Applied Geomatics--connecting the pp g dots between grapevine - - PowerPoint PPT Presentation

Applied Geomatics--connecting the pp g dots between grapevine physiology, terroir and remote sensing terroir, and remote sensing

Andrew Reynolds, Brock University Ralph Brown, University of Guelph Matthieu Marciniak; David Ledderhoff; Jim Matthieu Marciniak; David Ledderhoff; Jim Willwerth; Javad Hakimi, Brock University

Geomatics-Oriented Projects Geomatics Oriented Projects

• Chardonnay terroir (1998-2003) [Reynolds et al. Proc

ASEV/ES 2001; others STILL in preparation] ASEV/ES 2001; others STILL in preparation]

– Assessing within site terroir by mapping soil texture and vine vigor, and their relationships to numerous other variables (five sites) variables (five sites)

• Riesling terroir (1998-2003) [Reynolds et al. AJEV 2007]

– Similar goals as Chardonnay g y

• Riesling terroir II (2005-). [Jim Willwerth, PhD 2010].

– Assessing within site terroir by mapping soil and vine (10 i ) water status (10 sites)

• Cabernet Franc terroir (2005-). [Javad Hakimi, PhD 2009].

Similar goals as Riesling II (10 sites) – Similar goals as Riesling II (10 sites)

Projects contd Projects contd.

• Thirty Bench Riesling (2006-). [Matthieu Marciniak MSc

y g ( ) [

2010].

– Mapping six sous-terroirs in terms of water status; using low-elevation multispectral imaging to collect NDVI data (25 ) acres).

• Coyotes Run/ Lowrey (2008-). [David Ledderhof MSc

2010].

– Similar to Thirty Bench, using four Pinot noir blocks (each about 2 acres)

• Stratus Vineyard (2008-). [Vickie Tasker MA 2010].

Stratus Vineyard (2008 ). [Vickie Tasker MA 2010].

– Using a combination of multispectral imaging, plus a network of soil Profile Probes and wireless temperature sensors

Ways of Extending Geomatics R h I d Research to Industry

• Introducing mapping tools for

Introducing mapping tools for discriminating regions within vineyards with different yields, fruit composition, y p water status, disease or insect pressure

• Verifying sub-appellations

y g pp

• Combining this with remote sensing to

identify sub-blocks of superior quality y p q y

• Using identification of zonal differences to

more precisely manage vineyards p y g y

Discriminating regions within vineyards with different yields fruit vineyards with different yields, fruit composition, and water status. U d t di th b i f t i Understanding the basis for terroir

Basic Procedures

Using GPS to delineate blocks and to geo-locate vines

Data Collection Data Collection

• Leaf water potential

Leaf water potential

• Soil moisture
• Yield and yield components
• Yield and yield components
• Basic fruit composition

S i li d f it iti t

• Specialized fruit composition—terpenes;

phenolic analytes W i ht f i

• Weight of cane prunings
• And more……

Data Collection Data Collection

• Soil texture (sand silt clay)

Soil texture (sand, silt, clay)

• Soil composition (P, K, Ca, Mg, B)

S il h i l ti ( H CEC b

• Soil physical properties (pH, CEC, base

saturation, organic matter)

• Tissue elemental composition

Manipulation of the data Manipulation of the data

• Using things such as leaf water potential

Using things such as leaf water potential, vine size, soil texture as “treatments” (actually categories) and performing (actually categories) and performing standard ANOVA

• Correlations on all variables
• Correlations on all variables
• Spatial correlations on spatial variability

b t i bl between variables

• Temporal stability

Remote Sensing Remote Sensing

• Aerial flyovers collect multispectral

Aerial flyovers collect multispectral reflectance data

• Data are also collected on the ground to
• Data are also collected on the ground to

compare and verify A i l d t d t b i l t d i

• Aerial data need to be manipulated using

ENVI software to separate out canopy vs. il/ fl t soil/ cover crop reflectance

Riesling II Project (2005-)

Ji Will th PhD did t 2010 Jim Willwerth, PhD candidate 2010 Willwerth & Reynolds Progres Agricole et Viticole 2010 accepted

Project Objectives Project Objectives

• Use GPS & GIS to create spatial maps of

variability within 10 Riesling vineyard blocks f h f th 10 VQA b ll ti from each of the 10 VQA sub-appellations

• Identify zones within vineyard blocks based

mainly on vine water status and assess these for y fruit composition and wine sensory attributes

• Look for relationships between vine water status

and other variables and other variables

• Attempt to validate the VQA sub-appellations

based on sensory and chemical data

A B C “High” water status zones Spatial distribution of leaf water potential (-bars) Myers Vineyard Vineland ON; “Low” water status zones Spatial distribution of leaf water potential (-bars), Myers Vineyard, Vineland, ON; A: 2005; B: 2006; C: 2007. Consistent zones; temporally stable.

