Remote sensing for producing malaria risk mapsp g pRainer Sauerborn, Jean Pierre Lacaux, Cécile Vignolle (Toulouse)

TH

What is remote sensing?What can it do for infectious disease epidemiology?

DEP

T What can it do for infectious disease epidemiology?What can it do for DSS sites?Malaria risk maps based on surface water dynamics?

IND p y

Project with HDSS of Nouna and Ouaga

INDEPTH AGM Dar es Salaam, 22-26 September 2008

History of Remote SensingHistory of Remote Sensing

Balloon view of Boston by J MBalloon view of Boston by J. M. Black.

October 13, 1860. Altitude of 365 m.

Remote Sensing- Introduction

SENSOR

SOURCE

ATMOSPHERERECEIVER

TARGETSRECEIVER

Numerical and visual analysis of the imagesanalysis of the images

Final productsf th fi lfor the final user

Active and passive remote sensing

Sensors functioning in a passive modepassive modeSensors functioning in a passive modepassive modemeasure natural energy reflected or emitted from the

target

Reflected energyReflected energy => source of energy = Sunlight onlyReflected energyReflected energy => source of energy = Sunlight only detection possible during the day

concern visible and near infrared radiationEmitted energyEmitted energy => source of energy = Earth surface

detection possible day and nightconcern thermal infrared

Sensors functioning in an active modeactive modehave their own source of energy

The radiation produced by the sensor is directed towards the target of interest

The illuminated target reflects this artificial energy which is detected and measured by the sensordetected and measured by the sensor

detection possible anytimeconcern microwaves

Wavelengths (λ) Frequency (Hz)

The Electromagnetic Spectrum

300 000 km30 000 km3 000 km

1 Hz10 Hz

100 Hz

Wavelengths (λ) Frequency (Hz)

The electromagnetic spectrum shows the whole range of

wavelengths from the shorter 300 km30 km3 km

300 m30 m

1 KHz10 KHz

100 KHz1 MHz10 MHz

RadioLFMFHF

Audio (X-rays ) to the longer (radio waves)

30 m3 m

30 cm3 cm

0.3 cm

10 MHz100 MHz

1 GHz10 GHz100 GHz

Microwaves Red

Orange

0.7 µm

HFVHFUHF

300 µm30 µm3 µm

0.3 µm300Å

1012 Hz

1014 Hz

1016 Hz Ultraviolet

Infrared

Visible

Orange

Yellow

Green

FarThermalMiddleNear

300 30 3

0.3 0.03

Å

ÅÅ

ÅÅ

10 Hz

1018 Hz

1020 HzGamma

Ray

X Ray Blue

Violet0 4

BONN F., ROCHON G., Précis de télédétection : principes et méthodes

0.4 µm

Interactions with the targetReflection Absorption and Transmission

3 types of interactions are possible between the incident radiation (I) and the

Reflection, Absorption and Transmission

between the incident radiation (I) and the earth surface (target) :AbsorptionAbsorption (A)T i iT i i (T)TransmissionTransmission (T)ReflectionReflection (R)

Resolution in space and timeResolution in space and time

Satellite Spatial TemporalSatellite Spatial resolution

Temporal resolution

Meteosat 5 km 30 minMeteosat 5 km 30 min

Landsat 7 80m 16 d

Spot 5 5-10m 1 d

Ikonos 1m 1d

World Population mapping

6 000 m

How does remote sensing work?THE AIM

TO CREATE THEMATIC IMAGESTO CREATE THEMATIC IMAGES

Original spectral imagesClassified image Thematic mapsClassified image

Component n

Classification

p

irow

Component 1

Component n

Statistical resultscolumn j

Continuity in informationDiscontinuity in information

Physical measuresNature of pixel

Continuous valuesDiscrete values x

Nature of pixel

Component k0 255 Classes0 p

C. Vignolle, 2008

ClassificationSupervisedSupervised UnsupervisedUnsupervised

Identification of homogeneous representative l f th diff t f t

SupervisedSupervised

Spectral classes are grouped first, based only on the numerical information in the

UnsupervisedUnsupervised

samples of the different surface cover types (classes) of interest. These samples are called training areastraining areas

only on the numerical information in the data, and are then matched by the analyst to information classes (if possible). Clustering algorithms, are used to d t i th t l ( t ti ti l)determine the natural (statistical) groups (cluster).

