An overview of PastureTech research delivered to the Saskatchewan Forage Council (SFC) and members of the Saskatchewan Crop Insurance community in December 2016.

1. Using Satellite Imagery to Measure Pasture Production
Rick McConnell & Tom Crozier
Saskatchewan Meetings | SCIC and Forage Committee
December 2016
PastureTech.com

2. Purpose

3. Satellite imagery
• Measuring pasture
• Sponsored in part by the Canadian
Cattlemen’s Association (CCA)
• Focus on “ranch level” insurance
Linking two projects
Hydrology project
• Measuring flood, drought, excess moisture
• Sponsored in part by the Alberta Federation of
Agriculture (AFA)
• Link moisture deficiency for pasture (SSRB)
PastureTech.com @PastureTech 3
Both projects funded by AAFC; Agri-Risk Initiatives (Growing Forward 2)

4. Project purpose
• 3-season feasibility study focused on native pasture
• Determine the ability to use satellite imagery to
measure pasture production at the farm/ranch level
• If successful, could be used:
• To offer individual insurance coverage based on a
farm/ranch’s own records
• For area-based disaster insurance/compensation
centered on a farm/ranch to offset feed and/or
transportation costs
4PastureTech.com @PastureTech

5. What does this mean?
Pasture insurance could look like crop insurance
• 10-year average “pasture production” measured by the
satellite
• If current year’s production (measured by the satellite)
is less than the insurance trigger selected by the
rancher, there would be a pay out
• Insurance based on farm/ranch’s own production
records
5
Area-wide insurance or
compensation
PastureTech.com @PastureTech

6. Main challenge
• Satellite imagery accessible at various scales (e.g. 5m
to 1km); increasing costs for finer resolution
• Goal: Establish “X to Y” relationship between satellite
imagery and pasture production
• Require both satellite image measurement (X) and pasture
production measurement (Y) at the same resolution
• Transfer the “relative change” in a NDVI score to an “absolute
change” in pasture production
• Need many “Xs” and corresponding “Ys” to build a relationship
6PastureTech.com @PastureTech
Satellite Measurement (X)
PastureProduction(Y)
?

7. Solution
1. Use a hand-held spectrometer calibrated to an
accessible satellite system to take an “image” at a
one-half-meter resolution to get an “X” value
2. Clip the pasture within the one-half-meter area
“imaged” by the spectrometer to get a “Y” value
3. Confirm the spectrometer is in fact accurately
calibrated to the accessible satellite
4. Develop the “X to Y” relationship between
spectrometer and clips, and apply to the satellite
7PastureTech.com @PastureTech

8. Research

9. Pasture types
Is there a difference in the “X to Y” relationship among
broad pasture types?
- or -
Is it like “crop production”, where there are geographic
differences in yield but the methods used to measure
production are the same?
9PastureTech.com @PastureTech

10. • 250m x 250m resolution
• Free daily images
• What “picture” does the satellite take?
• Normalized difference vegetative index (NDVI)
• Other “indexes” possible [e.g. EVI (1 & 2), SA (1 & 2)]
10PastureTech.com @PastureTech
MODIS: Accessible satellite
Black squares: MODIS pixels
Yellow lines: Township boundaries
Green squares: Sample sites

11. What’s NDVI?
• Chlorophyll in plant absorbs “red” visible light
• Cell structure of plant reflects near infra-red light
• Difference between the two “light factors” can be used
to identify vegetation (e.g. trees from grass/tundra) or
healthy vegetation
11PastureTech.com @PastureTech
(0.50 – 0.08)
(0.50 + 0.08)
= 0.72
(0.40 – 0.30)
(0.4 + 0.30)
= 0.14

12. Sample sites
• Project is “linked” to AFSC
• 4 project sites (right: marked with red squares)
• 7 AFSC sites (right: marked with green circles)
• Thanks to the following volunteer ranches:
• Eddleston Ranch
• Osadczuk Ranch
• Hargraves Ranch
• Burke Creek Ranch
12PastureTech.com @PastureTech
(0.50 – 0.08)
(0.50 + 0.08)
= 0.72
(0.40 – 0.30)
(0.4 + 0.30)
= 0.14

13. Sample site layout
• Sites located from “centroid” of a known MODIS pixel
• 3 cages at each of the following compass points:
centre, north, east, south and west
• One cage for each of June, July and August (three site
visits)
• An “open” clip taken for each cage clip taken (e.g. 10
clips per site visit)
• 4 sites per ranch: 3 ranches with 2 summer and 2
winter sites, 1 ranch with 4 summer sites
13PastureTech.com @PastureTech

