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

6. NDVI Evolution and novelty detection Novelty/Anomoly

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

13. HUMANE vs. DETER Validation tests

16. HUMANE true positives 2004 2006 Parasid model is, sometimes more sensitive, and detects events that Deter doesn’t detect.

17. HUMANE True Positives It seems Parasid model detects quite small and isolate events which Deter doesn’t detect. 2006 2004

18. HUMANE False positives On the other hand, Parasid is more sensitive to false positives. Here, around a river. 2004 2006

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

21. HUMANE vs. FORMA Validation tests

23. Models’ output HUMANE detections First detection in 2004 FORMA probabilities First detection in 2000

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. Detailed comparison Top FORMA Bottom PARASID Images from google earth PARASID - FORMA Parasid is a bit more sensitive.

28. Detailed comparison Clear change in 2006 Softer change in 2008 Maybe vegetation degradation The pixel plotted is shown in red on the map .