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Andy Jarvis Parasid Near Real Time Monitoring Of Habitat Change Using A Neural Netwrok And Modis Data Conida Sept 2009

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Presentation on the PARASID tool, a habitat monitoring system for Latin America, developed jointly by TNC and CIAT. Presentation made in the Workshop on Integrated Space Technologies Applications for Sustainable Development in the Mountain Regions of Andean Countries on 15th September 2009.

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Near-real time monitoring of habitat change using a neural network and MODIS data: the PARASID approach Andy Jarvis, Louis Reymondin, Jerry Touval
Contents ,[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
NDVI Cleaning using HANTS ,[object Object],[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]
Sample novelty analysis
The Bottom-Line ,[object Object],[object Object],[object Object],[object Object],[object Object]
An Example - Caquetá ,[object Object],[object Object],[object Object],[object Object]
Detection results for Caquetá – Meta Analysis 25 May 2009 1.0 0.0 Novelty probabilities
Detection : See Caqueta-meta KML ,[object Object],[object Object],[object Object]
Deforestation Rates on the Rise
Some statistics ,[object Object],[object Object],[object Object]
 
Other Examples
Chile
Bolivia
Paraguay
Argentina
OTCA
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 ,[object Object],[object Object],[object Object],[object Object]
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 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],SELECTION CRITERIA
Conclusions ,[object Object],[object Object],[object Object]
GRACIAS!

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Andy Jarvis Parasid Near Real Time Monitoring Of Habitat Change Using A Neural Netwrok And Modis Data Conida Sept 2009

  • 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.
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  • 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
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  • 14. Detection results for Caquetá – Meta Analysis 25 May 2009 1.0 0.0 Novelty probabilities
  • 15.
  • 17.
  • 18.  
  • 20. Chile
  • 24. OTCA
  • 25. Model comparison PARASID vs. FORMA PARASID detections First detection in 2004 FORMA probabilities First detection in 2000
  • 26. PARASID vs DETER It seems Parasid model detects quite small and isolate events which Deter doesn’t detect. 2006 2004
  • 27.
  • 28. 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
  • 29.
  • 30.