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Andy  Jarvis PARASID Near Real Time Monitoring Of Habitat Change Using A Neural Network And  M O D I S Data  T N C  Brown  Bag  Sept 2009
 

Andy Jarvis PARASID Near Real Time Monitoring Of Habitat Change Using A Neural Network And M O D I S Data T N C Brown Bag Sept 2009

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Brown bag presentation for TNC in Washington 24th September 2009 on the PARASID habitat monitoring tool. Authored by Andy Jarvis, Louis Reymondin and Jerry Touval.

Brown bag presentation for TNC in Washington 24th September 2009 on the PARASID habitat monitoring tool. Authored by Andy Jarvis, Louis Reymondin and Jerry Touval.

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    Andy  Jarvis PARASID Near Real Time Monitoring Of Habitat Change Using A Neural Network And  M O D I S Data  T N C  Brown  Bag  Sept 2009 Andy Jarvis PARASID Near Real Time Monitoring Of Habitat Change Using A Neural Network And M O D I S Data T N C Brown Bag 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.
    • Any questions????
    • 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 1 month for NN learning
      • Detection takes 1 week
    • 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
    •  
    • 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
    • Caqueta, Jan 2004 – May 2009 Date
    • 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
    • Deforestation Rates
    • 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)
      USE CASES
    • 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
    • Methodological Enhancements
      • Detailed enhancements in mathematics
      • Use of daily MODIS data to reduce problems of frequent cloud cover
      • Validation with other reported deforestation statistics and other studies (e.g. Asner)
      • Inclusion of components which identify direction of change (reforestation vs. deforestation)
      • Linkages with fire datasets and better characterisation of flooding to avoid false positives
    • 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
    • GRACIAS!