Keynote at ISCRAM-China2008 conference: Vito and Disaster Management

1Harbin, July 6th 2008 Confidential – © 2007, VITO NV – All rights reserved

Disaster Management at VITO

N. Lewyckyj – VITOHarbin, July 6th 2008


Structure of the presentation

• VITO in a nutshell

• Disasters & sensors

• Main VITO projects dealing with DM

• Some conclusions and trends


VITO in a nutshell


• Autonomous public research company (shares 100 % owned by Flemish government)

• > 510 highly qualified researchers and technicians• Bridge between scientific knowledge and industrial applications or

government policy

Mission: As an independent and customer-driven research organization, VITO offers :

- innovating, technological solutions- scientifically based advice and support

aiming at : - stimulating sustainable development

- reinforcing the economic and social structure in Flanders and in Europe.

VITO : Flemish Institute for Technological Research



VITO activities

• VITO is an independent research organisation • 3 main research fields : energy-materials-environment• Research projects on behalf of European (EC, ESA)

and National authorities (policy support)• Technology development in cooperation with industry • Annual budget : 78 M€ (2007)


MEASUREMENTS AND EVALUATIONS

MEASUREMENTS AND EVALUATIONS

INNOVATION ANDRENOVATION

INNOVATION ANDRENOVATION

• Integrated environmental studies

• Environmental measurements

• Environmental toxicology

• Remote sensing and earth observation processes

• Energy technology

• Materials technology

• Environmental and process technology

VITO is structured according to seven “departments”


Integrated environmental studies

• Product and technology studies• BAT and EMIS• Risk evaluation and

environmental damage costs• Emission reduction strategies

• Water and soil pollution

• Electromagnetic environmental pollution

• Atmospheric processes

www.emis.vito.be


Different atmospheric models

• PC-Puff (local, using real-time meteo, different altitudes and back modeling possible)

• Imission Frequency Dispersion Model (local to regional, forecasts, more complex but also more accurate)

• Street canyon models (coupled to traffic emission models)

• Aurora (regional to national) to be run by specialists

• Dioxines models (IFDM coupled to HYSPLIT)

• …


Dispersion and exposure modelling of environmental pollutants

Dispersion modelling• air

– continuous or accidental emissions ↔ ambient air quality

– traffic ↔ urban air quality

– indoor product emission ↔ indoor air quality

• groundwater• surface water (continuous or accidental releases ↔ surface water quality)

Exposure modelling • exposure to air pollutant and related health effects • exposure to soil and water pollutants via the foodchain


Environmental measurements• Reference laboratory for environmental analysis and measurements

– responsible for accreditation of commercial labs– responsible for development and validation of recommended analytical procedures– state of the art analytical infrastructure

• Measurement and analysis of all types of pollution observed in our environmentwater, soil, solid waste, air (emissions, ambient air quality, indoor air quality)

• Advanced analytical equipment in- and organic analysesbio-assays and in-vitro testing to analyse directly probable health effects (genotoxicity, carcenogenicity, immune-system,...)


Environmental measurements

• Moving teams carrying “light weight” equipments

• Mobile car equipped with different sensors and with positioning system

• Biclycle equipped with an “utra fine particle” detector


Aerosols up to Ultra Fine Particles, video camera, gps, P-trak, noise measurements and PID-monitor

AëroFlex II: Biclycle equipped with an “utra fine particle” detector


AëroFlex II: values are not disturbed by the measurement method


Micro and mini UAVs to be equipped with air pollution sensors and wind measurements

25 - 40 cm

~ 2.5 m

MIRAMAP

~ 1.5 m


Environmental toxicology

Study of environmental and health risks associated with new products and environmental pollution

– Environment and health – Ecotoxicology– Biotechnology


Remote sensing and earth observation processes

Remote sensing and image processing– Space- and airborne sensors – Advanced research and image processing– Production & distribution of derived products


Remote sensing centre of expertise (TAP)

• Operational processing of VEGETATION images (Spot 4 & 5)

• Use of low resolution data for agriculture (Africa, ...)

