Disaster Management at VITO N. Lewyckyj – VITO Harbin, July 6 th  2008
Structure of the presentation <ul><li>VITO in a nutshell </li></ul><ul><li>Disasters & sensors </li></ul><ul><li>Main VITO...
VITO in a nutshell
<ul><li>Autonomous  public  research company (shares 100 % owned by Flemish government) </li></ul><ul><li>> 510 highly qua...
 
VITO  activities <ul><li>VITO is an independent research organisation  </li></ul><ul><li>3 main research fields : energy-m...
MEASUREMENTS AND  EVALUATIONS INNOVATION AND RENOVATION   <ul><li>Integrated environmental studies </li></ul><ul><li>Envir...
Integrated environmental studies <ul><li>Product and technology studies </li></ul><ul><li>BAT and EMIS </li></ul><ul><li>R...
Different atmospheric models <ul><li>PC-Puff (local, using real-time meteo, different altitudes and back modeling possible...
Dispersion and exposure modelling of environmental pollutants <ul><li>Dispersion modelling </li></ul><ul><li>air  </li></u...
Environmental measurements <ul><li>Reference laboratory for  environmental analysis and measurements </li></ul><ul><ul><li...
Environmental measurements <ul><li>Moving teams carrying “light weight” equipments </li></ul><ul><li>Mobile car equipped w...
Aerosols up to Ultra Fine Particles,  video camera, gps, P-trak,  noise measurements and  PID-monitor   AëroFlex II:  Bicl...
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 <ul><li>Study of  environmental and health risks  associated with new products and environmental ...
Remote sensing and  earth observation processes <ul><li>Remote sensing and image processing </li></ul><ul><ul><li>Space- a...
Remote sensing centre of expertise (TAP) <ul><li>Operational processing of VEGETATION images  (Spot 4 & 5) </li></ul><ul><...
RS sensors <ul><li>MEDUSA is a very light weight high resolution digital camera (GSD 30 cm from 18 km altitude) </li></ul>...
Environmental and process technology <ul><li>Water treatment  and re-use </li></ul><ul><li>Soil analysis and  decontaminat...
Scientific and technical expertise related to catastrophe management  <ul><li>Measurement and analysis of environmental po...
Disasters & sensors
Type of events affecting people <ul><li>Natural   avalanches, droughts, sand storm, earthquakes, floods, hurricanes, lands...
Other type of disasters <ul><li>Ecosystem destruction (e.g. oil spills) </li></ul><ul><li>Ressources destruction (e.g. bom...
Disaster  –  Crisis Characteristics <ul><li>Suddent event </li></ul><ul><li>Rapidly evolving </li></ul><ul><li>Needs radip...
Disaster properties define type of information required <ul><li>Geographic extend  gaz explosion vs Tsunami 12/06 </li></u...
Information is generated by analysing and fusing data coming from “sensors” Sensors (raw data) Data  (pre-) processing DSS...
Sensor types are multiple Stand-alone  vs networks On-site  vs   remote (incl. lab) Fixed vs  mobile Real vs virtual Autom...
Main VITO projects related to DM
Phases for disaster management Response • Activation of sub networks • Deployment of new networks • Refinement of data • A...
Prevention - monitoring
GMFS Project - Context <ul><li>GMES   </li></ul><ul><ul><li>G lobal  M onitoring for  E nvironment and  S ecurity </li></u...
Estimated and forecasted crop yield by main crop type at country level based on Agro-meteorological models integrated with...
Where?  <ul><li>Sub-Saharan Africa </li></ul><ul><ul><li>3 Regions  </li></ul></ul><ul><ul><ul><li>CILSS, IGAD, SADC </li>...
Yield Prediction   GMFS Yield Forecast for Millet (Senegal 2005 growing season)   (ULg) <ul><li>explanatory variables   </...
