Near Real Time Monitoring of Habitat Change Using

Neural Network and a MODIS data

Louis Reymondin – Alejandro Coca – Andy Jarvis – Karolina Argote - Jerry Touval – Andres Perez-Uribe –

Mark Mulligan

Terra-i is a system of habitat changes monitoring that uses different

mathematical models that combine vegetation data (MODIS NDVI) and

precipitation data (TRMM) to detect deviations from the natural cycle of

the vegetation over time and thus antrophogenic impacts on natural

ecosystems.

What is Terra-

It has maps of habitat loss every 16 days at the continental level with 250 meters of spatial resolution.

To use high-frequency imaging and moderatespatial resolution for ...

Monitoring the conversion of natural habitats in near real time. (Results 2

months after the date of capture)

Have a continental coverage of all types of habitat.

Be a support for government agencies in making decisions.

Quantifying habitat conversion rates and make analysis of trends from

2004 to date.

Monitor the impact on protected areas in Latin America.

Terra- goals

Terra-i is a model to predict the evolution of vegetation greenness intensity, based on measures of vegetation behavior in time and current weather measurements to detect significant habitat

changes..

Terra- approach

The intensity of vegetation greenness is a natural cycle that depends on climatic factors (precipitation, temperature), site variables (type of vegetation, soil characteristics) and disturbances (natural or anthropogenic).

Inputs data1. Vegetation Index (MOD13Q1 MODIS Product , 16 days, 250m)

Normalized difference vegetation index (NDVI) represents the amount and vigor of vegetation. In each area the values are closely related to vegetation

type and climatic conditions as well as the predominant land use pattern.

Tiles MODIS level analysis

This gives us greater automation of the process, synchronizing the stages download, pre-processing of MODIS data, Terra-i processing load and soon final

results in the map server and FTP.

Processed Terra-i data Incoming Test Tiles / Terra-i

Input data

2. Precipitation Data of the Tropical Rainfall Measuring Mission(3hours, 28km)

TRMM is led by NASA and the Japan Aerospace Exploration Agency (JAXA). It monitors and studies tropical and subtropical rainfall,

between 35 º N and 35 º S. It was released on November 27th, 1997 from Japan.

The methodology can be split into two main steps:

The training step (using data from 2000 to 2004)• Models are trained in order to find the relationship

between recent precipitation and the changes in the color of the vegetation (for different vegetation types)

The detection step (using data from 2004 to present)• The trained models output are compared with the

satellite measurements in order to detect anomalies in the vegetation state.

Research methodology overview

1.

2.

Model trainingNDVI and QA MODIS data MOD13Q1, Precipitation

(TRMM 3b42)(2000-2004)

Time series gap-filling and smoothing

ClusteringK-Means

Random pixels sampling for each cluster

Neural network training

To reduce the noise present in the data (clouds, atmospheric variations, shadows…)

To reduce processing duration, the NDVI time series with the same trends during the years are grouped together

Cleaned NDVI dataOriginal NDVI data

Time series gap-filling and smoothing

NDVI and QA MODIS data MOD13Q1, Precipitation (TRMM)

(2004-2011)

Difference between the NDVI sensor measurement and the NDVI predicted

by the neural network

Calibration using habitat changes maps generated with Landsat satellite images (30m)

Clasification of change

Vegetation changes maps

Rules

maps of change

probabilities

NDVI increase

NDVI decrease (anthropogenic)Results

NDVI Prediction from 2004 to 2011

Floods

Anomaly detection

Drought

OUTPUT: 16 day predicted NDVI

PredictionMultilayer perceptronBayesian Neural Network (BNN)

Model trainning and noise approximationScaled Conjugate Gradient (SCG)Gaussian noise

Input automatic selectionAutomatic relevance determination (ARD)

The goal of the model is to predict what is the NDVI value at the date t taking as input the NDVI values at t-1, t-2 … t-n and the previous rainfall.

INPUTS: Past NDVI (MODIS 13Q1)Previous rainfall (TRMM 3b42)

change

Methodology – Change detection

Calibration with Landsat Images

As Terra-i generates maps of conversion probabilities, we use Landsat images in order to calibrate the results and select the most appropriate probability threshold for each cluster to generate binary changed/unchanged maps.

2004

2009

Terra-i results comparation with local models

Terra-i results were compared with deforestation data produced by the National Institute for Space Research Instituto Nacional de Pesquisas Espaciais (INPE) from 2004 to 2009 through monitoring systems as PRODES and DETER.

PRODES The Project of estimation of deforestation in the Brazilian Amazon (PRODES) generated estimations from 2003 using a digital classification system with Landsat images (30m).

DETER DETER is a near real time deforestation detection system. It publishes fortnightly deforestation alerts for the Brazilian Amazon using MODIS images (500m).

The comparison shows a high correlation between Terra-i and PRODES systems.

Comparison with PRODESComparison with PRODES

% of PRODES detection within a MODIS pixels

% o

f mat

chin

g de

tecti

ons

The Software

Results

2004 – 2012

Habitat Loss in Colombia 2004-2011

*

Annual Rate : 118,026 Ha/yearTotal Loss: 944,206 Ha

Habitat Loss in The Biological corridor in Meta

Annual Rate: 1,789,138 Ha/yearTotal Loss: 13,418,538 Ha

Habitat Loss in Brasil 2004-June 2011

*

Road impact assessment

The Trans-Chaco Highway (2002-2006), Paraguay

Conclusions

• Very high levels of deforestation pre- and post- road construction

• But > 300% increase in deforestation rates since road finished, with a footprint that

likely goes beyond 50km buffer

Road: Trans-Chaco HighwayProject period: 2002-2006Average pre-road deforestation rate: 23,000Average post-road deforestation rate: 97,000 (+319%)Year of peak deforestation: 2010Footprint (modal deforestation distance): 30-40km

Road impact assessment

The Trans-Chaco Highway, Paraguay

Improve more and more our system by developing methodologies for analyzing the information generated.

Current Projects

Terra -

Deforestation patterns

Potential deforestation at T=0 Potential deforestation at T=150

Predicted deforestation Actual deforestation (Terra-i)

Base map

PROOF OF CONCEPT

Future Deforestation Scenarios

BR-364 Road, Brasil

• Terra-i can also be used within the WaterWorld and Co$ting Nature Policy Support Systems to

understand the impact of recent land cover change on hydrology and the production and

delivery of ecosystem services.

• Data: http://geodata.policysupport.org/

Integration Terra-i with others Policy Support Systems

Water flows Erosion

www.terra-i.org

&“The best way improve a system is to get people to use it”

Dr. Mulligan (Kings College of London)

A mapping and monitoring system for rapid assessment of land cover conversion at a medium scale (250m).

A tool for monitoring conversion of habitat at continental, national and regional level in close to real time.

A tool for understanding the effectiveness of protected areas and other conservation measures in stabilizing or reducing land cover conversion.

A spatial support system for decision making in public policy and private development initiatives.Through its linkage with WaterWorld and Co$ting Nature, a system for understanding the likely impacts of near real-time land cover change on a wide range of ecosystem services.

Conclusions

Terra-i is:

Terra-i isn’t:X Detailed monitoring tool in local level. For this it requires second-level monitoring (with high resolution images) and third level (field data).

X A system to monitor degradation.

Contact us:l.reymondin@cgiar.orga.coca@cgiar.orgwww.terra-i.org