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• Comparison with other

models• Plans and timelines

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

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 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

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)

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NDVI(t-2)

NDVI(t-n)

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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

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 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

Sample novelty analysis

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

An Example - Caquetá

• Training – From 2000 to

end of 2003• Detections

– From 2004 to May of 2009

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Detection results for Caquetá – Meta Analysis 25 May 2009

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

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%)

Other Examples

Chile

Bolivia

Paraguay

Argentina

OTCA

Model comparisonPARASID vs. FORMA

PARASID detectionsFirst detection in 2004

FORMA probabilitiesFirst detection in 2000

PARASID vs DETER

It seems Parasid model detects quite small and isolate events which Deter doesn’t detect.

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2004

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)

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

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

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

GRACIAS!