andy jarvis parasid near real time monitoring of habitat change using a neural netwrok and modis...
DESCRIPTION
Presentation on the PARASID tool, a habitat monitoring system for Latin America, developed jointly by TNC and CIAT. Presentation made in the Workshop on Integrated Space Technologies Applications for Sustainable Development in the Mountain Regions of Andean Countries on 15th September 2009.TRANSCRIPT
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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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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)
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w0
w1
w2
NDVI(t-1)
NDVI(t-2)
NDVI(t-n)
wp1
wp2
wp3
wo1
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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!