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Optical remote sensing applications in viticulture - a review

A HALL1,2,3, DW LAMB1,2, B HOLZAPFEL1,2 and J LOUIS1,2

Abridged Title: Optical remote sensing applications in viticulture

1Cooperative Research Centre for Viticulture, PO Box 154, Glen Osmond, SA 5064

2National Wine and Grape Industry Centre, Charles Sturt University, Locked Bag 588,

Wagga Wagga, NSW 2678

3Corresponding author: Andrew Hall, anhall@csu.edu.au, Fax: 02 6933 2737

Abstract

The emergence of precision agriculture technologies and an increasing demand for higher

quality grape products has led to a growing interest in the practice of precision viticulture;

monitoring and managing spatial variations in productivity-related variables within single

vineyards. Potentially, one of the most powerful tools in precision viticulture is the use of

remote sensing through its ability to rapidly provide a synoptic view of grapevine shape, size

and vigour over entire vineyards. Its potential for improving viticultural practice is evident

by the relationships that are known to exist between these canopy descriptors and grape

quality and yield. This paper introduces the reader to remote sensing and reviews its recent,

and potential, applications in viticulture.

Abbreviations

EM electromagnetic; GPS global positioning system; GIS geographical information system;

NDVI normalised difference vegetation index

Key words: remote sensing, precision viticulture, multispectral imaging, grapevine,

vegetative vigour

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Introduction

Grapevine (Vitis vinifera) health and productivity are influenced by numerous physical,

biological and chemical factors, including spatial variations in topography, physical and

chemical characteristics of soils and the incidence of pests and diseases. The spatial variation

in these factors effects a spatial variation in grape quality and yield within vineyards leading

to an overall reduction in wine quality and volume. With the likelihood of increased

differentiation in pricing between grapes based on measured quality attributes (Winemakers

Federation 1996), vineyard management decisions must account for spatial variability in

quality and yield in order to produce a higher-quality higher-value product. However, these

decisions rely on the availability of accurate and reliable data that describe spatial variability

in relevant vine descriptors.

The emergence of global positioning systems (GPS) technology means traditional on-site

measurements of physical, chemical and biological parameters associated with vine

productivity can now be linked to specific locations within vineyards. This information,

when used in conjunction with computer-based geographical information systems (GIS),

provides viticulturists with the capability to process and map spatial relationships between

attributes and make management decisions based on numerous layers of information (Taylor,

2000). The process of modulating cultural practices as a function of spatial and temporal

variation within agricultural fields is known as precision agriculture (Cook and Bramley

1998, Moran et al. 1997). In the context of the grape and wine industry, precision viticulture

may be defined as monitoring and managing spatial variation in productivity-related

variables (yield and quality) within single vineyards (Lamb and Bramley 2001).

In recent years, yield maps produced by grape-yield monitors in Australia have shown up to

eight-fold differences in yield can occur within a single vineyard block (Bramley and Proffitt

1999). Furthermore, there are considerable spatial variations in quality indicators such as

colour and baume (Bramley and Proffitt 2000). Relationships between yield and quality

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indicators are often inferred, however these relationships do vary significantly between

vineyards (Holzapfel et al. 1999, Holzapfel et al. 2000), and possibly within vineyards.

Moreover, preliminary data suggest regions of high and low-yielding vines in a vineyard

tend to remain stable in time, inferring that soils play a significant role in such variability

(Bramley et al. 2000). The accurate characterisation of spatial variations in those parameters

that influence vineyard productivity requires a considerable amount of data. Traditional

methods of generating such data are generally time consuming and expensive. For example,

measuring basic fruit quality and yield parameters of sixty sample sites in a one hectare

block requires more than thirty work-hours. The move toward on-the-go sensing of yield and

quality parameters by combining the latest sensor technology with GPS-equipped vehicles is

slow and currently limited to grape yield. However, rapid sensing techniques such as

measurement of baume using near infrared (NIR) spectroscopy (Williams 2000) and grape-

phenolic composition using visible-NIR spectroscopy (Celotti et al. 2001) are potential

candidates for on-the-go sensing. The use of rapid electromagnetic induction or EM-survey

techniques to accurately characterise soil structure is also becoming more widely used in the

grape and wine industry (Lamb and Bramley 2001).