Everybody with internet access* can use RS data

P i i l h t i tiPrincipals characteristic of remote sensing data

Exhaustive Available

Objective

Independent

Geographical referenced

IndependentD.W. Heath, 1989

* ..and some money

Application fields

R i i lt d i f t

Application fields

Resources in agriculture and in forestCrop monitoringWeather and climate monitoringWeather and climate monitoringWater managementNatural hazards managementNatural hazards managementInfectious diseases risksUrban and suburban surroundings follow-upArchaeological researchNational defenseNational defense...

Satellite-derived predictions of EIR for Africa

Satellite-based environmentalSatellite-based environmental data were able to discriminate between 5 equal-sized classes of EIR giving asized classes of EIR giving a high index of agreement (kappa statistic=0.77) between predicted values and training data. The satellite data included temperature variables i.e. reflectance in the middle infra-red (MIR) channel and land surface temperature (LST) and the cold cloud duration (CCD) ancold cloud duration (CCD), an index of precipitation

Rogers et al., (2002).

14Predicting the risk of dying from malaria in Nouna Predicting the risk of dying from malaria in Nouna through remote sensingthrough remote sensing

no prediction0 3 0

Lell, Rogers, Yé 2002

0 - 3.03.2 - 4.84.9 - 35.5malaria death

Research ideaResearch idea

Instead of using vegetation index (NDVI), T, rainfall g g ( )equivalents for mapping malariaCan we use surface water distribution and mosquito flight range for mapping malaria risk?range for mapping malaria risk?Does the predicted risk correspond to malaria transmission and to incidence of cases on the ground?Can these maps be used by health services to focus interventions to high risk areas?Is this cost-effective compared to interventions aimed atIs this cost-effective compared to interventions aimed at total population?Can this years malaria risk map predict next year`s based on

i it ti d t l ?precipitation data alone?Can the maps be scaled up to national level?

Population distribution

Gridded Population of the world 6km resolution

INDEPTH site population: 10 m resolution

INDEPTH HDSS populations: 3 0 million3.0 million

??

A remoteA remote--sensing tool applied to Rift Valley Fever (RVF) Monitoringsensing tool applied to Rift Valley Fever (RVF) MonitoringLacaux JP, Tourre Y

Analyses and processing of high-spatial resolution satellite images (SPOT 5, 10m)

Computation of ponds’ area their vegetation cover and turbidityComputation of ponds area, their vegetation cover and turbidity

Evaluation of Zones Potentially Occupied by Mosquitoes (ZPOM)Multi-spectral SPOT 5 Image (high-spatial resolution -10m)Multi spectral SPOT 5 Image (high spatial resolution 10m)

Map of Senegal –African Atlas (Jaguar Edit. )

43 k

m

Studied area : Ferlo region in Senegalg g

46 km

© CNES 2003, Distribution Spot Image SA

RemoteRemote--sensing applied to Rift Valley Fever (RVF) Monitoringsensing applied to Rift Valley Fever (RVF) Monitoring

Barkedji pond

Zoom – Pixel size : 10m

400 m©CNES 03 - distribution Spot Image

Baobab tree in the Barkedji pond Sept. 2003

Spot 5, multi-spectral high-spatial resolution (10-m) – August 26th, 2003

RemoteRemote--sensing applied to Rift Valley Fever (RVF) Monitoring (2)sensing applied to Rift Valley Fever (RVF) Monitoring (2)

Remote Sensing Pond characterizationRemote Sensing Pond characterization

Abundance of mosquitoes is linked to vegetation cover and turbidity of ponds Abundance of mosquitoes is linked to vegetation cover and turbidity of ponds