• Acquisition & use of airborne Hyperspectral data (soil contamination, sand movement, quality of water, …)

• Development & use of new technologies (Unmanned Aerial Vehicles and sensors)


RS sensors

• MEDUSA is a very light weight high resolution digital camera (GSD 30 cm from 18 km altitude)

• Airborne Prism EXperiment (APEX) is a unique airborne imaging spectrometer


Environmental and process technology• Water treatment and re-use

• Soil analysis and decontamination, incl. ground-water

• Waste

• Air

• Membrane technology

• Reactor technology

• Process optimalisation

• PRODEM: SME


Scientific and technical expertise related to catastrophe management

• Measurement and analysis of environmental pollutants (air, soil, water)

• High level expertise with regard to dispersion modelling of environmental pollutants in air, soil, surface water and ground water

• Toxicology study• Modelling of human exposure to environmental pollutants,

including effects on human health• Remote sensing (airborne, space borne, UAVs)


Disasters & sensors


Type of events affecting people• Natural

avalanches, droughts, sand storm, earthquakes, floods, hurricanes, landslides, tsunamis, volcanic eruptions, forest fires, …

• Technological transport accident, building collapse, explosion/fire,food poisonning, dam failure, industrial accident, pandemic (SARS),…

• Security terrorism, massa hysteria & panic, hooliganisms, bombing, biological agents, …


Other type of disasters

• Ecosystem destruction (e.g. oil spills)

• Ressources destruction (e.g. bomb fishing)

• Communication failure (e.g. financial world)

• Air pollution (e.g. smog events)


Disaster – Crisis Characteristics

• Suddent event• Rapidly evolving• Needs radip decisions (=> rapid information)• Large impact on society (human & environment)• Important uncertainties• Sometimes limited communication possibilities• Multi-disciplinary, multi-level (multi-end user)


Disaster properties define type of information required

• Geographic extend gaz explosion vs Tsunami 12/06

• Time scale and duration of the effectsroad chain-accident vs Chornobyl

• Amount of people affectedtrain accident vs Bopal

• Known riskEarthquake zones vs train accident

• …


Information is generated by analysing and fusing data coming from “sensors”

Sensors(raw data)

Data (pre-) processing

DSSdata fusion and

expert knowledge

Information for decision makers

Existing data bases

Rescue teams

Observators(population)

We do not consider here communication aspects although that are also

crucial


Sensor types are multiple

Stand-alone vs

networks

On-site vs

remote (incl. lab)

Fixedvs

mobile

Realvs

virtual

Automatic vs

manual

Hardware vs

human

Wired vs

wireless


Main VITO projects related to DM


Phases for disaster management

Response• Activation of sub networks• Deployment of new networks•Refinement of data•Access to wide data

••

Monitoring

CrisisAlert•Real time monitoring& forecasting

•Early warning

Post Disaster

Reconstruction

Recovery

Preparedness Scenarios developmentEmergency Planning maps

Months / years

Hours / days

Minutes / hours

Years / decades

Weeks / months

Continuous

Prevention and Monitoring•Deployment of monitoring networks•Improvement of modeling & prediction


Prevention - monitoring


GMFS Project - Context• GMESGMES

– GGlobal lobal MMonitoring for onitoring for EEnvironment and nvironment and SSecurityecurity– Joint EC and ESA initiativeJoint EC and ESA initiative

• GMFSGMFS– GGlobal lobal MMonitoring for onitoring for FFood ood SSecurityecurity– Is one of the demonstrator projects of the ESA Is one of the demonstrator projects of the ESA

TIGER initiative for AfricaTIGER initiative for Africa– Operational delivery of user-driven servicesOperational delivery of user-driven services– 2 phases 2 phases

• 2003-2004: Startup, consolidation & definition2003-2004: Startup, consolidation & definition• 2005-2008: Implementation2005-2008: Implementation


Service Product Name Description

Support to CFSAM

GMFS Support Kit for FAO/WFP CFSAM missions (SK)

The package contains a compilation of Geographic information on vegetation status, crop yield forecasting, production data, overall environmental conditions and problem areas, as per best information available (from GMFS and other sources) at the time of writing

Early warning Vegetation Productivity Indicator (VPI)

Assessment of vegetation/crop state based on to historical time series based on SPOT-VGT

Fraction of Absorbed Photo synthetically Active radiation (fAPAR) / DMP

Fraction of Absorbed Photo synthetically Active Radiation (fAPAR), indicator on state of the canopy, based on MERIS RR (end phase 1)

Agricultural mapping (60-70% accuracy)

Crop emergence date (CED)

Date of emergence of crops/vegetation, based on SAR data

Cultivated area (CA) Cultivated area over selected, localized areas based on SAR data and ground observations

Extent of cultivation (EoC)

Extent of the cultivated areas at country level, based on SAR optical data (MERIS/MODIS) and ground observations.

