Access the full text.
Sign up today, get DeepDyve free for 14 days.
R. Smart (1985)
Principles of Grapevine Canopy Microclimate Manipulation with Implications for Yield and Quality. A ReviewAmerican Journal of Enology and Viticulture
A. Huete, R. Jackson (1988)
Soil and Atmosphere Influences on the Spectra of Partial CanopiesRemote Sensing of Environment, 25
D. Turner, W. Cohen, R. Kennedy, Karin Fassnacht, J. Briggs (1999)
Relationships between leaf area index and Landsat TM spectral vegetation indices across three temperate zone sitesRemote Sensing of Environment, 70
A. McDonald, F. Gemmell, Philip Lewis (1998)
Investigation of the Utility of Spectral Vegetation Indices for Determining Information on Coniferous ForestsRemote Sensing of Environment, 66
N. Dokoozlian, W. Kliewer (1995)
The Light Environment Within Grapevine Canopies. I. Description and Seasonal Changes During Fruit DevelopmentAmerican Journal of Enology and Viticulture
L. Johnson, B. Lobitz, R. Armstrong, R. Baldy, E. Weber, J. Benedictis, D. Bosch (1996)
Airborne imaging aids vineyard canopy evaluationCalifornia Agriculture, 50
H. Liu, A. Huete (1995)
A feedback based modification of the NDVI to minimize canopy background and atmospheric noiseIEEE Transactions on Geoscience and Remote Sensing, 33
R. Pearson, L. Miller (1972)
Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie, Pawnee National Grasslands, Colorado
K. Sommer, A. Lang (1994)
Comparative analysis of two indirect methods of measuring leaf area index as applied to minimal and spur pruned grape vinesAustralian Journal of Plant Physiology, 21
A. Huete, R. Jackson, D. Post (1985)
Spectral response of a plant canopy with different soil backgroundsRemote Sensing of Environment, 17
J. Chen (1996)
Evaluation of Vegetation Indices and a Modified Simple Ratio for Boreal ApplicationsCanadian Journal of Remote Sensing, 22
A. Huete, C. Justice, H. Liu (1994)
Development of vegetation and soil indices for MODIS-EOSRemote Sensing of Environment, 49
J. Roujean, F. Bréon (1995)
Estimating PAR absorbed by vegetation from bidirectional reflectance measurementsRemote Sensing of Environment, 51
C. Wiegand, A. Richardson (1984)
Leaf Area, Light Interception, and Yield Estimates from Spectral Components Analysis1Agronomy Journal, 76
C. Tucker (1979)
Red and photographic infrared linear combinations for monitoring vegetationRemote Sensing of Environment, 8
G. Asner (1998)
Biophysical and Biochemical Sources of Variability in Canopy ReflectanceRemote Sensing of Environment, 64
Mabrouk Mabrouk, Carbonneau Carbonneau, Sinoquet Sinoquet (1997)
Canopy structure and radiation regime in grapevine. I. Spatial and angular distribution of leaf area in two canopy systemsVitis, 36
Schultz Schultz (1995)
Grape canopy structure, light microclimate and photosynthesis. I. A two‐dimensional model of the spatial distribution of surface area densities and leaf ages in two canopy systemsVitis, 34
S.B Verma, P.J Sellers, C. Walthall, F.G Hall, J. Kim, S.J Goetz (1993)
Photosynthesis and stomatal conductance related to reflectance on the canopy scaleRemote Sensing of Environment, 44
Grantz Grantz, Williams Williams (1993)
An empirical protocol for indirect measurements of leaf area index in grape ( Vitis vinifera L.)Horticultural Science, 28
J. Chen, S. Leblanc, John Miller, J. Freemantle, S. Loechel, C. Walthall, K. Innanen, H. White (1999)
Compact Airborne Spectrographic Imager (CASI) used for mapping biophysical parameters of boreal forestsJournal of Geophysical Research, 104
J. Gamon, C. Field, M. Goulden, K. Griffin, A. Hartley, G. Joel, J. Peñuelas, R. Valentini (1995)
Relationships Between NDVI, Canopy Structure, and Photosynthesis in Three Californian Vegetation TypesEcological Applications, 5
Ollat Ollat, Fermaud Fermaud, Tandonnet Tandonnet, Neveux Neveux (1998)
Evaluation of an indirect method for leaf area index determination in the vineyard: combined effects of cultivar, year and training systemVitis, 37
C. Wiegand, A. Richardson, D. Escobar, A. Gerbermann (1991)
Vegetation indices in crop assessmentsRemote Sensing of Environment, 35
A. Richardsons, A. Wiegand (1977)
DISTINGUISHING VEGETATION FROM SOIL BACKGROUND INFORMATIONPhotogrammetric Engineering and Remote Sensing, 43
Baldy Baldy, Benedicts Benedicts, Johnson Johnson, Weber Weber, Baldy Baldy, Osborn Osborn, Burleigh Burleigh (1996)
Leaf color and vine size are related to yield in a phylloxera infested vineyardVitis, 35
A. Huete, D. Post, R. Jackson (1984)
Soil spectral effects on 4-space vegetation discriminationRemote Sensing of Environment, 15
J. Colwell (1974)
Vegetation canopy reflectanceRemote Sensing of Environment, 3
W. Leeuwen, A. Huete (1996)
Effects of standing litter on the biophysical interpretation of plant canopies with spectral indicesRemote Sensing of Environment, 55
Smart Smart (1985)
Principles of grapevine canopy management micro‐climate manipulation with implications for yield and quality. A reviewAmerican Journal of Enology and Viticulture, 36
T. Carlson, D. Ripley (1997)
On the relation between NDVI, fractional vegetation cover, and leaf area indexRemote Sensing of Environment, 62
J. Price (1992)
Estimating vegetation amount from visible and near infrared reflectancesRemote Sensing of Environment, 41
C. Perry, L. Lautenschlager (1984)
Functional equivalence of spectral vegetation indicesRemote Sensing of Environment, 14
W. Bausch (1993)
Soil background effects on reflectance-based crop coefficients for corn☆Remote Sensing of Environment, 46
F. Baret, G. Guyot (1991)
Potentials and limits of vegetation indices for LAI and APAR assessmentRemote Sensing of Environment, 35
A. Huete, R. Jackson (1987)
Suitability of spectral indices for evaluating vegetation characteristics on arid rangelandsRemote Sensing of Environment, 23
Hatem Mabrouk, H. Sinoquet (1998)
Indices of light microclimate and canopy structure of grapevines determined by 3D digitising and image analysis, and their relationship to grape qualityAustralian Journal of Grape and Wine Research, 4
Three spectral vegetation indices were examined for their correlation with vertically shoot‐positioned canopy density (leaf area per metre of canopy) using both field spectroscopy data at the vine scale, and aerial image analysis at the vineyard scale. Vine canopy density accounted for a significant amount of the variability in all three vegetation indices (r2= 0.65 to 0.92). The perpendicular vegetation index (PVI) was found to have the greatest information cost as well as the poorest coefficient of determination value, and thus was the least desirable of the indices examined. The ratio‐based indices (RVI and NDVI) were shown to have similar information contents; however, the RVI was found to be more linearly related to canopy density over a broad range of values, and thus more desirable for vineyard remote sensing applications. Results from this analysis corroborate with findings from investigators in woodland and forest environments, and provide evidence of the complex nature of vineyard scene reflectance properties.
Australian Journal of Grape and Wine Research – Wiley
Published: Jul 1, 2002
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.