14. Site visits
• “Pre-clip” hand-held spectrometer reading taken at each clip
location
• Pictures and assessment of clip location
• Pasture is clipped, put into a marked bag and stored before
drying and sorting
• “Post-clip” hand-held spectrometer reading taken at each clip
location
• Systematic check of compass points to ensure accurate “X
to Y” measurement: first caged clips, then open clips
• “Walk-around” to verify spectrometer calibration with MODIS
satellite
14PastureTech.com @PastureTech

15. Sorting
• Samples stored in onion bags, dried to 0% moisture at
Lacombe federal research station
• Sorted into 3 categories: green vegetation, carry-over
(brown vegetation) and forbes
• Woody plants in clip sites are not clipped
• Categories weighed and recorded for “Y” value
• Small-size samples fully sorted
• Larger sample sizes partially sorted after test of impact
• Potential limiting impact on budget
15PastureTech.com @PastureTech

16. Analysis

17. COMPARISON OF WALK-AROUND AND MODIS NDVI VALUES:
2015 ALL RANCHES AND MONTHS (r=0.95, n=44)
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
1
0.00
0.03
0.05
0.08
0.10
0.13
0.15
0.18
0.20
0.23
0.25
0.28
0.30
0.33
0.35
0.38
0.40
0.43
0.45
0.48
0.50
0.53
0.55
0.58
0.60
0.63
0.65
0.68
0.70
0.73
0.75
0.78
0.80
0.83
0.85
0.88
0.90
0.93
0.95
0.98
1.00
MODIS NDVI
WALKNDVI
EST EQUAL
First Season (2015) Analysis Results
17PastureTech.com @PastureTech
• Spectrometer verified to be “highly correlated” to MODIS satellite
Comparison of Walk-Around and MODIS NDVI Values:
2015 All Ranches and Months (r=0.95, n=44)

18. Analysis
• No difference
• Cage vs open sites
• Summer vs winter pasture
• June, July and August
• Not enough data
• Production areas
• Carry-over effect on NDVI
• If no statistical difference, then all observations can be
explained by the same curve
18PastureTech.com @PastureTech

19. Analysis (cont’d)
19PastureTech.com @PastureTech
• Changing relative values of NDVI to lbs/acre of pasture production
• Relatively small changes in NDVI result in significant changes to production
• Curve flatness (need more definition at lower NDVI values)
0
200
400
600
800
1000
1200
1400
1600
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24
0.26
0.28
0.3
0.32
0.34
0.36
0.38
0.4
0.42
0.44
0.46
0.48
0.5
0.52
0.54
0.56
0.58
0.6
0.62
0.64
0.66
0.68
0.7
0.72
0.74
0.76
0.78
0.8
NDVI
LBS/AC
.10-.20 .20-.30 .30-.40 .40-.50 .50-.60 .60-.70 .70-.80
0
200
400
600
800
1000
1200
1400
1600
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24
0.26
0.28
LBS/AC
.10-.20 .20-.30
0
200
400
600
800
1000
1200
1400
1600
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24
0.26
0.28
0.3
0.32
0.34
0.36
0.38
LBS/AC
.10-.20 .20-.30 .30-.40
0
200
400
600
800
1000
1200
1400
1600
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24
0.26
0.28
0.3
0.32
0.34
0.36
0.38
0.4
0.42
0.44
0.46
NDVI
LBS/AC
.10-.20 .20-.30 .30-.40 .40-.50
0
200
400
600
800
1000
1200
1400
1600
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24
0.26
0.28
0.3
0.32
0.34
0.36
0.38
0.4
0.42
0.44
0.46
0.48
0.5
0.52
0.54
0.56
NDVI
LBS/AC
.10-.20 .20-.30 .30-.40 .40-.50 .50-.60
0
200
400
600
800
1000
1200
1400
1600
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24
0.26
0.28
0.3
0.32
0.34
0.36
0.38
0.4
0.42
0.44
0.46
0.48
0.5
0.52
0.54
0.56
0.58
0.6
0.62
0.64
NDVI
LBS/AC
.10-.20 .20-.30 .30-.40 .40-.50 .50-.60 .60-.70
0
200
400
600
800
1000
1200
1400
1600
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24
0.26
0.28
0.3
0.32
0.34
0.36
0.38
0.4
0.42
0.44
0.46
0.48
0.5
0.52
0.54
0.56
0.58
0.6
0.62
0.64
0.66
0.68
0.7
0.72
NDVI
LBS/AC
.10-.20 .20-.30 .30-.40 .40-.50 .50-.60 .60-.70 .70-.80
0
200
400
600
800
1000
1200
1400
1600
0.1
0.12
0.14
0.16
0.18
0.2
0.22
0.24
0.26
0.28
0.3
0.32
0.34
0.36
0.38
0.4
0.42
0.44
0.46
0.48
0.5
0.52
0.54
0.56
0.58
0.6
0.62
0.64
0.66
0.68
0.7
0.72
0.74
0.76
0.78
0.8
NDVI
LBS/AC
.10-.20 .20-.30 .30-.40 .40-.50 .50-.60 .60-.70 .70-.80

