Near-real time monitoring of deforestation using a neural network and MODIS data: the PARASID approach Andy Jarvis, Louis Reymondin, Jerry Touval CIAT and TNC

Contents The approach The implementation Some examples Comparison with DETER Comparison with FORMA Plans and timelines

The approach

The implementation

Some examples

Comparison with DETER

Comparison with FORMA

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

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

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

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

NDVI Evolution and novelty detection Novelty/Anomoly

What is “machine learning” ? 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

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

Methodology As required by the ARD algorithm, each input and the hidden output is a weights class with its own α α 0 α c INPUTS : Past NDVI (MODIS 3b42) Previous rainfall (TRMM) Temperature (WorldClim) OUTPUT : 16 day predicted NDVI NDVI t Precipitation (t) Temperature (t) … … w 0 w 1 w 2 NDVI (t-1) NDVI (t-2) NDVI (t-n) w p1 w p2 w p3 w o1 w o2 w o3

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.

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

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

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

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 Two animations for Mato Grosso

Two animations for Mato Grosso

PARASID vs. DETER Validation tests

Validation area Location Brazil Bottom left corner 09°40’00’’ S 60°00’00’’ O Size 160 * 120 [km 2 ] 307200 modis pixels Red square : Validation area

Location

Brazil

Bottom left corner

09°40’00’’ S

60°00’00’’ O

Size

160 * 120 [km 2 ]

307200 modis pixels

Data source Deter dataset has been downloaded on the website of the Deter project : http://www.obt.inpe.br/deter/indexdeter.php?id=6424 The satellite images of 2004 and 2006 used to compare the two models are extracted from images Mosaico LandSat 2004 (AMZ) Mosaico LandSat 2006 (AMZ) Also provide by the Deter project website.

Deter dataset has been downloaded on the website of the Deter project :

http://www.obt.inpe.br/deter/indexdeter.php?id=6424

The satellite images of 2004 and 2006 used to compare the two models are extracted from images

Mosaico LandSat 2004 (AMZ)

Mosaico LandSat 2006 (AMZ)

Also provide by the Deter project website.

PARASID true positives 2004 2006 Parasid model is, sometimes more sensitive, and detects events that Deter doesn’t detect.

PARASID True Positives It seems Parasid model detects quite small and isolate events which Deter doesn’t detect. 2006 2004

PARASID False positives On the other hand, Parasid is more sensitive to false positives. Here, around a river. 2004 2006

PARASID False Positives In this example, Parasid doesn’t detect as well as Deter the big new field (red circle) but, is more precise to detect the small fields on the top right corner (blue circle) . 2004 2006

Synthesis The general locations of detections are the same for both models, so we have in essence created a DETER that works more automatically and is applicable at the continental level Both PARASID and Deter models have problems with false positive events, but we can adjust for these using this and other validation data and a genetic algorithm. PARASID seems more sensitive for small and isolated events, but this sensitivity may also generate false positives. Again, this is fixable through calibration data.

The general locations of detections are the same for both models, so we have in essence created a DETER that works more automatically and is applicable at the continental level

Both PARASID and Deter models have problems with false positive events, but we can adjust for these using this and other validation data and a genetic algorithm.

PARASID seems more sensitive for small and isolated events, but this sensitivity may also generate false positives. Again, this is fixable through calibration data.

PARASID vs. FORMA Validation tests

Test area Brazil Latitude 8°36'7.50&quot;S Longitude 51°28'30.00“W NDVI 2000.02.18 NDVI 2004.01.01 NDVI 2009.01.01

Brazil

Latitude 8°36'7.50&quot;S

Longitude 51°28'30.00“W

Models’ output PARASID detections First detection in 2004 FORMA probabilities First detection in 2000

This map shows the models’ differencies The two models match Comparison PARASID – FORMA Red pixels show where FORMA’s probabilities are higher than the PARASID ones White pixels show where PARASID’s probabilities are higher than the FORMA’s ones

This map shows the models’ differencies

The two models match

Detailed comparison The marked areas show that most of the differences are due to the changes which happened between 2000 and 2004. Parasid can’t detect these changes as it started detecting in 2004. NDVI 2000.02.18 NDVI 2004.01.01 PARASID - FORMA

The marked areas show that most of the differences are due to the changes which happened between 2000 and 2004.

Parasid can’t detect these changes as it started detecting in 2004.

Detailed comparison Top FORMA Bottom PARASID Images from google earth PARASID - FORMA Maybe due to the rescaled pixel size from 250 [m] to 500 [m], FORMA model doesn’t fit perfectly some fields (the red bound around the fields on the comparison map).

Detailed comparison Top FORMA Bottom PARASID Images from google earth PARASID - FORMA Parasid is a bit more sensitive.

Detailed comparison Clear change in 2006 Softer change in 2008 Maybe vegetation degradation The pixel plotted is shown in red on the map .

Detailed comparison The two graphs in the previous slide show that the changes detected by PARASID and not by FORMA actually occurred (graph on the right). However these changes are more subtle than classic deforestation events (graph on the left). This could be due to vegetation degradation (e.g. selective logging) as the area where these changes occurred is surrounded by a high rural activity Also some benefits from 250m resolution PARASID and FORMA complementary in provision of monitoring

The two graphs in the previous slide show that the changes detected by PARASID and not by FORMA actually occurred (graph on the right).

However these changes are more subtle than classic deforestation events (graph on the left).

This could be due to vegetation degradation (e.g. selective logging) as the area where these changes occurred is surrounded by a high rural activity

Also some benefits from 250m resolution

PARASID and FORMA complementary in provision of monitoring

Conclusions and next steps Near-real time global monitoring is possible PARASID now functioning for Latin America Next milestones: Fully functioning web interface January 2010 Continental validation and calibration (January 2010) Global extent (2011) Additional models to identify type of change (drivers) (2011)

Near-real time global monitoring is possible

PARASID now functioning for Latin America

Next milestones:

Fully functioning web interface January 2010

Continental validation and calibration (January 2010)

Global extent (2011)

Additional models to identify type of change (drivers) (2011)

More info… [email_address]

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