This document discusses a project to develop methods for real-time pasture biomass estimation using active optical sensors (AOS). The goals are to evaluate AOS potential, develop calibrations for producers, and create a mobile app. Over 200 calibration samples across species and regions were collected. Preliminary results found height was often the best covariate. Two and three band sensor models improved correlations compared to NDVI alone. Future work includes integrating weather, satellite, and LiDAR data to validate estimates across more conditions. A mobile app would support producers using AOS for pasture management decisions.

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