Accurate quantification of the spatial variation of canopy size is crucial for vineyard managemen... more Accurate quantification of the spatial variation of canopy size is crucial for vineyard management in the context of Precision Viticulture. Biophysical parameters associated with canopy size, such as Leaf Area Index (LAI), can be estimated from Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), but in Vertical-Shoot-Positioned (VSP) vineyards, common satellite, or aerial imagery with moderate-resolution capture information at nadir of pixels whose values are a mix of canopy, sunlit soil, and shaded soil fractions and their respective spectral signatures. VI values for each fraction are considerably different. On a VSP vineyard, the illumination direction for each specific row orientation depends on the relative position of sun and earth. Respective proportions of shaded and sunlit soil fractions change as a function of solar elevation and azimuth, but canopy fraction is independent of these variations. The focus of this study is the interaction of ill...
Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine vigour in... more Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine vigour in several applications, including direct mapping of vegetative–reproductive balance (VRB). Normalized difference vegetation index (NDVI) has been successfully used to assess the spatial variability of estimated LAI. However, sometimes NDVI is unsuitable due to its lack of sensitivity at high LAI values. Moreover, the presence of hail protection with Grenbiule netting also affects incident light and reflection, and consequently spectral response. This study analyses the effect of protective netting in the LAI–NDVI relationship and, using NDVI as a reference index, compares several indices in terms of accuracy and sensitivity using linear and logarithmic models. Among the indices compared, results show NDVI to be the most accurate, and ratio vegetation index (RVI) to be the most sensitive. The wide dynamic range vegetation index (WDRVI) presented a good balance between accuracy and sensitiv...
Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine
vigour in... more Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine vigour in several applications, including direct mapping of vegetative–reproductive balance (VRB). Normalized di erence vegetation index (NDVI) has been successfully used to assess the spatial variability of estimated LAI. However, sometimes NDVI is unsuitable due to its lack of sensitivity at high LAI values. Moreover, the presence of hail protection with Grenbiule netting also a ects incident light and reflection, and consequently spectral response. This study analyses the e ect of protective netting in the LAI–NDVI relationship and, using NDVI as a reference index, compares several indices in terms of accuracy and sensitivity using linear and logarithmic models. Among the indices compared, results show NDVI to be the most accurate, and ratio vegetation index (RVI) to be the most sensitive. The wide dynamic range vegetation index (WDRVI) presented a good balance between accuracy and sensitivity. Soil-adjusted vegetation index 2 (SAVI2) appears to be the best estimator of LAI with linear models. Logarithmic models provided higher determination coecients, but this has little influence over the normal range of LAI values. A similar NDVI–LAI relationship holds for protected and unprotected canopies in initial vegetation stages, but di erent functions are preferable once the canopy is fully developed, in particular, if tipping is performed.
Accurate quantification of the spatial variation of canopy size is crucial for vineyard managemen... more Accurate quantification of the spatial variation of canopy size is crucial for vineyard management in the context of Precision Viticulture. Biophysical parameters associated with canopy size, such as Leaf Area Index (LAI), can be estimated from Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), but in Vertical-Shoot-Positioned (VSP) vineyards, common satellite, or aerial imagery with moderate-resolution capture information at nadir of pixels whose values are a mix of canopy, sunlit soil, and shaded soil fractions and their respective spectral signatures. VI values for each fraction are considerably different. On a VSP vineyard, the illumination direction for each specific row orientation depends on the relative position of sun and earth. Respective proportions of shaded and sunlit soil fractions change as a function of solar elevation and azimuth, but canopy fraction is independent of these variations. The focus of this study is the interaction of illumination direction with canopy orientation, and the corresponding effect on integrated NDVI. The results confirm that factors that intervene in determining the direction of illumination on a VSP will alter the integrated NDVI value. Shading induced considerable changes in the NDVI proportions affecting the final integrated NDVI value. However, the effect of shading decreases as the row orientation approaches the solar path. Therefore, models of biophysical parameters using moderate-resolution imagery should consider corrections for variations caused by factors affecting the angle of illumination to provide more general solutions that may enable canopy data to be obtained from mixed, integrated vine NDVI.
