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Computers & Geosciences 29 (2003) 813–822
Characterising and mapping vineyard canopy usinghigh-spatial-resolution aerial multispectral images
Andrew Halla,b,*, John Louisa,b, David Lamba,b,1
aCooperative Research Centre for Viticulture, PO Box 154, Glen Osmond SA 5064, AustraliabNational Wine and Grape Industry Centre, Charles Sturt University, Wagga Wagga NSW 2678, Australia
Received 5 April 2002; received in revised form 13 February 2003; accepted 10 March 2003
Abstract
Airborne digital images of vineyards have potential for yielding valuable information for viticulturists and vineyard
managers. This paper outlines a method of analysing high-spatial-resolution airborne images of vineyards to estimate
physical variables of individual grapevines in terms of local canopy shape and size. An algorithm (‘‘Vinecrawler’’) has
been developed to identify individual vine rows and extract sets of reflectance values (or combinations thereof) at quasi-
regular distances (approximately one pixel length) along the rows. Key vine canopy variables, including size, foliage
density and shape, were calculated from the sets of reflectance values collected by Vinecrawler. The algorithm precisely
identifies individual vines, allowing conversion from image coordinates (x-pixel, y-pixel) to a (row, vine) coordinate
system. The (row, vine) coordinate system is a valuable tool for directing vineyard managers to particular phenomena
identified from variables returned by Vinecrawler. This paper describes the computational methods used to identify vine
rows in raw airborne digital imagery and the operation of the Vinecrawler algorithm used to track along vine rows and
extract vine canopy size and shape descriptors and locational information.
r 2003 Elsevier Science Ltd. All rights reserved.
Keywords: Remote sensing; Precision viticulture; Digital image analysis; NDVI; Vitis vinifera L.
1. Introduction
Single variety blocks of grapevines (Vitis vinifera L.)
are generally subject to uniform management. However,
numerous physical, biological and chemical factors,
including spatial variations in topography, physical and
chemical characteristics of soils and the incidence of
pests and diseases, influence vine health and productivity
at the single vineyard block scale. A spatial variation in
environmental factors effects a spatial variation in grape
quality and yield (Hall et al., 2002). As differentiation in
pricing between grapes based on measured quality
attributes increases, greater emphasis is placed on
intelligent management of vineyard variability to
produce high-yielding vines and grapes with high
measurable quality attributes over entire vineyards.
Such management decisions rely upon the availability
of accurate and reliable data to describe the variability
exhibited by the vines (Hall et al., 2002). However,
mapping vineyard variables requires a considerable
amount of data, and traditional methods of generating
such data are time consuming and expensive. For
example, measuring six basic fruit quality and yield
variables of 60 sample sites in a 1-ha block requires more
than 30 work-hours.
By using remote-sensing technologies, the vegetative
characteristics of large areas of vineyard can be assessed
rapidly using airborne multispectral remote sensing.
ARTICLE IN PRESS
*Corresponding author. Cooperative Research Centre for
Viticulture, PO Box 154, Glen Osmond SA 5064, Australia.
Tel.: +61-2-6933-2744; fax: +61-2-6933-2737.
E-mail address: [email protected] (A. Hall).1Present address: School of Biological, Biomedical and
Molecular Sciences, University of New England, Armidale
NSW 2351, Australia.
0098-3004/03/$ - see front matter r 2003 Elsevier Science Ltd. All rights reserved.
doi:10.1016/S0098-3004(03)00082-7
Recent work has shown that differences in vine
performance can be identified from remotely sensed
data. For example, Johnson et al. (1996) reported that
NDVI values were negatively correlated to levels of the
highly damaging vine aphid, phylloxera. Significant
correlations have more latterly been achieved between
NDVI and canopy leaf area index (m2 leaf area per m2
of ground) and leaf area per vine (m2 per vine). These
correlations have been established over multiple vine-
yards using 4-m-resolution IKONOS satellite imagery
(Johnson et al., 2001). The research suggests that
remotely sensed images can be highly useful for
determining relative foliage density and extent (com-
monly referred to as vigour) within a vineyard.
Vine vigour is often reported to have a considerable
effect on fruit yield and quality (Dry, 2000; Haselgrove
et al., 2000; Petrie et al., 2000; Tisseyre et al., 1999;
Clingeleffer and Sommer, 1995; Iland et al., 1994).
