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Presentation made by Andy Jarvis from the Decision and Policy Analysis Program of the International Centre for Tropical Agriculture (CIAT). Delivered to the Science leadership Team in The Nature Conservancy (TNC) in December 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
• The approach
• The implementation
• Some examples
• What PARASID is, and what itis not
• Plans and timelines
Objectives of PARASID
HUman Impact Monitoring And Natural Ecosystems
• Provide near-real time monitoring of habitatchange (<3 month turn-around)
• Continental – global coverage (forests AND non-forests)
• Regularity in updates
The Approach
The change in greenness of a given pixel is a function of:
• Climate
• Site (vegetation, soil, geology)
• Human impact
Machine learning
We therefore try to learn how each pixel (site) responds to climate, and any anomolycorresponds to human impact
Machine learning (or neural-network), is a bio-inspired technology which emulates the basic mechanism of a brain.
It allows – To find a pattern in noisy dataset– To apply these patterns to new dataset
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1 2 3 4 5 6 7 8 9Time
ND
VI
Measurments
Predictions
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Interval min
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VI
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NDVI Evolution and novelty detection
Novelty/Anomoly
NDVI Cleaning using HANTS
Eliminate all short-term variations
Uses NDVI quality information
Iterative fitting of cleaned curve using
Fourier analysis
Least-square fitting to good quality values
Methodology
NDVIt
Precipitation (t)
Temperature(t)
…
…
w0
w1
w2
NDVI(t-1)
NDVI(t-2)
NDVI(t-n)
wp1
wp2
wp3
wo1
wo2
wo3
As required by the ARD algorithm, each
input and the hidden output is a weights
class with its own αα0
αc
INPUTS: Past NDVI (MODIS 13Q1)Previous rainfall (TRMM)Temperature (WorldClim)
OUTPUT: 16 day predicted NDVI
Methodology – Bayesian NN
• To detect novelties, Bayesian Neural Networks provide us two indicators
– The predicted value
– The probability repartition of where the value should be
• The first one allows us to detect abnormal measurements
• The second one allows us to say how sure we are a measurement is abnormal.
The Processing
• For South America alone, first calculationsapproximated 10 years of processing for the NN tolearn:– A map of 30720 by 37440 pixels
• 1,150,156,800 vectors• 23 vectors per year• 26,453,606,400 NDVI values to manage per year• 9.5 years of data• 251,309,260,800 individual data points
• Through various processes, optimizations and hardware acquisitions reduced time to 2 weeks for NN learning
• Detection takes 1 day
The Bottom-Line
• 250m resolution
• Latin American coverage (currently)
• 3 week turnaround from data being made available (4 week delay in MODIS going to NASA ftp) (3+4 = 7 weeks)
• Report every 16 days
• Measurement of scale of habitat change (0-1) and probability of event
Parasid Test cases
Introduction
• Different test cases with different vegetation and climate types
• All the test are done with the same parameters
– Training parameters• From 2000 to the end of 2003
– Detections parameters• From 2004 to May 2009
• A detection map is created each 16 days within this period
• The process is close to be fully automated
Colombia – Río Caquetá
• Size
– 480 * 300 [km2]
– 14400000 [ha]
• Vegetation type
– Tropical forest
Caqueta, Jan 2004 – May 2009Date
Colombia – Rio Caquetá
Cumulative detections in hectares
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Paraguay - Boquerón
• Size
– 240*240 [km2]
– 5760000 [ha]
• Vegetation type
– Savannah
– Chaco forest
Cumulative detection on time
Paraguay - Boquerón
Cumulative detections in hectares
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And now the tough one…
OTCAAmazon Cooperation Treaty
• Size
– 4228.75*3498 [km2]
– 1479216750 [ha]
• Vegetation type
– Tropical forest
OTCAAmazon Cooperation Treaty
Cumulative detections in hectares
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PARASID - Colombia
• Direct usage for developing negoatiation position of Colombia in Copenhagen
• September 2009 Colombia were going to COP15 with a figure of 100,000Ha/year deforestation
• PARASID analysis predicting MINIMUM 180,000Ha/year, most likely 250-300,000Ha/year
• Resulted in change in negotiation plan, and increased relevance of expansion of ChiribiquetiNP
• Discussions underway for PARASID to become a 1st tier monitoring tool for National Parks
Deteccion Acumulada en Hectareas
0.92
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0
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1/14/2004 5/28/2005 10/10/2006 2/22/2008 7/6/2009
Tiempo
Hecta
reas 0.96
Promedio
Prom + Desv
Prom - Desv
Detecciones
• 76% coverage of country
• Approx. 250,000Ha/year average
• 90% increase in deforestation rate 2004 - 2009
Tinigua National Park
1,300 Ha deforested between 2004 y 20090.5% of total area deforested in 5 years
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Hecta
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Cumulative detections in hectares
Series1
What PARASID is….
• 1st tier monitoring tool for looking at broad-scale patterns of habitat conversion
• National and regional platform for consistent measurement of habitat conversion
• Suitable early-warning system
• Important policy-influencing tool
What PARASID is not…..
• Detailed monitoring tool for examining local-scale impacts and changes – 2nd and 3rd tier analyses are needed
• A system for monitoring steady degradation
Outlook and next steps
• Three major pushes right now:1. Methodological development
• Long wish list….
2. Getting it out there• Adoption by countries
• Adoption by institutions
• Website and online data
3. Writing it up• Methodological paper imminent submission
• Latin American patterns in habitat change
• Effectiveness of Pas across the continent
• + many more…