Agricultural productivity (AP)

Qualitative assessment of potential productivity in the cultivated areas (High – low productivity)

Crop Yield assessment

Crop Yield (CY) Estimated and forecasted crop yield by main crop type at country level based on Agro-meteorological models integrated with Remote Sensing


Where?

• Sub-Saharan Africa

– 3 Regions • CILSS, IGAD, SADC

– 5 Countries• Senegal, Malawi• Sudan, Ethiopia, Zimbabwe


Yield Prediction

-

GMFS Yield Forecast for Millet (Senegal 2005 growing season) (ULg)

explanatory variables •phenological variables (FAO agrometshell

software)•The remote sensed variables •The meteorological variables

statistical study


Aïda: Advancing ICT for DRM in Africa

• EU FP7 project, just started• From Africa / for Africa• Multi-hazard approach• Aim: information communication• VITO acts as a coordinator


Objectives Reduce the risk of natural disasters Improve the capacity to respond to disasters Bridge the ICT information gap in Africa Provide stakeholders in Africa with decent access

to ICT information Promote existing, successful and adequate ICT

solution share this information

Open up the GEONETCast system for alerting purposes

Establish a long-term ICT-cooperation with and within Africa


Synergy?

Bottom up (AÏDA)

Half way (website/promotion

Irma tech demo/ workshop)

Top Down (IRMA)


Prepardness

40April 11, 2023 confidential – © 2007, VITO NV – all rights reserved

SEVESEOAn EO demonstration project

funded by the Data User Element of the Earth Observation Envelope Program(7 users from 4 countries)


SEVESEO IS Client – Substances viewer




RESDIM (in preparation)

• Simulation massive people avacuation

• Use real-time RS coupled with traffic modeling

• Contact with Police crisic centre in Leuven (Belgium)


DDK Project:Funded by the Belgian Science

Policystudying the vegetation dynamic to

avoid embankments breaks


DDK Project

• Funded by the Belgian Science Policy

• Studying the vegetation dynamic to avoid embankments breaks

• Using hyperspectral remote sensing


Unmixing results

Marram

GrassMoss


Alert


Smog (O3) prediction and alert system for Belgium

http://www.irceline.be/~celinair/smogstop/ozgraph_nl.html


VITO signs contract to supply air quality management system


Air Quality Monitoring and Forecasting in China (AMFIC)

http://www.amfic.eu/

• Prediction of pollutant concentrations in the atmosphere of the city of Shenyang

• Considered pollutants: O3, SO2, NO, NO2,CO, CH4 and PM

• Also air quality in the city of Beijing during the Olympic Games 2008


VITO uses its own developed AURORA air quality model


AURORA : input terrain data for 3-km domain

Domain : 150km x 150km @ 3km spatial resolutionTerrain data : - vegetation information : VEGETATION / SPOT - land use : GLC2000 - sea surface temperature : MODIS - topography : Digital Elevation Model


Trial simulation : E-MAP for Beijing

NOx emissions PM10 emissions

Beijing : - 50x50km², 1km resolution- August 2006


Hyperpeach: Hyperspectral remote sensing for crop assessment in peach orchards


Field campaign

Leaf level

Canopy level

ASD in situ reflectance measurements

SPAD measurements of foliar Chlorophyll

Biochemical leaf chlorophyll measurements

Airborne level

ASD in situ reflectance measurements

AHS airborne hyperspectral sensor


Stress (Chlorosis) can be detected on tree and canopy levels

Leaf: R2=0.95 (regression), R2=0.81 (inversion)

Canopy: R2=0.60 (regression), R2=0.49 (inversion)

Requirements on resolutionSpectral requirement: medium

Spatial requirement: high (tree identification!)

