Climate variability 11 year running mean

Transcription

1 Application of remote sensing technologies for mapping of cropping pattern and area estimations for major summer and winter crops across spatial and temporal scales: Lessons learnt in Australia A Potgieter

2 Climate variability 11 year running mean high variability at temporal & spatial

3 Impact (Economic, Natural, Social) Year Area (MHa) Production (Mtons)

4 perating within a Variable Climate Australian producers are excellent risk managers. Successful businesses within the world s most variable climate and without subsidies Climate risk and change is already built into the system Integration of climate forecasts with targeted decision making tools improved decision making Developed world-leading crop production systems frameworks & decision-support tools

5 Late-High Utility Utility of crop predictions is a function of timing and accuracy Early-Low

6 Yield (t/ha) Regional crop prediction framework Generating Yield Forecast Data Plumes Climate Forecast Set (e.g. at 1 June 2004) Start of fallow 1 October 2003 to 31 May Jun 19?? to 31 Dec 19?? 1 Jun 19?? to 31 Dec 20?? Forecast distribution based on climate forecast Best 10% Hindcast Qld 1994 SOI phases 1.5 Run model using weather data to date for current season Run model projections on subset of historical analogues from climate forecast to complete season Worst 10% Median Potgieter et. al. 2003, 2005, April May June July Aug Sept Oct Nov Month Forecast Worst10% Forecast-median Forecast Best10% Forecast Climatol Lt-median

7 Spatial & temporal forecast Predicted percentile median yield relative to all years 1 st July 2015 QAAFI 2010 Created 9/08/2010 [Slide 7]

8 EVI EVI Discriminating Crops: MISSING LINK Temporal scale SWQ Wheat SWQ Barley SWQ Chickpea CQ Wheat CQ Chickpea SEQ Wheat SEQ Barley Traditional approach: Single date approach e.g. DOY 225 does not capture all possibilities of peak crop canopy across regions (a) SWQ Wheat SWQ Barley DOY Proposed approach: Multi-temporal capturing all available crop growth information 6000 SWQ Chickpea CQ Wheat CQ Chickpea SEQ Wheat - Ability to accurately discriminate between crops (b) DOY

9 Greenness value (i.e. EVI) Reconstructing of crop growth profile Curve Fitting Procedures peak Time

10 EVI EVI Discriminating between crops Actual Reconstructed 2005 HANTS (Verhoef et al 1996) Harmonic analysis of time series 5000 Barley from satellite 4000 (a) Phase Amplitude (a ) DOY (b) Actual Reconstructed Wheat from satellite 2006 (c) Day of Year (DOY) 2000 (Verhoef et al 1996; Potgieter et al. 2007, 2010, 2011 ) (b ) DOY

11 Validation & Training ground truth data Paddock Watch

12 Example of Winter cropping: Waggamba & Moree 2015

13 Ground truth & Accuracies: Waggamba 2015 EVI multi-temporal canopy profiles Accuracy of area estimates during AVG: 87% Wheat-78%; Chickpea- 98% Barley-96% Jan May Nov 25 May 26 Jun 28 Jul 29 Aug 30 Sep 17 Nov

14 Crop Area Estimates Waggamba Moree

15 Other outputs: Land Use: Aug 2013 to Jul 2014

16 Cropping trajectories over time QLD: Increasing trend in Summer cropping of 36k Ha per year AVG: 602k Ha

17 Regional scale commodity forecasting framework Potgieter et. al. 2003, 2005, 2006

18 Specific & Total Crop Area & Production Summer 2014/15 Winter 2014

19 Land use patterns Bangladesh (source: Perry Poulton CSIRO) Southern Bangladesh 16 th Jan 2007 Lat:22.89ºN Long:91.40ºE(7173 km 2 ) Classified image Original image Land use class Area (%) Area (ha) 1. Fallow Trees Ponds Cropping (*other) Total

20 What lessons have we learnt? Utility of any prediction is based on accuracy and timing Framework high efficacy in predicting crop area estimates: at crop type level Successfully integrated to determine crop production estimates at regional scale What spatial scale is needed? - horses for courses; depending on what the issue is. Temporal resolution of outputs are sometimes more useful than accuracy: TRENDS Ground truthing fields are critical in crop discrimination

21 Which Satellite platform? Vegetation type: 75 current and planned sensors from 2015 to 203 Temporal & spatial resolution Accessibility of data: 1 m to 1 km resolution once every 10 minutes to ~once every month (source: NASA)

22 KEEP IT SIMPLE!!! The right answer to the wrong question therefore Asking the right questions before designing any framework

23 Thankyou