Near-real time monitoring of deforestation using a neural network and MODIS data:  the HUMANE approach Andy Jarvis, Louis ...
Contents <ul><li>The approach </li></ul><ul><li>The implementation </li></ul><ul><li>Some examples </li></ul><ul><li>Compa...
Objectives of HUMANE <ul><li>HUman Impact Monitoring And Natural Ecosystems </li></ul><ul><li>Provide near-real time monit...
The Approach <ul><li>The change in greenness of a given pixel is a function of: </li></ul><ul><ul><ul><li>Climate </li></u...
Machine learning <ul><li>We therefore try to learn how each pixel (site) responds to climate, and any anomoly corresponds ...
NDVI Evolution and novelty detection Novelty/Anomoly
What is “machine learning” ? <ul><li>Machine learning (or neural-network), is a bio-inspired technology which emulates the...
Methodology As required by the ARD algorithm, each input and the hidden output is a weights class with its own  α   α 0 α ...
Methodology – Bayesian NN <ul><li>To detect novelties, Bayesian Neural Networks provide us two indicators </li></ul><ul><u...
The Processing <ul><li>For South America alone, first calculations approximated 10 years of processing for the NN to learn...
The Bottom-Line <ul><li>250m resolution </li></ul><ul><li>Latin American coverage (currently) </li></ul><ul><li>3 week tur...
An Example <ul><li>Two animations for Mato Grosso  </li></ul>
HUMANE vs. DETER Validation tests
Validation area <ul><li>Location  </li></ul><ul><ul><li>Brazil </li></ul></ul><ul><ul><li>Bottom left corner </li></ul></u...
Data source <ul><li>Deter dataset has been downloaded on the website of the Deter project :  </li></ul><ul><ul><li>http://...
HUMANE true positives 2004 2006 Parasid model is, sometimes more sensitive, and detects events that Deter doesn’t detect.
HUMANE True Positives It seems Parasid model detects quite small and isolate events which Deter doesn’t detect. 2006 2004
HUMANE False positives On the other hand, Parasid is more sensitive to false positives. Here, around a river. 2004 2006
HUMANE False Positives In this example, Parasid doesn’t detect as well as Deter the big new field (red circle) but, is mor...
Synthesis <ul><li>The general locations of detections are the same for both   models, so we have in essence created a DETE...
HUMANE vs. FORMA Validation tests
Test area <ul><li>Brazil </li></ul><ul><ul><li>Latitude  8°36'7.50&quot;S </li></ul></ul><ul><ul><li>Longitude  51°28'30.0...
Models’ output HUMANE detections First detection in 2004 FORMA probabilities First detection in 2000
<ul><li>This map shows  the models’ differencies </li></ul><ul><ul><li>The two models match </li></ul></ul>Comparison PARA...
Detailed comparison <ul><li>The marked areas show that most of the differences are due to the changes which happened betwe...
Detailed comparison Top FORMA  Bottom PARASID Images from google earth PARASID - FORMA Maybe due to the rescaled pixel siz...
Detailed comparison Top FORMA  Bottom PARASID Images from google earth PARASID - FORMA Parasid is a bit more sensitive.
Detailed comparison Clear change in 2006 Softer change in 2008 Maybe vegetation degradation The pixel plotted is  shown in...
Detailed comparison <ul><li>The two graphs in the previous slide show that the changes detected by HUMANE and not by FORMA...
Conclusions and next steps <ul><li>Near-real time global monitoring  is  possible </li></ul><ul><li>HUMANE now functioning...
