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ARS Home » Plains Area » Sidney, Montana » Northern Plains Agricultural Research Laboratory » Agricultural Systems Research » Research » Publications at this Location » Publication #125528


item Anderson, Gerald
item Prosser, Chadley

Submitted to: International Symposium on Biological Control of Weeds
Publication Type: Proceedings
Publication Acceptance Date: 4/1/2000
Publication Date: 6/1/2000
Citation: Anderson, G. L., E. S. Delfosse, N. R. Spencer, C. W. Prosser, and R. D. Richard. 2000. Biological Control of Leafy Spurge: An Emerging Success Story. In: Proceedings, X International Biological Control Symposium, Bozeman, MT, July 4-9. pp. 15-25.

Interpretive Summary: The use of standard statistical methods to determine the mathematical relationship between two sets of data is often difficult when the variables of interest are subject to distortions. Even though the two data sources (e.g. images) may look very similar, the inherent distortions or "cross-noise" substantially reduces our ability to relate one image to the other. Alternative analytical techniques are available that can remove a substantial amount of the errors that exist between two sources of information. This paper examines the use of a unique field mask, image rotation, Wavelet Transformation, and the Fast Fourier Transform to reduce the cross-noise between images and produce usable mathematical relationships that can help predict crop yields weeks or even months prior to harvest.

Technical Abstract: In many remote sensing studies it is desired to quantify the functional relationship between images of a given target that were acquired by different sensors. Such comparisons are problematic because when the pixel values of one image are plotted versus the other, the "cross- noise" is quite high. Typically, the correlation coefficient is quite low, even when the compared images look very much alike. The underlying assumption of classical regression is that Y is absolutely correct while X is erroneous. Thus, the objective is to fit X to Y by choosing the parameters of Y=f(X), which minimize the "residuals" (Y-Y). The alternative, FFT regression method presented herein comprises a two- stage sensor fusion approach, whereby the initially low correlation between X and Y is increased and the residuals are drastically decreased. The practical utility of FFT regression is demonstrated by examples wherein remotely sensed NDVI image X are used for predicting yield distributions in agricultural fields. Reference yield maps Y, were derived by combine yield monitors, which measure the flow rate of the crop, while it is being harvested.