Aïda:  Advancing ICT for DRM in Africa   <ul><li>EU FP7 project, just started </li></ul><ul><li>From Africa / for Africa <...
Objectives <ul><li>Reduce the risk of natural disasters </li></ul><ul><li>Improve the capacity to respond to disasters </l...
Synergy? Bottom up (AÏDA) Half way (website/promotion Irma tech demo/ workshop) Top Down (IRMA)
Prepardness
SEVESEO An EO demonstration project  funded by the Data User Element of the Earth Observation Envelope Program (7 users fr...
SEVESEO IS Client – Substances viewer
 
 
RESDIM (in preparation) <ul><li>Simulation massive people avacuation </li></ul><ul><li>Use real-time RS coupled with traff...
DDK Project: Funded by the Belgian Science Policy studying the vegetation dynamic to avoid embankments breaks
DDK Project <ul><li>Funded by the Belgian Science Policy </li></ul><ul><li>Studying the vegetation dynamic to avoid embank...
Unmixing results Marram Grass Moss
Alert
Smog (O 3 ) 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/ <ul><li>Prediction of pollutant concentration...
VITO uses its own developed AURORA air quality model
AURORA : input terrain data for 3-km domain Domain : 150km x 150km @ 3km spatial resolution Terrain data :  - vegetation i...
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 <ul><ul><li>ASD in situ reflectance measurements </li></ul></ul><ul><ul><li>SPAD me...
Vegetation indices <ul><ul><li>Model inversion < regression (training!) </li></ul></ul><ul><ul><li>Standard inversion < Ad...
Response
h ttp:// www.osiris-fp6.eu O pen architecture for  S mart and  I nteroperable networks in  R isk management based on  I n-...
The OSIRIS project <ul><li>International EU FP6 project : 10 partners + 4 end-users </li></ul><ul><li>Use of sensors & sen...
Mobile Ground Control Station Central Data Processing Centre (CDPC) in Mol SWE PC MONITORING / SUPERVISION REAL TIME IMAGE...
User interfaces Firemen : 19 Trucks : 3 The user will access some displays, based on OSIRIS generic applications on fireme...
Mobile Ground Control Station Central Data Processing Centre (CDPC) at VITO (Mol) RAW DATA REAL TIME RAW  DATA COMPRESSED ...
We will generate georeferenced images in near real time from video stream
 
 
Belgian Dioxin crisis in 1999 <ul><li>Problem : huge contamination of the Belgian human food chain by dioxins (PCDD/PCDF) ...
Oil fire in Brussels <ul><li>Fire at the MARLY company </li></ul><ul><li>Fire during several days with continuous release ...
Case Arcelor-Mittal : Integrated approach exposure population to Cr and Ni and estimation health risks . <ul><li>Measureme...
Hg-pollution in the Brussels region <ul><li>Sending the measurement car </li></ul><ul><li>Samples taken and rapidly analys...
Post crisis – dammage assesment
Seasonal robustness of an empirical SPM algorithm for the Scheldt (Belgium) Private partner       Research partner Titel d...
Introduction:  Conventional methods for measuring SPM concentrations <ul><li>Turbidity measurements </li></ul><ul><li>Poin...
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. Sterc...
Similar bottom types in different geomorphological units are finally manual fused to obtaine 17 meaningful classes. Introd...
Introduction Field survey Hyperspectral data Classification - MNF - Geomorfology - Endmembers - Benthic cover map - Labell...
Stress detection of Heavy-metal Contaminated Trees  :  The Maatheide track The area is contaminated by different  Zn, Pb, ...
Visualization of EGFN along the track Location of the former zinc factory. In the neighborhood of the zinc factory the pin...
Some conclusions and trends
VITO is active in all the phases of a disaster   Response Prevention and Monitoring Preparedness   Monitoring Crisis Alert...
End-users are local, regional, national and international authorities <ul><li>Flemish fire brigades, municipalities </li><...
