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Near-real time monitoring of deforestation using a neural network and MODIS data:  the PARASID approach Andy Jarvis, Louis Reymondin, Jerry Touval CIAT and TNC
Contents ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Objectives of PARASID ,[object Object],[object Object],[object Object],[object Object]
The Approach ,[object Object],[object Object],[object Object],[object Object]
Machine learning ,[object Object],[object Object],[object Object],[object Object],[object Object]
NDVI Evolution and novelty detection Novelty/Anomoly
What is “machine learning” ? ,[object Object],[object Object],[object Object],[object Object]
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 ,[object Object],[object Object],[object Object],[object Object],[object Object]
The Processing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
The Bottom-Line ,[object Object],[object Object],[object Object],[object Object],[object Object]
An Example ,[object Object]
 
PARASID vs. DETER Validation tests
Validation area ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Red square : Validation area
Data source ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
PARASID true positives 2004 2006 Parasid model is, sometimes more sensitive, and detects events that Deter doesn’t detect.
PARASID True Positives It seems Parasid model detects quite small and isolate events which Deter doesn’t detect. 2006 2004
PARASID False positives On the other hand, Parasid is more sensitive to false positives. Here, around a river. 2004 2006
PARASID 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
Synthesis ,[object Object],[object Object],[object Object]
PARASID vs. FORMA Validation tests
Test area ,[object Object],[object Object],[object Object],NDVI 2000.02.18 NDVI 2004.01.01 NDVI 2009.01.01
Models’ output PARASID detections First detection in 2004 FORMA probabilities First detection in 2000
[object Object],[object Object],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
Detailed comparison ,[object Object],[object Object],NDVI 2000.02.18 NDVI 2004.01.01 PARASID - FORMA
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).
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 red on the map .
Detailed comparison ,[object Object],[object Object],[object Object],[object Object],[object Object]
Conclusions and next steps ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
More info… ,[object Object]

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Andy Jarvis and Louis Reymondin - PARASID Near Real Time Monitoring Of Deforestation Using A Neural Aug 2009

  • 1. Near-real time monitoring of deforestation using a neural network and MODIS data: the PARASID approach Andy Jarvis, Louis Reymondin, Jerry Touval CIAT and TNC
  • 2.
  • 3.
  • 4.
  • 5.
  • 6. NDVI Evolution and novelty detection Novelty/Anomoly
  • 7.
  • 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.
  • 10.
  • 11.
  • 12.
  • 13.  
  • 14. PARASID vs. DETER Validation tests
  • 15.
  • 16.
  • 17. PARASID true positives 2004 2006 Parasid model is, sometimes more sensitive, and detects events that Deter doesn’t detect.
  • 18. PARASID True Positives It seems Parasid model detects quite small and isolate events which Deter doesn’t detect. 2006 2004
  • 19. PARASID False positives On the other hand, Parasid is more sensitive to false positives. Here, around a river. 2004 2006
  • 20. PARASID 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
  • 21.
  • 22. PARASID vs. FORMA Validation tests
  • 23.
  • 24. Models’ output PARASID detections First detection in 2004 FORMA probabilities First detection in 2000
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  • 27. 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).
  • 28. Detailed comparison Top FORMA Bottom PARASID Images from google earth PARASID - FORMA Parasid is a bit more sensitive.
  • 29. Detailed comparison Clear change in 2006 Softer change in 2008 Maybe vegetation degradation The pixel plotted is shown in red on the map .
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  • 32.