Remote Sensing and Precision Viticulture · Why the interest in precision viticulture? • Highly variable regions in Niagara due to unique geological history and location– topography,

Airborne Remote Sensing for Precision Viticulture in Niagara

Ralph BrownSchool of EngineeringUniversity of Guelph

Why the interest in precision viticulture?• Highly variable regions in Niagara due to unique

geological history and location– topography, soil type, micro-climate (terroir)

• Different areas in same block may differ in vigour, nutrient availability, water status, fruit quality, etc.

• Can apply spatially-variable management to try to even out production (e.g., fertilizing, irrigation, thinning) or…

• Adapt to variability by managing zones differently and segregating fruit at harvest for unique character ‘reserve’ wines

Napa, Australia and New Zealand – the beginning of remote sensing for viticulture

• Started with the development of a tool to monitor phylloxera spread ~ 20 years ago

• Napa work started with NASA (Lee Johnson) - Developed tool to monitor phylloxera spread and found that RS data had other uses:

– Crop scouting– Vineyard management– Harvest planning to maximize reserve

wine production• Commercial RS services began 1999

Spectral differences in grape canopy• Typical green vegetation reflectance

– chlorophyll absorbs at 420, 490 nm, green peak ~ 540-560 nm

– second chlorophyll trough at 660-680 nm– red edge to NIR plateau 700-740 nm– water overtone troughs at 1450 and 1940 nm

• Stressed leaves reflect more strongly than healthy leaves in green-yellow-orange (540-640 nm) and in the red (660-700 nm), lower in NIR

• Reflectance in spectral bands combined as indices to emphasize soil or vegetation, e.g. NIR+red or NIR+green for Normalized Difference Vegetation Index (NDVI)

Leaf Reflectance – spectral differences between phylloxera-infested and healthy vines (CSU 2002)

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NDVI Green Ratio (R740 – R550 )/ (R740 + R550 ) used to separate healthy from infested vines

RS images contain other information too!

• Spatial patterns reveal underlying variability in soil type, moisture, fertility, disease, etc.

• Time series of images show temporal effects – e.g., weather effects (drought), disease spread, insect infestation

• Spatial information in a geo-referenced image (i.e., image elements tagged with geographic position) useful for determining areas, GPS location in vineyard, etc.

RS images from 4-band CMOS cameras in small aircraft at 3500 ft AGL

Multi-band images co-registered – e.g., RGB colour

Red band

Green band

Blue band

NIR, red and green gives colour-infrared composite (CIR)

NIR band

Red band

Green band

CIR highlights vegetative canopy (red)

Active canopy

Little canopy

Many vineyard blocks show canopy variation, reveal underlying variability of site

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June 29, 2007 August 28, 2007

Band reflectance in multi-spectral image used to classify and interpret image

Red is grape canopy (and trees in bush)

Green is other vegetation(floor)

Black is soil

NDVI highlights canopy vigour – yellow (low) to bright green (high) for one date - August 28, 2007

Change in NDVI shows canopy development from July 20 to August 14, 2007

green = +ve change, blue = -ve change

canopy increase

30-Bench Winemakers Project

• Large Riesling block• Divide into zones

based on vigour• Harvest fruit and

vinify separately• Determine variability

of fruit, wine• Stability of zones

across years

30-Bench vineyard management zones from 2005 RS images - canopy vigour from NDVI (red)

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Triangle

Geo-referenced image also contains spatial information e.g., Area of zones? GPS for vines?

7,393 sq m

4,286 sq m

7,296 sq m 8,660 sq m

Length of 1st row= 233.9 m

+ = GPS coordinatesof sentinel vines

Sentinel vines are used to make ground measurements, chart stability of zones

• Vines are flagged and geo-referenced (GPS)• Same vines are revisited year after year• Collect canopy, soil, fruit characteristic data• Fruit from sentinel vines grouped by water

stress for small wine batches• Fruit from each management zone harvested

separately• Winery keeps zone batches separate through

process

Ground data collected from sentinel vines

• Soil moisture from portable TDR

• Vine water status from pressure bomb

• Leaf reflectance spectrum • Harvest data – yield, berry

weight, Brix, pH, etc. • Sensory and chemical wine

data

Measuring leaf reflectance in management zones

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Yield per vine (kg) in 2006 season

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Sugar (Brix) in 2006 season

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Total monoterpenes in fruit (2006 season)

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2006 cool and wet – pattern of variability

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Soil Moisture Yield per vine

Brix Total monoterpenes

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Yield per vine (kg) in 2007 season

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2007 hot and dry – pattern of variability

Soil Moisture Yield per vine

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Are the zones stable?

• Well, yes they seem to be – in this vineyard diagonal zones appear in all 3 years

• Zones are evident in aerial images• Due to soil type and topography variation• Effects of zones change from wet year to dry

year – are these predictable in advance?• How can we manage this?• What next?

Re-draw Zones – based on 2 year dataset

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Thermal (long-wave) infrared imaging

• Lakeshore Rd NOTL• Evenly spaced

flights dawn to dusk• Changes in surface

temperature due to canopy, soil, moisture

• Heating and cooling

Thermal image shows surface temperature differences – canopy is cool (blue) soil surface is warmer (orange)

Colour-near infrared image

Thermal image

Thermal sequence heating pattern shows problem areas (warmer), active canopy (cooler) – useful for

irrigation scheduling?

Morning Noon

AfternoonEvening

Remote Sensing and Weather Data Integrated On-line System for Vineyard Management

Partnership formed to develop and commercialize remote sensing servicesfor viticulture in Ontario.

System is needed to acquire, process and deliver imagery to users in a useful form.

Geo-spatial information is extracted, combined with weather data as input tomodels for prediction of vine stress.

Integrated as an on-line system, outputsare useful for decision making – spraying, irrigation, harvesting.

Thanks to all those involved…

• Ontario Centres of Excellence Etech

• NSERC• Andrew Peller Ltd.• 30-Bench Winemakers• Brock University CCOVI• Weather Innovations Inc.• Aviation International• Lakeview Harvesters

• Dr. Andy Reynolds• Darryl Brooker• Matthieu Marciniak• Linda Tremblay• David Ledderhof• Lucas Baissas• Aiman Soliman• Jim Willwerth• Javad Hakimi• …many others