The use of remote sensing as a means of monitoring crop growth and development is

attracting interest from researchers and commercial organisations alike. This interest is

primarily driven by the opportunities for cost-effective generation of spatial data amenable to

support precision agriculture activities (Lamb 2000). To date, limited use is being made of

this technology in the grape and wine industry, either for research support or as a

commercial monitoring tool. This paper presents some of the key principles of remote

sensing, reviews the current status of remote sensing in viticulture, and discusses the

potential of remote sensing as part of an integrated management tool for vineyards.

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How does remote sensing work?

Remote sensing involves measuring features on the earth's surface using remote satellite or

aircraft-mounted sensors. In terms of optical remote sensing, sensors detect and record

sunlight reflected from the surface of objects on the ground. The ability of a sensor to detect

these objects is quantified in terms of the sensor's spatial, radiometric, spectral and temporal

resolution.

Spatial resolution is a measure of the smallest object detectable on the ground. The number

of available image-forming pixels in the sensor itself, and its distance from the ground,

contribute to determining the pixel-size on the ground and the overall image footprint. For

example, the American Landsat satellite, orbiting at a height of 705 km above the Earth’s

surface is capable of recording images with a 30 m x 30 m pixel size (referred to as a 30 m

pixel), and a footprint of 185 km x 185 km. The French SPOT satellite orbits 832 km above

the earth' s surface, generating full scenes of 60 km x 60 km and a 20 m pixel. This means

the smallest object that can be directly detected by the sensor is 30 m (Landsat) or 20 m

(SPOT) in each dimension (Barret and Curtis 1999) (Figure 1). More recently, high-

resolution satellites such as IKONOS, which provides 4-m resolution multispectral imagery,

have come on line, however, the cost of such data remains a significant impediment to its

widespread use (Lamb et al. 2001b). Airborne mounted sensors such as airborne digital

cameras or video systems, which are flown up to 3 km above the ground, generally have 1-

to 2-m pixels and corresponding image footprints of the order of 100 Ha (Figure 2) (eg Lamb

2000). Figures 1 and 2 illustrate that while Landsat and SPOT satellite imagery, with spatial

resolution of the order of tens-of-metres, is suitable for applications requiring regional

coverage, the pixel size precludes its use in the investigation of targets of the size of typical

vineyard blocks, and of features that may vary within vineyards.

Radiometric resolution specifies the number of discrete radiometric levels available to

individual pixels to record the intensity of measured radiation from a target in a given

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waveband. For example, 8-bit radiometric resolution means there are 28 = 256 levels

available (0 = darkest, 255 = brightest) while 10-bit sensors have 210 = 1024 levels available

to each image pixel. In practise, however, n-bit systems tend to only have (n-2)-bits of

information in image pixels as usually the lowest 2-bits of data carries the system noise,

including dark-current and thermal noise (King 1992, Louis et al. 1995).

Temporal resolution or, more simply, revisit-frequency is an important attribute of any

sensor when used for commercial monitoring or management purposes. Typical commercial

satellites like the American Landsat and French SPOT satellites have revisit intervals of 16

and 26 days, respectively. In the case of SPOT imagery, a target-pointing capability during

different overpasses could reduce this interval to as low as 2 days (Barrett and Curtis 1999).

Aircraft mounted sensors, on the other hand, are more amenable to user-defined visitations,

and have the added advantage of being able to operate under a high-cloud base (Figure 3).

The spectral resolution is the number of wavebands of data that can be simultaneously

recorded at each pixel. The amount of sunlight reflected off a target is described in terms of

the target's reflectance profile. The spectral reflectance profiles for Cabernet Sauvignon

vines, underlying covercrop (chick-peas) and bare soil are given in Figure 4. These profiles

indicate the amount of sunlight these targets reflect as a function of the wavelength (or

colour). All photosynthesising plants, including vine canopies and covercrops, do not reflect

much light in blue or red wavelengths because chlorophylls (and related pigments) absorb

much of the incident energy in these wavelengths for the process of photosynthesis.

