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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ND
VI
Measurments
Predictions
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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
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Paraguay - Boquerón
• Size
– 240*240 [km2]
– 5760000 [ha]
• Vegetation type
– Savannah
– Chaco forest
Cumulative detection on time
Paraguay - Boquerón
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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
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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
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1/14/2004 5/28/2005 10/10/2006 2/22/2008 7/6/2009
Tiempo
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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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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…