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

Bremer, Dale J.; Lee, Hyeonju; Su, Kemin; Keeley, Steven J.

doi:10.2135/cropsci2010.12.0728

Cited by 35 publications

(38 citation statements)

References 22 publications

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“…These findings with NDVI also demonstrated the importance of combining correlation analysis with ANOVA in evaluating objective color parameters. Previous reports by Bremer et al (2011aBremer et al ( , 2011b also indicated the limitations and species dependence of predicting turf quality based on NDVI.…”

Section: Nitrogen Fertility In Bermudagrass and Creeping Bentgrassmentioning

confidence: 92%

Evaluation of Key Methodology for Digital Image Analysis of Turfgrass Color Using Open‐Source Software

et al. 2016

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Digital image analysis is a frequently used research technique to provide an objective measure of turfgrass color, in addition to the traditional visual rating. A commonly used method relies on commercial software package SigmaScan Pro to quantify mean hue angle, saturation, and brightness values from turf images, and to calculate a dark green color index as the measure of color. To enable turf image analysis to function on an open‐source platform, a method was developed within ImageJ to batch process turf images for color parameters. This Java‐based ImageJ plugin quantifies hue angle, saturation, and brightness values and calculates a dark green color index. In addition, information on the variability of these color parameters can be simultaneously acquired. This new method was used to quantify color parameters of turf images collected from field plots of tall fescue (Schedonorus arundinacea Shreb. Dumort.), Kentucky bluegrass (Poa pratensis L.), ryegrass (Lolium ssp.), hybrid bermudagrass (Cynodon dactylon (L.) Pers. × C. transvaalensis Burtt‐Davy), and creeping bentgrass (Agrostis stolonifera L.). While color parameter values differed little between ImageJ and SigmaScan, the time saved in processing images using ImageJ was considerable. Aside from software, analysis of color parameters acquired from the five turfgrass species indicated that hue angle alone can adequately measure turf color in digital images. Results also demonstrated that, in addition to light source, camera settings should remain fixed during photo capture to avoid introducing errors. The ImageJ plug‐in developed in this study is made available at http://www.turffiles.ncsu.edu.

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Section: Nitrogen Fertility In Bermudagrass and Creeping Bentgrassmentioning

confidence: 92%

Evaluation of Key Methodology for Digital Image Analysis of Turfgrass Color Using Open‐Source Software

et al. 2016

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“…Normalized difference vegetative index is a measurement of red and near‐infrared light reflectance and has become the most commonly used vegetation index for agronomic crops and maintained turfgrass (Carrow et al, 2010; Murphy et al, 2014; Rouse et al, 1974). Several factors that contribute to overall turf quality have been associated with this index including color, canopy structure, density, and chlorophyll concentrations (Bremer et al, 2011; Stiegler et al, 2005; Trenholm et al, 1999, 2000). Previous research has shown that chlorophyll concentrations are negatively impacted by drought stress (DaCosta et al, 2004).…”

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confidence: 99%

Enhanced Soil Moisture Assessment using Narrowband Reflectance Vegetation Indices in Creeping Bentgrass

McCall

Zhang

Sullivan³

et al. 2017

Crop Science

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Turfgrasses are measured aesthetically and by their ability to withstand stressors. Historically, researchers quantified acceptability by visual quality, but inconsistencies necessitate the use of vegetation indices (VIs) as an objective measurement. Indiscernible relationships have been established between turfgrass canopy normalized difference vegetation index (NDVI) and important variables such as soil moisture and leaf chlorophyll content. Alternative and variable–specific indices have been established in cropping systems. The water band index (WBI) is used for predicting water availability in cropping systems but has not been explored within turfgrass systems. The objective of this study was to compare the relationships of established VIs with chlorophyll content of ‘L‐93’ creeping bentgrass (Agrostis stolonifera L.) and water content of a sand‐based root zone maintained under greenhouse conditions. All VIs were moderately to strongly correlated to total chlorophyll content (r = 0.49–0.85). Turf color was more closely related to chlorophyll indices than to wet‐lab quantification. Only WBI (r ≥ 0.80) and the green/red ratio index (GRI; r ≥ 0.50) were consistently related to soil water content. The NDVI was weakly related to soil water content in one trial (r = 0.49). Nonlinear regression showed that WBI can be useful for estimating a decline in soil water content as water first becomes limiting for creeping bentgrass and may offer a viable means to assess water availability independently of non‐moisture‐related stresses and more accurately compared with previous indices. Future research will evaluate WBI on broader geospatial scales to assess practical application for turfgrass irrigation management.

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“…Reflectance‐based methods are more objective than traditional visual ratings, and the two methods are well correlated (Trenholm et al, 1999; Bell et al, 2009; Bremer et al, 2011a; Schiavon et al, 2011; Rimi et al, 2013; Leinauer et al, 2014). Still, several studies have demonstrated the limitations of replacing visual ratings with reflectance‐based methods.…”

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confidence: 99%

Plant Colorants Interfere with Reflectance‐Based Vegetation Indices

Obear

Kreuser

Hubbard³

et al. 2017

Crop Science

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Reflectance‐based vegetation indices are commonly used to quantify turfgrass color, estimate chlorophyll content, and make inferences about plant health. These methods are often substituted for visual color ratings, as they are well correlated but more objective. This study examines the utility of reflectance‐based vegetation indices for evaluating turfgrass treated with iron or plant colorants. In a study in Madison, WI, creeping bentgrass (Agrostis stolonifera L.) was treated with ammonium sulfate (2.4 or 9.8 kg nitrogen [N] ha−1), Turf Screen (untreated or 4.0 L ha−1), or ferrous sulfate (untreated or 12.2 kg Fe ha−1). Visual color ratings, chlorophyll index (CI), normalized difference vegetation index, normalized difference red edge, red, and near‐infrared (NIR) reflectance were able to detect differences in N rate. When colorant or iron was applied, however, reflectance methods could no longer detect these differences in N rate. In a study in Ithaca, NY, the application of four colorants increased visual color ratings. The pigments Harmonizer and Foursome also increased CI, whereas Turf Screen and Green Lawnger did not. Three of the four pigments decreased the chlorophyll content in leaf tissue from 1 July to 22 August, despite having greater or equal CI values. Spectral analysis of six colorants revealed that these products reflect light in the red and NIR regions and likely interfere with reflectance‐based vegetation indices, which measure leaf reflectance in these same regions of the spectrum. Overall, these findings suggest that reflectance‐based vegetation indices should not be used to make inferences about plant health in studies where a colorant or iron is applied.

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

Cited by 35 publications

References 22 publications

Evaluation of Key Methodology for Digital Image Analysis of Turfgrass Color Using Open‐Source Software

Evaluation of Key Methodology for Digital Image Analysis of Turfgrass Color Using Open‐Source Software

Enhanced Soil Moisture Assessment using Narrowband Reflectance Vegetation Indices in Creeping Bentgrass

Plant Colorants Interfere with Reflectance‐Based Vegetation Indices

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