20. Analysis (cont’d)
20PastureTech.com @PastureTech
Estimates of GGF at differing ranges of NDVI using a five-observations data format from
samples collected at Eddleston, Hargraves and Osadczuk Ranches (June, July, August
and Pooled 2015) excluding outliers of + or - 2.5 standard deviation
Range NDVI Ln NDVI LN GGF GGF (lbs/acre)
1 0.10 – 0.20 -2.30259 to -1.60944 1.966 – 3.735 7 – 42
2 0.20 – 0.30 -1.60944 to -1.20397 3.735 – 4.774 42 – 118
3 0.30 – 0.40 -1.20397 to -0.91629 4.774 – 5.512 118 – 248
4 0.40 – 0.50 -0.91629 to -0.69315 5.512 – 6.084 248 – 439
5 0.50 – 0.60 -0.69315 to -0.51083 6.084 – 6.551 439 – 700
6 0.60 – 0.70 -0.51083 to -0.35667 6.551 – 6.946 700 – 1039
7 0.70 – 0.80 -0.35667 to -0.22314 6.946 – 7.289 1039 – 1463

21. Making sense of
NDVI values

22. Example
22PastureTech.com @PastureTech
Ranch level satellite vegetation index values: Osadczuk Ranch

23. Example
23PastureTech.com @PastureTech
NDVI profile: Osadczuk Ranch
0.224
0.262
0.277
0.303
0.333
0.356
0.388
0.409
0.441
0.472
0.500
0.523
0.517 0.514
0.490
0.462
0.440
0.431
0.424
0.415
0.405
0.396 0.398
0.200
0.250
0.300
0.350
0.400
0.450
0.500
0.550
15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
AP AP AP M M M M M/J JN JN JN JN JL JL JL JL JL/AU AU AU AU AU SP SP
NDVI

24. Comparison of summer & winter
24PastureTech.com @PastureTech
Burton and Osadczuk grazed lands:
Average 7-day cloud-adjusted NDVI values (2000-2016) mid-April to mid-September

25. Comparison of NDVI values on four ranches
25PastureTech.com @PastureTech
Average 7-day cloud-adjusted NDVI values (2000-2016) mid-April to mid-September

26. Example
26PastureTech.com @PastureTech
Eddleston Ranch summer grazed:
NDVI as a % of average (2000-2016) each of May, June and July

27. Example
27PastureTech.com @PastureTech
Hargrave Ranch summer & winter grazed:
Annual NDVI as a % of average (2000-2016) May, June and July weighted 20%, 50% and
30% respectively

28. Comparison of four volunteer ranches
28PastureTech.com @PastureTech
NDVI % of average (2000-2016) by year
Beginning of May to end of July (Weighting: May 25%, June 60%, July 15%)

