Hostname: page-component-7dd5485656-wxk4p Total loading time: 0 Render date: 2025-10-31T16:38:34.037Z Has data issue: false hasContentIssue false
  • English
  • Français

Translation of remote sensing data into weed management decisions

Published online by Cambridge University Press:  20 January 2017

David R. Shaw
Get access Rights & Permissions [Opens in a new window]

Abstract

Remote sensing and associated spatial technologies provide tremendous opportunity to enhance weed management and improve–protect the environment through judicious use of the most efficacious control methods for a given site. They can also be invaluable assets for detection of invasions, assessment of infestation levels, monitoring rate of spread, and determining the efficacy of mitigation efforts for weed management. In combination with other technologies such as global positioning systems and geographic information systems, sampling strategies can be devised to efficiently determine the location of weed populations in agricultural and wildland situations. Maps created from remote sensing or sampling (or both) allow site-specific weed management of only the areas requiring corrective action. Potential benefits to the land managers and the ecosystem as a whole will come from reductions in inputs, reduced environmental liability from the detrimental effects of applying control measures to entire areas, crop yield increases through better management decisions, and early detection and effective management of invading species. Improvements in spatial and spectral resolution, temporal frequency, image turnaround time, and cost of image acquisition, combined with the realization of the value of the data, are enhancing the acceptance and usage of remote sensing technologies. However, remote sensing will be best used by providing accurate, site-specific data that can be converted into information used by decision support systems (DSSs). Advances in these DSSs, and their ability to incorporate remote sensing data, have been rapid and widespread in the past 10 yr. As a result, federal management and research agencies, academic institutions, and private entities have collectively developed efforts to use this information in monitoring and management efforts for invasive species in western rangelands, aquatic ecosystems and forestry, and site-specific weed management in agronomics.

Keywords

Decision support systemsinvasive speciesspatial technologies

Information

Type
Symposium
Information
Weed Science , Volume 53 , Issue 2 , April 2005 , pp. 264 - 273
Copyright
Copyright © 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.)