However, these targets reflect a higher proportion of light in the green wavelengths, again

due to chlorophylls and related pigments, and this is why such targets appear green when

viewed by the human eye. However, in the near infrared wavelengths (wavelengths greater

than about 700 nm) photosynthesising plants reflect large proportions of the incident sunlight

(in excess of 65%). These wavelengths, to which the human eye is insensitive, can be

detected by appropriate instruments. The amount of sunlight reflected in these wavelengths

is very sensitive to leaf cell structure and this is influenced by water content (Campbell 1996,

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pp. 456-459). Figures 5(a) and (b) show the reflectance profile of a typical vegetated target.

Superimposed on these profiles are a set of wavebands corresponding to the sensitivity of a

hypothetical instrument and the reflectance profile that would be inferred from the response

of that instrument to the ground target. In Figure 5(a), the hypothetical instrument measures

the spectral signature of the target in four wavebands. While an accurate measure of the

target reflectance would be extracted at the four specified wavebands, the shape of the

reflectance profile of the vegetated target is only poorly described. Using thirteen closely

spaced wavebands (Figure 5(b)), the reflectance of the target is recorded for each waveband

and the shape of the entire spectral profile is more accurately described. In an application

where fine detail in the shape of the spectral profile is required, the higher spectral-resolution

instrument (Figure 5(b)) would be appropriate.

A consequence of the upper limit on the amount of data that can be processed and stored in

real-time by any remote sensing system is the compromise between spatial, radiometric and

spectral resolution. In general, this equates to a trade-off between spatial and spectral

resolution. The terms multispectral and hyperspectral are often interchanged, although they

usually define instruments according to the number of wavebands of information that is

recorded for each image pixel. The more general adjective ‘multispectral’ is used to describe

instruments that record information in only a small number of wavebands; typically 2-10.

Hyperspectral instruments record information in a large number of wavebands, typically

greater than 10.

Spectral vegetation indices reduce the multiple-waveband data at each image pixel to a

single numerical value (index), and many have been developed to highlight changes in

vegetation condition (eg Wiegand et al. 1991, Price and Bausch 1995). Vegetation indices

utilise the significant differences in reflectance of vegetation at green, red and near infrared

wavelengths. For example, Normalised Difference Vegetation Index (NDVI) images are

created by transforming each multi-waveband image pixel according to the relation:

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)( + )()( - )(

redrednear infraredrednear infraNDVI = (Equation 1)

where ‘near infrared’ and ‘red’ are respectively the reflectances in each band (Rouse et al.

1973). The NDVI, a number between –1 and +1, quantifies the relative difference between

the near infrared reflectance ‘peak’ and red reflectance ‘trough’ in the spectral signature

(refer to Figure 4 for an example). This index is the most widely used indicator of plant

vigour or relative biomass. For highly vegetated targets, the NDVI value will be close to

unity, while for non-vegetated targets the NDVI will be close to zero. Negative values of

NDVI rarely occur in natural targets.

One important advantage of ratio indices such as the NDVI is that the intensity of the total

light reflected from a target does not influence the calculation. An object under shadow will

reflect light reduced by approximately the same amount across the entire spectrum. Although

there is a reduction in the precision of NDVI for areas in shadow, because of a reduction in

the total range of reflectance levels, the ratio of two spectrally similar features should be the

same. Through the use of vegetation indices, shadows, which may otherwise be a significant

problem in imaging a vineyard with closely spaced rows, are effectively removed.

Airborne imaging systems

The use of airborne colour and colour-infrared photography for monitoring crops in

Australia was established in the early 1970’s (eg Harris and Haney 1973). These techniques

were later extended to detect weeds in crops and pastures (Barrett and Leggett 1979, Arnold

et al. 1985). However, limitations of aerial photography for crop monitoring include the

absence of a quantitative data acquisition capability, the high cost and availability of colour

infrared film and processing, and the requirement for manual scanning or digitising. The

intrinsic analogue nature of the imagery results in significant additional processing and delay

prior to incorporating the imagery into a GIS.

