Translation of remote sensing data into weed management decisions

Weed Science

Volumn 53, Issue 2, 2005, Pages 264-273

  Shaw, David R ^a  

^a MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

Decision support systems; Invasive species; Spatial technologies

Indexed keywords

DECISION SUPPORT SYSTEM; DETECTION METHOD; IMAGE PROCESSING; INVASIVE SPECIES; MANAGEMENT PRACTICE; MAP; MITIGATION; REMOTE SENSING; WEED CONTROL;

EID: 33644905081     PISSN: 00431745     EISSN: None     Source Type: Journal    
DOI: 10.1614/WS-04-072R1     Document Type: Conference Paper

References (81)

1. Anderson, J. E., R. L. Fischer, and S. R. Deloach. 1999. Remote sensing and precision agriculture: ready for harvest or still maturing? Photogram. Eng. Remote Sens. 65:1118-1123.
2. Auld, B. A. and C. A. Tisdell. 1988. Influence of spatial distribution of weeds on crop yield loss. Plant Prot. Q. 3:81.
3. Barnes, E. M., M. G. Baker, P. J. Pinter, Jr., and D. D. Jones. 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.
4. Bartlett, D. S., M. A. Hardisky, R. W. Johnson, M. F. Gross, V. Klemas, and J. M. Hartman. 1998. Continental scale variability in vegetation reflectance and its relationship to canopy morphology. Int. J. Remote Sens. 9:1223-1241.
5. 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.
6. Blazquez, C. H. and G. J. Edwards. 1986. Spectral reflectance of healthy and diseased watermelon leaves. Ann. Appl. Biol. 108:243-249.
7. Brain, P. and R. Cousens. 1990. The effect of weed distribution on predictions of yield loss. J. Appl. Ecol. 27:735-742.
8. 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.
9. Brown, R. B. and J.-P.G.A. Steckler. 1995. Precision maps for spatially variable herbicide application in no-till corn. Trans. ASAE 37:297-302.
10. Burrough, P. A., A. K. Bregt, M. J. deHeus, and E. G. Kloosterman. 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.
11. Campanella, R. and M. R. Seal. 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.
12. Cardina, J., G. A. Johnson, and D. H. Sparrow. 1997. The nature and consequence of weed spatial distribution. Weed Sci. 45:364-373.
13. Carlson, R. E., D. N. Yarger, and R. H. Shaw. 1971. Factors affecting the spectral properties of leaves with special emphasis on leaf water status. Agron. J. 63:486-489.
14. Carson, H. W., L. W. Lass, and R. H. Callihan. 1995. Detection of yellow hawkweed (Hieracium pratense) with high resolution multispectral digital imagery. Weed Technol. 9:477-483.
15. Carter, P. G. and C. J. Johannsen. 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.
16. Chen, C. W., J. Herr, L. Ziemelis, R. A. Goldstein, and L. Olmsted. 1999. Decision support system for total maximum daily load. J. Environ. Eng. 125:635-659.
17. Coleman, T. L. and O. L. Montgomery. 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.
18. Delannay, X., T. T. Bauman, D. H. Beighley, et al. 1995. Yield evaluation of a glyphosate-tolerant Glycine max line after treatment with glyphosate. Crop Sci. 35:1461-1467.
19. Easley, J. W, D. R. Shaw, and C. J. Gray. 2004. Aerial imagery for site-specific herbicide applications in soybean. Proc. South. Weed Sci. Soc. 57:239.
20. Estes, J. E., M. R. Mel, and J. O. Hooper. 1997. Measuring soil moisture with an airborne imaging passive microwave radiometer. Photogram. Eng. Remote Sens. 43:1273-1281.
21. Everitt, J. H. and C. J. Deloach. 1990. Remote sensing of Chinese tamarisk (Tamarix chinensis) and associated vegetation. Weed Sci. 38:273-278.
22. 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. 65:43, 586-43, 670.
23. Felton, W. L., A. F. Doss, P. G. Nash, and K. R. McCloy. 1991. To selectively spot spray weeds. Am. Soc. Agric. Eng. Symp. 11-91:427-432.
24. Fitzgerald, G. J., S. J. Maas, and W. R. DeTar. 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.
25. Fritz, L. W. 1996. The era of commercial earth observation satellites. Photogram. Eng. Remote Sens. 62:40-44.
26. Garegnani, J., J. A. Gualtieri, S. Chettri, J. Robinson, J. P. Hunt, M. Bechdol, and A. Vermeulen. 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.
27. Givens, W. A., D. .R. Shaw, and M. C. Smith. 2004. Using multiresolutional textural analysis to detect weeds in soybean. Proc. South. Weed Sci. Soc. 57:346.