A B C High water status Low water status

Spatial distribution of berry weight (g), Myers Vineyard, Vineland, ON; A: 2005; B: 2006; C:2007. Higher LWP = higher berry weight.

A B C

Spatial distribution of berry Brix, Myers Vineyard, Vineland ON; A: 2005; B: 2006; C: 2007. Low LWP = highest Brix.

A B C

Spatial distribution of berry titratable acidity (g/L), Myers Vineyard, Vineland, ON; A: 2005; B: 2006; C:2007. Low LWP = lowest TA.

A B C

Spatial distribution of leaf water potential (-bars), Chateau des Charmes (Paul Bosc Estate), Niagara-on-the-Lake, ON; A: 2005; B: 2006; C: 2007. Once again, temporally stable spatial patterns.

A B C

Spatial distribution of berry potentially volatile terpenes (mg/L), Chateau Spatial distribution of berry potentially volatile terpenes (mg/L), Chateau des Charmes (Paul Bosc Estate), Niagara-on-the-Lake, ON; A: 2005; B: 2006; C: 2007. Low LWP = highest PVT.

Sensory Map of Significant Sensory Attributes, Twenty Mile Bench; 2005 , y ;

Factors contributing to sensory profile p

Soil and vine water status responsible for 75% of the variability in the data set

Verifying sub-appellations Verifying sub-appellations

Cabernet Franc Project

J d H ki i PhD 2009 Javad Hakimi, PhD 2009 Hakimi and Reynolds AJEV 2010 in press

Project Objectives j j

• Use GPS & GIS to create spatial maps of variability

within 10 Cabernet Franc vineyard blocks from each of the 10 VQA sub-appellations each of the 10 VQA sub-appellations

• Identify zones within vineyard blocks based mainly
• n vine water status and assess these for fruit

iti d i tt ib t composition and wine sensory attributes

• Look for relationships between vine water status

and other variables a d ot e a ab es

• Attempt to validate the VQA sub-appellations

based on sensory and chemical data

PCA of Sensory Data, Cabernet Franc 2005

Variables (axes F1 and F2: 63.94 %) Observations (axes F1 and F2: 63.94 %)

Green bean associated with high water potential Lakeshore or riverfront sites

( )

Acidity bell pepper green bean black pepper BELL PEPPER GREEN BEAN 0 5 0.75 1

( )

Cave sp George Harbour 2 3 Color black currant black cherry BLACK CHERRY 0.25 0.5

26.13 %)

Buis Reif 1

26.13 %)

High water status

Bitterness Astringency red fruit BLACK PEPPER BLACK CURRANT

• 0.5
• 0.25

F2 (2

Buis Vieni HOP Hernder

• 2
• 1

F2 (2

Low water status

RED FRUIT

• 1
• 0.75
• 1
• 0.75
• 0.5
• 0.25

0.25 0.5 0.75 1

F1 (37 81 %)

CDC

• 4
• 3
• 4
• 3
• 2
• 1

1 2 3 4 5 6

F1 (37.81 % )

F1 (37.81 %)

Partial Least Squares (PLS) Partial Least Squares (PLS)

Correlations with t on axes t1 and t2 (84.3%) Clusters

Berry wt

Hue

sand

Bl CURRANT Astringency Color

0.75 1

Yield y

TA

WP

SM

Color RED FRUIT BLACK PEPPER BELL PEP red fruit

green bean Bitterness Acidity Color

0.25 0.5

24.3%)

vine size

Brix

pH

Phenols Soil pH OM Ca

CEC

BS

GREEN BEAN

bl cherry

black pepper bell pep

Acidity

• 0.5
• 0.25

t2 2

Anthocyanin clay

P K Mg

CEC BLACK CHERRY bl currant

• 1
• 0.75
• 1
• 0.75
• 0.5
• 0.25

0.25 0.5 0.75 1

t1 (60 0% ) t1 (60.0% )