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Airborne imaging systems, incorporating in-flight or post-flight image digitisation can

provide sub-metre resolution images of crops at any revisit frequency and in a timely and

cost-effective manner. Multispectral imaging systems provide user-selectable spectral bands,

some as narrow as 10 nm bandwidth. These bands are commonly available in the visible and

near infrared (NIR) (eg Manzer and Cooper 1982, Louis et al. 1995, Anderson and Yang

1996, Sun et al. 1997), and the mid-infrared bands (SWIR) (Everitt et al. 1986, Everitt et al.

1987). Hyperspectral imaging systems also provide user-selectable wavebands. Systems such

as the Compact Airborne Spectrographic Imager (CASI-2) offers up to 288 wavebands with

approximately a 2.2 nm bandwidth in the range of 400 - 900 nm (ITRES 2001). By

comparison, Hymap imagery offers up to 200 wavebands in the visible, NIR, SWIR and

thermal infrared (TIR) (Intspec 2001). However, due to power and stability requirements,

airborne hyperspectral imaging systems are confined to operation in larger twin-engine

aircraft and this makes them significantly more expensive to operate than multispectral

imaging systems which can be deployed in single-engine aircraft. Airborne multispectral and

hyperspectral systems are ideal for quantification of crop growth in agricultural research

applications. These systems have spectral bands in the visible green (555-580 nm) and red

(665-700 nm) wavelengths, and in the near-infrared (740-900 nm) wavelengths, and provide

the high temporal and spatial resolution needed for agricultural research plot evaluation

(Clevers 1986, Clevers 1988a, Clevers 1988b, Lamb 2000). Insights provided by such

research and the increasing affordability of multispectral imaging systems have resulted in

them becoming more widely used over a wide variety of Australian crops (Lamb 2000).

Remote sensing as a tool for precision viticulture

Remote sensing of soils

Along with climate and topography, soil is a key factor influencing vineyard productivity

(Jackson 2000). At the regional or between-vineyard scale, soil has been described as having

the least significant effect on grape wine and quality (eg Rankine et al. 1971, Wahl 1988).

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However, at the scale of individual vineyards, soil and topography are often strongly

connected (Jackson 2000, Yule et al. 2001, Taylor and McBratney 2001), as are topography

and climate, in particular microclimate (Percival et al. 1994, Hutchinson 2001). On-ground

physical measurements of soil structure and condition in vineyards have demonstrated

significant variations can exist within single vineyards. For example, using EM-surveying to

measure soil electrical conductivity in two contrasting Australian vineyards demonstrated up

to three-fold differences in conductivity existed within each. In the case of the 7 hectare

Coonawarra vineyard used in this study, conductivity was highly correlated to soil depth and

the latter varied by a factor of two (Bramley et al. 2000). Similarly, large variations in petiole

nutrient levels in the same vineyard suggested large spatial differences in soil mineral

content (Bramley 2001a). Spatially referenced grape yield maps acquired from a number of

Australian vineyards over the past three years suggest regions of high- and low-yielding

vines tend to remain stable in time. This suggests that soils, and their association with

topography and microclimate, play an important role in characterising the spatial

characteristics of within-vineyard variability. Therefore, it is no surprise that considerable

effort in precision viticulture research is targeting measuring and mapping spatial variability

in soils at the single vineyard scale.

Often, different soils, because of differences in intrinsic colour, moisture levels and organic

and mineral constituents, have different optical reflectance characteristics (Condit 1970,

Colwell 1983, Escadafal et al. 1989). However, care must be taken when using optical

remote sensing for mapping soil structure on the basis of surface reflectance as visible and

near infrared radiation penetrates only to within a few millimetres of the soil surface (Lee

1978). Numerous studies have reported varying levels of agreement in comparing bare-soil

images with other on-ground soil data such as EM survey (Pitcher-Campbell et al. 2001) and

traditional soil sampling (Grierson and Bolt 1995, Ryan and Lewis 2001). In a situation

where the ground has been ploughed, as in the preparation of a new vineyard site, the soil

surface may more accurately reflect soil variations in the vicinity of the vine root-zone.

