Real-time pasture biomass estimation by Karl Andersson, SPAA Symposium Aug 2016.

1. Real-time pasture biomass
estimation
Mark Trotter, Karl Andersson, Andrew Robson, Derek
Schneider, Ashley Saint, Lucy Frizell
Participant teams: Lewis Kahn, Paul Reynolds, Tony Butler,
Brad Wooldridge, Chris Blore, Peter Schroder, Jim Shovelton,
Ian Gamble

2. B.GSM.0010, P4.18
Real Time Pasture Biomass Estimation

4. Aims and why
What we’re doing
Some results
Mobile app
Future directions

5. Aims
1. Evaluate the potential for Active Optical
Sensors (AOS)
2. Develop a series calibrations for use by
producers
3. Develop a Mobile Device Application
(MDA) to support AOS

6. Aims:
Current
biomass
Growth
rate
Paddock 963 23
Black leg 750 21
Plumb tree 894 25
Parkers 675 11
Parkers west 762 18
Gumtree 1 1256 31
Gumtree 2 1766 34
500
1000
1500
Export data

7. Why?
Pasture utilisation
Estimate biomass compared to benchmarks
Help make objective decisions on stocking rates
Calibrate other methods (e.g. pastures from
space)

8. Why?
 Pasture utilisation
 Help make objective decisions on stocking rates
 Estimate biomass compared to benchmarks
 Calibrate other methods (e.g. pastures from space)

9. Why?
 Pasture utilisation
 Help make objective decisions on stocking rates
 Estimate biomass compared to benchmarks
 Calibrate other methods (e.g. pastures from space)

10. Why?
 Pasture utilisation
 Help make objective decisions on stocking rates
 Estimate biomass compared to
benchmarks
 Calibrate other methods

11. Correlating sensors to GDM…
 >200 samples taken from across Australia
 Each sample consists of 8-12 individual cuts
 Each site is scanned with a Greenseeker Handheld
 Height measured by plate meter
 Digital image (before and after cut)
 Quadrat is harvested using clippers or knives – cut
to ground

12. Preliminary results…
• The ok, more
common
• Tablelands Fescue
• The good,
• Tasmanian
Ryegrass

13. Preliminary results…
• The problems…
• Poor calibrations • NDVI Saturation

15. Effect of plant mix

16. Season State Species Input variable Model type Model R2
1 Winter NSW Fescue NDVI*Height LM -204 + 368.8 x 0.95
2 Winter Vic Ryegrass NDVI*Height GLM exp^(6.1 + 0.21 x) 0.79
3 Winter Vic Phalaris_Ryegrass NDVI*Height LM -189 + 244.7 x 0.68
4 Winter Vic Phalaris NDVIxLHt LM -62 + 862.3 x 0.67
5 Winter Vic Mixed LNDVI LM -22 -811.5 x 0.33
6 Winter NSW Fescue NDVI GLM exp^(4.2 + 5.75 x) 0.91
7 Winter NSW Lucerne NDVI LM -571 + 2464.7 x 0.92
8 Winter Vic Mixed NDVI LM -75157 + 104337.6 x 0.6
9 Winter Vic Phalaris_Clover NDVIxLHt LM -167 + 945.8 x 0.93
10 Winter Vic Phalaris LHt LM -1378 + 1731.9 x 0.75
11 Winter Vic Phalaris_Ryegrass_Clove
r
LHt LM -835 + 1509.6 x 0.53
12 Winter Vic Phalaris_Clover NDVIxLHt GLM exp^(4.9 + 1.35 x) 0.86
14 Winter NSW Fescue LNDVI GLM exp^(9.2 + 2.61 x) 0.95
15 Winter Vic Phalaris_Clover NDVIxLHt QLM -30 + 456.1 x + 178.8 x^2 0.92
16 Winter Vic Phalaris LNDVI GLM exp^(9.1 + 10.2 x) 0.73
17 Winter Vic Ryegrass NDVI*Height GLM exp^(4.4 + 0.41 x) 0.71
18 Winter Vic Phalaris_Clover NDVIxLHt LM -347 + 1613.8 x 0.81
19 Winter Vic Phalaris_Ryegrass_Clove
r
Height GLM exp^(6.7 + 0.15 x) 0.84
20 Spring Vic Ryegrass NDVI*Height LM 925 + 256.1 x 0.87
23 Spring Vic Phalaris_Clover NDVI*Height GLM exp^(8.1 + 0.14 x) 0.47
24 Spring NSW Fescue NDVIxLHt LM -592 + 2141.8 x 0.87
25 Spring NSW Lucerne NDVI GLM exp^(2.2 + 6.79 x) 0.76
26 Spring NSW Phalaris LHt LM -93 + 1113.7 x 0.96
27 Spring Vic Mixed LHt GLM exp^(3.3 + 1.72 x) 0.72
28 Spring Vic Mixed NDVI*Height LM 1333 + 443.3 x 0.51
29 Spring Vic Ryegrass NDVI GLM exp^(-5.9 + 16.2 x) 0.68
30 Spring Vic Phalaris NDVIxLHt GLM exp^(4.6 + 1.64 x) 0.89
31 Spring Vic Phalaris_Clover Height LM 242 + 191.2 x 0.92
32 Spring NSW Cocksfoot_Fescue NDVIxLHt LM -348 + 2069.1 x 0.7
33 Spring NSW Cocksfoot_Fescue_Clove
r
LNDVI GLM exp^(8.4 + 1.88 x) 0.71
34 Spring Vic Ryegrass_Clover NDVI*Height GLM exp^(6.5 + 0.12 x) 0.86
35 Spring Vic Phalaris_Clover NDVIxLHt LM -75 + 1418.4 x 0.91
36 Spring NSW Fescue_Clover NDVIxLHt LM -60 + 1145.7 x 0.65
37 Spring NSW Ryegrass NDVI LM -578 + 5083.8 x 0.63
38 Spring NSW Fescue NDVIxLHt LM 174 + 1641.4 x 0.74
39 Spring NSW Mixed NDVIxLHt LM -633 + 2381.6 x 0.9
40 Spring Vic Mixed LNDVI LM 8218 + 7283.1 x 0.82
41 Spring Vic Ryegrass NDVIxLHt LM -1112 + 2783.3 x 0.9
42 Spring Vic Phalaris_Ryegrass_Clove
r
LNDVI LM 3410 + 2241.5 x 0.83
43 Spring Vic Phalaris Clover NDVIxLHt LM 91 + 2256 8 x 0 95