More info… <ul><li>[email_address] </li></ul>
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Andy J Humane Near Real Time Monitoring Of Deforestation Using A Neural Aug 2009

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Andy J Humane Near Real Time Monitoring Of Deforestation Using A Neural Aug 2009

  1. 1. Near-real time monitoring of deforestation using a neural network and MODIS data: the HUMANE approach Andy Jarvis, Louis Reymondin, Jerry Touval CIAT and TNC
  2. 2. Contents <ul><li>The approach </li></ul><ul><li>The implementation </li></ul><ul><li>Some examples </li></ul><ul><li>Comparison with DETER </li></ul><ul><li>Comparison with FORMA </li></ul><ul><li>Plans and timelines </li></ul>
  3. 3. Objectives of HUMANE <ul><li>HUman Impact Monitoring And Natural Ecosystems </li></ul><ul><li>Provide near-real time monitoring of habitat change (<3 month turn-around) </li></ul><ul><li>Continental – global coverage (forests AND non-forests) </li></ul><ul><li>Regularity in updates </li></ul>
  4. 4. The Approach <ul><li>The change in greenness of a given pixel is a function of: </li></ul><ul><ul><ul><li>Climate </li></ul></ul></ul><ul><ul><ul><li>Site (vegetation, soil, geology) </li></ul></ul></ul><ul><ul><ul><li>Human impact </li></ul></ul></ul>
  5. 5. Machine learning <ul><li>We therefore try to learn how each pixel (site) responds to climate, and any anomoly corresponds to human impact </li></ul><ul><li>Machine learning (or neural-network), is a bio-inspired technology which emulates the basic mechanism of a brain. </li></ul><ul><li>It allows </li></ul><ul><ul><li>To find a pattern in noisy dataset </li></ul></ul><ul><ul><li>To apply these patterns to new dataset </li></ul></ul>
  6. 6. NDVI Evolution and novelty detection Novelty/Anomoly
  7. 7. What is “machine learning” ? <ul><li>Machine learning (or neural-network), is a bio-inspired technology which emulates the basic mechanism of a brain. </li></ul><ul><li>It allows </li></ul><ul><ul><li>To find a pattern in noisy dataset </li></ul></ul><ul><ul><li>To apply these patterns to new dataset </li></ul></ul>
  8. 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. 9. Methodology – Bayesian NN <ul><li>To detect novelties, Bayesian Neural Networks provide us two indicators </li></ul><ul><ul><li>The predicted value </li></ul></ul><ul><ul><li>The probability repartition of where the value should be </li></ul></ul><ul><li>The first one allows us to detect abnormal measurements </li></ul><ul><li>The second one allows us to say how sure we are a measurement is abnormal. </li></ul>
  10. 10. The Processing <ul><li>For South America alone, first calculations approximated 10 years of processing for the NN to learn: </li></ul><ul><ul><li>A map of 30720 by 37440 pixels </li></ul></ul><ul><ul><ul><li> 1,150,156,800 vectors </li></ul></ul></ul><ul><ul><ul><li> 23 vectors per year </li></ul></ul></ul><ul><ul><ul><li> 26,453,606,400 NDVI values to manage per year </li></ul></ul></ul><ul><ul><ul><li> 9.5 years of data </li></ul></ul></ul><ul><ul><ul><li> 251,309,260,800 individual data points </li></ul></ul></ul><ul><li>Through various processes, optimizations and hardware acquisitions reduced time to 3 months for NN learning </li></ul><ul><li>Detection takes 2-3 days </li></ul>
  11. 11. The Bottom-Line <ul><li>250m resolution </li></ul><ul><li>Latin American coverage (currently) </li></ul><ul><li>3 week turnaround from data being made available (4 week delay in MODIS going to NASA ftp) (3+4 = 7 weeks) </li></ul><ul><li>Report every 16 days </li></ul><ul><li>Measurement of scale of habitat change (0-1) and probability of event </li></ul>
  12. 12. An Example <ul><li>Two animations for Mato Grosso </li></ul>
  13. 13. HUMANE vs. DETER Validation tests
  14. 14. Validation area <ul><li>Location </li></ul><ul><ul><li>Brazil </li></ul></ul><ul><ul><li>Bottom left corner </li></ul></ul><ul><ul><ul><li>09°40’00’’ S </li></ul></ul></ul><ul><ul><ul><li>60°00’00’’ O </li></ul></ul></ul><ul><li>Size </li></ul><ul><ul><li>160 * 120 [km 2 ] </li></ul></ul><ul><ul><li>307200 modis pixels </li></ul></ul>Red square : Validation area
  15. 15. Data source <ul><li>Deter dataset has been downloaded on the website of the Deter project : </li></ul><ul><ul><li>http://www.obt.inpe.br/deter/indexdeter.php?id=6424 </li></ul></ul><ul><li>The satellite images of 2004 and 2006 used to compare the two models are extracted from images </li></ul><ul><ul><li>Mosaico LandSat 2004 (AMZ) </li></ul></ul><ul><ul><li>Mosaico LandSat 2006 (AMZ) </li></ul></ul><ul><li>Also provide by the Deter project website. </li></ul>
  16. 16. HUMANE true positives 2004 2006 Parasid model is, sometimes more sensitive, and detects events that Deter doesn’t detect.