Approach

Model inversion < regression (training!)

Standard inversion < Adapted simulated annealing + filtering

Vegetation indicesVegetation indices were developed and high correlation was

found between iron stress and chlorophyll content


Response


http:// www.osiris-fp6.eu

Open architecture for Smart and Interoperable networks in Risk management based on In-situ Sensors


The OSIRIS project

International EU FP6 project : 10 partners + 4 end-users

Use of sensors & sensor networks for DM (sensor web)

4 demonstrations: - forest fire, - industrial accident, - air quality and - water quality

Each time 2 phases: monitoring & crisis

VITO: RS for forest fires & dispersion models for air quality


MobileGround Control Station

Central Data Processing

Centre (CDPC) in Mol

SWE

PC

MONITORING / SUPERVISION

REAL TIME IMAGERY

UNPROCESSED DATA

RAW DATA COMPRESSED

PROCESSED DATA

RS systemMONITORING / SUPERVISION

Remote sensing system

OSIRIS TRUCK

WIRELESS SMART IMAGING SENSOR

BU BU

BU

SWE

POSITIONNING SENSOR SYSTEM

MAP

Firemen : 19

Trucks : 3

HOUSES

ROAD

FOREST

FIRE

SATELLITE COM

WIRELESS COM

WIRED COM

LEGEND

SATELLITE COM

WIRELESS COM

WIRED COM

SATELLITE COM

WIRELESS COM

WIRED COM

LEGEND

DISPLAYOPERATOR

SWE

Forest fire global deployment

BU

BU


User interfaces

Firemen : 19

Trucks : 3

MAP

PC

VSAT

GCS1

UWB1

UWB2

UWB3

UWB4

CAM1

CAM2

CAM3

ARSS

Houses

ROAD

FOREST

FIRE

WIND DIRECTION

The user will access some displays, based on OSIRIS generic applications on firemen existing displays. The goal is to provide displays fusing all sensor data information in the most operational way.

lewyckyn

Attention: I have dded a possible trajectory for the HAp (in yellow)


MobileGround Control Station

Central Data Processing

Centre (CDPC) at VITO (Mol)

RA

W D

AT

A R

EA

L TIM

E

RAW DATA COMPRESSED

PROCESSED DATA

RS SYSTEM

Description of the RS system within OSIRIS

TE

LEM

ET

RY

Platform

TA

SK

ING

COMPRESSED DATA

SWEµ-PAF

HAP TASKIN

G

METE

ORO

LOG

ICAL

DATA

AB3 Satellite

REQUEST FOR DATA

RS platform(s)


We will generate georeferenced images in near real time from video stream

MercatorLow.mpg




Belgian Dioxin crisis in 1999

• Problem : huge contamination of the Belgian human food chain by dioxins (PCDD/PCDF)

• Reason : reuse of contaminated waste animal fat in the animal food chain (feed of pigs and chickens)

• Threat to human health and enormous economic damage • VITO was established immediately by the Belgian

government as the reference lab to organise and perform the control analyses

• Development of new fast screening methods (CALUX) • Development of a food chain model to assess consumer

exposure and effects to public health


Oil fire in Brussels

• Fire at the MARLY company

• Fire during several days with continuous release of toxic gases in the atmosphere

• VITO was asked to assess the situation

• Use of the IFDM atmospheric model + environmental measurements


Case Arcelor-Mittal : Integrated approach exposure population to Cr and Ni and estimation health risks.

