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Steven C. Wiest

Digitized photographic images of turf plots composed of bermudagrass, buffalo grass, tall fescue, and zoysiagrass were taken at a height of about 150 cm with a 28-mm lens. Fast Fourier transforms of these images were performed, and a radial plot of the power spectrum was obtained from each image. Hurst plots (log frequency vs. log intensity) were used to subtract “background” from the power spectra, so peaks would be more evident. The peak of the power spectrum occurs at the average spacing between leaves (more precisely, between areas of the canopy that reflects a significant amount of light) and defines the characteristic dimension. Zoysiagrass had the lowest characteristic dimension, while tall fescue had the highest. The width of the power spectrum is indicative of the variability of the characteristic dimension within the canopy. The minimum characteristic dimension (occurring at the highest frequency) was less than 1.7 cm, whereas all the other species had about the same minimum characteristic dimension of ≈1.9 cm. The maximum characteristic dimension was greatest for fescue (6.9 cm), followed by buffalo grass (3.8 cm), bermudagrass (3.3 cm), and zoysiagrass (2.8 cm). These results indicate that the characteristic dimension can be a useful tool for discriminating between turfgrass species in digitized images.

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Steven C. Wiest

A system for the digital analysis of photographic prints of turfgrass plots is being developed. The 3-year-old turfgrass plots included Meyer zoysiagrass, Midlawn bermudagrass, Prairie buffalograss and Mustang tall fescue. The plots were photographed by a camera with a small dual bubble level on the camera back and a 28-mm-wide angle lens. Photographs were digitized with flatbed scanners. The images can then be analyzed in a variety of ways. For example, a series of photographs were taken from mid-Sept. through late Oct 1995 and spectral analysis of the resultant digital images were made. The initial RGB (red-greenblue) format of the images was converted to HSI (hue-saturation-intensity) for analysis. The results indicate, obviously, that hue changed from 104 (i.e., green) to 75.7 degrees (i.e., brownish) between the beginning and end of Oct. 1995. Similarly, intensity changed from ≈0.12 to ≈0.16 during the same time period, indicating that the images became darker over time. These phenomena were observed in all four species examined. However, the saturation value evoked a significant species * date interaction. The three warm-season species showed a decrease in saturation, while Mustang had no significant decrease during Oct. Spectral as well as textural analysis are likely the two most useful techniques in the digital analysis of turfgrass plots. Examples of both will be presented.

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Steven C Wiest and Edward W. Hellman

Scanning electron micrographs of grape berry surfaces, which resemble mountainscapes, contain a wealth of structural information. A typical statistical characterization of features such as root mean square peak-to-peak spacings, peak density, etc., is readily performed on these images. However, a much richer base of information is accessible by analyzing the images with fractal geometry. Fractal box dimension is a quantitative measure of surface roughness, and varies with the contour at which it is determined in both cultivars `Foch' and `Perlette', suggesting that the surfaces are multifractal structures. Fourier spectral analyses of the surfaces produce a similar conclusion. Thus, the unambiguous quantitative resolution of cultivars on the basis of their wax surface structure looks promising, but requires further work.

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Steven C. Wiest and Roth E. Gaussoin

The following model simulates hourly temperature fluctuations at 6 Kansas stations:

\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \[T_{h}=\frac{(T_{x}-T_{n})}{2}\left[\mathrm{exp}\left(\frac{0.693h}{DL_{M}}\right)-1\right]+T_{n};{\ }0{\leq}h{\leq}DL_{M}\] \end{document}
\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \[T_{h}=\frac{(T_{x}-T_{n})}{2}\left[1+\mathrm{sin}\frac{{\pi}(h-DL_{M})}{2(23-DL_{M})}\right]+T_{n};{\ }DL_{M}{\leq}h{\leq}23\] \end{document}
where h = time (hours after sunrise), DLM = 20.6 - 0.6 * daylength (DL), Th = temperature at time h, and TX and Tn = maximum and minimum temperature, respectively. Required inputs are daily TX and Tn and site latitude (for the calculation of DL). Whereas other models have been derived by fitting equations to chronological temperatures, this model was derived by daily fitting of hourly temperatures sorted by amplitude. Errors from this model are generally lower, and less seasonally biased, than those from other models tested.