The “perfect” sensor system do not exist <ul><li>Only combination of systems provides adequate information, especially for...
Some trends are  <ul><li>Standardization (e.g. OGC for geographic information) </li></ul><ul><li>Development of smart sens...
Unmanned Aerial Vehicles (UAVs) From  EURO UVS report Micro Flying Robot   - Japan Helios (Aerovironment/NASA, USA ) Sansw...
Altitude Satellites HAPs Manned systems Small UAVs
The  Pegasus  system
<ul><li>Platform: Mercator-1 </li></ul><ul><ul><li>32 kg,  18 m wingspan </li></ul></ul><ul><ul><li>Rapid launch possible ...
Pegasus  status <ul><li>Platform: Mercator-1 </li></ul><ul><ul><li>Preliminary tests successful abroad (world record) </li...
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 Coverag...
 
Update ~ 1 min Coverage ~ 72  km² Update ~ 25 min Coverage ~ 314  km² Pegasus   with  GSD = 1 m  Swath = 10 km Update ~ 4....
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
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Keynote at ISCRAM-China2008 conference: Vito and Disaster Management

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  • Keynote at ISCRAM-China2008 conference: Vito and Disaster Management

    1. 1. Disaster Management at VITO N. Lewyckyj – VITO Harbin, July 6 th 2008
    2. 2. Structure of the presentation <ul><li>VITO in a nutshell </li></ul><ul><li>Disasters & sensors </li></ul><ul><li>Main VITO projects dealing with DM </li></ul><ul><li>Some conclusions and trends </li></ul>
    3. 3. VITO in a nutshell
    4. 4. <ul><li>Autonomous public research company (shares 100 % owned by Flemish government) </li></ul><ul><li>> 510 highly qualified researchers and technicians </li></ul><ul><li>Bridge between scientific knowledge and industrial applications or government policy </li></ul><ul><li>Mission : </li></ul><ul><li>As an independent and customer-driven research organization, VITO offers : - innovating, technological solutions </li></ul><ul><li>- scientifically based advice and support </li></ul><ul><li>aiming at : </li></ul><ul><li>- stimulating sustainable development </li></ul><ul><li>- reinforcing the economic and social structure in Flanders and in Europe. </li></ul>VITO : Flemish Institute for Technological Research
    5. 6. VITO activities <ul><li>VITO is an independent research organisation </li></ul><ul><li>3 main research fields : energy-materials-environment </li></ul><ul><li>Research projects on behalf of European (EC, ESA) and National authorities (policy support) </li></ul><ul><li>Technology development in cooperation with industry </li></ul><ul><li>Annual budget : 78 M€ (2007) </li></ul>
    6. 7. MEASUREMENTS AND EVALUATIONS INNOVATION AND RENOVATION <ul><li>Integrated environmental studies </li></ul><ul><li>Environmental measurements </li></ul><ul><li>Environmental toxicology </li></ul><ul><li>Remote sensing and earth observation processes </li></ul><ul><li>Energy technology </li></ul><ul><li>Materials technology </li></ul><ul><li>Environmental and process technology </li></ul>VITO is structured according to seven “departments”
    7. 8. Integrated environmental studies <ul><li>Product and technology studies </li></ul><ul><li>BAT and EMIS </li></ul><ul><li>Risk evaluation and environmental damage costs </li></ul><ul><li>Emission reduction strategies </li></ul><ul><li>Water and soil pollution </li></ul><ul><li>Electromagnetic environmental pollution </li></ul><ul><li>Atmospheric processes </li></ul>www.emis.vito.be