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Imagery, highlighting differences in the vigour of the pre-existing pasture or crop, may also

be useful in identifying different soil zones in a potential vineyard site before cultivation,

(for example, see figure 2 in Lamb 1999). Recent work, involving the Hymap hyperspectral

sensor demonstrated the enormous potential of high-order image processing of many

wavebands of spectral information (Ryan and Lewis 2001). Ryan and Lewis contended that

using 128 spectral wavebands of Hymap allowed them to discriminate numerous soil zones

underneath mature vines. However, the extent to which these soil analyses relied on direct

soil spectral information versus indirect measurements of the subtle variations in vine vigour

was not established.

By identifying regions of similar soils and matching suitable varieties and clones to the

particular soil types, remotely sensed images can be a valuable tool at the planning stage of

vineyard development. Soil-related effects in a given field will vary from season to season,

and may completely reverse under different rainfall conditions (eg Lamb 2000). However,

positioning varietal blocks so that they are contained within only one soil type with its own

irrigation system allows easy management and a more consistent product (Grierson and Bolt

1995). Although physical soil sampling will remain an essential requirement of ground-

truthing, the major advantage of remote sensing is in reducing the amount of soil sampling

required to adequately characterise and delineate soil zones. A single imaging mission, with

a view to segregating a site into homogeneous blocks has potential to characterise variability

and increase overall quality and productivity of a vineyard development. The economic

benefits of planning in this way are considerable, as the cost of imaging at this stage can be

inexpensive (Grierson and Bolt 1995) yet beneficial to a vineyard over its entire lifetime.

Remote sensing of vines

Despite its increasing level of application usage in the analysis of broadacre agriculture

crops, airborne imaging is yet to be fully evaluated over established vineyards with respect to

quantifying attributes of the vines themselves. Two distinct functions of imaging established

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vineyards have so far been identified. The first is the general mapping of vines to accurately

establish numbers of different varietals within vineyards, and the second is the mapping of

levels of relative vigour to establish spatial differences in vine performance within single

varietal blocks.

In terms of general mapping, accurate information concerning the location and size of blocks

containing different varietals allows for more accurate forecasting of regional productivity

and the allocation of resources for subsequent winemaking (Bramley 2001b). Subtle

differences in leaf spectral signature and phenology, and vine shape/size, suggest that it may

be possible to discriminate and map different varietals using remote sensing. However, such

differences may be quite small and would likely require a sensor with a combination of

metre-resolution imagery and a large number of spectral wavebands. To date, only

hyperspectral instruments such as CASI have been successfully used to discriminate

different varietals within vineyards and to identify mis-planting of one variety within a block

containing another (eg Bradey and Wiley 2000).

Information regarding relative vigour levels has many applications for improving

management at the precision scale, such as the early detection of certain vine diseases or the

identification of discrete management zones. Spatial differences in environmental factors

result in significant spatial variations in vigour throughout a vineyard. Vine vigour is

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, Iland et al. 1994). For example, in a single

block of Cabernet Sauvignon, researchers demonstrated that yields of vigorous vines were

nearly double that of stressed vines (Clingeleffer and Sommer 1995). In the same study,

considerable delays in fruit maturation were also associated with the more-vigorous and

higher-yielding vines. Three levels of vine vigour used in the study produced significant

differences in juice and wine parameters; higher-yielding vines produced grapes of lower

quality. Based on such observations, vine vigour could be used as surrogate indicators of

vine yield and grape quality.

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In addition to vine vigour, links between canopy shape and vine physiology have also been

reported by several studies. For example, Intrieri et al. (1997) describe significant differences

in total vine assimilation of CO2 by vines before and after various canopy shape and

thickness manipulations. Similarly, Smithyman et al. (1997) report on the influence of three

different canopy configurations on vegetative development, yield and composition of

grapevines. Furthermore, several studies have shown a link between fruit exposure on the

vine and some of its characteristics at harvest. This has led to practices such as basal leaf

removal; a late season trimming technique where leaves are removed from around fruit

clusters to improve ripening conditions. This method of leaf removal has been associated

with increased evaporation potential, wind speed, higher temperature and improved light

exposure around the fruit (Thomas et al. 1988). As well as leaf removal increasing fruit

exposure to light and air movement, the resulting improvement in access of chemical sprays

to the fruit produce a less favourable environment for the development of fungal infections.