Therefore, measures of vine canopy can be used to
estimate differences in fruit yield and quality. The use of
airborne remote sensing as a means of monitoring vine
canopy characteristics is attracting interest because of
opportunities for cost-effective generation of spatial
data amenable to precision agriculture activities (Lamb,
2000). Two factors that need to be addressed in order to
achieve a more objective and accurate analysis of
vineyard images are: (1) to increase the precision with
which individual vines can be identified and then located
within an image and (2) to estimate and then map
descriptors of vine vigour (Tisseyre et al., 1999).
This paper outlines a methodology for identifying
vine rows in an airborne multispectral image, and then
describes the Vinecrawler algorithm which tracks along
vine rows and estimates physical vine variables relating
to vine vigour.
2. Airborne multispectral imaging of the vineyard
Multispectral images of a block of Charles Sturt
University’s commercial vineyard, at Wagga Wagga,
New South Wales, Australia, were acquired during
2000–2001. The site is a well-established hedge-pruned
block (ca. 1 ha) of Cabernet Sauvignon subject to
uniform management. There is a space of 3.6m between
rows, and individual vines are separated by 1.8m along
the rows. The block is on a shallow (o10�) inclinesloping towards the east and with rows planted
perpendicular to the slope. The site was imaged at key
phenological stages in the annual growth cycle: bud-
burst, flowering, veraison (onset of berry ripening) and
immediately prior to harvest. Vine foliage has a
distinctly non-Lambertian surface. Therefore, imaging
was conducted as close to solar noon as possible from a
position directly above the vineyard.
The multispectral airborne imaging system used to
acquire vineyard images consisted of four digital video
cameras, each having a 740� 576 pixel array and fittedwith 12mm focal length lenses. Images were acquired at
an altitude of 305m delivering a 25 cm� 25 cm pixel
footprint on the ground. Each camera was fitted with an
interchangeable inference filter in near infrared (757.5–
782.5 nm), red (637.5–662.5 nm), green (537.5–562.5 nm)
and blue (437.5–462.5 nm) wavebands to allow produc-
tion of true-colour, false-colour and vegetation index
composite images. A standard desktop computer con-
taining a four-channel frame-grabber board captured
and digitised four-band composite images from the
cameras during flight. High- and low-reflectance targets
of known spectral characteristics, measuring 2m� 2m,were placed on the ground and included in the image.
These were used to calibrate image pixels from raw
digital numbers to reflectance values following the
procedure of Spackman et al. (2000). Images were also
corrected for radiometric and geometric distortion
(Louis et al., 1995; Spackman et al., 2000). Any
vegetation growing between and under the vines was
removed with herbicide spray in the weeks prior to the
imaging overflights. This ensured that most photo-
synthetically active vegetation detected in the vineyard
block would be from vines alone.
The four-band raster images were converted to a
single vegetation index, the normalised difference
vegetative index (NDVI). NDVI images were created
by transforming each multispectral image pixel accord-
ing to the relation (Rouse et al., 1973)
NDVI ¼ðnear infraredÞ � ðredÞðnear infraredÞ þ ðredÞ
; ð1Þ
where ‘near infrared’ and ‘red’ are the reflectances in
each band, respectively. The NDVI, a number between –
1 and +1, is a widely used indicator of plant vigour or
relative biomass. For highly vegetated targets, the
NDVI value is close to unity, while for non-vegetated
targets the NDVI is close to zero. Negative values of
NDVI rarely occur in natural targets.
3. Vineyard image processing
3.1. Characteristics of an NDVI image and image
thresholding
An image of a vineyard block composes of pixels
corresponding to vines themselves and the space
between the vine rows (inter-row spacing). Depending
on the exact composition of the image, other pixels may
comprise additional features such as outbuildings, roads
and tracks and trees. Fig. 1 is an NDVI image of a
vineyard block calculated pixel by pixel using the near
infrared and red bands of a multispectral image. In this
ARTICLE IN PRESSA. Hall et al. / Computers & Geosciences 29 (2003) 813–822814
grey-scale, white pixels represent pixels with the highest
NDVI value while black pixels represent pixels with the
lowest NDVI values. White pixels in this image include
those corresponding to vines and other healthy vegeta-
tion, for example, the trees in the top left corner.
