Near-real time monitoring of habitat change using a neural network and MODIS data: the PARASID approach Andy Jarvis, Louis Reymondin, Jerry Touval

NDVI Evolution and novelty detection Novelty/Anomoly

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 13Q1) 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

Any questions????

Sample novelty analysis

Parasid Test cases

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

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

NDVI 2004.01.01 NDVI 2009.01.01 Anomalies probability 2009.01.01

Cumulative detection on time

Paraguay - Boquerón

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

And now the tough one…

OTCA Amazon Cooperation Treaty

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

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

GRACIAS!