Article purchase

Temporarily unavailable

References

Literature Cited

Anderson, J. E., Fischer, R. L., and Deloach, S. R. 1999. Remote sensing and precision agriculture: ready for harvest or still maturing? Photogram. Eng. Remote Sens 65:1118–1123.Google Scholar
Auld, B. A. and Tisdell, C. A. 1988. Influence of spatial distribution of weeds on crop yield loss. Plant Prot. Q 3:81.Google Scholar
Barnes, E. M., Baker, M. G., Pinter, P. J. Jr., and Jones, D. D. 1998. Integration of remote sensing and crop models to provide decision support for precision crop management. Pages 211–213 in Proceedings of the First International Conference on Geospatial Information in Agriculture and Forestry; Lake Buena Vista, FL; January 1–3, 1998. Volume 1.Google Scholar
Bartlett, D. S., Hardisky, M. A., Johnson, R. W., Gross, M. F., Klemas, V., and Hartman, J. M. 1998. Continental scale variability in vegetation reflectance and its relationship to canopy morphology. Int. J. Remote Sens 9:1223–1241.Google Scholar
Blackburn, G. A. 1998. Quantifying chlorophylls and carotenoids at leaf and canopy scales: an evaluation of some hyperspectral approaches. Remote Sens. Environ 66:273–285.CrossRefGoogle Scholar
Blazquez, C. H. and Edwards, G. J. 1986. Spectral reflectance of healthy and diseased watermelon leaves. Ann. Appl. Biol 108:243–249.CrossRefGoogle Scholar
Brain, P. and Cousens, R. 1990. The effect of weed distribution on predictions of yield loss. J. Appl. Ecol 27:735–742.Google Scholar
Brown, N. 2000. Practical commercial remote sensing applications in the Ontario precision agriculture marketplace. Pages 6–13 in Proceedings of the Second International Conference on Geospatial Information in Agriculture and Forestry; Lake Buena Vista, FL; January 10–12, 2000. Volume 1.Google Scholar
Brown, R. B. and Steckler, J-P. G. A. 1995. Precision maps for spatially variable herbicide application in no-till corn. Trans. ASAE 37:297–302.Google Scholar
Burrough, P. A., Bregt, A. K., deHeus, M. J., and Kloosterman, E. G. 1985. Complementary use of thermal imagery and spectral analysis of soil properties and wheat yields to reveal cyclic patterns in the Flevopolder. J. Soil Sci 36:141–152.Google Scholar
Campanella, R. and Seal, M. R. 2000. Patterns among seeding rates, normalized difference vegetation index, and yield on a Mississippi cotton farm. Pages 232–239 in Proceedings of the Second International Conference on Geospatial Information in Agriculture and Forestry; Lake Buena Vista, FL; January 10–12, 2000. Volume 1.Google Scholar
Cardina, J., Johnson, G. A., and Sparrow, D. H. 1997. The nature and consequence of weed spatial distribution. Weed Sci 45:364–373.Google Scholar
Carlson, R. E., Yarger, D. N., and Shaw, R. H. 1971. Factors affecting the spectral properties of leaves with special emphasis on leaf water status. Agron. J 63:486–489.Google Scholar
Carson, H. W., Lass, L. W., and Callihan, R. H. 1995. Detection of yellow hawkweed (Hieracium pratense) with high resolution multispectral digital imagery. Weed Technol 9:477–483.Google Scholar
Carter, P. G. and Johannsen, C. J. 2000. Crop vegetation anomaly identification from observed patterns found within remote sensing data. Pages 152–156 in Proceedings of the Second International Conference on Geospatial Information in Agriculture and Forestry; Lake Buena Vista, FL; January 10–12, 2000. Volume 1.Google Scholar
Chen, C. W., Herr, J., Ziemelis, L., Goldstein, R. A., and Olmsted, L. 1999. Decision support system for total maximum daily load. J. Environ. Eng 125:635–659.CrossRefGoogle Scholar
Coleman, T. L. and Montgomery, O. L. 1987. Soil moisture, organic matter and iron content effect on spectral characteristics of selected vertisols and alfisols in Alabama. Photogram. Eng. Remote Sens 12:1659–1663.Google Scholar
Delannay, X., Bauman, T. T., and Beighley, D. H. et al. 1995. Yield evaluation of a glyphosate-tolerant Glycine max line after treatment with glyphosate. Crop Sci 35:1461–1467.Google Scholar
Easley, J. W., Shaw, D. R., and Gray, C. J. 2004. Aerial imagery for site-specific herbicide applications in soybean. Proc. South. Weed Sci. Soc 57:239.Google Scholar