Photosynthetically inert structures such as roads, bare
soil and fences appear dark (low NDVI). The inter-row
spacing generally appears dark since it is predominantly
dead undergrowth. Fig. 2 is a histogram of the NDVI
values extracted from the region of Fig. 1 comprising the
main vineyard block. A large moving average (51-
points) was applied to smooth all but the vine and inter-
row space features from the histogram. The smoothed
histogram of NDVI pixels illustrates a bimodal dis-
tribution of pixel values, with two distinct peaks (P1 &
P2) and a trough (T). A comparison of a subset of pixels
with very high NDVI values (over 0.67) against true-
and false-colour images confirms that pixels with very
high NDVI values are almost exclusive where vines are
present. The unusually high NDVI values indicate very
dense and deep foliage characteristic of mature hedge
pruned vines. With a pixel size of 25 cm, pixels at the
fringes between vine rows and inter-row space contain
sunlight reflected from both the grapevines and the
ground. Again, by comparing a subset of pixels that
have values between 0.38 and 0.67, these ‘‘mixed’’ pixels
are mostly around the fringes of the vine rows.
Using the histogram, image pixels were grouped into
one of the three categories: non-vine, vine and mixed.
The mid-point on the x-axis of Fig. 2 between P1 and T
separated the non-vine pixels from the mixed pixels. The
mid-point on the x-axis between T and P2 separated the
mixed pixels from the vine pixels. The NDVI value at
the mid-point between T and P2 was used as a threshold
to eliminate all non-vine and mixed pixels: any pixel
with an NDVI value less than 0.67 was set to zero. The
ARTICLE IN PRESS
Fig. 1. Grey-scale NDVI image calculated from multispectral
image of Cabernet Sauvignon block (approximately 1.5 ha).
White pixels represent highest NDVI values and black pixels
represent lowest NDVI values. Note that white pixels corre-
spond to vigorous vegetative surfaces such as grapevines and
trees.
Fig. 2. Histogram of NDVI values extracted from an image taken when vines were flowering. Histogram has been smoothed with a
51-point moving average. Distribution shows clearly defined peaks (P1 and P2) and a trough (T). Two vertical lines separate three
classes of pixels. Pixels included in an analysis are only those to right of line at 0.67 on x-axis.
A. Hall et al. / Computers & Geosciences 29 (2003) 813–822 815
resultant ‘‘thresholded’’ image is depicted in Fig. 3.
White pixels represent the highest levels of vegetative
vigour (highest NDVI). The lower the NDVI value
(the darker the pixel), the lower the level of vegetative
vigour. Lower NDVI values are thought to be caused
by a greater presence of a ground component
and/or by lower foliage density in the pixel. Since all
pixels below the threshold NDVI value have been
removed, it is clear that vine rows are distinguishable
from inter-row space. Furthermore, it is evident
from Fig. 3 that different levels of vine vigour can be
observed in the vineyard as differences in the size of vine
canopies.
3.2. Data extraction—the ‘‘vinecrawler’’ algorithm
Following the process of image thresholding, extrac-
tion of canopy variables can take place. Fig. 4 illustrates
the geometry involved in the data extraction process.
The locations of image pixels were specified according to
their integer coordinates (x; y), where the origin is
located at the top left corner. As it progresses along
the vine rows, the algorithm transforms image coordi-
nates into a vine-based coordinate system (u; v), where u
is the direction along the centre of the vine row given by
u ¼ x cos y� y sin y ð2Þ
and v specifies the axis normal to the direction of the
vine row:
v ¼ x sin yþ y cos y: ð3Þ
In Eqs. (2) and (3), y is the mean angle of deviation ofthe row direction from the horizontal direction within
the image. While the pixel coordinates in the image-
based coordinate system are integers, the vine-based
coordinate system utilises real numbers to reflect the
additional accuracy required when working with inter-
polated pixel locations. The algorithm used to extract
variables of vine architecture, aptly named ‘‘Vinecraw-
ler’’, follows eight logic steps, summarised in Fig. 5.
Referring to Fig. 5, the steps are:
1. Locate the starting point of the first or next vine row.
2. Search both up and down in the u direction for the
upper and lower edge pixels of the vine row
according to the location of the nominated NDVI
threshold value (in this instance 0.67).