In uniform-cover crops like wheat and canola, different levels of plant vigour often appear as

differences in the crop density against a background of underlying soil. Generally, a region

of healthy crop has a high plant density. Such a region would appear as all crop plants,

typically a deep green as viewed by the eye. Conversely, poor crops with a lower plant

density would appear as a mixture of soil and crop. These crops would appear to look green-

brown as viewed by the eye (Lamb 2000). Grapevines, however, express vigour not only in

terms of the density of the canopy, but also in the spatial extent of the canopy itself.

Therefore, the relationship between spatial variations in vine vigour, as perceived by a

remote sensing instrument, and spatial variations in vine productivity (yield and quality) may

be complex. Identifying the most appropriate means of quantifying vine vigour in remotely

sensed imagery is currently the subject of research worldwide.

Conceptually, and from a computational point of view, the most convenient approach to

quantifying vine vigour is in blending canopy spectral signature, a combination of single leaf

spectral characteristics and canopy density, with canopy size/shape. This is achieved by

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using remotely sensed imagery with a spatial resolution comparable to the inter-row spacing

of the target vines (Figure 6). Assuming the background covercrop is uniform, or at the least

shaded by vines, this process produces image pixels that are a local average of vine and

inter-row space (non-vine) spectral signatures. Changes in leaf spectral signature or the

proportion of vine and non-vine area within single image pixels will change the average

pixel value (Figure 7).

Johnson et al. (1996) have successfully used this technique to identify broad areas of

vineyard infested with the highly damaging vine aphid, phylloxera (Daktulosphaira

vitifoliae). This was achieved by relating the level of phylloxera incidence to the level of

vine vigour. The level of vine vigour on the ground was quantified in terms of pruning

weight where the largest or most dense vines yielded the greatest weight of vegetation during

subsequent pruning. Correlations established between the NDVI values extracted from

imagery and canopy pruning weights were used to indicate areas subject to phylloxera

infestation. Significant correlations have also 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 vineyards using 4 metre-resolution

IKONOS satellite imagery (Johnson et al. 2001).

A consequence of the link between canopy vigour and grape yield is that significant

correlations between image-derived NDVI values and subsequent grape yield is possible

(Baldy et al. 1996, Lamb et al. 2001a). These relationships remain valid regardless of

whether the driving influence behind the spatial variation is water and nutrient status (eg

Clingeleffer and Sommer 1995) or pests and diseases (eg Baldy et al. 1996, Munkvold et al.

1994). Similarly, studies involving assessment of the effect of canopy morphology on fruit

characteristics have suggested some qualities of the fruit may also be inferable from vine

size/shape or vigour. Where it can be established from remotely sensed imagery that vines

within a block have, for example a more open canopy, it could be expected that the fruit

character and other biophysical properties of the vine are being influenced. Numerous

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researchers worldwide have indicated that links between remotely sensed imagery and grape

quality indices are being investigated (eg Vintage 2001, CRCV 2001). However, outcomes

have yet to be reported in the scientific literature.

Separation of leaf spectral signature from vine size/shape characteristics in remotely sensed

imagery can only be achieved through using more complex data-extraction procedures.

Furthermore, extraction of vine size/shape descriptors requires images of spatial resolution

of tens of centimetres, as large numbers of image pixels must be covered by individual vines.

Hall et al. (2001) have reported developing a "vinecrawler" algorithm for extracting both

spectral-signature and canopy dimension information from ultra-high (25-cm) resolution

multispectral images of vines. This process first involves the classification of vine and non-

vine pixels. The inter-row space, which would otherwise confound vine shape/size

measurement, can be eliminated from the analysis (Figure 8). The vinecrawler algorithm

progressively moves along the centre of the classified vine rows and records spectral

signature parameters as well as size/shape descriptors such as the width of the canopy cross-

section (number of pixels), skew and kurtosis (Figure 9). Importantly, this technique has

allowed the identification of individual vines, which allows the generation of a row-vine

coordinate system from vineyard imagery. This has an immediate application in terms of

directing on-ground field visitations to regions identified from remotely sensed imagery

(Hall et al. 2001). Work on linking these complex vine descriptors with grape quality indices

is reported to be in progress.