NDVI (vegetation)NDTI (turbidity)

vegetation activity gradient

Pond detection CharacterizationNiaka

Satellite Images Turbidity Gradient

Barkedji

Ponds’ characterizationPonds’ area Ponds characterization

% vegetation cover

% Turbidity

Ponds area

SPOT 5 Image 10 meters26/08/2003 t

RemoteRemote--sensing applied to sensing applied to Rift Valley Fever (RVF) Monitoring (3)Rift Valley Fever (RVF) Monitoring (3)

1

26/08/2003 wet season

4 km

Pond south-west Barkedji15 ha (peak of rainy season)

55% covers by vegetation45% free water

1- pond vegetation1.4

© CNES 2003, Distribution Spot Image SA

2

1.4 km

False color composite

The new Normalized Difference Pond Index or:NDPI ( MIR G )/ (MIR G )

2-sahelian savanna

NDPI = ( MIR-Green)/ (MIR+Green)

ZPOMZPOMs….

e.g. Tourre et al. (2006) for Rift Valley Fever, Senegal

Pilot studyflight range of mosquitoes:capture, marking, release

Main study: Identify iso-moustique for A. gambiae

overlay population on surface water mapPredict malaria based on mathematicalPredict malaria based on mathematicalModel and compare to predictions base

on remote-sensed risk maps

Linking health surveillance and environmental monitoringg

Local: LUCC, meteo:Health Surveillance: 47 villages, 73 000 people

January 2003Page 23

SPOT 5, 10m C, Nouna (Burkina Faso), 23/09/2007

1

7

89

18

1

3

5

8

Nouna10

1415

17

2

4

610

11

12

13

15

16

19

20

21

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SPOT 5, 10m C, Nouna, 23/09/2207

4km

© CNES 2007, distribution Spot Image © CNES 2007, distribution Spot Image

False color composite NDPI NDVI4km

© CNES 2007, distribution Spot Image

Bare soilsWater bodies

Vegetation Gradient

-

Zone 1

Hierarchical Classification

Vegetation Gradient

+Clouds© Medias product, CNES 2007, distribution Spot Image

Project proposal (EU)Project proposal (EU)

CNFRP, Ouaga: entomological classification of ponds, flight g g p grangesCRSN, Nouna: population maps, cohorts with incident malaria cases household survey (cases) DSS (deaths)malaria cases, household survey (cases), DSS (deaths)Toulouse (Remote sensing technology) Heidelberg: cost-effectivenessg

All: modelling of risk maps

Thank youMaybe it‘s a small contribution to keep this child healthyMaybe it s a small contribution to keep this child healthy

Thank you, asanti sani, abarka

Vegetation indicesTarget behaviour

Most of the information on vegetation are contained in the red and NIR wavebands, so it was logical to combine them to study vegetation activity

NDVI is one of the most often used

This index is a good indicator of the state of

vegetation

Band 2 (Red)SPOT

imagesvegetation

Band 3 (NIR)NDVI

NDVI =XS3 - XS2

XS3 + XS2

Image analysis

Creation of new information thanks to remote sensing data

Remote sensinggdata

(different bands anddifferent dates)

Classified image

Waterimage

=Land cover

mapSummer crops

Forest

map

Environmental information systemy

Classical GIS of DSClassical GIS of DSInclusion of non-inhabited space

Di it l El ti M d l (DEM) f K i P i (B ki F )Ground mappingRemote sensing

Land surface model 400

420

440

460

Digital Elevation Model (DEM) of Kossi-Province (Burkina Faso)

Elevation[m + msl]

Land surface modelMeteorological information

10 d t ti260

280

300

320

340

360

380

Dedougou

K O S S I

10 ground stationsRemote sensing: land use, surface water

Nouna

January 2003Page 30

0 20000 40000 60000Meters

Air quality measurement stations (5)

-Fine particles (PM10)-Carbon monoxide