using satellite imagery to measure pasture production

Using Satellite Imagery to Measure Pasture ProductionRick McConnell & Tom CrozierSaskatchewan Meetings | SCIC and Forage CommitteeDecember 2016

PastureTech.com

Purpose

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)

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

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

50 km

150

km

Area-wide insurance or compensation

PastureTech.com @PastureTech

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


Satellite Measurement (X)

Pas

ture

Pro

duct

ion

(Y)

?

Solution1. 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


Research

Pasture typesIs 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?


• 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)]


MODIS: Accessible satellite

Black squares: MODIS pixels

Yellow lines: Township boundaries

Green squares: Sample sites

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


(0.50 – 0.08)

(0.50 + 0.08)= 0.72

(0.40 – 0.30)

(0.4 + 0.30)= 0.14

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


(0.50 – 0.08)

(0.50 + 0.08)= 0.72

(0.40 – 0.30)

(0.4 + 0.30)= 0.14

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


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


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


Analysis

COMPARISON OF WALK-AROUND AND MODIS NDVI VALUES: 2015 ALL RANCHES AND MONTHS (r=0.95, n=44)

00.050.10.150.20.250.30.350.40.45

0.50.550.60.650.70.750.80.850.90.95

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

WAL

K ND

VI

EST EQUAL

First Season (2015) Analysis Results


• 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)

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


Analysis (cont’d)


• 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.

20.

220.

240.

260.

28 0.3

0.32

0.34

0.36

0.38 0.

40.

420.

440.

460.

48 0.5

0.52

0.54

0.56

0.58 0.

60.

620.

640.

660.

68 0.7

0.72

0.74

0.76

0.78 0.

8

NDVI

LBS/

AC

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.

30.

320.

340.

360.

38 0.4

0.42

0.44

0.46

0.48 0.

50.

520.

540.

560.

58 0.6

0.62

0.64

0.66

0.68 0.

70.

720.

740.

760.

78 0.8

.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.

30.

320.

340.

360.

38 0.4

0.42

0.44

0.46

0.48 0.

50.

520.

540.

560.

58 0.6

0.62

0.64

0.66

0.68 0.

70.

720.

740.

760.

78 0.8

.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.

20.

220.

240.

260.

28 0.3

0.32

0.34

0.36

0.38 0.

40.

420.

44

0.46

0.48 0.

50.

520.

540.

560.

58 0.6

0.62

0.64

0.66

0.68 0.

70.

720.

740.

760.

78 0.8

.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.

30.

320.

340.

360.

38 0.4

0.42

0.44

0.46

0.48 0.

50.

520.

540.

560.

58 0.6

0.62

0.64

0.66

0.68 0.

70.

720.

740.

760.

78 0.8

.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.

30.

320.

340.

360.

38 0.4

0.42

0.44

0.46

0.48 0.

50.

520.

540.

560.

58 0.6

0.62

0.64

0.66

0.68 0.

70.

720.

740.

760.

78 0.8

.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.

20.

220.

240.

260.

28 0.3

0.32

0.34

0.36

0.38 0.

40.

420.

440.

460.

48 0.5

0.52

0.54

0.56

0.58 0.

60.

620.

640.

660.

68 0.7

0.72

0.74

0.76

0.78 0.

8

.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.

30.

320.

340.

360.

38 0.4

0.42

0.44

0.46

0.48 0.

50.

520.

540.

560.

58 0.6

0.62

0.64

0.66

0.68 0.

70.

720.

740.

760.

78 0.8

.10-.20 .20-.30 .30-.40 .40-.50 .50-.60 .60-.70 .70-.80

Analysis (cont’d)


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

Making sense of NDVI values

Example


Ranch level satellite vegetation index values: Osadczuk Ranch

Example


NDVI profile: Osadczuk Ranch

0.224

0.2620.277

0.303

0.333

0.356

0.388

0.409

0.441

0.472

0.500

0.5230.517 0.514

0.490

0.462

0.4400.431

0.4240.415

0.4050.396 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

Comparison of summer & winter


Burton and Osadczuk grazed lands: Average 7-day cloud-adjusted NDVI values (2000-2016) mid-April to mid-September

Comparison of NDVI values on four ranches


Average 7-day cloud-adjusted NDVI values (2000-2016) mid-April to mid-September

Example


Eddleston Ranch summer grazed:NDVI as a % of average (2000-2016) each of May, June and July

Example


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

Comparison of four volunteer ranches


NDVI % of average (2000-2016) by yearBeginning of May to end of July (Weighting: May 25%, June 60%, July 15%)

5 best and worst growing seasons by NDVI


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 Y R 2002 2002 2002 2009 2001 2001 2000 2000 2000 2000 2000 2000 2000 2000 20002ND WO RST 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 20034TH WORST Y R 2000 2001 2000 2000 2002 2016 2016 2007 2007 2007 2003 2003 2003 2003 20075TH WORST Y R 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 AUWEEK: 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

BEST Y R 2016 2007 2007 2016 2015 2015 2011 2011 2011 2011 2008 2012 2004 2013 2013

2ND BEST Y R 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 Y R 2003 2014 2006 2007 2003 2011 2013 2004 2004 2008 2013 2013 2013 2004 2002

5TH BEST Y R 2015 2006 2012 2015 2011 2014 2014 2006 2010 2004 2004 2011 2010 2002 2004

GREEN BOLD REFLECTS TYPI CAL MA X GROWTH PERIOD; LIGHT BLUE A RE TYPI CALLY T HE 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 Y R 2002 2002 2002 2009 2001 2001 2000 2000 2000 2000 2000 2000 2000 2000 20002ND WO RST 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 20034TH WORST Y R 2000 2001 2000 2000 2002 2016 2016 2007 2007 2007 2003 2003 2003 2003 20075TH WORST Y R 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 AUWEEK: 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

BEST Y R 2016 2007 2007 2016 2015 2015 2011 2011 2011 2011 2008 2012 2004 2013 2013

2ND BEST Y R 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 Y R 2003 2014 2006 2007 2003 2011 2013 2004 2004 2008 2013 2013 2013 2004 2002

5TH BEST Y R 2015 2006 2012 2015 2011 2014 2014 2006 2010 2004 2004 2011 2010 2002 2004

GREEN BOLD REFLECTS TYPI CAL MA X GROWTH PERIOD; LIGHT BLUE A RE TYPI CALLY T HE 5-6 HIGHEST WEEKS OF NDVI

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)


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)


Link to hydrology project

Hydrology


Current state of HGS model simulations


Basin Scale

Sub-basin Scale

Local Scale

Steady-State Transient

Steady-State Transient

Steady-State

Steady-State

Red DeerBowOldman Lower

SSR

South Saskatchewan River Basin Boundary


Sub-basins within SSRB


Model mesh example


Soil monitoring sites


Pasture (and irrigation) sites in Alberta SSRB


Discussion

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


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?


pasturetech.com @pasturetech

Follow our progress!

Thank you for your time! Questions?

using satellite imagery to measure pasture production

Science