Andy Jarvis - Parasid Near Real Time Monitoring Of Habitat Change Using A Neural Network And Modis Data Ideam Sept 2009
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Andy Jarvis - Parasid Near Real Time Monitoring Of Habitat Change Using A Neural Network And Modis Data Ideam Sept 2009

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Presentation on the PARASID tool, a habitat monitoring system for Latin America, developed jointly by TNC and CIAT. Presented in a meeting with IDEAM, Bogota, Colombia on 19th September 2009.

Presentation on the PARASID tool, a habitat monitoring system for Latin America, developed jointly by TNC and CIAT. Presented in a meeting with IDEAM, Bogota, Colombia on 19th September 2009.

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Andy Jarvis - Parasid Near Real Time Monitoring Of Habitat Change Using A Neural Network And Modis Data Ideam Sept 2009 Presentation 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
    • The approach
    • The implementation
    • Some examples
    • Comparison with other models
    • Plans and timelines
  • 3. Objectives of PARASID
    • HUman Impact Monitoring And Natural Ecosystems
    • Provide near-real time monitoring of habitat change (<3 month turn-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
    • We therefore try to learn how each pixel (site) responds to climate, and any anomoly corresponds to human impact
    • 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
    • Uses NDVI quality information
    • Iterative fitting of cleaned curve using
      • Fourier analysis
      • Least-square fitting to good quality values
  • 8. Methodology As required by the ARD algorithm, each input and the hidden output is a weights class with its own α α 0 α c INPUTS : Past NDVI (MODIS 3b42) Previous rainfall (TRMM) Temperature (WorldClim) OUTPUT : 16 day predicted NDVI NDVI t Precipitation (t) Temperature (t) … … w 0 w 1 w 2 NDVI (t-1) NDVI (t-2) NDVI (t-n) w p1 w p2 w p3 w o1 w o2 w o3
  • 9. 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.
  • 10. The Processing
    • For South America alone, first calculations approximated 10 years of processing for the NN to learn:
      • 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 3 months for NN learning
    • Detection takes 2-3 days
  • 11. Sample novelty analysis
  • 12. 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
  • 13. Some statistics
    • 75% of deforestation occurs in December and January
    • 50,000 Ha deforested in Dec/Jan of 2008/2009 compared with 7,500 Ha in 2004/2005
    • During 16 days of Christmas in 2008 16,000 Ha lost, compared with 500 Ha in 2004 (3%)
  • 14.  
  • 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 near to be fully automated
  • 17. Colombia – Río Caquetá
    • Size
      • 480 * 300 [km 2 ]
      • 14400000 [ha]
    • Vegetation type
      • Tropical forest
  • 18. Detection : See Caqueta-meta KML
    • See http://www.youtube.com/watch?v=exGmzc70PrQ
    • Pink : Too many clouds to analyse
    • Red : 3 consecutive times detected with more than 95% confidence
  • 19. NDVI 2004.01.01 NDVI 2009.01.01 Anomalies probability 2009.01.01
  • 20. Colombia – Rio Caquetá
  • 21. Colombia – Rio Caquetá
  • 22. Colombia – Rio Caquetá
    • Comments
      • 0.22% deforestation rate per year
      • The model is working well in this area where deforestation seems accelerating
  • 23. Colombia – Serranía San Lucas
    • Size
      • 180*960 [km 2 ]
      • 4320000 [ha]
    • Vegetation type
      • Lowland tropical forest
      • Montane forest
  • 24. Colombia – Serranía San Lucas NDVI 2004.01.01 NDVI 2009.01.01 anomalies probability 2009.01.01 Cumulative detection on time
  • 25. Colombia – Serranía San Lucas
  • 26. Colombia – Serranía San Lucas
    • Comments
      • 0.63% deforestation rate per year
      • The main area of deforestation in the center is a really strong change which could be flooding.
      • A deeper analysis is needed to explain the different types of changes
      • The model seems to work well even if the place is a bit cloudy
  • 27. Colombia – Sierra Nevada
    • Size
      • 120*120 [km 2 ]
      • 1440000 [ha]
    • Vegetation type
      • Dry forest
      • Montane forest
  • 28. Colombia – Sierra Nevada NDVI 2004.01.01 NDVI 2009.01.01 Anomalies probability 2009.01.01 Cumulative detection on time
  • 29. Colombia – Sierra Nevada
