Andy Jarvis - Parasid Near Real Time Monitoring Of Habitat Change Tnc Slt


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Presentation made by Andy Jarvis from the Decision and Policy Analysis Program of the International Centre for Tropical Agriculture (CIAT). Delivered to the Science leadership Team in The Nature Conservancy (TNC) in December 2009.

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Andy Jarvis - Parasid Near Real Time Monitoring Of Habitat Change Tnc Slt

  1. 1. Near-real time monitoring of habitat change using a neural network and MODIS data: the PARASID approach<br />Andy Jarvis, Louis Reymondin, Jerry Touval<br />
  2. 2. Contents<br />Theapproach<br />Theimplementation<br />Someexamples<br />What PARASID is, and whatitisnot<br />Plans and timelines<br />
  3. 3. Objectives of PARASID<br />HUmanImpactMonitoring And Natural Ecosystems<br />Providenear-real time monitoring of habitatchange (&lt;3 monthturn-around)<br />Continental – global coverage (forests AND non-forests)<br />Regularity in updates<br />
  4. 4. The Approach<br />The change in greenness of a given pixel is a function of:<br />Climate<br />Site (vegetation, soil, geology)<br />Human impact<br />
  5. 5. Machine learning<br />Wetherefore try tolearnhoweach pixel (site) respondstoclimate, and anyanomolycorrespondstohumanimpact<br />Machine learning (or neural-network), is a bio-inspired technology which emulates the basic mechanism of a brain.<br />It allows <br />To find a pattern in noisy dataset<br />To apply these patterns to new dataset<br />
  6. 6. NDVI Evolution and novelty detection<br />Novelty/Anomoly<br />
  7. 7. NDVI Cleaning using HANTS<br /><ul><li>Eliminate all short-term variations
  8. 8. Uses NDVI quality information
  9. 9. Iterative fitting of cleaned curve using
  10. 10. Fourier analysis
  11. 11. Least-square fitting to good quality values</li></li></ul><li>w0<br />NDVI(t-1)<br />NDVI(t-2)<br />…<br />wo1<br />w1<br />NDVI(t-n)<br />wp1<br />NDVIt<br />wo2<br />Precipitation (t)<br />Temperature(t)<br />…<br />wp2<br />w2<br />wp3<br />wo3<br />Methodology<br />As required by the ARD algorithm, each input and the hidden output is a weights class with its own α<br />α0<br />αc<br />INPUTS: Past NDVI (MODIS 13Q1)<br /> Previous rainfall (TRMM)<br /> Temperature (WorldClim)<br />OUTPUT: 16 day predicted NDVI<br />
  12. 12. Methodology – Bayesian NN<br />To detect novelties, Bayesian Neural Networks provide us two indicators<br />The predicted value<br />The probability repartition of where the value should be<br />The first one allows us to detect abnormal measurements<br />The second one allows us to say how sure we are a measurement is abnormal. <br />
  13. 13. The Processing<br />For South Americaalone, firstcalculationsapproximated 10 years of processingforthe NN tolearn:<br />A map of 30720 by 37440 pixels <br /> 1,150,156,800 vectors<br /> 23 vectors per year<br /> 26,453,606,400 NDVI values to manage per year<br /> 9.5 years of data<br /> 251,309,260,800 individual data points<br />Through various processes, optimizations and hardware acquisitions reduced time to 2 weeks for NN learning<br />Detection takes 1 day<br />
  14. 14. The Bottom-Line<br />250m resolution<br />Latin American coverage (currently)<br />3 week turnaround from data being made available (4 week delay in MODIS going to NASA ftp) (3+4 = 7 weeks)<br />Report every 16 days<br />Measurement of scale of habitat change (0-1) and probability of event<br />
  15. 15. Parasid Test cases<br />
  16. 16. Introduction<br />Different test cases with different vegetation and climate types<br />All the test are done with the same parameters<br />Training parameters<br />From 2000 to the end of 2003 <br />Detections parameters<br />From 2004 to May 2009<br />A detection map is created each 16 days within this period<br />The process is close to be fully automated<br />
  17. 17. Colombia – Río Caquetá<br />Size <br />480 * 300 [km2]<br />14400000 [ha]<br />Vegetation type<br />Tropical forest<br />
  18. 18. Caqueta, Jan 2004 – May 2009<br />Date<br />
  19. 19.
  20. 20. Colombia – Rio Caquetá<br />
  21. 21. Paraguay - Boquerón<br />Size<br />240*240 [km2]<br />5760000 [ha]<br />Vegetation type<br />Savannah<br />Chaco forest<br />
  22. 22. Cumulative detection on time<br />
  23. 23. Paraguay - Boquerón<br />
  24. 24. And now the tough one…<br />
  25. 25. OTCAAmazon Cooperation Treaty<br />Size<br />4228.75*3498 [km2]<br />1479216750 [ha]<br />Vegetation type<br />Tropical forest<br />
  26. 26.
  27. 27. OTCAAmazon Cooperation Treaty<br />
  28. 28. PARASID - Colombia<br />Direct usage for developing negoatiation position of Colombia in Copenhagen<br />September 2009 Colombia were going to COP15 with a figure of 100,000Ha/year deforestation<br />PARASID analysis predicting MINIMUM 180,000Ha/year, most likely 250-300,000Ha/year<br />Resulted in change in negotiation plan, and increased relevance of expansion of Chiribiqueti NP<br />Discussions underway for PARASID to become a 1st tier monitoring tool for National Parks<br />
  29. 29. 76% coverage of country<br />Approx. 250,000Ha/year average<br />90% increase in deforestation rate 2004 - 2009<br />
  30. 30. TiniguaNational Park<br />1,300 Ha deforestedbetween 2004 y 2009<br />0.5% of total areadeforested in 5 years<br />
  31. 31. What PARASID is….<br />1st tier monitoring tool for looking at broad-scale patterns of habitat conversion<br />National and regional platform for consistent measurement of habitat conversion<br />Suitable early-warning system<br />Important policy-influencing tool<br />
  32. 32. What PARASID is not…..<br />Detailed monitoring tool for examining local-scale impacts and changes – 2nd and 3rd tier analyses are needed<br />A system for monitoring steady degradation<br />
  33. 33. Outlook and next steps<br />Three major pushes right now:<br />Methodological development<br />Long wish list….<br />Getting it out there<br />Adoption by countries<br />Adoption by institutions<br />Website and online data<br />Writing it up<br />Methodological paper imminent submission<br />Latin American patterns in habitat change<br />Effectiveness of Pas across the continent<br />+ many more…<br />
  34. 34.<br />