characterising and mapping vineyard canopy using high-spatial-resolution aerial multispectral images

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.

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*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

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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

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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).

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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

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Fig. 4. Graphical representation of pixels and basic geometry used in data extraction algorithm.


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

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Fig. 5. (a,b) Flow chart depicting logic steps used by Vinecrawler algorithm.


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

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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.


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

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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.


(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

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ARTICLE IN PRESSA. Hall et al. / Computers & Geosciences 29 (2003) 813–822822

characterising and mapping vineyard canopy using high-spatial-resolution aerial multispectral images

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