• Measurements on the company terrain diffuse and derived emissions of fine particules (PM10)

• Measurement of the environment in the surrounding• Use of dispersion models• measurements : on the company personel aiming at

assessing the exposure • measurements arround the company: via different exposure

pathways: hovering particles – deposited particules – inside and outside

• exposure modelling on the basis of the performed measurements

• speciation of Ni (NiS, NiO, metallic Ni) and Cr (Cr(VI), Cr(III))


Hg-pollution in the Brussels region

• Sending the measurement car

• Samples taken and rapidly analysed in the labo

• Analyse on the basis of atmospheric models

• Source of van Hg-pollutie identified


Post crisis – dammage assesment


Seasonal robustness of an empirical SPM algorithm for the Scheldt (Belgium)

Private partner Research partner

Titel die ik heb opgegeven: Retrieval of suspended sediment concentrations in tidal rivers"


Introduction: Conventional methods for measuring SPM concentrations

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Turbidity measurements• Point measurements are expensive

• difficult to install in some locations

• The number of points is always limited

• the extent of the area for which measurements are relevant is often unknown

OBS turbidity meter


SPM concentration maps 2005 - examples

At high tide 2h after high tide


The Kabar project (Tanimbar, Indonesia)

Mapping of coral reefs using hyperspectral data;

L. Bertels, E. Knaeps, S. Sterckx, B. DerondeFlemish Institute for Technological Research (VITO), Belgium

Tony Vanderstraete Stijn Van Coillie, Rudi GoossensGeography Department, Ghent University, Belgium

The Indonesian archipelago with its many coral species is called the ‘centrum of biodiversity’.

biological richness & valuable socio-economic resources

Many threats are posing stress on coral reefs.

pollution, sedimentation and unsustainable fishing activities Objectives: monitoring system efficient mapping

- provide information for decision making

- protecting coral reef environments

KArang TanimBAR

Pulau Nukaha


Number of Number of Number of classes Number of classes spectra extracted clusters obtained after cluster aggregation after manual fusion Fore reef 2236 237 20 12 Reef crest 288 36 10 6 Back reef 758 31 10 8 Shallow lagoon 2134 162 20 6 Deep lagoon 963 52 12 4 Total 6379 518 72 36 17

Similar bottom types in different geomorphological unitsare finally manual fused to obtaine 17 meaningful classes.

IntroductionIntroductionField surveyField surveyHyperspectral dataHyperspectral dataClassification - MNF- MNF - Geomorfology- Geomorfology - Endmembers- Endmembers - Benthic cover map - Labelling- Labelling - Accuracy - Accuracy ConclusionConclusionAcknowledgmentsAcknowledgments

benthic coverage Sand 46%

Algea 24% Ag 1 44.1 %Ag 2 21.8 %Ag 3 34.1 %

Corals 30% Cg 1 18.9 %Cg 2 3.1 %Cg 3 8.3 %Cg 4 6.8 %Cg 5 33.9 %Cg 6 1.6 %Cg 7 2.0 %Cg 8 7.6 %Cg 9 10.0 %Cg 10 1.8 %Cg 11 6.0 %


IntroductionIntroductionField surveyField surveyHyperspectral dataHyperspectral dataClassification - MNF- MNF - Geomorfology- Geomorfology - Endmembers- Endmembers - Benthic cover map- Benthic cover map - Labelling - Accuracy - Accuracy ConclusionConclusionAcknowledgmentsAcknowledgments

Coral Group 6: Fore reef / ± -2 m ↔ ± -7 mHard coral on calcified rock, minor soft coral.

Acropora sp.; Typical: Acropora palifera

Coral Group 7: Lagoon / ± -2 m ↔ ± -9 mPatch coral.

Different species of hard and soft coral.

Coral Group 10: Fore reef /± -7 m ↔ ± -15 mSoft coral on sandy bottom, minor hard coral.

Sarcophyton, sp.; Gorgonians

Algae Group 1: Back reef / ± -2 mCalcified rock covered with turf algae.

Sparse macro algae are present.


Stress detection of Heavy-metal

Contaminated Trees : The Maatheide track

The area is contaminated by different Zn, Pb, Cu & Cd –rich minerals.

NFormer location

of thezinc factory

Mol

Lommel

Heavy metal Concentration

(mg/kg)

Zn 10000

Pb 1700

Cu 1000

Cd 10-70

J.Vangronsveld et al., 1995


Visualization of EGFN along the track

Location of the formerzinc factory.

In the neighborhood of the zinc factory thepine trees show high stress levels. Going further

east or west stress level decreases.