Accurate quantification of the spatial variation of canopy size is crucial for vineyard managemen... more Accurate quantification of the spatial variation of canopy size is crucial for vineyard management in the context of Precision Viticulture. Biophysical parameters associated with canopy size, such as Leaf Area Index (LAI), can be estimated from Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), but in Vertical-Shoot-Positioned (VSP) vineyards, common satellite, or aerial imagery with moderate-resolution capture information at nadir of pixels whose values are a mix of canopy, sunlit soil, and shaded soil fractions and their respective spectral signatures. VI values for each fraction are considerably different. On a VSP vineyard, the illumination direction for each specific row orientation depends on the relative position of sun and earth. Respective proportions of shaded and sunlit soil fractions change as a function of solar elevation and azimuth, but canopy fraction is independent of these variations. The focus of this study is the interaction of ill...
Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine vigour in... more Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine vigour in several applications, including direct mapping of vegetative–reproductive balance (VRB). Normalized difference vegetation index (NDVI) has been successfully used to assess the spatial variability of estimated LAI. However, sometimes NDVI is unsuitable due to its lack of sensitivity at high LAI values. Moreover, the presence of hail protection with Grenbiule netting also affects incident light and reflection, and consequently spectral response. This study analyses the effect of protective netting in the LAI–NDVI relationship and, using NDVI as a reference index, compares several indices in terms of accuracy and sensitivity using linear and logarithmic models. Among the indices compared, results show NDVI to be the most accurate, and ratio vegetation index (RVI) to be the most sensitive. The wide dynamic range vegetation index (WDRVI) presented a good balance between accuracy and sensitiv...
Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine
vigour in... more Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine vigour in several applications, including direct mapping of vegetative–reproductive balance (VRB). Normalized di erence vegetation index (NDVI) has been successfully used to assess the spatial variability of estimated LAI. However, sometimes NDVI is unsuitable due to its lack of sensitivity at high LAI values. Moreover, the presence of hail protection with Grenbiule netting also a ects incident light and reflection, and consequently spectral response. This study analyses the e ect of protective netting in the LAI–NDVI relationship and, using NDVI as a reference index, compares several indices in terms of accuracy and sensitivity using linear and logarithmic models. Among the indices compared, results show NDVI to be the most accurate, and ratio vegetation index (RVI) to be the most sensitive. The wide dynamic range vegetation index (WDRVI) presented a good balance between accuracy and sensitivity. Soil-adjusted vegetation index 2 (SAVI2) appears to be the best estimator of LAI with linear models. Logarithmic models provided higher determination coecients, but this has little influence over the normal range of LAI values. A similar NDVI–LAI relationship holds for protected and unprotected canopies in initial vegetation stages, but di erent functions are preferable once the canopy is fully developed, in particular, if tipping is performed.
Accurate quantification of the spatial variation of canopy size is crucial for vineyard managemen... more Accurate quantification of the spatial variation of canopy size is crucial for vineyard management in the context of Precision Viticulture. Biophysical parameters associated with canopy size, such as Leaf Area Index (LAI), can be estimated from Vegetation Indices (VI) such as the Normalized Difference Vegetation Index (NDVI), but in Vertical-Shoot-Positioned (VSP) vineyards, common satellite, or aerial imagery with moderate-resolution capture information at nadir of pixels whose values are a mix of canopy, sunlit soil, and shaded soil fractions and their respective spectral signatures. VI values for each fraction are considerably different. On a VSP vineyard, the illumination direction for each specific row orientation depends on the relative position of sun and earth. Respective proportions of shaded and sunlit soil fractions change as a function of solar elevation and azimuth, but canopy fraction is independent of these variations. The focus of this study is the interaction of illumination direction with canopy orientation, and the corresponding effect on integrated NDVI. The results confirm that factors that intervene in determining the direction of illumination on a VSP will alter the integrated NDVI value. Shading induced considerable changes in the NDVI proportions affecting the final integrated NDVI value. However, the effect of shading decreases as the row orientation approaches the solar path. Therefore, models of biophysical parameters using moderate-resolution imagery should consider corrections for variations caused by factors affecting the angle of illumination to provide more general solutions that may enable canopy data to be obtained from mixed, integrated vine NDVI.
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Papers by Pedro Towers
vigour in several applications, including direct mapping of vegetative–reproductive balance (VRB).
