applied geomatics--connecting the dots between grapevine ... · applied geomatics--connecting the...
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Applied Geomatics--connecting the pp gdots between grapevine physiology,
terroir and remote sensingterroir, and remote sensing
Andrew Reynolds, Brock UniversityRalph Brown, University of Guelph
Matthieu Marciniak; David Ledderhoff; JimMatthieu Marciniak; David Ledderhoff; Jim Willwerth; Javad Hakimi, Brock University
Geomatics-Oriented ProjectsGeomatics 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 Chardonnayg 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 contdProjects 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 dResearch to Industry
• Introducing mapping tools forIntroducing mapping tools for discriminating regions within vineyardswith different yields, fruit composition, y pwater status, disease or insect pressure
• Verifying sub-appellationsy g pp• Combining this with remote sensing to
identify sub-blocks of superior qualityy p q y• Using identification of zonal differences to
more precisely manage vineyardsp y g y
Discriminating regions within vineyards with different yields fruitvineyards with different yields, fruit
composition, and water status. U d t di th b i f t iUnderstanding the basis for terroir
Basic ProceduresUsing GPS to delineate blocks and to geo-locate vines
Data CollectionData Collection
• Leaf water potentialLeaf 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 analytesW i ht f i• Weight of cane prunings
• And more……
Data CollectionData 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 dataManipulation of the data
• Using things such as leaf water potentialUsing 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 blbetween variables• Temporal stability
Remote SensingRemote Sensing
• Aerial flyovers collect multispectralAerial flyovers collect multispectral reflectance data
• Data are also collected on the ground to• Data are also collected on the ground to compare and verifyA 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 tsoil/ cover crop reflectance
Riesling II Project (2005-)Ji Will th PhD did t 2010Jim Willwerth, PhD candidate 2010
Willwerth & Reynolds Progres Agricole et Viticole 2010 accepted
Project ObjectivesProject Objectives• Use GPS & GIS to create spatial maps of
variability within 10 Riesling vineyard blocks f h f th 10 VQA b ll tifrom each of the 10 VQA sub-appellations
• Identify zones within vineyard blocks based mainly on vine water status and assess these for yfruit composition and wine sensory attributes
• Look for relationships between vine water status and other variablesand 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 CHigh 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), ChateauSpatial 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 profilep
Soil and vine water status responsible for 75% of the variability in the
data set
Verifying sub-appellationsVerifying sub-appellations
Cabernet Franc ProjectJ d H ki i PhD 2009Javad Hakimi, PhD 2009
Hakimi and Reynolds AJEV 2010 in press
Project Objectivesj j• Use GPS & GIS to create spatial maps of variability
within 10 Cabernet Franc vineyard blocks from each of the 10 VQA sub-appellationseach of the 10 VQA sub-appellations
• Identify zones within vineyard blocks based mainly on vine water status and assess these for fruit
iti d i tt ib tcomposition and wine sensory attributes• Look for relationships between vine water status
and other variablesa 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 BEAN0 5
0.75
1
( )
Cave sp
George Harbour
2
3
Color black currant
black cherry
BLACK CHERRY
0
0.25
0.5
26.1
3 %
)
Buis
Reif
0
1
26.1
3 %
)
High water status
BitternessAstringency
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 0.25 0.5 0.75 1
F1 (37 81 %)
CDC
-4
-3
-4 -3 -2 -1 0 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%)
ClustersBerry wt
Huesand
Bl CURRANTAstringency
Color0.75
1
Yield
yTA
WPSM
Color
RED FRUITBLACK PEPPER
BELL PEPred fruitgreen bean
Bitterness
Acidity
Color
0
0.25
0.5
24.3
%)
vine size Brix
pHPhenols
Soil pHOMCa
CEC
BS
GREEN BEAN
bl cherryblack pepper
bell pep
Acidity
-0.5
-0.25
t2 2
AnthocyaninclayP
KMg
CEC
BLACK CHERRY bl currant-1
-0.75
-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1
t1 (60 0%)t1 (60.0%)
Using remote sensing to identify sub blocks of superior qualitysub-blocks of superior quality
Thirty Bench ProjectM tthi M i i k MS did t 2010Matthieu 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 & ivineyard characteristics and fruit & wine composition of Riesling
• Use GPS & GIS to create spatial maps ofUse GPS & GIS to create spatial maps of variability within vineyard blocks
• Identify zones for premium wine production y p pand/or precision management zones within vineyard blocks based mainly on vine water statusstatus
Thirty Bench- View of the Study Sitey yCourtesy Ralph Brown
Sentinel VinesSentinel Vines
Spatial variation in soil moisture over four vintagesover 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 2007Soil 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 2007Leaf Water Potential 2006
Leaf Water Potential 2008 Leaf Water Potential 2009
YieldOnce again, clear temporal stability is present (yellow/orange areas are highest yields)
2006 2007
2008
Weight of cane prunings 2009Some inverse spatial correlations with water potential and soil
moisture
Brix and TA 2006Brix and TA 2006
WPYWPYWPBWPB
18.09
18.82
18.82
19 55WPYWPYWPBWPB
TriangleTriangleSPYSPY