The way ahead

Although it is undergoing rapid growth on the heels of precision viticulture, the application

of remote sensing in viticulture is in its infancy. With the proliferation of newer, more

advanced remote sensing technologies, growers are being tempted by the promise of value-

added products such as yield and quality maps. Scientific investigations are only now in

progress to evaluate the capability and utility of remote sensing to directly estimate yield and

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quality parameters. In the meantime, research has demonstrated the ability of remote sensing

for simply monitoring and mapping vine-canopy vigour within vineyards. The link between

remotely sensed imagery and simple canopy spectral signature and size/shape descriptors is

more clearly understood. The ability of remote sensing to provide a synoptic snapshot of

vineyard variability could be used for directing in-vineyard sampling to ascertain causes of

variability, or as a means of detecting changes in spatial variations of vine vigour during or

between seasons. It is recommended that such information be used as part of a greater

management strategy.

Acknowledgments

This work is supported by the Commonwealth Cooperative Research Centres Program and is

conducted by the CRC for Viticulture. The authors appreciate ongoing support provided by

Charles Sturt University’s Spatial Analysis Unit (CSU-SPAN).

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Figure 1 Satellite image (French SPOT) of the Wagga Wagga region, SE NSW, acquired on

October 1998. Pixel size = 20 m. (a) 60 km x 60 km Full scene, (b) magnified, 13 km x 13

km, sub-scene of Wagga Wagga, (c) magnified, 3 km x 3 km, sub-scene of Charles Sturt

University Wagga Wagga Campus, (d) magnified, 400 m x 600 m (24 Ha), sub-scene of

Charles Sturt University Vineyard Stages IIA & IV.

Figure 2 Multispectral airborne image (false-colour) of Charles Sturt University Wagga

Wagga Vineyard acquired January, 2001. (a) Altitude = 2.25 km, pixel size = 1.5 m, area

coverage = 110 Ha, (b) Altitude = 1.5 km, pixel size = 1.0 m, area coverage = 49 Ha, (c)

Altitude = 750 m, pixel size = 50 cm, area coverage = 12 Ha (d) Altitude = 300 m, pixel size

= 20 cm, area coverage = 2 Ha.

Figure 3 Multispectral airborne image (false-colour) of Charles Sturt University Wagga

Wagga Vineyard acquired late February, 2001 under (a) full cloud-cover with cloudbase at

2.4 km, imaging altitude = 1.5 km, pixel size = 1.0 m, (b) clear skies, imaging altitude = 1.5

km, pixel size = 1.0 m. Note the absence of shadows in 3(a).

Figure 4 Spectral reflectance profiles for Cabernet Sauvignon, covercrop (chick-peas) and

exposed red-brown soil. (Percentage of reflected sunlight = 100 x Relative reflectance). Data

acquired from Charles Sturt University's vineyard in Wagga Wagga, NSW.

Figure 5 Comparison between an actual vegetation reflectance profile and an inferred

reflectance profile using (a) 4 wavebands (multispectral), and (b) 13 wavebands

(hyperspectral).

34

Figure 6 NDVI images of a Cabernet Sauvignon block with different spatial resolutions. (a)

20 cm, (b) 1 m, and (c) 3m. Vine row spacing = 3 m. Extracted from Lamb et al (2001).

Figure 7 Synthetic NDVI image of a block of vines having the same spatial characteristics

and vigour. Pixels with dimensions equal to the vine-row spacing will give the same

combined signature regardless of where they lie relative to vines or inter-row space.

Modified from Lamb et al (2001).

Figure 8 Pseudo-colour NDVI image of CSU vineyard with fully developed canopy, January

1999. With the inter-row space eliminated from this image, a good indication of vine size as

well as overall vigour is conveyed.

Figure 9 Grey-scale representation of a single vine-canopy unit extracted from high-

resolution (25 cm) imagery. Light grey areas represent a high NDVI and dark grey represent

low NDVI.