Using remote sensing to identify sub blocks of superior quality sub-blocks of superior quality

Thirty Bench Project

M tthi M i i k MS did t 2010 Matthieu Marciniak, MSc candidate 2010 Reynolds et al. Progres Agricole et Viticole 2010 accepted

Project Objectives

• Correlate remotely sensed spectral data to

i d h t i ti d f it & i vineyard characteristics and fruit & wine composition of Riesling

• Use GPS & GIS to create spatial maps of

Use GPS & GIS to create spatial maps of variability within vineyard blocks

• Identify zones for premium wine production

y p p and/or precision management zones within vineyard blocks based mainly on vine water status status

Thirty Bench- View of the Study Site y y

Courtesy Ralph Brown

Sentinel Vines Sentinel Vines

Spatial variation in soil moisture

• ver four vintages
• ver four vintages

Temporal stability is apparent (orange areas = lowest soil moisture 2007-0; blue = lowest 2009) S il M i t 2006 S il M i t 2007 Soil Moisture 2006 Soil Moisture 2007 Soil Moisture 2008 Soil Moisture 2009

Spatial variation in leaf ψ over four vintages

Again temporal stability is apparent, as are spatial correlations between soil moisture and leaf ψ; yellow and orange areas are highest absolute values of leaf ψ (i.e. most negative or lowest) Leaf Water Potential 2007 Leaf Water Potential 2006 Leaf Water Potential 2008 Leaf Water Potential 2009

Yield

Once again, clear temporal stability is present (yellow/orange areas are highest yields) 2006 2007 2008

Weight of cane prunings 2009

Some inverse spatial correlations with water potential and soil moisture

Brix and TA 2006 Brix and TA 2006

WPY WPY WPB WPB

WPY WPY WPB WPB Triangle Triangle SPY SPY SPB SPB

LE LE

• Brix. Has been temporally consistent over

three vintages. Note the higher Brix (orange) in the low water status zones

WPY WPY WPB WPB

LE LE Triangle Triangle SPY SPY SPB SPB

Titratable acidity. Also has been temporally

consistent over three vintages. Note the lower TA (blue) in the low water status zones

Potentially-volatile terpenes 2006 y p

Highest in the low-vigor zones

WPY WPY WPB WPB SPB SPB

2.0 2 3 2.6 1.78

SPY SPY Triangle Triangle

2.6 2.3 2.3 2.3 2.30299

LE LE

2.3 1 78 2.3 1.78

Potentially-volatile terpenes 2009 y p

Once again highest in the low-vigor zones, particularly Steel Post & Triangle

Spatial Correlations between variables within the same vintage variables within the same vintage

Low leaf water potential associated with higher Brix values Berry Brix 2007 Leaf Water Potential 2007 Note: Orange areas represent highest Brix and highest absolute values of water potential (i.e. most negative or lowest)

Yield and NDVI green 2006

A clear and temporally stable relationship between the two variables

WPB WPB WPY WPY

SPY SPY SPB SPB

Yield “Y” zones (high

• Yield. Y” zones (high

vigor) = high-yielding too

NDVI green

Leaf water potential and NDVI 2006

Spatial patterns and relationships that are temporally stable

Mean leaf water potential (absolute value)

WPY WPY WPB WPB

• 10.9

SPY SPY SPB SPB

• 11.5
• 12.1
• 10.3

SPY SPY

• 9.7

NDVI green

NDVI green 2008 g

Temporally stable compared to previous years

NDVI 2009

Once again, temporal stability was apparent relative to prior years

NDVI green NDVI red

Differences between ‘small lot’ blocks Differences between small lot blocks

Variables (axes D1 and D2: 67.95 %) after Varimax rotation

Observations (axes D1 and D2: 67.95 %) after Varimax rotation

Berry wt Berry pH Berry Brix Vine Size 0.5 0.75 1

Triangle 2 3

Berry TVT Berry PVT Berry FVT 0 25 0.25

D2 (25.98 %)

1 2

D2 (25.98 %)

M ean SM M ean WP Berry TA Yield

• 0.75
• 0.5
• 0.25

D

SPY SPB LE WPY WPB

• 1

D

Elevation

• 1
• 1
• 0.75
• 0.5
• 0.25

0.25 0.5 0.75 1

D1 (41.97 %)

SPY

• 1
• 3
• 2
• 1

1 2 3

D1 (41.97 %)

The Triangle Block has consistently won the most awards at Ontario wine The Triangle Block has consistently won the most awards at Ontario wine

• competitions. Might we then use remote sensing to pick out blocks like

Triangle in other cultivars?