29. 5 best and worst growing seasons by NDVI
29PastureTech.com @PastureTech
Burton summer and winter grazed lands combined
MONTH: M M/JN JN JN JN JN JL JL JL JL JL/AU AU AU AU AU
WEEK: 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
WORST YR 2002 2002 2002 2009 2001 2001 2000 2000 2000 2000 2000 2000 2000 2000 2000
2ND WORST YR 2009 2008 2011 2001 2000 2000 2001 2001 2001 2001 2001 2001 2001 2001 2001
3RD WORST YR 2008 2009 2009 2002 2009 2009 2009 2009 2003 2003 2007 2007 2007 2007 2003
4TH WORST YR 2000 2001 2000 2000 2002 2016 2016 2007 2007 2007 2003 2003 2003 2003 2007
5TH WORST YR 2014 2000 2001 2004 2004 2010 2010 2016 2009 2002 2006 2006 2006 2006 2008
MONTH: M M/J JN JN JN JN JL JL JL JL JL/AU AU AU AU AU
WEEK: 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
BEST YR 2016 2007 2007 2016 2015 2015 2011 2011 2011 2011 2008 2012 2004 2013 2013
2ND BEST YR 2005 2003 2016 2006 2006 2013 2012 2014 2012 2010 2011 2004 2012 2010 2009
3RD BEST YR 2007 2016 2003 2003 2007 2006 2006 2012 2008 2012 2012 2008 2002 2016 2010
4TH BEST YR 2003 2014 2006 2007 2003 2011 2013 2004 2004 2008 2013 2013 2013 2004 2002
5TH BEST YR 2015 2006 2012 2015 2011 2014 2014 2006 2010 2004 2004 2011 2010 2002 2004
GREEN BOLD REFLECTS TYPICAL MAX GROWTH PERIOD; LIGHT BLUE ARE TYPICALLY THE 5-6 HIGHEST WEEKS OF NDVI
MONTH: M M/JN JN JN JN JN JL JL JL JL JL/AU AU AU AU AU
WEEK: 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
WORST YR 2002 2002 2002 2009 2001 2001 2000 2000 2000 2000 2000 2000 2000 2000 2000
2ND WORST YR 2009 2008 2011 2001 2000 2000 2001 2001 2001 2001 2001 2001 2001 2001 2001
3RD WORST YR 2008 2009 2009 2002 2009 2009 2009 2009 2003 2003 2007 2007 2007 2007 2003
4TH WORST YR 2000 2001 2000 2000 2002 2016 2016 2007 2007 2007 2003 2003 2003 2003 2007
5TH WORST YR 2014 2000 2001 2004 2004 2010 2010 2016 2009 2002 2006 2006 2006 2006 2008
MONTH: M M/J JN JN JN JN JL JL JL JL JL/AU AU AU AU AU
WEEK: 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35
BEST YR 2016 2007 2007 2016 2015 2015 2011 2011 2011 2011 2008 2012 2004 2013 2013
2ND BEST YR 2005 2003 2016 2006 2006 2013 2012 2014 2012 2010 2011 2004 2012 2010 2009
3RD BEST YR 2007 2016 2003 2003 2007 2006 2006 2012 2008 2012 2012 2008 2002 2016 2010
4TH BEST YR 2003 2014 2006 2007 2003 2011 2013 2004 2004 2008 2013 2013 2013 2004 2002
5TH BEST YR 2015 2006 2012 2015 2011 2014 2014 2006 2010 2004 2004 2011 2010 2002 2004
GREEN BOLD REFLECTS TYPICAL MAX GROWTH PERIOD; LIGHT BLUE ARE TYPICALLY THE 5-6 HIGHEST WEEKS OF NDVI

30. Further project research
• Complete sorting and analysis, incorporating all data to
date; test with “hay colour instrument”
• Present findings to project committee, ranchers and
others to gain input; expand technical report and blog
• Blind test: Use algorithm to estimate GGF prior to
sorting (selection of samples) and compare to sorted
samples
• Expand ranch participation in secondary study; use
algorithm to estimate historical pasture production and
verify results with ranchers (Alberta and
Saskatchewan)
30PastureTech.com @PastureTech

31. Further research (cont’d)
• Develop potential insurance designs
• Split season (Alberta)
• Consecutive weeks of moisture deficiency (Spain)
• Pasture growth curve deficiency (Mexico, ad hoc)
• Back-cast insurance designs and review results with
project committee, ranchers and others
• Work closely with crop insurance agencies; e.g. input
advisory groups (AFSC)
• Link satellite imagery to soil moisture (hydrology
project)
31PastureTech.com @PastureTech

32. Link to hydrology
project

33. Hydrology
33PastureTech.com @PastureTech

34. Current state of HGS model simulations
34PastureTech.com @PastureTech
Basin Scale
Sub-basin
Scale
Local Scale
Steady-
State
Transient
Steady-
State
Transient
Steady-
State
Steady-
State
Red
Deer
BowOldman
Lower
SSR

35. South Saskatchewan River Basin Boundary
35PastureTech.com @PastureTech

36. Sub-basins within SSRB
36PastureTech.com @PastureTech

37. Model mesh example
37PastureTech.com @PastureTech

38. Soil monitoring sites
38PastureTech.com @PastureTech

39. Pasture (and irrigation) sites in Alberta SSRB
39PastureTech.com @PastureTech

40. Discussion

41. Satellite
• Cost of satellite imagery
• Can satellite differentiate pasture, crops, trees and
weeds?
• What is the smallest pixel size feasible? Are there
implications to geographical coverage?
• Use of satellite for native pasture vs. tame; forages,
silage
41PastureTech.com @PastureTech

42. Pasture
• What do ranchers want to insure?
• How do ranchers use their pasture? What is important
to them (early season vs. late season)?
• Does pasture growth come down to quantity in early
season and quality in late season?
• Does normal to greater grass in spring mean annual production
has been obtained?
• Is there as much food value in grass once it “browns off” or
does less water mean more nutrition and weight gain?
42PastureTech.com @PastureTech

43. pasturetech.com
@pasturetech
Follow our progress!
Thank you for your time! Questions?