18. Solutions?
 We examined
other reflectance
bands
 Excellent
correlation but
not consistent
 The best co-
variate turns out
to be plate
height.
Question % of possible
sites
Proportion of sites where ACS470 bands are better than GS NDVI? 97%
Proportion of these sites where there is a substantive improvement (increase of
more than r2
0.10)?
71%
Proportion of sites where ACS470 bands are better than GS NDVI, Height or a
combination?
78%
Proportion of these sites where there is a substantive improvement (increase of
more than r2
0.10)?
29%
Date State
Locatio
n
Species/past
ure type
Best 2 band
sensor model r2
Best 3 band sensor
model r2
4/06/2014 NSW UNE
Fescue
Fescue Ln GDM = Ln
NDVI ((760‐
700)/760+700)
0.83 Ln GDM = Band 590,
Ln Band 730
0.86
23/06/2014 NSW Sundow
n
Lucerne GDM = Ln SR
(590/730)
0.91 GDM = SR
(590/730), SR
(730/760)
0.95
23/06/2014 NSW Sundow
n
Fescue GDM = SR
(590/670)
0.93 GDM = SR(730/670),
SR(760/590)
0.95
25/07/2014 NSW Sundow
n
Fescue Ln GDM =
SR(530/760)
0.96 Ln GDM = Band 730,
Ln NDVI ((760‐
530)/760+530))
0.98
18/09/2015 NSW Sundow
n
Lucerne GDM = Ln((760‐
530)/760+530))
0.93 Ln GDM =
SR(760/730), Ln
Band 700
0.98
18/09/2014 NSW Sundow
n
Fescue Ln GDM = Band
530
0.85 Ln GDM = Ln Band
760, NDVI ((760‐
530)/760+530))
0.90
23/09/2014 NSW Kirby Phalaris GDM = NDVI
((760‐
730)/(760+730))
0.88 GDM = Ln
SR(670/760), Ln
SR(700/730)
0.98

19. a) b) c)

20. Region
Combined
Winter+Spring
All cuts
Northern
Tablelands
r2
0.70 0.45
n 289 545
Mean 2191 2616
RMSE 982 1312
Central
Victoria
r2
0.77
n 66
Mean 1373
RMSE 541
Southern
Victoria
r2
0.62
n 82
Mean 1743
RMSE 375
Western
Victoria
r2
0.77
n 326
Mean 1206
RMSE 469
Tasmania
r2
0.66 0.68
n 82 153
Mean 1189 1120
RMSE 403 380

21. WA and SA pastures

22. 0 units
21 units
42 units
63 units
0.51 0.55
0.65 0.78

26. DIY calibration

27. Where to from here?
 Integrate with weather and satellite data
 Integrate with LiDAR
 Integration with feed budgeting/stocking rate software
 Quality and pasture growth rates

28. Calibration shifts

29. Dealing with calibration shifts…
NDVI profiling

30. Soil moisture

31. LiDAR data
Lidar data

32. Take home messages
 NDVI from affordable AOS have the potential to provide pastures
biomass estimates
 In many pastures, the inclusion of height measures improved the
correlation with GDM, and was a better universal covariate than other
AOS bands
 Work is still to be done to validate estimates, and provide seasonal,
regional, species and calibrations
 LiDAR may provide convenient height measures, though further
development is required