28. Griffin, J. L., D. B. Reynolds, D. L. Jordan, L. M. Prochaska, and R. L. Rogers. 1994. Evaluation of roundup ready transgenic soybean in Louisiana. La. Agric. 37:23.
29. Hart, W. G. and V. I. Myers. 1968. Infrared aerial color photography for the detection of population of brown soft scale on citrus groves. J. Econ. Entomol. 61:617-624.
30. Heald, C. M., W. H. Thames, and C. L. Wiegand. 1972. Detection of Rotylenchus reniformis infestations by aerial infrared photography. J. Nematol. 4:298-300.
31. Hirano, A., M. Madden, and R. Welch. 2003. Hyperspectral image data for mapping wetland vegetation. Wetlands 23:436-448.
32. Holmberg, H. 2000. Sky-high Scouting, www.agriculture.com/sfonline/sf/ 2000/may-june/0007skyhigh.html.
33. Holsapple, C. W. and A. B. Whinston. 1996. Decision Support Systems: A Knowledge Based Approach. Eagan, MN: West. Pp. 1-29.
34. Jackson, R. D., C. A. Jones, G. Uehara, and L. T. Santo. 1980. Remote detection of nutrient and water deficiency in sugarcane under variable cloudiness. Remote Sens. Environ. 11:327-331.
35. Johannsen, C. J., P. G. Carter, D. K. Morris, K. Ross, and B. Erickson. 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.
36. John, B. 1992. Soil moisture detection with airborne passive and active microwave sensors. Int. J. Remote Sens. 13:481-491.
37. King, B. A., R. A. Brady, I. R. McCann, and J. C. Stark. 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.
38. Lamb, D. W., M. M. Weedon, and L. J. Rew. 1999. Evaluating the accuracy of mapping weeds in seedling crops using airborne digital imaging: Avena spp. in seedling triticale. Weed Res. 39:481-492.
39. Lass, L. W., H. W. Carson, and R. H. Callihan. 1996. Detection of yellow starthistle (Centaurea solstitialis) and common St. Johnswort (Hypericum perforatum) with multispectral digital imagery. Weed Technol. 10: 466-474.
40. Leone, A. P., G. G. Wright, and C. Corves. 1995. The application of satellite remote sensing for soil studies in upland areas of southern Italy. Int. J. Remote Sens. 16:447-466.
41. Lindquist, J. L., J. A. Dieleman, D. A. Mortensen, G. A. Johnson, and D. Y. Wyse-Pester. 1998. Economic importance of managing spatially heterogeneous weed populations. Weed Technol. 12:7-13.
42. Lowenberg-DeBoer, J. 1999. Risk management potential of precision farming technologies. J. Agric. Appl. Econ. 31:275-285.
43. Manzer, F. E. and G. R. Cooper. 1967. Aerial photographic methods of potato disease detection. Maine Agric. Exp. Stn. Bull. 646:1-14.
44. Marshall, E.J.P. 1988. Field-scale estimates of grass weed populations in arable land. Weed Res. 28:191-198.
45. Maxwell, B. D. and C. Colliver. 1995. Expanding economic thresholds by including spatial and temporal weed dynamics. Proc. Brighton Crop Prot. Conf. Weeds 13:1069-1076.
46. Medlin, C. R. and D. R. Shaw. 2000. Economic comparison of broadcast and site-specific herbicide applications in nontransgenic and glyphosate-tolerant Glycine max. Weed Sci. 48:653-661.
47. Medlin, C. R., D. R. Shaw, P. D. Gerard, and F. E. LaMastus. 2000. Using remote sensing to detect weed infestations in Glycine max. Weed Sci. 48:393-398.
48. Menges, R. M., P. R. Nixon, and A. J. Richardson. 1985. Light reflectance and remote sensing of weeds in agronomic and horticultural crops. Weed Sci. 33:569-581.
49. 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.
50. Moran, M. S., Y. Inoue, and E. M. Barnes. 1997. Opportunities and limitations for image-based remote sensing in precision crop management. Remote Sens. Environ. 61:319-346.
51. Penuelas, J., J. A. Gamon, A. L. Fredeen, J. Merino, and C. B. Field. 1994. Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves. Remote Sens. Environ. 48:135-146.
52. Pilesjo, P., L. Thylen, and A. Persson. 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.
53. Powell, W. G., J. H. Massey, M. S. Cox, L. M. Bruce, D. L. Evans, M. L. Tagert, and D. R. Shaw. 2003. Environmental applications of remote sensing: determination of crop residues on soil surfaces using hyperspectral reflectance. Proc. South. Weed Sci. Soc. 56:311.
54. Price, A. J., D. W. Krueger, G. Roberson, and G. G. Wilkerson. 2003. Evaluation of variable-rate herbicide applications in soybean. Proc. South. Weed Sci. Soc. 56:300.