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Steven C. Wiest and Roth E. Gaussoin

The following model simulates hourly temperature fluctuations at 6 Kansas stations:

\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \[T_{h}=\frac{(T_{x}-T_{n})}{2}\left[\mathrm{exp}\left(\frac{0.693h}{DL_{M}}\right)-1\right]+T_{n};{\ }0{\leq}h{\leq}DL_{M}\] \end{document}
\batchmode \documentclass[fleqn,10pt,legalpaper]{article} \usepackage{amssymb} \usepackage{amsfonts} \usepackage{amsmath} \pagestyle{empty} \begin{document} \[T_{h}=\frac{(T_{x}-T_{n})}{2}\left[1+\mathrm{sin}\frac{{\pi}(h-DL_{M})}{2(23-DL_{M})}\right]+T_{n};{\ }DL_{M}{\leq}h{\leq}23\] \end{document}
where h = time (hours after sunrise), DLM = 20.6 - 0.6 * daylength (DL), Th = temperature at time h, and TX and Tn = maximum and minimum temperature, respectively. Required inputs are daily TX and Tn and site latitude (for the calculation of DL). Whereas other models have been derived by fitting equations to chronological temperatures, this model was derived by daily fitting of hourly temperatures sorted by amplitude. Errors from this model are generally lower, and less seasonally biased, than those from other models tested.

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Steven C. Wiest and David L. Hensley

The prediction of which species will do well in various microclimates is of obvious interest to horticulturists as well as homeowners. To this end, the following 5 species of trees and shrubs where planted at 5 disparate sites across Kansas in spring 1985 and growth and environment measured for the 4 following years: Phellodendron amurense, Acer rubrum, Acer platanoides `Greenlace', Quercus acutissima, and Cercocarpus montanus. Preliminary analysis of trunk diameter growth vs. environment indicates few simple relationships and several rather complex relationships. Rather simplistic linear relationships (growth vs. a single environmental parameter) are largely meaningless, and often misleading. For instance, growth of Q. acutissima was negatively correlated with the highest maximum temperature prior to the growing season and positively correlated with the lowest minimum temperature prior to the growing season. More complex, and reasonable, relationships will be presented.

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Jack D. Fry, Steven C. Wiest, and Ward S. Upham

Evapotranspiration from tall fescue, perennial ryegrass and zoysiagrass turfs during the summers of 1992-3 was compared to evapotranspiration estimates from an evaporation pan, a black Bellani plate, and several empirical combination models, Actual measurement of turf water use was made with small weighing lysimeters. Soil was maintained at field capacity. Data were collected on 51 dates between June and September. Tall fescue was clipped weekly at 7.6 cm whereas ryegrass and zoysiagrass were clipped 3 times weekly at 2.5 cm, Although differences between the grass species existed, in general the rankings of estimate precision were Bellani plate > evaporation pan > empirical models when compared with measured evapotranspiration rates.

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Steven C. Wiest, Jack D. Fry, and Ward S. Upham

A relatively accurate estimate of turfgrass evapotranspiration (ET) using environmental parameters readily obtainable from a local weather station would be of benefit to golf course superintendents, landscape managers, and homeowners. The Penman–Monteith model is clearly a poorer estimate than that obtained by Bellani plates or spheres. It has been suggested that, while the Penman–Monteith model is good in the drier climate of the southwestern United States, other models may be of greater practicable utility in climates such as are common in Kansas. Thus, other models have been evaluated for their suitability as turfgrass ET estimates in Kansas-like climates. Turfgrass ET was measured via lysimeters in 1992–94. Specifically, measurements were taken on three tall fescue varieties mowed at 6.35 or 7.62 cm, and zoysiagrass and perennial ryegrass mowed at 2.54 cm. Evaporation from black Bellani plates was measured simultaneously. These evaporation and ET rates were compared to those estimated by various empirical models whose data came from a weather station located within 31 m of the Bellani plates and lysimeters. Empirical models included temperature methods (e.g., FAO-24 Blaney–Criddle), radiation methods (e.g., Jensen–Haise, Hargreaves–Samani), combination equations (e.g., Priestly–Taylor, Penman), and variants. The best model(s) determined from these comparisons will likely become the method(s) of choice for estimating turfgrass ET in Kansas.