Normalized di erence vegetation index (NDVI) has been successfully used to assess the spatial
variability of estimated LAI. However, sometimes NDVI is unsuitable due to its lack of sensitivity
at high LAI values. Moreover, the presence of hail protection with Grenbiule netting also a ects
incident light and reflection, and consequently spectral response. This study analyses the e ect of
protective netting in the LAI–NDVI relationship and, using NDVI as a reference index, compares
several indices in terms of accuracy and sensitivity using linear and logarithmic models. Among the
indices compared, results show NDVI to be the most accurate, and ratio vegetation index (RVI) to be
the most sensitive. The wide dynamic range vegetation index (WDRVI) presented a good balance
between accuracy and sensitivity. Soil-adjusted vegetation index 2 (SAVI2) appears to be the best
estimator of LAI with linear models. Logarithmic models provided higher determination coecients,
but this has little influence over the normal range of LAI values. A similar NDVI–LAI relationship
holds for protected and unprotected canopies in initial vegetation stages, but di erent functions are
preferable once the canopy is fully developed, in particular, if tipping is performed.
in the context of Precision Viticulture. Biophysical parameters associated with canopy size,
such as Leaf Area Index (LAI), can be estimated from Vegetation Indices (VI) such as the Normalized
Difference Vegetation Index (NDVI), but in Vertical-Shoot-Positioned (VSP) vineyards, common satellite,
or aerial imagery with moderate-resolution capture information at nadir of pixels whose values
are a mix of canopy, sunlit soil, and shaded soil fractions and their respective spectral signatures. VI
values for each fraction are considerably different. On a VSP vineyard, the illumination direction
for each specific row orientation depends on the relative position of sun and earth. Respective
proportions of shaded and sunlit soil fractions change as a function of solar elevation and azimuth,
but canopy fraction is independent of these variations. The focus of this study is the interaction of
illumination direction with canopy orientation, and the corresponding effect on integrated NDVI.
The results confirm that factors that intervene in determining the direction of illumination on a
VSP will alter the integrated NDVI value. Shading induced considerable changes in the NDVI
proportions affecting the final integrated NDVI value. However, the effect of shading decreases as
the row orientation approaches the solar path. Therefore, models of biophysical parameters using
moderate-resolution imagery should consider corrections for variations caused by factors affecting
the angle of illumination to provide more general solutions that may enable canopy data to be
obtained from mixed, integrated vine NDVI.
vigour in several applications, including direct mapping of vegetative–reproductive balance (VRB).
Normalized di erence vegetation index (NDVI) has been successfully used to assess the spatial
variability of estimated LAI. However, sometimes NDVI is unsuitable due to its lack of sensitivity
at high LAI values. Moreover, the presence of hail protection with Grenbiule netting also a ects
incident light and reflection, and consequently spectral response. This study analyses the e ect of
protective netting in the LAI–NDVI relationship and, using NDVI as a reference index, compares
several indices in terms of accuracy and sensitivity using linear and logarithmic models. Among the
indices compared, results show NDVI to be the most accurate, and ratio vegetation index (RVI) to be
the most sensitive. The wide dynamic range vegetation index (WDRVI) presented a good balance
between accuracy and sensitivity. Soil-adjusted vegetation index 2 (SAVI2) appears to be the best
estimator of LAI with linear models. Logarithmic models provided higher determination coecients,
but this has little influence over the normal range of LAI values. A similar NDVI–LAI relationship
holds for protected and unprotected canopies in initial vegetation stages, but di erent functions are
preferable once the canopy is fully developed, in particular, if tipping is performed.
in the context of Precision Viticulture. Biophysical parameters associated with canopy size,
such as Leaf Area Index (LAI), can be estimated from Vegetation Indices (VI) such as the Normalized
Difference Vegetation Index (NDVI), but in Vertical-Shoot-Positioned (VSP) vineyards, common satellite,
or aerial imagery with moderate-resolution capture information at nadir of pixels whose values
are a mix of canopy, sunlit soil, and shaded soil fractions and their respective spectral signatures. VI
values for each fraction are considerably different. On a VSP vineyard, the illumination direction
for each specific row orientation depends on the relative position of sun and earth. Respective
proportions of shaded and sunlit soil fractions change as a function of solar elevation and azimuth,
but canopy fraction is independent of these variations. The focus of this study is the interaction of
illumination direction with canopy orientation, and the corresponding effect on integrated NDVI.
The results confirm that factors that intervene in determining the direction of illumination on a
VSP will alter the integrated NDVI value. Shading induced considerable changes in the NDVI
proportions affecting the final integrated NDVI value. However, the effect of shading decreases as
the row orientation approaches the solar path. Therefore, models of biophysical parameters using
moderate-resolution imagery should consider corrections for variations caused by factors affecting
the angle of illumination to provide more general solutions that may enable canopy data to be
obtained from mixed, integrated vine NDVI.