Using remote sensing to identify sub blocks of superior quality in sub-blocks of superior quality in red wine cultivars

Coyotes Run/ Lowrey Project y y j

(Images and text courtesy David Ledderhof MSc candidate 2010)

Project Objectives Project Objectives

• Correlate remotely sensed spectral data to

vineyard characteristics and fruit & wine vineyard characteristics and fruit & wine composition of Pinot noir

• Use GPS & GIS to create spatial maps of

Use GPS & GIS to create spatial maps of variability within vineyard blocks

• Identify zones for premium wine

Identify zones for premium wine production and/or precision management zones within vineyard blocks y

Study Sites and Vineyard D t C ll ti Data Collection

Study sites C t ' R R d P & Bl k P

• Coyote's Run: Red Paw & Black Paw

Vineyards (three blocks)

• Lowrey's Farm (one block)

y ( )

• Variety of soil types, age of vines, clones

Data collection

Geolocating Sentinel Vines Soil Sample Collection & Analysis

C ( )

Aerial Image Capture (x4 in 2008 and 2009) TDR - Soil Moisture

P B b Vi W t St t

Pressure Bomb – Vine Water Status Ground-based Leaf Reflectance

Relative Location of Blocks:

• St. David's, Ontario

Image Source: Niagara Navigator {http://navigator.yourniagara.ca/navigator/#}

Coyote's Run Pinot noir y

Images: July 29, 2008

Sample Results: Red Paw 2 Sample Results: Red Paw 2

% silt % clay % sand

Note: Different scale for each map

yield Leaf ψ NDVI-red

Red Paw 2 NDVI

The challenge e tracting NDVI data from co er The challenge-extracting NDVI data from cover- cropped vineyards without assessing the cover crop R d P 2 NDVI Red Paw 2 NDVI Red Paw 2 masked NDVI Red Paw 2 NDVI map

Using identification of zonal diff t i l differences to more precisely manage vineyards g y

Stratus Vineyards Project y j

(Vickie Tasker, MA 2010 pending)

Project Objectives j j

• Correlate remotely sensed spectral data to vineyard

characteristics and fruit & wine composition of several Vitis vinifera cultivars (Chardonnay, Cabernet Franc, Vitis vinifera cultivars (Chardonnay, Cabernet Franc, Semillon)

• Use GPS & GIS to create spatial maps of variability

within vineyard blocks within vineyard blocks

• Set up a network of wireless temperature sensors and

corresponding Profile Probe sites on a grid throughout th i d the vineyard

• Attempt to see if localized soil moisture and/or canopy

temperatures have major impacts upon fruit composition p j p p p

Stratus Vineyards Project Stratus Vineyards Project

Other Project Objectives

• Evaluate airborne digital imagery for the purpose
• Evaluate airborne digital imagery for the purpose
• f determining canopy variability and spatial

patterns of interest in the vineyard. D l th l i t f th

• Develop a thermal environment map of the

Stratus vineyard based upon in-situ temperature sensors at the canopy and soil level and aerial py thermal infrared imagery.

• Develop a GIS database for Stratus that

incorporates all currently available soils incorporates all currently available soils, drainage, and vine (clone, age and rootstock), in a format that is consistent with overlaying digital airborne remote sensing maps airborne remote sensing maps.

Stratus- General Soils and Varieties

Images courtesy Ralph Brown Images courtesy Ralph Brown

CF1

CF2

CH1

Conclusions Conclusions

• Geomatics has allowed us to conclude that the

so-called terroir effect is based highly on vine water status

• This technology has permitted verification of

This technology has permitted verification of sub-appellations in the Niagara region

• Coupling this with remote sensing might provide

th d t id tif i b bl k b d a method to identify premium sub-blocks based

• n e.g. water status using NDVI measurement
• In every instance, any vineyard variable can be

e e y sta ce, a y eya d a ab e ca be mapped and this spatial variability can be checked for temporal stability—permitting implementation of precision viticulture implementation of precision viticulture