Estes, J. E., Mel, M. R., and Hooper, J. O. 1997. Measuring soil moisture with an airborne imaging passive microwave radiometer. Photogram. Eng. Remote Sens 43:1273–1281.Google Scholar
Everitt, J. H. and Deloach, C. J. 1990. Remote sensing of Chinese tamarisk (Tamarix chinensis) and associated vegetation. Weed Sci 38:273–278.CrossRefGoogle Scholar
Federal Register. 2000. Environmental Protection Agency: revisions to the water quality planning and management regulation and revisions to the national pollutant discharge elimination system program in support of revisions to the water quality planning and management regulation; final rules. Fed. Reg 65:43586–43670.Google Scholar
Felton, W. L., Doss, A. F., Nash, P. G., and McCloy, K. R. 1991. To selectively spot spray weeds. Am. Soc. Agric. Eng. Symp 11–91:427–432.Google Scholar
Fitzgerald, G. J., Maas, S. J., and DeTar, W. R. 2000. Early detection of spider mites in cotton using multispectral remote sensing. Pages 1022–1024 in Proceedings of the Beltwide Cotton Conference; San Antonio, TX; January 4–8, 2000. Memphis, TN: Cotton.Google Scholar
Fritz, L. W. 1996. The era of commercial earth observation satellites. Photogram. Eng. Remote Sens 62:40–44.Google Scholar
Garegnani, J., Gualtieri, J. A., Chettri, S., Robinson, J., Hunt, J. P., Bechdol, M., and Vermeulen, A. 2000. Preliminary test of the utility of hyperspectral image data to precision farming. Pages 2519–2521 in Proceedings of the IEEE; July 24–28, 2000. Piscataway, NJ: IEEE.Google Scholar
Givens, W. A., Shaw, D. R., and Smith, M. C. 2004. Using multiresolutional textural analysis to detect weeds in soybean. Proc. South. Weed Sci. Soc 57:346.Google Scholar
Griffin, J. L., Reynolds, D. B., Jordan, D. L., Prochaska, L. M., and Rogers, R. L. 1994. Evaluation of roundup ready transgenic soybean in Louisiana. La. Agric 37:23.Google Scholar
Hart, W. G. and Myers, V. I. 1968. Infrared aerial color photography for the detection of population of brown soft scale on citrus groves. J. Econ. Entomol 61:617–624.Google Scholar
Heald, C. M., Thames, W. H., and Wiegand, C. L. 1972. Detection of Rotylenchus reniformis infestations by aerial infrared photography. J. Nematol 4:298–300.Google Scholar
Hirano, A., Madden, M., and Welch, R. 2003. Hyperspectral image data for mapping wetland vegetation. Wetlands 23:436–448.Google Scholar
Holmberg, H. 2000. Sky-high Scouting. www.agriculture.com/sfonline/sf/2000/may-june/0007skyhigh.html.Google Scholar
Holsapple, C. W. and Whinston, A. B. 1996. Decision Support Systems: A Knowledge Based Approach. Eagan, MN: West. Pp. 1–29.Google Scholar
Jackson, R. D., Jones, C. A., Uehara, G., and Santo, L. T. 1980. Remote detection of nutrient and water deficiency in sugarcane under variable cloudiness. Remote Sens. Environ 11:327–331.Google Scholar
Johannsen, C. J., Carter, P. G., Morris, D. K., Ross, K., and Erickson, B. 2000. The real applications of remote sensing to agriculture. Pages 1–5 in Proceedings of the Second International Conference on Geospatial Information in Agriculture and Forestry; Lake Buena Vista, FL; January 10–12, 2000. Volume 1.Google Scholar
John, B. 1992. Soil moisture detection with airborne passive and active microwave sensors. Int. J. Remote Sens 13:481–491.Google Scholar
King, B. A., Brady, R. A., McCann, I. R., and Stark, J. C. 1994. Variable rate water application through sprinkler irrigation. Pages 427–439 in Proceedings on Site-specific Management for Agricultural Systems. Madison, WI: ASA/CSSA/SSSA.Google Scholar
Lamb, D. W., Weedon, M. M., and Rew, L. J. 1999. Evaluating the accuracy of mapping weeds in seedling crops using airborne digital imaging: Avena spp. in seedling triticale. Weed Res 39:481–492.CrossRefGoogle 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
Leone, A. P., Wright, G. G., and Corves, C. 1995. The application of satellite remote sensing for soil studies in upland areas of southern Italy. Int. J. Remote Sens 16:447–466.Google Scholar
Lindquist, J. L., Dieleman, J. A., Mortensen, D. A., Johnson, G. A., and Wyse-Pester, D. Y. 1998. Economic importance of managing spatially heterogeneous weed populations. Weed Technol 12:7–13.Google Scholar
