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

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

Aims and whyWhat we’re doingSome resultsMobile appFuture directions

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

Aims:

Current biomass

Growth rate

Paddock 963 23Black leg 750 21Plumb tree 894 25Parkers 675 11Parkers west 762 18Gumtree 1 1256 31Gumtree 2 1766 34

500

1000

1500

Export data

Why?

Pasture utilisationEstimate biomass compared to benchmarksHelp make objective decisions on stocking ratesCalibrate other methods (e.g. pastures from

space)

Why?Pasture utilisationHelp make objective decisions on stocking ratesEstimate biomass compared to benchmarksCalibrate other methods (e.g. pastures from space)

Why?Pasture utilisationHelp make objective decisions on stocking ratesEstimate biomass compared to benchmarksCalibrate other methods (e.g. pastures from space)

Why?Pasture utilisationHelp make objective decisions on stocking ratesEstimate biomass compared to

benchmarksCalibrate other methods

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

Preliminary results…

• The ok, more common

• Tablelands Fescue• The good,• Tasmanian

Ryegrass

Preliminary results…• The problems…

• Poor calibrations • NDVI Saturation

Effect of plant mix

Season State Species Input variable Model type Model R21 Winter NSW Fescue NDVI*Height LM -204 + 368.8 x 0.952 Winter Vic Ryegrass NDVI*Height GLM exp^(6.1 + 0.21 x) 0.793 Winter Vic Phalaris_Ryegrass NDVI*Height LM -189 + 244.7 x 0.684 Winter Vic Phalaris NDVIxLHt LM -62 + 862.3 x 0.675 Winter Vic Mixed LNDVI LM -22 -811.5 x 0.336 Winter NSW Fescue NDVI GLM exp^(4.2 + 5.75 x) 0.917 Winter NSW Lucerne NDVI LM -571 + 2464.7 x 0.928 Winter Vic Mixed NDVI LM -75157 + 104337.6 x 0.69 Winter Vic Phalaris_Clover NDVIxLHt LM -167 + 945.8 x 0.93

10 Winter Vic Phalaris LHt LM -1378 + 1731.9 x 0.7511 Winter Vic Phalaris_Ryegrass_Clove

rLHt LM -835 + 1509.6 x 0.53

12 Winter Vic Phalaris_Clover NDVIxLHt GLM exp^(4.9 + 1.35 x) 0.8614 Winter NSW Fescue LNDVI GLM exp^(9.2 + 2.61 x) 0.9515 Winter Vic Phalaris_Clover NDVIxLHt QLM -30 + 456.1 x + 178.8 x^2 0.9216 Winter Vic Phalaris LNDVI GLM exp^(9.1 + 10.2 x) 0.7317 Winter Vic Ryegrass NDVI*Height GLM exp^(4.4 + 0.41 x) 0.7118 Winter Vic Phalaris_Clover NDVIxLHt LM -347 + 1613.8 x 0.8119 Winter Vic Phalaris_Ryegrass_Clove

rHeight GLM exp^(6.7 + 0.15 x) 0.84

20 Spring Vic Ryegrass NDVI*Height LM 925 + 256.1 x 0.8723 Spring Vic Phalaris_Clover NDVI*Height GLM exp^(8.1 + 0.14 x) 0.4724 Spring NSW Fescue NDVIxLHt LM -592 + 2141.8 x 0.8725 Spring NSW Lucerne NDVI GLM exp^(2.2 + 6.79 x) 0.7626 Spring NSW Phalaris LHt LM -93 + 1113.7 x 0.9627 Spring Vic Mixed LHt GLM exp^(3.3 + 1.72 x) 0.7228 Spring Vic Mixed NDVI*Height LM 1333 + 443.3 x 0.5129 Spring Vic Ryegrass NDVI GLM exp^(-5.9 + 16.2 x) 0.6830 Spring Vic Phalaris NDVIxLHt GLM exp^(4.6 + 1.64 x) 0.8931 Spring Vic Phalaris_Clover Height LM 242 + 191.2 x 0.9232 Spring NSW Cocksfoot_Fescue NDVIxLHt LM -348 + 2069.1 x 0.733 Spring NSW Cocksfoot_Fescue_Clove

rLNDVI GLM exp^(8.4 + 1.88 x) 0.71

34 Spring Vic Ryegrass_Clover NDVI*Height GLM exp^(6.5 + 0.12 x) 0.8635 Spring Vic Phalaris_Clover NDVIxLHt LM -75 + 1418.4 x 0.9136 Spring NSW Fescue_Clover NDVIxLHt LM -60 + 1145.7 x 0.6537 Spring NSW Ryegrass NDVI LM -578 + 5083.8 x 0.6338 Spring NSW Fescue NDVIxLHt LM 174 + 1641.4 x 0.7439 Spring NSW Mixed NDVIxLHt LM -633 + 2381.6 x 0.940 Spring Vic Mixed LNDVI LM 8218 + 7283.1 x 0.8241 Spring Vic Ryegrass NDVIxLHt LM -1112 + 2783.3 x 0.942 Spring Vic Phalaris_Ryegrass_Clove

rLNDVI LM 3410 + 2241.5 x 0.83

43 Spring Vic Phalaris Clover NDVIxLHt LM 91 + 2256 8 x 0 95

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/pasture 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 Sundown 

Lucerne  GDM = Ln SR (590/730) 

0.91 GDM = SR (590/730), SR (730/760) 

0.95 

23/06/2014 NSW Sundown 

Fescue GDM = SR (590/670) 

0.93 GDM = SR(730/670), SR(760/590) 

0.95 

25/07/2014 NSW Sundown 

Fescue Ln GDM = SR(530/760) 

0.96 Ln GDM = Band 730, Ln NDVI ((760‐530)/760+530)) 

0.98 

18/09/2015 NSW Sundown 

Lucerne  GDM = Ln((760‐530)/760+530)) 

0.93 Ln GDM = SR(760/730), Ln Band 700 

0.98 

18/09/2014 NSW Sundown 

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 

 

a) b) c)

Region     Combined Winter+Spring All cuts

Northern Tablelands 

r2  0.70 0.45n  289 545Mean 2191 2616RMSE  982 1312

Central Victoria 

r2  0.77   n  66Mean  1373RMSE  541   

Southern Victoria 

r2  0.62   n  82Mean  1743RMSE 375

Western Victoria 

r2  0.77   n  326Mean  1206RMSE  469   

Tasmania 

r2  0.66 0.68n  82 153Mean  1189 1120RMSE  403 380

WA and SA pastures

0 units21 units

42 units

63 units

0.51 0.55

0.65 0.78

DIY calibration

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

Calibration shifts

Dealing with calibration shifts…NDVI profiling

Soil moisture

LiDAR data

Lidar data

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