Andy Jarvis - Parasid Near Real Time Monitoring Of Habitat Change Tnc Slt

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

4500

5000

5500

6000

6500

7000

7500

8000

8500

9000

1 2 3 4 5 6 7 8 9Time

ND

VI

Measurments

Predictions

Interval max

Interval min

4500

5000

5500

6000

6500

7000

7500

8000

8500

9000

1 2 3 4 5 6 7 8 9Time

ND

VI

Measurments

Predictions

Interval max

Interval min

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

Cumulative detections in hectares

0

20000

40000

60000

80000

100000

120000

140000

160000

180000

200000

1/1

/2004

4/1

/2004

7/1

/2004

10/1

/2004

1/1

/2005

4/1

/2005

7/1

/2005

10/1

/2005

1/1

/2006

4/1

/2006

7/1

/2006

10/1

/2006

1/1

/2007

4/1

/2007

7/1

/2007

10/1

/2007

1/1

/2008

4/1

/2008

7/1

/2008

10/1

/2008

1/1

/2009

4/1

/2009

Time

Hecta

res

Paraguay - Boquerón

• Size

– 240*240 [km2]

– 5760000 [ha]

• Vegetation type

– Savannah

– Chaco forest

Cumulative detection on time

Paraguay - Boquerón

Cumulative detections in hectares

0

50000

100000

150000

200000

250000

300000

1/1

/2004

4/1

/2004

7/1

/2004

10/1

/2004

1/1

/2005

4/1

/2005

7/1

/2005

10/1

/2005

1/1

/2006

4/1

/2006

7/1

/2006

10/1

/2006

1/1

/2007

4/1

/2007

7/1

/2007

10/1

/2007

1/1

/2008

4/1

/2008

7/1

/2008

10/1

/2008

1/1

/2009

4/1

/2009

Time

Hecta

res

And now the tough one…

OTCAAmazon Cooperation Treaty

• Size

– 4228.75*3498 [km2]

– 1479216750 [ha]

• Vegetation type

– Tropical forest

OTCAAmazon Cooperation Treaty

Cumulative detections in hectares

0

2000000

4000000

6000000

8000000

10000000

12000000

14000000

16000000

18000000

20000000

1/1

/2004

4/1

/2004

7/1

/2004

10/1

/2004

1/1

/2005

4/1

/2005

7/1

/2005

10/1

/2005

1/1

/2006

4/1

/2006

7/1

/2006

10/1

/2006

1/1

/2007

4/1

/2007

7/1

/2007

10/1

/2007

1/1

/2008

4/1

/2008

7/1

/2008

10/1

/2008

1/1

/2009

4/1

/2009

Time

Hecta

res

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

0.92

0.94

0.96

0.98

0.90

0

500000

1000000

1500000

2000000

2500000

1/14/2004 5/28/2005 10/10/2006 2/22/2008 7/6/2009

Tiempo

Hecta

reas 0.96

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

0

200

400

600

800

1000

1200

1400

1/1

/20

04

7/1

/2004

1/1

/20

05

7/1

/20

05

1/1

/20

06

7/1

/20

06

1/1

/20

07

7/1

/20

07

1/1

/20

08

7/1

/20

08

1/1

/20

09

Hecta

res

Time

Cumulative detections in hectares

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…

a.jarvis@cgiar.org