    8. 9. Different atmospheric models <ul><li>PC-Puff (local, using real-time meteo, different altitudes and back modeling possible) </li></ul><ul><li>Imission Frequency Dispersion Model (local to regional, forecasts, more complex but also more accurate) </li></ul><ul><li>Street canyon models (coupled to traffic emission models) </li></ul><ul><li>Aurora (regional to national) to be run by specialists </li></ul><ul><li>Dioxines models (IFDM coupled to HYSPLIT) </li></ul><ul><li>… </li></ul>
    9. 10. Dispersion and exposure modelling of environmental pollutants <ul><li>Dispersion modelling </li></ul><ul><li>air </li></ul><ul><ul><li>continuous or accidental emissions ↔ ambient air quality </li></ul></ul><ul><ul><li>traffic ↔ urban air quality </li></ul></ul><ul><ul><li>indoor product emission ↔ indoor air quality </li></ul></ul><ul><li>groundwater </li></ul><ul><li>surface water ( continuous or accidental releases ↔ surface water quality ) </li></ul><ul><li>Exposure modelling </li></ul><ul><li>exposure to air pollutant and related health effects </li></ul><ul><li>exposure to soil and water pollutants via the foodchain </li></ul>
    10. 11. Environmental measurements <ul><li>Reference laboratory for environmental analysis and measurements </li></ul><ul><ul><li>responsible for accreditation of commercial labs </li></ul></ul><ul><ul><li>responsible for development and validation of recommended analytical procedures </li></ul></ul><ul><ul><li>state of the art analytical infrastructure </li></ul></ul><ul><li>Measurement and analysis of all types of pollution observed in our environment water, soil, solid waste, air (emissions, ambient air quality, indoor air quality) </li></ul><ul><li>Advanced analytical equipment in- and organic analyses bio-assays and in-vitro testing to analyse directly probable health effects (genotoxicity, carcenogenicity, immune-system,...) </li></ul>
    11. 12. Environmental measurements <ul><li>Moving teams carrying “light weight” equipments </li></ul><ul><li>Mobile car equipped with different sensors and with positioning system </li></ul><ul><li>Biclycle equipped with an “utra fine particle” detector </li></ul>
    12. 13. 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
    13. 14. AëroFlex II: values are not disturbed by the measurement method
    14. 15. Micro and mini UAVs to be equipped with air pollution sensors and wind measurements 25 - 40 cm ~ 2.5 m MIRAMAP ~ 1.5 m
    15. 16. Environmental toxicology <ul><li>Study of environmental and health risks associated with new products and environmental pollution </li></ul><ul><ul><li>Environment and health </li></ul></ul><ul><ul><li>Ecotoxicology </li></ul></ul><ul><ul><li>Biotechnology </li></ul></ul>
    16. 17. Remote sensing and earth observation processes <ul><li>Remote sensing and image processing </li></ul><ul><ul><li>Space- and airborne sensors </li></ul></ul><ul><ul><li>Advanced research and image processing </li></ul></ul><ul><ul><li>Production & distribution of derived products </li></ul></ul>
    17. 18. Remote sensing centre of expertise (TAP) <ul><li>Operational processing of VEGETATION images (Spot 4 & 5) </li></ul><ul><li>Use of low resolution data for agriculture (Africa, ...) </li></ul><ul><li>Acquisition & use of airborne Hyperspectral data (soil contamination, sand movement, quality of water, …) </li></ul><ul><li>Development & use of new technologies (Unmanned Aerial Vehicles and sensors) </li></ul>
    18. 19. RS sensors <ul><li>MEDUSA is a very light weight high resolution digital camera (GSD 30 cm from 18 km altitude) </li></ul><ul><li>Airborne Prism EXperiment (APEX) is a unique airborne imaging spectrometer </li></ul>