ARTICLE IN PRESS
Fig. 3. Grey-scale thresholded NDVI image of vineyard block. All pixels with NDVI values below a threshold (here 0.67) were set to
zero and are therefore black in image.
A. Hall et al. / Computers & Geosciences 29 (2003) 813–822816
A pixel with a value of zero indicates a pixel
beyond the edge of the vine row. Occasionally, vine
growth can envelop the inter-row space at sections
along certain rows and there will be no pixel
with a zero value at the edge of the row. Without
intervention, the algorithm would continue across
the rows until the next pixel with a zero value is
found. Therefore, the search is restricted to a distance
of half the vine row spacing (generally known
by vineyard managers to within an accuracy of
0.1m). The pixel at this position is considered the
end pixel.
3. Obtain the individual NDVI values along the vector
specified in the v direction between the upper and
lower edge pixels. This characterises the vegetation
across the vine row at this location, u:4. Locate the mid-point of the vector of NDVI values
that represent this segment of the vine row.
The mid-point of a vector (ump; vmp) is located at
‘‘the weighted centre’’ of the vector. The weighting of
each pixel is its NDVI value. For example, a vector
may be represented by a set of NDVI values of (0.84,
0.94, 0.54, 0.62, 0.56). The equivalent cumulative
vector is (0.84, 1.78, 2.32, 2.94, 3.50). The mid-
point is calculated as half the sum of the vector, i.e.
3.50/2=1.75. This value is less than the second value
of the cumulative vector (1.78); therefore, the centre
pixel is the second along the vector.
The distance (dmp) to the mid-point of the vector is
precisely calculated by linear interpolation along the
line transecting the vine row, i.e. (0.94+(1.78–1.75))/
0.94+1E1.97 pixels along the transect. The coordi-nates of the central point in the row segment
(xmp; ymp) are calculated using the coordinates of
the upper edge pixel (xupper; yupper) and the mean angle
of the row direction to the horizontal direction of the
image (y), i.e.
xmp ¼ xupper þ dmp sin y; ð4Þ
ymp ¼ yupper þ dmp cos y: ð5Þ
5. Output the pixel coordinates of the mid-point
(xmp; ymp) and the NDVI set to a file. The coordinates
will be the location of the variables calculated for this
particular point in the vines.
6. Move along one pixel width from the mid-point in
the direction of the mean row direction (u direction).
In practice, xmp is increased by cos y and ymp is
increased by sin y: On occasion, the pixel located atthis position has been thresholded to zero, due to
missing or dead vines. If this occurs the algorithm
executes a sub-process to search for the next pixel in
the row that is not zero. A search is made in the u
direction both up and down a distance of 40% of the
row spacing. If no pixel above zero is found, the
search will move on to one-pixel width in the v
direction and search in the u direction similarly again.
This is repeated until a pixel that does not have an
NDVI value of zero is found.
7. Repeat steps 2–6 until the end of the image is
reached.
8. Repeat process until all rows in the block have been
mapped.
The outcome of executing the algorithm is a table
containing one row of data for every set of NDVI values
between the upper and lower edge pixels for each
ARTICLE IN PRESS
Fig. 4. Graphical representation of pixels and basic geometry used in data extraction algorithm.
A. Hall et al. / Computers & Geosciences 29 (2003) 813–822 817
transect of each vine row in the image. Each row
contains an identifier of the vine row from which the
vector originated, pixel coordinates of the centre of the
transect, and a set of ordered NDVI values. An example
of such an output is shown in Table 1. The vine row
identification number is simply an integer that is 1 for
the first row and increases by one for every new row
encountered in the image. The NDVI set and pixel
coordinates (xmp; ymp) are formed as described by steps 3
and 4 of the algorithm.
3.3. Quantifying vine characteristics from extracted data
Table 1 lists variables that describe some aspect of the
NDVI vector at each vine row transect. The first three
columns list the vine row number, the coordinates of the
weighted centre of the vine row transect, and the NDVI
values for each pixel along the vector. The mean NDVI,
calculated as the mean of all pixels belonging to the
specific vector, quantifies the density of vine biomass.
