Proceedings International Geoscience and Remote Sensing Symposium (IGARSS). Toronto, Canada.CrossRefGoogle Scholar
Murtha, P. A. 1978. Remote sensing and vegetation damage: a theory for detection and assessment. Photogramm. Eng. Remote Sens 44:1147–1158.Google Scholar
[NASA] National Aeronautic and Space Agency 2008. Landsat 7, Science Data Users Handbook; Instrument Calibration. http://landsathandbook.gsfc.nasa.gov/handbook/handbook_htmls/chapter8/chapter8.html. Accessed June 12, 2008.Google Scholar
Nelson, L. S., Skogerboe, J. G., and Getsinger, K. D. 2001. Herbicide evaluation against giant salvinia. J. Aquat. Plant Manag 39:48–53.Google Scholar
Nutter, F. W., Littrell, R. H. Jr, and Brenneman, T. B. 1990. Utilization of a multispectral radiometer to evaluate fungicide efficacy to control late leaf spot in peanut. Phytopathology 80:102–108.CrossRefGoogle Scholar
Penfound, W. T. and Earle, T. T. 1948. The biology of the waterhyacinth. Ecol. Monogr 18:447–472.CrossRefGoogle Scholar
Peñuelas, J., Gamon, J. A., Griffin, K. L., and Field, C. B. 1993. Assessing community type, plant biomass, pigment composition, and photosynthetic efficiency of aquatic vegetation from spectral reflectance. Remote Sens. Environ 46:110–118.CrossRefGoogle Scholar
Senseman, S. A. ed. 2007. Herbicide Handbook. 9th ed. Lawrence, KS Weed Science Society of America. 458 p.Google Scholar
Spencer, D., Sher, A., Thornby, D., Liow, P., Ksander, G., and Tan, W. 2007. Non-destructive assessment of Arundo donax (Poaceae) leaf quality. J. Freshw. Ecol 22:277–285.CrossRefGoogle Scholar
Thelen, K. D., Kravchenko, A. N., and Lee, C. D. 2004. Use of optical remote sensing for detecting herbicide injury in soybean. Weed Technol 18:292–297.CrossRefGoogle Scholar
Toft, J. D., Simenstad, C. A., Cordell, J. R., and Grimaldo, L. F. 2003. The effects of introduced water hyacinth on habitat structure, invertebrate assemblages, and fish diets. Estuaries 26:746–758.CrossRefGoogle Scholar
Ustin, S. L., Gitelson, A. A., Jacquemoud, S., Schaepman, M., Asner, G. P., Gamon, J. A., and Zarco-Tejada, P. 2009. Retrieval of foliar information about plant pigment systems from high resolution spectroscopy. Remote Sens. Environ 113:S67–S77.CrossRefGoogle Scholar
Van, T. K., Vandiver, V. V. Jr, and Conant, R. D. Jr. 1986. Effect of herbicide rate and carrier volume on glyphosate phytotoxicity. J. Aquat. Plant Manag 24:66–69.Google Scholar
Willard, C. J. 1958. Rating scales for weed control experiments. Weeds 6:327–328.CrossRefGoogle Scholar
Wunderlich, W. E. 1962. History of water hyacinth control in Louisiana. Hyacinth Control J 1:14–16.Google Scholar