  • 30. Colombia – Sierra Nevada
    • Comments
      • 0.01% deforestation rate per year
      • Difficult to detect changes in this area
      • An analysis of the probabilities is needed to show places with anomalies
  • 31. Bolivia – Santa Cruz
    • Size
      • 480*420 [km 2 ]
      • 20160000 [ha]
    • Vegetation type
      • Tropical forest
  • 32. NDVI 2004.01.01 NDVI 2009.01.01 Anomalies probability 2009.01.01
  • 33. Bolivia – Santa Cruz Cumulative detection on time
  • 34. Bolivia – Santa Cruz 0.09% deforestation rate
  • 35. Paraguay - Boquerón
    • Size
      • 240*240 [km 2 ]
      • 5760000 [ha]
    • Vegetation type
      • Savannah
  • 36. NDVI 2004.01.01 NDVI 2009.01.01 Anomalies probability 2009.01.01
  • 37. Cumulative detection on time
  • 38. Paraguay - Boquerón
  • 39. Paraguay - Boquerón
    • Comments
      • 0.87% deforestation rate
      • Savannah and tropical forest have a totally different environment
      • The model seems to work well even if the changes are more subtle
  • 40. Chile – Region del Bio Bio
    • Size
      • 240*120 [km 2 ]
      • 2880000 [ha]
    • Vegetation type
      • Tempered forest
  • 41. Chile – Region del Bio Bio NDVI 2004.01.01 NDVI 2009.01.01 Anomalies probability 2009.01.01 Cumulative detection on time
  • 42. Chile – Region del Bio Bio
  • 43. Chile – Region del Bio Bio
    • Comments
      • 0.31% deforestation rate
      • The model seems to work with a tempered climate and non-tropical forests
  • 44. And now the tough one…
  • 45. OTCA Amazon Cooperation Treaty
    • Size
      • 4228.75*3498 [km 2 ]
      • 1479216750 [ha]
    • Vegetation type
      • Tropical forest
  • 46.  
  • 47. OTCA Amazon Cooperation Treaty
  • 48. OTCA Amazon Cooperation Treaty
    • Comments
      • Average 0.22% deofrestation rate
      • Still a bit noisy in the center
        • Due to clouds undetected during the cleaning process
      • Most of the detections are valid
      • The system seems stable over big areas and a certain amount of consecutive dates (detections over 120 dates)
  • 49. Time processing statistics
    • For an area of the size of OTCA with
      • One Dell server
        • 16 [GB] of RAM
        • 8 processors Intel Xeon X5365 3 [GHz]
    • Cleaning process
      • Cleaning 214 date
      • 12 hours
    • Clustering process
      • 6 Clusters
      • Clustered on the years 2000 to the end of 2003
      • 12 hours
    • Modeling process
      • 3 Models per clusters
      • 2000 pixels as training dataset
      • 5000 pixels as validation dataset
      • 3 hours
    • Detections process for 2004 to 2009
      • 120 detections grids
      • 70 hours
    • Whole process
      • Only 4 days processing from the raw data
  • 50. Tasas de deforestación
  • 51. Model comparison PARASID vs. FORMA PARASID detections First detection in 2004 FORMA probabilities First detection in 2000
  • 52. PARASID vs DETER It seems Parasid model detects quite small and isolate events which Deter doesn’t detect. 2006 2004
  • 53. Next Steps
      • Fully functioning web interface January 2010
      • Preliminary continental validation and calibration (January 2010)
      • Global extent (2011)
      • Additional models to identify type of change (drivers) (2011)
  • 54. Analysis of three images between the years 2000 and 2009. MATO-GROSSO – BRASIL LAT: - 10.1, LON: - 51.3 10/10/2000 LANDSAT 7 SLC ON 29/06/2009 LANDSAT 7 SLC OFF CLASSIFIED IMAGES IN ERDAS Forest Uncoverage Change 00-09 Unchanged CHANGE DETECTION IN ERDAS
  • 55. SAMPLING POINTS IN LATIN-AMERICA
    • 1. Covering the whole Latin-America
    • 2. Sampling of different land use type
      • Tropical forest
      • Andes
      • Savanna
      • Desert
    • 3. Selection of areas with high risk of change
      • Near to cities
      • Near to road
      • Near to rivers
      • With crops already existing
    SELECTION CRITERIA
  • 56. Conclusions
    • Near-real time global monitoring is possible
    • PARASID now functioning for Latin America
    • Providing first approximations of deforestation rates in over a decade for some parts of Latin America
  • 57. Puntos de mejoramiento
    • Corridas detalladas para zonas
    • Mejoramiento significativo en precisión y cobertura (por nubes) usando datos crudos de MODIS diarios
    • Con calibración y validación en campo
    • Estudio conjunto IDEAM con CIAT/TNC para aproximación nacionales
  • 58. GRACIAS!