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

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

    • Near-real time monitoring of habitat change using a neural network and MODIS data: the PARASID approach Andy Jarvis, Louis Reymondin, Jerry Touval
    • Contents
      • The approach
      • The implementation
      • Some examples
      • Comparison with other models
      • Plans and timelines
    • 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
    • The Approach
      • The change in greenness of a given pixel is a function of:
          • Climate
          • Site (vegetation, soil, geology)
          • Human impact
    • 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
    • NDVI Evolution and novelty detection Novelty/Anomoly
    • 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
    • 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
    • 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.
    • 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
    • Sample novelty analysis
    • 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
    • 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%)
    •  
    • Parasid Test cases
    • 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
    • Colombia – Río Caquetá
      • Size
        • 480 * 300 [km 2 ]
        • 14400000 [ha]
      • Vegetation type
        • Tropical forest
    • 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
    • NDVI 2004.01.01 NDVI 2009.01.01 Anomalies probability 2009.01.01
    • Colombia – Rio Caquetá
    • Colombia – Rio Caquetá
    • Colombia – Rio Caquetá
      • Comments
        • 0.22% deforestation rate per year
        • The model is working well in this area where deforestation seems accelerating
    • Colombia – Serranía San Lucas
      • Size
        • 180*960 [km 2 ]
        • 4320000 [ha]
      • Vegetation type
        • Lowland tropical forest
        • Montane forest
    • Colombia – Serranía San Lucas NDVI 2004.01.01 NDVI 2009.01.01 anomalies probability 2009.01.01 Cumulative detection on time
    • Colombia – Serranía San Lucas
    • 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
    • Colombia – Sierra Nevada
      • Size
        • 120*120 [km 2 ]
        • 1440000 [ha]
      • Vegetation type
        • Dry forest
        • Montane forest
    • Colombia – Sierra Nevada NDVI 2004.01.01 NDVI 2009.01.01 Anomalies probability 2009.01.01 Cumulative detection on time
    • Colombia – Sierra Nevada
    • 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
    • Bolivia – Santa Cruz
      • Size
        • 480*420 [km 2 ]
        • 20160000 [ha]
      • Vegetation type
        • Tropical forest
    • NDVI 2004.01.01 NDVI 2009.01.01 Anomalies probability 2009.01.01
    • Bolivia – Santa Cruz Cumulative detection on time
    • Bolivia – Santa Cruz 0.09% deforestation rate
    • Paraguay - Boquerón
      • Size
        • 240*240 [km 2 ]
        • 5760000 [ha]
      • Vegetation type
        • Savannah
    • NDVI 2004.01.01 NDVI 2009.01.01 Anomalies probability 2009.01.01
    • Cumulative detection on time
    • Paraguay - Boquerón
    • 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
    • Chile – Region del Bio Bio
      • Size
        • 240*120 [km 2 ]
        • 2880000 [ha]
      • Vegetation type
        • Tempered forest
    • Chile – Region del Bio Bio NDVI 2004.01.01 NDVI 2009.01.01 Anomalies probability 2009.01.01 Cumulative detection on time
    • Chile – Region del Bio Bio
    • Chile – Region del Bio Bio
      • Comments
        • 0.31% deforestation rate
        • The model seems to work with a tempered climate and non-tropical forests
    • And now the tough one…
    • OTCA Amazon Cooperation Treaty
      • Size
        • 4228.75*3498 [km 2 ]
        • 1479216750 [ha]
      • Vegetation type
        • Tropical forest
    •  
    • OTCA Amazon Cooperation Treaty
    • 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)
    • 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
    • Tasas de deforestación
    • Model comparison PARASID vs. FORMA PARASID detections First detection in 2004 FORMA probabilities First detection in 2000
    • PARASID vs DETER It seems Parasid model detects quite small and isolate events which Deter doesn’t detect. 2006 2004
    • 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)
    • 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
    • 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
    • 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
    • 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
    • GRACIAS!