SPBSPB
17.36 17.36
18.09
18.09
18.09
18.09
18.09
18.09
18.09
18.09
18.82
18.82
18 82
19.55
19.55
19.55
19.55
19 55
19.55
19.55
20.28
20.28
20.28
21.01
21.01
21.01
21.01
21.01
21.74
21.74 21.74
21.74
22.4701
22.4701
LELE
18.09
18.82
18.82 19.55
19.55
19.55
19.55
20.28
21.74
Brix. Has been temporally consistent over three vintages. Note the higher Brix (orange)in the low water status zones
WPYWPYWPBWPB
11.3
11.3
11.7
11.3
10.9
11.7
11.3
8.4
8.8
10.50
LELE
TriangleTriangleSPYSPY
SPBSPB
10.5
10.511.3
11.3
10.911.3
10.5
9.610.5
10.9
10.5
11.312.1
10.9
11.3
11.7
10.5
10.5
11.7
12.1
10.5
10.9
10.1
9.6
11.310.9
9 2
9.6
10 1
10.1
11.2716
Titratable acidity. Also has been temporally 10.5
10.9
10.5
11.7
11.79.2
10.9
10.1
11.4
consistent over three vintages. Note the lower TA (blue) in the low water status zones
Potentially-volatile terpenes 2006y pHighest in the low-vigor zones
WPYWPYWPBWPBSPBSPB2.0
2 32.6
1.78
SPYSPYTriangleTriangle
2.6
2.32.3
2.3
2.30299
LELE2.3
1 78
2.3
1.78
Potentially-volatile terpenes 2009y pOnce again highest in the low-vigor zones, particularly Steel Post & Triangle
Spatial Correlations between variables within the same vintagevariables 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 2006A clear and temporally stable relationship between the two variables
WPBWPB WPYWPY3.91
3.91
3.913.17
4.66
4.66
3.174.66
5.41
SPYSPY
SPBSPB
6.90
2.42 5.41
4.66
3.17
Yield “Y” zones (highYield. Y” zones (high vigor) =high-yielding too
0.660.67
0.66
0.66
0.670.65
0.68
0.64
0.65
0.71
0.69
0.70
NDVI green
Leaf water potential and NDVI 2006Spatial patterns and relationships that are temporally stable
Mean leaf water potential(absolute value)
WPYWPYWPBWPB -10.9
SPYSPY
SPBSPB-11.5
-12.1
-10.3
SPYSPY
-9.7
0.670.65
0.660.67
0.66
0.66
0.68
0.70
0.64
0.65
0.71
0.69
NDVI green
NDVI green 2008gTemporally stable compared to previous years
NDVI 2009Once again, temporal stability was apparent relative to prior years
NDVI green
NDVI red
Differences between ‘small lot’ blocksDifferences between small lot blocksVariables (axes D1 and D2: 67.95 %)
after Varimax rotationObservations (axes D1 and D2: 67.95 %)
after Varimax rotation
Berry wt
Berry pH Berry Brix
Vine Size
0.5
0.75
1
Triangle2
3
Berry TVTBerry PVT
Berry FVT
0 25
0
0.25
D2
(25.
98 %
)
1
2
D2
(25.
98 %
)M ean SM
M ean WP
Berry TA
Yield
-0.75
-0.5
-0.25D
SPY
SPB
LEWPY
WPB
-1
0D
Elevat ion-1
-1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1
D1 (41.97 %)
SPY-1-3 -2 -1 0 1 2 3
D1 (41.97 %)
The Triangle Block has consistently won the most awards at Ontario wineThe 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 insub-blocks of superior quality in
red wine cultivars
Coyotes Run/ Lowrey Projecty y j(Images and text courtesy David Ledderhof MSc candidate 2010)
Project ObjectivesProject Objectives• Correlate remotely sensed spectral data to
vineyard characteristics and fruit & winevineyard characteristics and fruit & wine composition of Pinot noir
• Use GPS & GIS to create spatial maps ofUse GPS & GIS to create spatial maps of variability within vineyard blocks
• Identify zones for premium wineIdentify zones for premium wine production and/or precision management zones within vineyard blocksy
Study Sites and Vineyard D t C ll tiData CollectionStudy 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 collectionGeolocating Sentinel VinesSoil Sample Collection & Analysis
C ( )Aerial Image Capture (x4 in 2008 and 2009)TDR - Soil MoistureP B b Vi W t St tPressure Bomb – Vine Water StatusGround-based Leaf Reflectance
Relative Location of Blocks: St. David's, Ontario
Image Source: Niagara Navigator {http://navigator.yourniagara.ca/navigator/#}
Coyote's Run Pinot noiry
Images: July 29, 2008
Sample Results: Red Paw 2Sample Results: Red Paw 2
% silt % clay% sand
Note: Different scale for each mapyield Leaf ψ NDVI-red
Red Paw 2 NDVIThe challenge e tracting NDVI data from co erThe challenge-extracting NDVI data from cover-
cropped vineyards without assessing the cover crop
R d P 2 NDVIRed Paw 2 NDVI Red Paw 2 masked NDVIRed Paw 2 NDVI map
Using identification of zonal diff t i ldifferences to more precisely
manage vineyardsg y
Stratus Vineyards Projecty j(Vickie Tasker, MA 2010 pending)
Project Objectivesj 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 blockswithin vineyard blocks
• Set up a network of wireless temperature sensors and corresponding Profile Probe sites on a grid throughout th i dthe 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 ProjectStratus Vineyards ProjectOther Project Objectives• Evaluate airborne digital imagery for the purpose• Evaluate airborne digital imagery for the purpose
of 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 pythermal infrared imagery.
• Develop a GIS database for Stratus that incorporates all currently available soilsincorporates all currently available soils, drainage, and vine (clone, age and rootstock), in a format that is consistent with overlaying digital airborne remote sensing mapsairborne remote sensing maps.
Stratus- General Soils and VarietiesImages courtesy Ralph BrownImages courtesy Ralph Brown
CF1
CF2
CH1
ConclusionsConclusions• Geomatics has allowed us to conclude that the
so-called terroir effect is based highly on vine water status
• This technology has permitted verification ofThis 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 da method to identify premium sub-blocks based
on 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 viticultureimplementation of precision viticulture