55. Randall, J. M. 1996. Weed control for the preservation of biodiversity. Weed Technol. 10:370-383.
56. Rankins, A., Jr., W. F. Bloodworth, and D. B. Reynolds. 2003. HADSS validation for use in Mississippi cotton. Proc. South. Weed Sci. Soc. 56:197.
57. Rankins, A., Jr., D. R. Shaw, and J. D. Byrd, Jr. 1998. HERB and MSU-HERB field validation for soybean (Glycine max) weed control in Mississippi. Weed Technol. 12:88-96.
58. Rew, L. J., B. Whelan, and A. B. McBratney. 2001. Does kriging predict weed distributions accurately enough for site-specific weed control? Weed Res. 41:245-263.
59. Robert, P. C. 1997. Remote sensing: a potentially powerful technique for precision agriculture. In Proceedings of the Land Satellite Information in the Next Decade II; Washington, DC; December 2-5, 1997. ASPRS. Pp. 151-159.
60. Robert, P. C., R. H. Rust, and W. E. Larson. 1994. Preface. Pages xiii-xiv in Proceedings of the Site-specific Management for Agricultural Systems. Madison, WI: ASA/CSSA/SSSA.
61. Safir, G. R., G. H. Svits, and A. H. Ellingboe. 1972. Spectral reflectance and transmittance of corn leaves infected with Helminthosporium maydis. Phytopathology 62:1210-1213.
62. Sassenrath-Cole, G. F., K. B. Hood, V. J. Alarcon, R. Olson, L. Tarpley, J. Varco, and M. Seal. 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.
63. Schnase, J. L., T. J. Stohlgren, and J. A. Smith. 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.
64. Servilla, M. 1998. Bridging the barriers. Agriculture remote sensing: part two. Mod. Agric. 1:18-20. [www.eomonline.com/modernagsite/archives/index. html.]
65. Shaw, D. R. and R. E. Mack. 1991. Application timing of herbicides for the control of redvine (Brunnichia ovata). Weed Technol. 5:125-129.
66. 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.
67. Shibayama, R. and T. Akiyama. 1991. Estimating grain yield of maturing rice canopies using high spectral resolution reflectance measurements. Remote Sens. Environ. 36:45-53.
68. Sudbrink D. L., Jr., F. A. Harris, J. T. Robbins, G. L. Snodgrass, and S. J. Thomson. 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.
69. Thenkabail, P. S., R. B. Smith, and E. D. Pauw. 2000. Hyperspectral vegetation indices and their relationships with agricultural crop characteristics. Remote Sens. Environ. 71:158-182.
70. Thenkabail, P. S., A. D. Ward, and J. G. Lyon. 1995. Landsat-5 thematic mapper models of soybean and corn crop characteristics. Int. J. Remote Sens. 15:49-61.
71. Thornton, P. K., R. H. Fawcett, J. B. Dent, and H. D. Coble. 1991. Spatial weed distributions and economic thresholds for weed control. Crop Prot. 9:337-342.
72. Tucker, C. J. 1980. Remote sensing of leaf water content in the near infrared. Remote Sens. Environ. 10:23-32.
73. [U.S. EPA] United States Environmental Protection Agency. 1999. Implementing the Food Quality Protection Act: Progress Report. www.epa.gov/oppfead1/fqpa/fqpareport.pdf.
74. Varvel, G. E., M. R. Schlemmer, and J. S. Schepers. 1999. Relationship between spectral data from an aerial image and soil organic matter and phosphorus levels. Prec. Agric. 1:291-300.
75. Walburg, G., M. E. Bauer, C.S.T. Daughtry, and T. L. Housley. 1982. Effects of nitrogen nutrition on the growth, yield, and reflectance characteristics of corn canopies. Agron. J. 74:677-683.
76. Westbrook, R. 1998. Invasive Plant Fact Book. Washington, DC: Federal Interagency Committee for the Management of Noxious and Exotic Weeds. 8 p.
77. 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.
78. Wiegand, C. L. and A. J. Richardson. 1990. Use of spectral vegetation indices to infer leaf area, evapotranspiration, and yield. Agron. J. 86: 623-629.
79. Wiegand, C. L., A. J. Richardson, D. E. Escobar, and A. H. Gerbermann. 1991. Vegetation indices in crop assessments. Remote Sens. Environ. 105:105-119.
80. Wilkerson, G. G., S. A. Modena, and H. D. Coble. 1991. HERB: decision model for postemergence weed control in soybean. Agron. J. 83:413-417.
81. Willers, J. L., M. R. Seal, and R. G. Luttrell. 1999. Remote sensing, line-intercept sampling for tarnished plant bugs (Heteroptera: Miridae) in mid-south cotron. J. Cotton Sci. 3:160-170.