Column length refers to the length of the vector (in
pixels) and this quantifies the width of the vine row at
that particular point along the row. The maximum
NDVI is the highest NDVI value existing in the
particular vector and gives an indication of the highest
level of vigour for a vine. Although not shown in this
table, another valuable variable is the sum of the NDVI
values of a vector. This quantifies the total biomass at
that segment of the row.
The final column of Table 1 (‘‘second derivative’’)
gives an example of a variable developed to describe a
shape characteristic of the vine at a particular point
ARTICLE IN PRESS
Fig. 5. (a,b) Flow chart depicting logic steps used by Vinecrawler algorithm.
A. Hall et al. / Computers & Geosciences 29 (2003) 813–822818
along the row segment. The value contained in this
column is the second derivative of a quadratic line of
best fit applied to a single vector. This variable quantifies
the curvature of the spatial distribution of the NDVI
values of the vector. Fig. 6 shows a selection of
quadratic lines of best fit for the NDVI vectors shown
in Table 1. Usually the pattern results in a convex curve.
The greater the convexity of the curve, the higher (in
absolute terms) the second derivative of the fitted
quadratic. For example, the curve resulting from vector
4 results in a highly convex curve and a high value for
the variable (�1.78� 10�2), whereas for the flatter linedescribed by vector 3, a lower value results
(�3.15� 10�3). A linear across-row (or vector) profile
results in a value close to zero (e.g. vector 6). On
occasion, a concave curve results in a positive second
derivative.
The second derivative variable is a useful descriptor of
the density of vegetation in the centre of the row in
comparison to the outside of the row, and may be useful
in discriminating between differences in canopy archi-
tecture. It is suspected that vines with high values for the
second derivative may show significantly different berry
characteristics due to the various degrees of shading
associated with different canopy shapes that are known
to affect anthocyanin development in grapes.
4. Mapping vine descriptors
The pixel coordinates (x; y) used to map the extractedvectors are transformed into UTM (datum: WGS-84)
map coordinates (X ;Y ) using first-order polynomialspatial warping, where
X ¼X
i;j
ai;jxjyi; ð6Þ
Y ¼X
i;j
bi;jxjyi: ð7Þ
The coefficients of the polynomial functions (ai;j and bi;j)
are calculated using on-ground GPS measurements as
the spatial reference. It should be noted that images are
not resampled in this process, thereby retaining the
spectral integrity of the original images.
By plotting a 1-D line-graph of the row vector values
corresponding to a particular vine attribute along each
row, a particular quality of the vine can be visualised
along that row. Calculating the distance from the row
start point to each row vector, using the UTM pixel
coordinates gives a good spatial representation of the
extracted vine properties. For example, Fig. 7 is a graph
of the summed-NDVI values, a good indication of total
vine biomass, along each row vector as a function of
distance along a single vine row from the starting point
(v ¼ 0).Whole-vine variables can be calculated using an
appropriate number of row vectors. The vegetative
ARTICLE IN PRESS
Table 1
Section of table produced by data extraction algorithm. Four columns added at right describe qualities of each NDVI vector
Vine row Coordinates NDVI vector Mean
NDVI
Column
length
Max.
NDVI
Second
derivative
x y 1 2 3 4 5 6 7
5 250.99 205.72 0.73 0.82 0.88 0.85 0.82 4 0.88 �2.80� 10�2
5 250.07 206.80 0.76 0.81 0.87 0.84 0.88 0.73 0.77 0.81 7 0.88 �1.11� 10�2
5 250.15 207.79 0.77 0.82 0.84 0.83 0.82 0.72 0.84 0.80 7 0.84 �3.15� 10�3
5 251.22 208.71 0.78 0.83 0.87 0.84 0.83 0.72 0.81 6 0.87 �1.78� 10�2
5 251.30 209.71 0.79 0.84 0.87 0.84 0.82 0.83 5 0.87 �1.34� 10�2
5 251.38 210.70 0.82 0.84 0.90 0.82 0.91 0.86 5 0.91 �1.79� 10�4
5 251.46 211.70 0.84 0.86 0.85 0.78 0.88 0.84 5 0.88 5.91� 10�3
5 251.54 212.70 0.73 0.87 0.85 0.83 0.74 0.75 0.80 6 0.87 �1.73� 10�2
5 251.62 213.70 0.77 0.88 0.86 0.84 0.70 0.83 0.81 6 0.88 �5.84� 10�3
5 251.69 214.69 0.80 0.88 0.86 0.89 0.72 0.83 0.83 6 0.89 �7.47� 10�3
Fig. 6. Selection of quadratic lines of best fit for NDVI vectors
shown in Table 1.