    19. 20. Environmental and process technology <ul><li>Water treatment and re-use </li></ul><ul><li>Soil analysis and decontamination , incl. ground-water </li></ul><ul><li>Waste </li></ul><ul><li>Air </li></ul><ul><li>Membrane technology </li></ul><ul><li>Reactor technology </li></ul><ul><li>Process optimalisation </li></ul><ul><li>PRODEM: SME </li></ul>
    20. 21. Scientific and technical expertise related to catastrophe management <ul><li>Measurement and analysis of environmental pollutants (air, soil, water) </li></ul><ul><li>High level expertise with regard to dispersion modelling of environmental pollutants in air, soil, surface water and ground water </li></ul><ul><li>Toxicology study </li></ul><ul><li>Modelling of human exposure to environmental pollutants, including effects on human health </li></ul><ul><li>Remote sensing (airborne, space borne, UAVs) </li></ul>
    21. 22. Disasters & sensors
    22. 23. Type of events affecting people <ul><li>Natural avalanches, droughts, sand storm, earthquakes, floods, hurricanes, landslides, tsunamis, volcanic eruptions, forest fires, … </li></ul><ul><li>Technological transport accident, building collapse, explosion/fire,food poisonning, dam failure, industrial accident, pandemic (SARS),… </li></ul><ul><li>Security terrorism, massa hysteria & panic, hooliganisms, bombing, biological agents, … </li></ul>
    23. 24. Other type of disasters <ul><li>Ecosystem destruction (e.g. oil spills) </li></ul><ul><li>Ressources destruction (e.g. bomb fishing) </li></ul><ul><li>Communication failure (e.g. financial world) </li></ul><ul><li>Air pollution (e.g. smog events) </li></ul>
    24. 25. Disaster – Crisis Characteristics <ul><li>Suddent event </li></ul><ul><li>Rapidly evolving </li></ul><ul><li>Needs radip decisions (=> rapid information) </li></ul><ul><li>Large impact on society (human & environment) </li></ul><ul><li>Important uncertainties </li></ul><ul><li>Sometimes limited communication possibilities </li></ul><ul><li>Multi-disciplinary, multi-level (multi-end user) </li></ul>
    25. 26. Disaster properties define type of information required <ul><li>Geographic extend gaz explosion vs Tsunami 12/06 </li></ul><ul><li>Time scale and duration of the effects road chain-accident vs Chornobyl </li></ul><ul><li>Amount of people affected train accident vs Bopal </li></ul><ul><li>Known risk Earthquake zones vs train accident </li></ul><ul><li>… </li></ul>
    26. 27. Information is generated by analysing and fusing data coming from “sensors” Sensors (raw data) Data (pre-) processing DSS data 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
    27. 28. Sensor types are multiple Stand-alone vs networks On-site vs remote (incl. lab) Fixed vs mobile Real vs virtual Automatic vs manual Hardware vs human Wired vs wireless
    28. 29. Main VITO projects related to DM
    29. 30. Phases for disaster management Response • Activation of sub networks • Deployment of new networks • Refinement of data • Access to wide data • • Monitoring Crisis Alert • Real time monitoring & forecasting • Early warning Post Disaster Reconstruction Recovery Preparedness Scenarios development Emergency Planning maps Months / years Hours / days Minutes / hours Years / decades Weeks / months Continuous Prevention and Monitoring • Deployment of monitoring networks • Improvement of modeling & prediction
    30. 31. Prevention - monitoring
    31. 32. GMFS Project - Context <ul><li>GMES </li></ul><ul><ul><li>G lobal M onitoring for E nvironment and S ecurity </li></ul></ul><ul><ul><li>Joint EC and ESA initiative </li></ul></ul><ul><li>GMFS </li></ul><ul><ul><li>G lobal M onitoring for F ood S ecurity </li></ul></ul><ul><ul><li>Is one of the demonstrator projects of the ESA TIGER initiative for Africa </li></ul></ul><ul><ul><li>Operational delivery of user-driven services </li></ul></ul><ul><ul><li>2 phases </li></ul></ul><ul><ul><ul><li>2003-2004: Startup, consolidation & definition </li></ul></ul></ul><ul><ul><ul><li>2005-2008: Implementation </li></ul></ul></ul>
