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-1)

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

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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.

Any questions????

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 1 month for NN learning

• Detection takes 1 week

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

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 near to be fully automated

Colombia – Río Caquetá

• Size – 480 * 300 [km2]– 14400000 [ha]

• Vegetation type– Tropical forest

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

Caqueta, Jan 2004 – May 2009Date

NDVI 2004.01.01 NDVI 2009.01.01

Anomalies probability 2009.01.01

Colombia – Rio Caquetá

Colombia – Rio Caquetá

Cumulative detections in hectares

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Colombia – Rio Caquetá

• Comments– 0.22% deforestation rate per year– The model is working well in this area where

deforestation seems accelerating

Bolivia – Santa Cruz

• Size– 480*420 [km2]– 20160000 [ha]

• Vegetation type– Tropical forest– Chaco– Savannah

NDVI 2004.01.01

NDVI 2009.01.01

Anomalies probability2009.01.01

Bolivia – Santa Cruz

Cumulative detection on time

Bolivia – Santa Cruz

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0.09% deforestation rate

Paraguay - Boquerón

• Size– 240*240 [km2]– 5760000 [ha]

• Vegetation type– Savannah– Chaco forest

NDVI 2004.01.01

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Anomalies probability2009.01.01

Cumulative detection on time

Paraguay - Boquerón

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Paraguay - Boquerón

• Comments– 0.87% deforestation rate– Savannah and tropical forest have a totally

different environment– The model seems to work well even if the

changes are more subtle

Chile – Region del Bio Bio

• Size– 240*120 [km2]– 2880000 [ha]

• Vegetation type– Temperate forest

Chile – Region del Bio Bio

NDVI 2004.01.01 NDVI 2009.01.01

Anomalies probability2009.01.01 Cumulative detection on time

Chile – Region del Bio Bio

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Chile – Region del Bio Bio

• Comments– 0.31% deforestation rate– The model seems to work with a tempered

climate and non-tropical forests

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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OTCAAmazon Cooperation Treaty

• Comments– Average 0.22% deofrestation rate– Still a bit noisy in the center

• Due to clouds undetected during the cleaning process

– Most of the detections are valid– The system seems stable over big areas and

a certain amount of consecutive dates (detections over 120 dates)

Time processing statistics• For an area of the size of OTCA with

– One Dell server • 16 [GB] of RAM • 8 processors Intel Xeon X5365 3 [GHz]

• Cleaning process– Cleaning 214 date– 12 hours

• Clustering process– 6 Clusters– Clustered on the years 2000 to the end of 2003– 12 hours

• Modeling process– 3 Models per clusters– 2000 pixels as training dataset– 5000 pixels as validation dataset– 3 hours

• Detections process for 2004 to 2009– 120 detections grids– 70 hours

• Whole process – Only 4 days processing from the raw data

Deforestation Rates

Country Region Deforestation RateParaguay Boqueron, Chaco 0.87%Colombia Serrania San lucas 0.63%Chile Region Bio Bio 0.31%Colombia Rio Caqueta 0.22%Multiple OTCA 0.22%Bolivia Santa Cruz 0.09%Colombia Nevado de Santa Marta 0.01%

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.

2006

2004

Next Steps

– Fully functioning web interface January 2010

– Preliminary continental validation and calibration (January 2010)

– Global extent (2011)– Additional models to identify type of

change (drivers) (2011) US

E C

AS

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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

SAMPLING POINTS IN LATIN-AMERICASAMPLING POINTS IN LATIN-AMERICA

1. Covering the whole Latin-America

2. Sampling of different land use type

a. Tropical forestb. Andesc. Savannad. Desert

3. Selection of areas with high risk of change

a. Near to citiesb. Near to roadc. Near to riversd. With crops already existing

SELECTION CRITERIASELECTION CRITERIA

Methodological Enhancements

• Detailed enhancements in mathematics

• Use of daily MODIS data to reduce problems of frequent cloud cover

• Validation with other reported deforestation statistics and other studies (e.g. Asner)

• Inclusion of components which identify direction of change (reforestation vs. deforestation)

• Linkages with fire datasets and better characterisation of flooding to avoid false positives

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!