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

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.

Published in: Technology, Education
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Transcript

  • 1. Near-real time monitoring of habitat change using a neural network and MODIS data: the PARASID approach
    Andy Jarvis, Louis Reymondin, Jerry Touval
  • 2. Contents
    Theapproach
    Theimplementation
    Someexamples
    What PARASID is, and whatitisnot
    Plans and timelines
  • 3. Objectives of PARASID
    HUmanImpactMonitoring And Natural Ecosystems
    Providenear-real time monitoring of habitatchange (<3 monthturn-around)
    Continental – global coverage (forests AND non-forests)
    Regularity in updates
  • 4. The Approach
    The change in greenness of a given pixel is a function of:
    Climate
    Site (vegetation, soil, geology)
    Human impact
  • 5. Machine learning
    Wetherefore try tolearnhoweach pixel (site) respondstoclimate, and anyanomolycorrespondstohumanimpact
    Machine learning (or neural-network), is a bio-inspired technology which emulates the basic mechanism of a brain.
    It allows
    To find a pattern in noisy dataset
    To apply these patterns to new dataset
  • 6. NDVI Evolution and novelty detection
    Novelty/Anomoly
  • 7. NDVI Cleaning using HANTS
    • Eliminate all short-term variations
    • 8. Uses NDVI quality information
    • 9. Iterative fitting of cleaned curve using
    • 10. Fourier analysis
    • 11. Least-square fitting to good quality values
  • w0
    NDVI(t-1)
    NDVI(t-2)

    wo1
    w1
    NDVI(t-n)
    wp1
    NDVIt
    wo2
    Precipitation (t)
    Temperature(t)

    wp2
    w2
    wp3
    wo3
    Methodology
    As required by the ARD algorithm, each input and the hidden output is a weights class with its own α
    α0
    αc
    INPUTS: Past NDVI (MODIS 13Q1)
    Previous rainfall (TRMM)
    Temperature (WorldClim)
    OUTPUT: 16 day predicted NDVI
  • 12. Methodology – Bayesian NN
    To detect novelties, Bayesian Neural Networks provide us two indicators
    The predicted value
    The probability repartition of where the value should be
    The first one allows us to detect abnormal measurements
    The second one allows us to say how sure we are a measurement is abnormal.
  • 13. The Processing
    For South Americaalone, firstcalculationsapproximated 10 years of processingforthe NN tolearn:
    A map of 30720 by 37440 pixels
     1,150,156,800 vectors
     23 vectors per year
     26,453,606,400 NDVI values to manage per year
     9.5 years of data
     251,309,260,800 individual data points
    Through various processes, optimizations and hardware acquisitions reduced time to 2 weeks for NN learning
    Detection takes 1 day
  • 14. The Bottom-Line
    250m resolution
    Latin American coverage (currently)
    3 week turnaround from data being made available (4 week delay in MODIS going to NASA ftp) (3+4 = 7 weeks)
    Report every 16 days
    Measurement of scale of habitat change (0-1) and probability of event
  • 15. Parasid Test cases
  • 16. Introduction
    Different test cases with different vegetation and climate types
    All the test are done with the same parameters
    Training parameters
    From 2000 to the end of 2003
    Detections parameters
    From 2004 to May 2009
    A detection map is created each 16 days within this period
    The process is close to be fully automated
  • 17. Colombia – Río Caquetá
    Size
    480 * 300 [km2]
    14400000 [ha]
    Vegetation type
    Tropical forest
  • 18. Caqueta, Jan 2004 – May 2009
    Date
  • 19.
  • 20. Colombia – Rio Caquetá
  • 21. Paraguay - Boquerón
    Size
    240*240 [km2]
    5760000 [ha]
    Vegetation type
    Savannah
    Chaco forest
  • 22. Cumulative detection on time
  • 23. Paraguay - Boquerón
  • 24. And now the tough one…
  • 25. OTCAAmazon Cooperation Treaty
    Size
    4228.75*3498 [km2]
    1479216750 [ha]
    Vegetation type
    Tropical forest
  • 26.
  • 27. OTCAAmazon Cooperation Treaty
  • 28. PARASID - Colombia
    Direct usage for developing negoatiation position of Colombia in Copenhagen
    September 2009 Colombia were going to COP15 with a figure of 100,000Ha/year deforestation
    PARASID analysis predicting MINIMUM 180,000Ha/year, most likely 250-300,000Ha/year
    Resulted in change in negotiation plan, and increased relevance of expansion of Chiribiqueti NP
    Discussions underway for PARASID to become a 1st tier monitoring tool for National Parks
  • 29. 76% coverage of country
    Approx. 250,000Ha/year average
    90% increase in deforestation rate 2004 - 2009
  • 30. TiniguaNational Park
    1,300 Ha deforestedbetween 2004 y 2009
    0.5% of total areadeforested in 5 years
  • 31. What PARASID is….
    1st tier monitoring tool for looking at broad-scale patterns of habitat conversion
    National and regional platform for consistent measurement of habitat conversion
    Suitable early-warning system
    Important policy-influencing tool
  • 32. What PARASID is not…..
    Detailed monitoring tool for examining local-scale impacts and changes – 2nd and 3rd tier analyses are needed
    A system for monitoring steady degradation
  • 33. Outlook and next steps
    Three major pushes right now:
    Methodological development
    Long wish list….
    Getting it out there
    Adoption by countries
    Adoption by institutions
    Website and online data
    Writing it up
    Methodological paper imminent submission
    Latin American patterns in habitat change
    Effectiveness of Pas across the continent
    + many more…
  • 34. a.jarvis@cgiar.org

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