    32. 33. Estimated and forecasted crop yield by main crop type at country level based on Agro-meteorological models integrated with Remote Sensing Crop Yield (CY) Crop Yield assessment Qualitative assessment of potential productivity in the cultivated areas (High – low productivity) Agricultural productivity (AP) Extent of the cultivated areas at country level, based on SAR optical data (MERIS/MODIS) and ground observations. Extent of cultivation (EoC) Cultivated area over selected, localized areas based on SAR data and ground observations Cultivated area (CA) Date of emergence of crops/vegetation, based on SAR data Crop emergence date (CED) Agricultural mapping (60-70% accuracy) Fraction of Absorbed Photo synthetically Active Radiation (fAPAR), indicator on state of the canopy, based on MERIS RR (end phase 1) Fraction of Absorbed Photo synthetically Active radiation (fAPAR) / DMP Assessment of vegetation/crop state based on to historical time series based on SPOT-VGT Vegetation Productivity Indicator (VPI) Early warning 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 GMFS Support Kit for FAO/WFP CFSAM missions (SK) Support to CFSAM Description Product Name Service
    33. 34. Where? <ul><li>Sub-Saharan Africa </li></ul><ul><ul><li>3 Regions </li></ul></ul><ul><ul><ul><li>CILSS, IGAD, SADC </li></ul></ul></ul><ul><ul><li>5 Countries </li></ul></ul><ul><ul><ul><li>Senegal, Malawi </li></ul></ul></ul><ul><ul><ul><li>Sudan, Ethiopia, Zimbabwe </li></ul></ul></ul>
    34. 35. Yield Prediction GMFS Yield Forecast for Millet (Senegal 2005 growing season) (ULg) <ul><li>explanatory variables </li></ul><ul><ul><li>phenological variables ( FAO agrometshell software ) </li></ul></ul><ul><ul><li>The remote sensed variables </li></ul></ul><ul><ul><li>The meteorological variables </li></ul></ul><ul><li>statistical study </li></ul>
    35. 36. Aïda: Advancing ICT for DRM in Africa <ul><li>EU FP7 project, just started </li></ul><ul><li>From Africa / for Africa </li></ul><ul><li>Multi-hazard approach </li></ul><ul><li>Aim: information communication </li></ul><ul><li>VITO acts as a coordinator </li></ul>
    36. 37. Objectives <ul><li>Reduce the risk of natural disasters </li></ul><ul><li>Improve the capacity to respond to disasters </li></ul><ul><li>Bridge the ICT information gap in Africa </li></ul><ul><li>Provide stakeholders in Africa with decent access to ICT information </li></ul><ul><li>Promote existing, successful and adequate ICT solution </li></ul><ul><ul><li>share this information </li></ul></ul><ul><li>Open up the GEONETCast system for alerting purposes </li></ul><ul><li>Establish a long-term ICT-cooperation with and within Africa </li></ul>
    37. 38. Synergy? Bottom up (AÏDA) Half way (website/promotion Irma tech demo/ workshop) Top Down (IRMA)
    38. 39. Prepardness
    39. 40. SEVESEO An EO demonstration project funded by the Data User Element of the Earth Observation Envelope Program (7 users from 4 countries)
    40. 41. SEVESEO IS Client – Substances viewer
    41. 44. RESDIM (in preparation) <ul><li>Simulation massive people avacuation </li></ul><ul><li>Use real-time RS coupled with traffic modeling </li></ul><ul><li>Contact with Police crisic centre in Leuven (Belgium) </li></ul>
    42. 45. DDK Project: Funded by the Belgian Science Policy studying the vegetation dynamic to avoid embankments breaks