A. Hall et al. / Computers & Geosciences 29 (2003) 813–822 819
characteristics of a single sample vine were calculated as
either the sum or mean of several concurrent row
vectors. With 25 cm image resolution, and a vine-to-vine
spacing of 1.8m, a set of 7 (or, occasionally, 8) vectors
were included. Whole-vine variables provide a spatially
exact description of the vegetative shape or quantity that
can be used in statistical analyses to assess relationships
between image data and actual on-ground biophysical
data collected in the vineyard.
As is usual in viticultural practice, individual vine
location is determined using (row, vine) coordinates.
Linking UTM coordinates to this system provides a
viticulturist with an intuitive mapping system. Fig. 8
illustrates a basic vineyard coordinate system. The row
number is the number of rows from the edge of the block
and the vine number is the number of vines along the
row from one end of the block to the vine. In this work,
the distance along a row to a vine was measured in the
vineyard. In Fig. 8, the distances to (row 18, vine 5) and
the distance to (row 17, vine 2) are highlighted. It should
be noted that the sample area from which on-ground
biophysical data were taken is the space between the
trunk of the vine and the trunk of the next vine along the
row. Using these distance measurements, coordinates of
(row, distance along a row) were formed.
A point along a row in an image was found using this
same coordinate system. The location of a vine in the
image was calculated by interpolating a line in the u
direction from the row start point the same distance as
measured in the vineyard. In effect, then, the only
coordinates required from the field to interpret the data
were (row, distance along the row). Therefore, rather
than accurately surveying each individual sample loca-
tion, only a few GPS locations were required to initially
georectify the image. Fig. 9 depicts a 2-D map of
individual vine canopy size and mean NDVI per vine
across the entire vineyard block.
Preliminary analyses of relationships between biophy-
sical data and vine variables derived from the image data
corroborate some suspected relationships between ca-
nopy structure and vine performance (Hall et al., 2001).
For example, from a sample set of 60 vines, vine biomass
ARTICLE IN PRESS
Fig. 7. Plot of biomass row vector, illustrating how total biomass (sum of NDVI values for a column) changes with distance along row
18 at harvest.
Fig. 8. Close-up view of a section of vineyard, illustrating (vine, row) coordinate system and measurements used to map extracted
image data.
A. Hall et al. / Computers & Geosciences 29 (2003) 813–822820
(sum of NDVI) at harvest 2001 had a negative linear
relationship (r ¼ �0:63) with the anthocyanin content ofthe berries (an important quality indicator). At the same
time, vine size was shown to be positively correlated
(r ¼ 0:65) to plant nitrogen levels.
5. Conclusion
A procedure of analysing high-spatial-resolution
multispectral imagery of vineyard blocks has been
developed. Imagery is first converted into a single-
number vegetation index and then, by a process of
thresholding, is separated into non-vine and vine pixels.
An algorithm has then been developed to move along
single vine rows and extract variables of canopy
architecture related to both biomass density and canopy
size and shape. These variables can then be mapped
either as 1-D along-row profiles, or as 2-D spatial maps.
Image pixel coordinates can also be transformed into a
(vine, row) coordinate system which, subsequent to the
identification of vines of notably different canopy
characteristics, allows the accurate location of these
vines on the ground.
Acknowledgements
This project is supported by the Commonwealth
Cooperative Research Centres Program and is con-
ducted by the CRC for Viticulture. The authors
appreciate ongoing support provided by Charles Sturt
University’s Spatial Analysis Unit (CSU-SPAN). The
authors are also most grateful to Bruno Holzapfel for
viticultural and vineyard sampling advice; and Charles
Sturt University Winery for access and support in
preparation and ongoing maintenance of the vineyard
block used in this study. The authors also wish to
acknowledge the valuable comments of an anonymous
reviewer.
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Fig. 9. Symbolic map of vineyard block at harvest, providing
information on vine size and mean NDVI. Each circle
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