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Andy Jarvis Parasid Near Real Time Monitoring Of Habitat Change Using A Neural Netwrok And Modis...
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Transcript of Andy Jarvis Parasid Near Real Time Monitoring Of Habitat Change Using A Neural Netwrok And Modis...
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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
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Contents
• The approach• The implementation• Some examples• Comparison with other
models• Plans and timelines
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Objectives of PARASID
HUman Impact Monitoring And Natural Ecosystems
• Provide near-real time monitoring of habitat change (<3 month turn-around)
• Continental – global coverage (forests AND non-forests)
• Regularity in updates
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The Approach
The change in greenness of a given pixel is a function of:
• Climate• Site (vegetation, soil, geology)• Human impact
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Machine learning
We therefore try to learn how each pixel (site) responds to climate, and any anomoly corresponds 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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Predictions
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NDVI Evolution and novelty detection
Novelty/Anomoly
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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
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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 α α0
αc
INPUTS: Past NDVI (MODIS 3b42) Previous rainfall (TRMM) Temperature (WorldClim)
OUTPUT: 16 day predicted NDVI
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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.
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The Processing
• For South America alone, first calculations approximated 10 years of processing for the NN to learn:– 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 3 months for NN learning
• Detection takes 2-3 days
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Sample novelty analysis
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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
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An Example - Caquetá
• Training – From 2000 to
end of 2003• Detections
– From 2004 to May of 2009
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1.01.0
0.00.0
Novelty probabilitiesNovelty probabilities
Detection results for Caquetá – Meta Analysis 25 May 2009
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Detection : See Caqueta-meta KML
• See http://www.youtube.com/watch?v=exGmzc70PrQ
• Pink : Too many clouds to analyse• Red : 3 consecutive times detected with
more than 95% confidence
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Deforestation Rates on the RiseCumulative deforestation 2004 - present in Caqueta region of
Colombia
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Some statistics
• 75% of deforestation occurs in December and January
• 50,000 Ha deforested in Dec/Jan of 2008/2009 compared with 7,500 Ha in 2004/2005
• During 16 days of Christmas in 2008 16,000 Ha lost, compared with 500 Ha in 2004 (3%)
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Other Examples
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Chile
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Bolivia
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Paraguay
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Argentina
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OTCA
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Model comparisonPARASID vs. FORMA
PARASID detectionsFirst detection in 2004
FORMA probabilitiesFirst detection in 2000
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PARASID vs DETER
It seems Parasid model detects quite small and isolate events which Deter doesn’t detect.
2006
2004
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Next Steps
– Fully functioning web interface January 2010– Continental validation and calibration
(January 2010)– Global extent (2011)– Additional models to identify type of change
(drivers) (2011)
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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
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SAMPLING POINTS IN LATIN-AMERICASAMPLING POINTS IN LATIN-AMERICA
1. Covering the whole Latin-America
2. Sampling of different land use type
● Tropical forest● Andes● Savanna● Desert
3. Selection of areas with high risk of change
● Near to cities● Near to road● Near to rivers● With crops already existing
SELECTION CRITERIASELECTION CRITERIA
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Conclusions
• Near-real time global monitoring is possible
• PARASID now functioning for Latin America
• Providing first approximations of deforestation rates in over a decade for some parts of Latin America
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GRACIAS!