Vehicle-Mounted Optical Sensing

Bell, G. E.; Martin, Dennis L.; Wiese, S. G.; Dobson, D. D.; Smith, Michael W.; Stone, M. L.; Solie, John B.

doi:10.2135/cropsci2002.0197

Cited by 14 publications

(12 citation statements)

References 15 publications

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“…The coefficients of determination {r) between NDVI and visual quality ranged from 0.38 to 0.83 in the bluegrasses and were lesser, albeit significant, in tall fescue (Table 1). These r^ values are similar to results from other sttidies that have indicated significant relationships between NDVI and visual quality (Trenholm et al, 1999;Bell et al, 2002;Fitz-Rodriguez and Choi, 2002;Keskin et al, 2008;Lee et al, 2011).…”

Section: Resultssupporting

confidence: 88%

“…To determine irrigation amounts, evapotranspiration was calculated by using the Penman-Monteith equation (Allen et al, 1998) and climatological data obtained at an on-site weather station. Plots were mowed at 7.6 cm twice a week with a walk-behind rotary mower.…”

Section: Materials and Methods Study Sitementioning

confidence: 99%

“…X C. transvaalensis Burtt Davy, 'Midiron']. In other studies, vegetation indices calculated from reflectance data were also strongly correlated with visual quality in a number of turfgrass species and under different cultural practices (Bell et al, 2002;Fitz-Rodriguez and Choi, 2002;Keskin et al, 2008;Lee et al, 2011).…”

mentioning

confidence: 87%

“…Although this method is relatively fast to implement, it is subjective. Some researchers have contended that visual ratings may vary significantly among evaluators or even with the same evaluator over time, and that such ratings tend to be inaccurate and nonreproducible (Horst et al, 1984;Bell et al, 2002).…”

mentioning

confidence: 99%

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Relationships between Normalized Difference Vegetation Index and Visual Quality in Cool‐Season Turfgrass: I. Variation among Species and Cultivars

Bremer

Lee

et al. 2011

Crop Science

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Canopy spectrai reflectance may provide an objective means to evaluate visual quality of turfgrass, but evaluations of visual quality may be confounded by differences in reflectance among species or cultivars. In this 3-yr study near Manhattan, KS, we examined effects of species and cultivars on relationships between normalized difference vegetation index (NDVI) and visual quality ratings in Kentucky bluegrass {Poa pratensis L., 'Apollo'), two Kentucky bluegrass x Texas biuegrass {Poa arachnifera Ton.) hybrids (Thermal Blue' and 'Reveille'), and tall fescue {Festuca arundinacea Schreb., 'Dynasty'). A broad range of visual quality was imposed on all four grasses through deficit irrigation and NDVI was measured using broadband spectral radiometry across this range for each grass. Distinct linear regression models of visual quality were found for each grass, and models were also distinct among years in each grass. Relationships between NDVI and visual quality were stronger in the bluegrasses (r^ = 0.41 to 0.83) because they had a greater range in quality under deficit irrigation than tall fescue. The 95% confidence intervals surrounding predictions of visual quality from NDVI ranged from ± 1.25 to 2.10 (on a 1 to 9 scale). Results indicated that the requirement to develop separate models for each grass and in each year, combined with relatively wide confidence intervals, represents a practical limitation to predicting visual quality with NDVI.

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Section: Resultssupporting

confidence: 88%

Section: Materials and Methods Study Sitementioning

confidence: 99%

mentioning

confidence: 87%

mentioning

confidence: 99%

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Relationships between Normalized Difference Vegetation Index and Visual Quality in Cool‐Season Turfgrass: I. Variation among Species and Cultivars

Bremer

Lee

et al. 2011

Crop Science

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“…In turfgrass, spectral vegetation indices have been developed to evaluate visual quality, chlorophyll content, injury, and environmental stress in turfgrass (Trenholm, Carrow, and Duncan 1999;Bell et al 2002;Duncan 2003, 2004;Jiang and Carrow 2005;Bremer et al 2011aBremer et al , 2011b. In some turfgrass studies, spectral indices have been used to estimate LAI (Trenholm, Carrow, and Duncan 1999;Duncan 2003, 2004;Jiang and Carrow 2007;DaCosta and Huang 2006;Merewitz et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Using hyperspectral radiometry to predict the green leaf area index of turfgrass

Goldsby

Price

et al. 2015

International Journal of Remote Sensing

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The green leaf area index (LAI) is an important indicator of the photosynthetic capacity of turfgrass canopies. The measurement of LAI is typically destructive and requires large plots to allow for multiple sampling dates. Hyperspectral radiometry may provide a rapid, non-destructive means for estimating LAI. Our objectives were to: (1) evaluate the utility of hyperspectral radiometry to predict the LAI of Kentucky bluegrass (Poa Pratensis L.); and (2) determine regions of the spectrum that provide the best LAI predictions. An empirical prediction model of spectral data for LAI was conducted with partial least squares regression (PLSR). The PLSR method created viable, first-iteration models for five of 11 sampling dates (the coefficient of determination (R 2 ) is 0.52-0.85). Each model had its own set of factors that were analysed to determine their 'weights', or specific regions of the spectrum by which they were most strongly influenced. Second iterations of each model were then created using only those regions most strongly influenced, centred on 600, 690, 761, 960, 1330, and 1420 nm (±10 nm). Four of the five second-iteration models had LAI estimation capabilities greater than or similar to the first-iteration models (R 2 = 0.72-0.86), indicating that the information contained in all other wavelengths was redundant or irrelevant in regard to predictions of LAI. The robustness of prediction models varied over the growing season, possibly related to changes in canopy properties with environmental conditions. Results suggest hyperspectral radiometry has a significant potential to predict LAI in turfgrass, although different models may be required throughout the growing season.

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Fluorescence-based versus reflectance proximal sensing of nitrogen content in Paspalum vaginatum and Zoysia matrella turfgrasses

Agati

Foschi

Grossi

et al. 2013

European Journal of Agronomy

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Vehicle-Mounted Optical Sensing

Cited by 14 publications

References 15 publications

Relationships between Normalized Difference Vegetation Index and Visual Quality in Cool‐Season Turfgrass: I. Variation among Species and Cultivars

Relationships between Normalized Difference Vegetation Index and Visual Quality in Cool‐Season Turfgrass: I. Variation among Species and Cultivars

Using hyperspectral radiometry to predict the green leaf area index of turfgrass

Fluorescence-based versus reflectance proximal sensing of nitrogen content in Paspalum vaginatum and Zoysia matrella turfgrasses

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