X


Some conclusions and trends


VITO is active in all the phases of a disaster

Response

Prevention and Monitoring

Preparedness

Monitoring

Crisis

Alert

Post Disaster

ReconstructionRecovery

but not always on an operationnal basis


End-users are local, regional, national and international authorities

• Flemish fire brigades, municipalities• Antwerp harbour, private industry• Belgian Federal Police and Civil Protection• French and Dutch fire brigades (OSIRIS,

Miramap)• China• African partners (GMFS, AIDA)• …


The “perfect” sensor system do not exist

• Only combination of systems provides adequate information, especially for real-time issues

• Interoperability is therefore a key issue

• Selection of information is crucial (avoid data overflow)

• Understanding the end-user needs is not always obvious but is crucial• The end-users are following the technology developments and are mostly

open to innovative trials but theuy mostly trust much more a person than a machine

• During demos, the proposed solution should enhance the performances but not disturb the operationnal system


Some trends are

• Standardization (e.g. OGC for geographic information)• Development of smart sensors• Direct involvment of the population• Miniaturization and cost lowering• Development of sensor networks/constellations• Higher resolution => more data• Data fusion (all type) will increase• Processing will be more and more automated• Data processing in limited specialized centres

-> communication is very important• Concerning RS, UAV’s will play a major role during the next

decade


Unmanned Aerial Vehicles (UAVs)

From EURO UVS report

Micro Flying Robot - Japan

Helios (Aerovironment/NASA, USA)

Sanswire (USA)

TU Delft de Delfly Micro


Satellites

HAPs

Manned systems

Altitude

Small UAVs


The Pegasus system


Platform: Mercator-1– 32 kg, 18 m wingspan– Rapid launch possible– Solar cells + batteries– Flying attitude : night 14 km - day 18 km– Ground launch or using balloon– Operationnal speed ~ 20 m/s– Endurance: weeks to months– Data downlink (170 Km LOS)

Ground control station– Mobile (container)– Control of UAV– Reception telemetry– Programming trajectories– Reception payload data + archiving buffer– Transmission to VITO-CDPC– Automatic flight planning

Pegasus is a project

Payload: Medusa (ESA contract)– High resolution RGB + PAN camera (GSD 30 cm)– 2kg, 100 cm length, 11 cm frontal diameter– CMOS sensor 10.000 x 1.200 pixels– Up to 1 image every 2 seconds– Max. data rate downlinked : 20 Mbits/sec– Equipped with IMU, GPS, transmittor-receptor, CDHU,

…– Work: only during day

Central Data Processing Center (at VITO)– Software for processing raw data into information– HDF5 self decsribing format (NASA)– Up to level 4 data on request– Archiving of all data (database)– Actually ~60 TB available– Parallel processing


Mirror Lens groups Lens groups

FPA IMU

GPS GPS antenna

Transmitter

CDHU

E-box tray

• Platform: Mercator-1– Preliminary tests successful abroad (world record)– Scale model tested in Helchteren in June 06– Problem of batteries solved– Ready to fly - wait at Verhaert Space premises

• Payload: Medusa (ESA contract)– Phase B achieved– Phase C/D started– Payload tested and ready for fly : expected for 03/08

• Ground control station– Most part of hardware available– Software still under development at Verhaert– Integration will be done soon by VITO– Customisation for OSIRIS (done by VITO)

• Central Data Processing Center (at VITO)– Software under development– Some test already performed– Next tests (real-time) during Flanders day (22/04)

Pegasus status


R ~ swath

GSD = 0.3 m

R ~ 3 km

Update ~ 8.2 min

Coverage ~ 28.3 km²

R ~ 0.5 swath

GSD = 0.3 m

R ~ 2.7 km

Update ~ 1.6 min

Coverage ~ 5.7 km²

Possible coverage offered by the HAP



R ~ 0.5 swath

Update ~ 1 min

Coverage ~ 72 km²

Update ~ 25 min

Coverage ~ 314 km²

R ~ swath

Pegasus with GSD = 1 m Swath = 10 km

Update ~ 4.7 hours

Coverage ~ 2500 km²

50 km

50 km


Pegasus 1m


Zephyr : endurance WR (54+33 hours)


Zephyr in White Sand

http://www.newscientist.com/blog/technology/labels/UAVs.html


Thank you for your attention

http://www.vito.be