    43. 46. DDK Project <ul><li>Funded by the Belgian Science Policy </li></ul><ul><li>Studying the vegetation dynamic to avoid embankments breaks </li></ul><ul><li>Using hyperspectral remote sensing </li></ul>
    44. 47. Unmixing results Marram Grass Moss
    45. 48. Alert
    46. 49. Smog (O 3 ) prediction and alert system for Belgium http://www.irceline.be/~celinair/smogstop/ozgraph_nl.html
    47. 50. VITO signs contract to supply air quality management system
    48. 51. Air Quality Monitoring and Forecasting in China (AMFIC) http://www.amfic.eu/ <ul><li>Prediction of pollutant concentrations in the atmosphere of the city of Shenyang </li></ul><ul><li>Considered pollutants: O3, SO2, NO, NO2,CO, CH4 and PM </li></ul><ul><li>Also air quality in the city of Beijing during the Olympic Games 2008 </li></ul>
    49. 52. VITO uses its own developed AURORA air quality model
    50. 53. AURORA : input terrain data for 3-km domain Domain : 150km x 150km @ 3km spatial resolution Terrain data : - vegetation information : VEGETATION / SPOT - land use : GLC2000 - sea surface temperature : MODIS - topography : Digital Elevation Model
    51. 54. Trial simulation : E-MAP for Beijing NOx emissions PM10 emissions Beijing : - 50x50km², 1km resolution - August 2006
    52. 55. Hyperpeach: Hyperspectral remote sensing for crop assessment in peach orchards
    53. 56. Field campaign Leaf level Canopy level <ul><ul><li>ASD in situ reflectance measurements </li></ul></ul><ul><ul><li>SPAD measurements of foliar Chlorophyll </li></ul></ul><ul><ul><li>Biochemical leaf chlorophyll measurements </li></ul></ul>Airborne level <ul><ul><li>ASD in situ reflectance measurements </li></ul></ul><ul><ul><li>AHS airborne hyperspectral sensor </li></ul></ul>
    54. 57. Vegetation indices <ul><ul><li>Model inversion < regression (training!) </li></ul></ul><ul><ul><li>Standard inversion < Adapted simulated annealing + filtering </li></ul></ul>Approach <ul><ul><li>Spectral requirement: medium </li></ul></ul><ul><ul><li>Spatial requirement: high (tree identification!) </li></ul></ul>Requirements on resolution <ul><ul><li>Leaf: R 2 =0.95 (regression), R 2 =0.81 (inversion) </li></ul></ul><ul><ul><li>Canopy: R 2 =0.60 (regression), R 2 =0.49 (inversion) </li></ul></ul>Vegetation indices were developed and high correlation was found between iron stress and chlorophyll content Stress (Chlorosis) can be detected on tree and canopy levels
    55. 58. Response
    56. 59. h ttp:// www.osiris-fp6.eu O pen architecture for S mart and I nteroperable networks in R isk management based on I n-situ S ensors
    57. 60. The OSIRIS project <ul><li>International EU FP6 project : 10 partners + 4 end-users </li></ul><ul><li>Use of sensors & sensor networks for DM (sensor web) </li></ul><ul><li>4 demonstrations: </li></ul><ul><li>forest fire, </li></ul><ul><li>industrial accident, </li></ul><ul><li>air quality and </li></ul><ul><li>water quality </li></ul><ul><li>Each time 2 phases: monitoring & crisis </li></ul><ul><li>VITO: RS for forest fires & dispersion models for air quality </li></ul>
    58. 61. Mobile Ground Control Station Central Data Processing Centre (CDPC) in Mol SWE PC MONITORING / SUPERVISION REAL TIME IMAGERY UNPROCESSED DATA RAW DATA COMPRESSED PROCESSED DATA RS system MONITORING / SUPERVISION Remote sensing system OSIRIS TRUCK WIRELESS SMART IMAGING SENSOR BU BU BU SWE POSITIONNING SENSOR SYSTEM DISPLAY OPERATOR SWE Forest fire global deployment BU BU
    59. 62. User interfaces Firemen : 19 Trucks : 3 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. MAP PC VSAT GCS1 UWB1 UWB2 UWB3 UWB4 CAM1 CAM2 CAM3 ARSS Houses ROAD FOREST FIRE WIND DIRECTION