  17. 17. HUMANE True Positives It seems Parasid model detects quite small and isolate events which Deter doesn’t detect. 2006 2004
  18. 18. HUMANE False positives On the other hand, Parasid is more sensitive to false positives. Here, around a river. 2004 2006
  19. 19. HUMANE False Positives In this example, Parasid doesn’t detect as well as Deter the big new field (red circle) but, is more precise to detect the small fields on the top right corner (blue circle) . 2004 2006
  20. 20. Synthesis <ul><li>The general locations of detections are the same for both models, so we have in essence created a DETER that works more automatically and is applicable at the continental level </li></ul><ul><li>Both HUMANE and Deter models have problems with false positive events, but we can adjust for these using this and other validation data and a genetic algorithm. </li></ul><ul><li>HUMANE seems more sensitive for small and isolated events, but this sensitivity may also generate false positives. Again, this is fixable through calibration data. </li></ul>
  21. 21. HUMANE vs. FORMA Validation tests
  22. 22. Test area <ul><li>Brazil </li></ul><ul><ul><li>Latitude 8°36'7.50&quot;S </li></ul></ul><ul><ul><li>Longitude 51°28'30.00“W </li></ul></ul>NDVI 2000.02.18 NDVI 2004.01.01 NDVI 2009.01.01
  23. 23. Models’ output HUMANE detections First detection in 2004 FORMA probabilities First detection in 2000
  24. 24. <ul><li>This map shows the models’ differencies </li></ul><ul><ul><li>The two models match </li></ul></ul>Comparison PARASID – FORMA Red pixels show where FORMA’s probabilities are higher than the PARASID ones White pixels show where PARASID’s probabilities are higher than the FORMA’s ones
  25. 25. Detailed comparison <ul><li>The marked areas show that most of the differences are due to the changes which happened between 2000 and 2004. </li></ul><ul><li>Parasid can’t detect these changes as it started detecting in 2004. </li></ul>NDVI 2000.02.18 NDVI 2004.01.01 PARASID - FORMA
  26. 26. Detailed comparison Top FORMA Bottom PARASID Images from google earth PARASID - FORMA Maybe due to the rescaled pixel size from 250 [m] to 500 [m], FORMA model doesn’t fit perfectly some fields (the red bound around the fields on the comparison map).
  27. 27. Detailed comparison Top FORMA Bottom PARASID Images from google earth PARASID - FORMA Parasid is a bit more sensitive.
  28. 28. Detailed comparison Clear change in 2006 Softer change in 2008 Maybe vegetation degradation The pixel plotted is shown in red on the map .
  29. 29. Detailed comparison <ul><li>The two graphs in the previous slide show that the changes detected by HUMANE and not by FORMA actually occurred (graph on the right). </li></ul><ul><li>However these changes are more subtle than classic deforestation events (graph on the left). </li></ul><ul><li>This could be due to vegetation degradation (e.g. selective logging) as the area where these changes occurred is surrounded by a high rural activity </li></ul><ul><li>Also some benefits from 250m resolution </li></ul><ul><li>HUMANE and FORMA complementary in provision of monitoring </li></ul>
  30. 30. Conclusions and next steps <ul><li>Near-real time global monitoring is possible </li></ul><ul><li>HUMANE now functioning for Latin America </li></ul><ul><li>Next milestones: </li></ul><ul><ul><li>Fully functioning web interface January 2010 </li></ul></ul><ul><ul><li>Continental validation and calibration (January 2010) </li></ul></ul><ul><ul><li>Global extent (2011) </li></ul></ul><ul><ul><li>Additional models to identify type of change (drivers) (2011) </li></ul></ul>
  31. 31. More info… <ul><li>[email_address] </li></ul>
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