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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]
Some statistics ,[object Object],[object Object],[object Object]
 
Parasid  Test cases
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Colombia – Río Caquetá ,[object Object],[object Object],[object Object],[object Object],[object Object]
Detection : See Caqueta-meta KML ,[object Object],[object Object],[object Object]
NDVI 2004.01.01 NDVI 2009.01.01 Anomalies probability 2009.01.01
Colombia – Rio Caquetá
Colombia – Rio Caquetá
Colombia – Rio Caquetá ,[object Object],[object Object],[object Object]
Colombia – Serranía San Lucas ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
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 ,[object Object],[object Object],[object Object],[object Object],[object Object]
Colombia – Sierra Nevada ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
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 ,[object Object],[object Object],[object Object],[object Object]
Bolivia – Santa Cruz ,[object Object],[object Object],[object Object],[object Object],[object Object]
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 ,[object Object],[object Object],[object Object],[object Object],[object Object]
NDVI 2004.01.01 NDVI 2009.01.01 Anomalies probability 2009.01.01
Cumulative detection on time
Paraguay - Boquerón
Paraguay - Boquerón ,[object Object],[object Object],[object Object],[object Object]
Chile – Region del Bio Bio ,[object Object],[object Object],[object Object],[object Object],[object Object]
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 ,[object Object],[object Object],[object Object]
And now the tough one…
OTCA Amazon Cooperation Treaty ,[object Object],[object Object],[object Object],[object Object],[object Object]
 
OTCA Amazon Cooperation Treaty
OTCA Amazon Cooperation Treaty ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Time processing statistics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
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 ,[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]
Puntos de mejoramiento ,[object Object],[object Object],[object Object],[object Object]
GRACIAS!

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