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Detection of pitted morningglory (Ipomoea lacunosa) with hyperspectral remote sensing. II. Effects of vegetation ground cover and reflectance properties

Published online by Cambridge University Press:  20 January 2017

Clifford H. Koger ,
David R. Shaw ,
Krishna N. Reddy  and
Lori M. Bruce
David R. Shaw
Affiliation:
Department of Plant and Soil Sciences, Mississippi State University, Mississippi State, MS 39762
Krishna N. Reddy
Affiliation:
USDA-ARS, Southern Weed Science Research Unit, Stoneville, MS 38776
Lori M. Bruce
Affiliation:
Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS 39762
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Abstract

Field research was conducted to determine the potential of hyperspectral remote sensing for discriminating plots of soybean intermixed with pitted morningglory and weed-free soybean with similar and different proportions of vegetation ground cover. Hyperspectral data were collected using a handheld spectroradiometer when pitted morningglory was in the cotyledon to two-leaf, two- to four-leaf, and four- to six-leaf growth stages. Synthesized reflectance measurements containing equal and unequal proportions of reflectance from vegetation were obtained, and seven 50-nm spectral bands (one ultraviolet, two visible, and four near-infrared) derived from each hyperspectral reflectance measurement were used as discrimination variables to differentiate weed-free soybean and soybean intermixed with pitted morningglory. Discrimination accuracy was 93 to 100% regardless of pitted morningglory growth stage and whether equal or unequal proportions of reflectance from vegetation existed in weed-free soybean and soybean intermixed with pitted morningglory. Discrimination accuracy was 88 to 98% when using the discriminant model developed for one experiment to discriminate soybean intermixed with pitted morningglory and weed-free soybean plots of the other experiment. Reflectance in the near-infrared spectrum was higher for weed-free soybean compared with soybean intermixed with pitted morningglory, and this difference affected the ability to discriminate weed-free soybean from soybean intermixed with pitted morningglory.

Keywords

Pitted morningglory, Ipomoea lacunosa L. IPOLAsoybean, Glycine max (L.) Merr. ‘Asgrow 4702RR’Conventional tillagediscriminant analysislinear mixingno tillageremote sensing
Type
Weed Biology and Ecology
Information
Weed Science , Volume 52 , Issue 2 , April 2004 , pp. 230 - 235
Copyright
Copyright © Weed Science Society of America 

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Footnotes

Present address: USDA-ARS, Southern Weed Science Research Unit, P.O. Box 350, Stoneville, MS 38776; ckoger@ars.usda.gov

References

Literature Cited

Balough, G. R. and Bookhout, T. A. 1989. Remote detection and measurement of purple loosestrife stands. Widl. Soc. Bull 17:66–67.Google Scholar
Bruce, L. M., Morgan, C., and Larsen, S. 2001. Automated detection of subpixel targets with continuous and discrete wavelet transforms. IEEE Trans. Geosci. Remote Sens 39:2217–2226.Google Scholar
Everitt, J. H., Escobar, D. E., Alaniz, M. A., Davis, M. R., and Richardson, J. V. 1996. Using spatial information technologies to map Chinese tamarisk (Tamarix chinensis) infestations. Weed Sci 44:194–201.Google Scholar
Franz, E. M., Gebhardt, R., and Unklesbay, K. B. 1991. The use of local spectral properties of leaves as an aid for identifying weed seedlings in digital images. Trans. Am. Soc. Agric. Eng 34:682–687.Google Scholar
Lass, L. W., Carson, H. W., and Callihan, R. H. 1996. Detection of yellow starthistle (Centaurea solstitalis) and common St. Johnswort (Hypericum perforatum) with multispectral digital imagery. Weed Technol 10:466–474.Google Scholar
Li, J. 2002. Linear Unmixing of Hyperspectral Signals via Wavelet Feature Extraction. Ph.D. dissertation. Mississippi State University, Mississippi State. 152 p.Google Scholar
Koger, C. H., Shaw, D. R., Reddy, K. N., and Bruce, L. M. 2004. Detection of pitted morningglory with hyperspectral remote sensing. I. Effects of tillage and cover crop residue. Weed Sci 52:222–229.Google Scholar
Manolakis, D., Siracusa, C., and Shaw, G. 2001. Hyperspectral subpixel target detection using the linear mixed model. IEEE Trans. Geosci. Remote Sens 39:1392–1409.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.CrossRefGoogle Scholar
Richardson, A. J., Menges, R. M., and Nixon, P. R. 1985. Distinguishing weed from crop plants using video remote sensing. Photogram. Eng. Remote Sens 51:1785–1790.Google Scholar
Schowengerdt, R. A. 1997. Thematic classification. Pages 457–451 in Remote Sensing: Models and Methods for Image Processing. 2nd ed. San Diego, CA: Academic.CrossRefGoogle Scholar
Settle, J. and Campbell, N. 1998. On the errors of two estimators of sub-pixel fractional cover when mixing is linear. IEEE Trans. Geosci. Remote Sens 36:163–170.Google Scholar