Lowenberg-DeBoer, J. 1999. Risk management potential of precision farming technologies. J. Agric. Appl. Econ 31:275–285.Google Scholar
Manzer, F. E. and Cooper, G. R. 1967. Aerial photographic methods of potato disease detection. Maine Agric. Exp. Stn. Bull 646:1–14.Google Scholar
Marshall, E. J. P. 1988. Field-scale estimates of grass weed populations in arable land. Weed Res 28:191–198.Google Scholar
Maxwell, B. D. and Colliver, C. 1995. Expanding economic thresholds by including spatial and temporal weed dynamics. Proc. Brighton Crop Prot. Conf. Weeds 13:1069–1076.Google Scholar
Medlin, C. R. and Shaw, D. R. 2000. Economic comparison of broadcast and site-specific herbicide applications in nontransgenic and glyphosate-tolerant Glycine max . Weed Sci 48:653–661.Google Scholar
Medlin, C. R., Shaw, D. R., Gerard, P. D., and LaMastus, F. E. 2000. Using remote sensing to detect weed infestations in Glycine max . Weed Sci 48:393–398.Google Scholar
Menges, R. M., Nixon, P. R., and Richardson, A. J. 1985. Light reflectance and remote sensing of weeds in agronomic and horticultural crops. Weed Sci 33:569–581.Google Scholar
Moran, M. S. 2000. Image-based remote sensing for agricultural management—perspectives of image providers, research scientists and users. Pages 23–29 in Proceedings of the Second International Conference on Geospatial Information in Agriculture and Forestry; Lake Buena Vista, FL; January 10–12, 2000. Volume 1.Google Scholar
Moran, M. S., Inoue, Y., and Barnes, E. M. 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens. Environ 61:319–346.Google Scholar
Penuelas, J., Gamon, J. A., Fredeen, A. L., Merino, J., and Field, C. B. 1994. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sens. Environ 48:135–146.Google Scholar
Pilesjo, P., Thylen, L., and Persson, A. 2000. Digital elevation models for delineation of agricultural management zones. Pages 224–231 in Proceedings of the Second International Conference on Geospatial Information in Agriculture and Forestry; Lake Buena Vista, FL; January 10–12, 2000. Volume 1.Google Scholar
Powell, W. G., Massey, J. H., Cox, M. S., Bruce, L. M., Evans, D. L., Tagert, M. L., and Shaw, D. R. 2003. Environmental applications of remote sensing: determination of crop residues on soil surfaces using hyperspectral reflectance. Proc. South. Weed Sci. Soc 56:311.Google Scholar
Price, A. J., Krueger, D. W., Roberson, G., and Wilkerson, G. G. 2003. Evaluation of variable-rate herbicide applications in soybean. Proc. South. Weed Sci. Soc 56:300.Google Scholar
Randall, J. M. 1996. Weed control for the preservation of biodiversity. Weed Technol 10:370–383.Google Scholar
Rankins, A. Jr., Bloodworth, W. F., and Reynolds, D. B. 2003. HADSS validation for use in Mississippi cotton. Proc. South. Weed Sci. Soc 56:197.Google Scholar
Rankins, A. Jr., Shaw, D. R., and Byrd, J. D. Jr. 1998. HERB and MSU-HERB field validation for soybean (Glycine max) weed control in Mississippi. Weed Technol 12:88–96.Google Scholar
Rew, L. J., Whelan, B., and McBratney, A. B. 2001. Does kriging predict weed distributions accurately enough for site-specific weed control? Weed Res 41:245–263.Google Scholar
Robert, P. C. 1997. Remote sensing: a potentially powerful technique for precision agriculture. in Proceeding of the Land Satellite Information in the Next Decade II; Washington, DC; December 2–5, 1997. ASPRS. Pp. 151–159.Google Scholar
Robert, P. C., Rust, R. H., and Larson, W. E. 1994. Preface. Pages xiii–xiv in Proceeding of the Site-specific Management for Agricultural Systems. Madison, WI: ASA/CSSA/SSSA.Google Scholar
Safir, G. R., Svits, G. H., and Ellingboe, A. H. 1972. Spectral reflectance and transmittance of corn leaves infected with Helminthosporium maydis . Phytopathology 62:1210–1213.Google Scholar
Sassenrath-Cole, G. F., Hood, K. B., Alarcon, V. J., Olson, R., Tarpley, L., Varco, J., and Seal, M. 1998. Remote sensing of crop physiological function: applications for development of variable rate inputs. Pages 604–608 in Proceedings of the First International Conference on Geospatial Information in Agriculture and Forestry; Lake Buena Vista, FL; June 1–3, 1998. Volume 2.Google Scholar
Schnase, J. L., Stohlgren, T. J., and Smith, J. A. 2003. The national invasive species forecasting system: a strategic NASA/USGS partnership to manage biological invasions. Page 79 in Proceedings of the Invasive Plants in Natural and Managed Systems. 7th International Conference on the Ecology and Management of Alien Plant Invasions; Ft. Lauderdale, FL; November 3–7. Lawrence, KS: Weed Science Society of America.Google Scholar
Servilla, M. 1998. Bridging the barriers. Agriculture remote sensing: part two. Mod. Agric 1:18–20. [www.eomonline.com/modernagsite/archives/index.html.].Google Scholar
Shaw, D. R. and Mack, R. E. 1991. Application timing of herbicides for the control of redvine (Brunnichia ovata). Weed Technol 5:125–129.Google Scholar
Sheffner, E. 2003. The national invasive species forecasting system: a strategic NASA/USGS partnership to manage biological invasions. Page 81 in Proceedings of the Invasive Plants in Natural and Managed Systems. 7th International Conference on the Ecology and Management of Alien Plant Invasions; Ft. Lauderdale, FL; November 3–7. Lawrence, KS: Weed Science Society of America.Google Scholar
Shibayama, R. and Akiyama, T. 1991. Estimating grain yield of maturing rice canopies using high spectral resolution reflectance measurements. Remote Sens. Environ 36:45–53.Google Scholar
Sudbrink, D. L. Jr., Harris, F. A., Robbins, J. T., Snodgrass, G. L., and Thomson, S. J. 2000. Remote sensing of late-season pest damage to cotton and wild host plants of tarnished plant bug in the Mississippi Delta. Pages 1220–1223 in Proceedings of the Beltwide Cotton Conference; San Antonio, TX; January 4–8, 2000. Memphis, TN: Cotton.Google Scholar
Thenkabail, P. S., Smith, R. B., and Pauw, E. D. 2000. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sens. Environ 71:158–182.Google Scholar
Thenkabail, P. S., Ward, A. D., and Lyon, J. G. 1995. Landsat-5 thematic mapper models of soybean and corn crop characteristics. Int. J. Remote Sens 15:49–61.Google Scholar
Thornton, P. K., Fawcett, R. H., Dent, J. B., and Coble, H. D. 1991. Spatial weed distributions and economic thresholds for weed control. Crop Prot 9:337–342.Google Scholar
Tucker, C. J. 1980. Remote sensing of leaf water content in the near infrared. Remote Sens. Environ 10:23–32.Google Scholar
[U.S. EPA] United States Environmental Protection Agency. 1999. Implementing the Food Quality Protection Act: Progress Report. www.epa.gov/oppfead1/fqpa/fqpareport.pdf.Google Scholar
Varvel, G. E., Schlemmer, M. R., and Schepers, J. S. 1999. Relationship between spectral data from an aerial image and soil organic matter and phosphorus levels. Prec. Agric 1:291–300.Google Scholar
Walburg, G., Bauer, M. E., Daughtry, C. S. T., and Housley, T. L. 1982. Effects of nitrogen nutrition on the growth, yield, and reflectance characteristics of corn canopies. Agron. J 74:677–683.CrossRefGoogle Scholar
Westbrook, R. 1998. Invasive Plant Fact Book. Washington, DC: Federal Interagency Committee for the Management of Noxious and Exotic Weeds. 8 p.Google Scholar
Westbrook, R. G. 2003. Overview of the U.S. national early warning and rapid response system for invasive plants. Page 96 in Proceedings of the Invasive Plants in Natural and Managed Systems. 7th International Conference on the Ecology and Management of Alien Plant Invasions; Ft. Lauderdale, FL; November 3–7. Lawrence, KS: Weed Science Society of America.Google Scholar
Wiegand, C. L. and Richardson, A. J. 1990. Use of spectral vegetation indices to infer leaf area, evapotranspiration, and yield. Agron. J 86:623–629.Google Scholar
Wiegand, C. L., Richardson, A. J., Escobar, D. E., and Gerbermann, A. H. 1991. Vegetation indices in crop assessments. Remote Sens. Environ 105:105–119.Google Scholar
Wilkerson, G. G., Modena, S. A., and Coble, H. D. 1991. HERB: decision model for postemergence weed control in soybean. Agron. J 83:413–417.Google Scholar
Willers, J. L., Seal, M. R., and Luttrell, R. G. 1999. Remote sensing, line-intercept sampling for tarnished plant bugs (Heteroptera: Miridae) in mid-south cotton. J. Cotton Sci 3:160–170.Google Scholar