    60. 63. Mobile Ground Control Station Central Data Processing Centre (CDPC) at VITO (Mol) RAW DATA REAL TIME RAW DATA COMPRESSED PROCESSED DATA RS SYSTEM Description of the RS system within OSIRIS TELEMETRY Platform TASKING COMPRESSED DATA SWE µ-PAF HAP TASKING METEOROLOGICAL DATA REQUEST FOR DATA RS platform(s) AB3 Satellite
    61. 64. We will generate georeferenced images in near real time from video stream
    62. 67. Belgian Dioxin crisis in 1999 <ul><li>Problem : huge contamination of the Belgian human food chain by dioxins (PCDD/PCDF) </li></ul><ul><li>Reason : reuse of contaminated waste animal fat in the animal food chain (feed of pigs and chickens) </li></ul><ul><li>Threat to human health and enormous economic damage </li></ul><ul><li>VITO was established immediately by the Belgian government as the reference lab to organise and perform the control analyses </li></ul><ul><li>Development of new fast screening methods (CALUX) </li></ul><ul><li>Development of a food chain model to assess consumer exposure and effects to public health </li></ul>
    63. 68. Oil fire in Brussels <ul><li>Fire at the MARLY company </li></ul><ul><li>Fire during several days with continuous release of toxic gases in the atmosphere </li></ul><ul><li>VITO was asked to assess the situation </li></ul><ul><li>Use of the IFDM atmospheric model + environmental measurements </li></ul>
    64. 69. Case Arcelor-Mittal : Integrated approach exposure population to Cr and Ni and estimation health risks . <ul><li>Measurements on the company terrain diffuse and derived emissions of fine particules (PM10) </li></ul><ul><li>Measurement of the environment in the surrounding </li></ul><ul><li>Use of dispersion models </li></ul><ul><li>measurements : on the company personel aiming at assessing the exposure </li></ul><ul><li>measurements arround the company: via different exposure pathways: hovering particles – deposited particules – inside and outside </li></ul><ul><li>exposure modelling on the basis of the performed measurements </li></ul><ul><li>speciation of Ni (NiS, NiO, metallic Ni) and Cr (Cr(VI), Cr(III)) </li></ul>
    65. 70. Hg-pollution in the Brussels region <ul><li>Sending the measurement car </li></ul><ul><li>Samples taken and rapidly analysed in the labo </li></ul><ul><li>Analyse on the basis of atmospheric models </li></ul><ul><li>Source of van Hg-pollutie identified </li></ul>
    66. 71. Post crisis – dammage assesment
    67. 72. 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&quot;
    68. 73. Introduction: Conventional methods for measuring SPM concentrations <ul><li>Turbidity measurements </li></ul><ul><li>Point measurements are expensive </li></ul><ul><li>difficult to install in some locations </li></ul><ul><li>The number of points is always limited </li></ul><ul><li>the extent of the area for which measurements are relevant is often unknown </li></ul>OBS turbidity meter
    69. 74. SPM concentration maps 2005 - examples At high tide 2h after high tide
    70. 75. The Kabar project ( Tanimbar, Indonesia) Mapping of coral reefs using hyperspectral data; L. Bertels, E. Knaeps, S. Sterckx, B. Deronde Flemish Institute for Technological Research (VITO), Belgium Tony Vanderstraete Stijn Van Coillie, Rudi Goossens Geography Department, Ghent University, Belgium <ul><li>The Indonesian archipelago with its many coral species is called the </li></ul><ul><li>‘ centrum of biodiversity’. </li></ul><ul><ul><li>biological richness & valuable socio-economic resources </li></ul></ul><ul><li>Many threats are posing stress on coral reefs. </li></ul><ul><li> pollution, sedimentation and unsustainable fishing activities </li></ul>Objectives:  monitoring system  efficient mapping - provide information for decision making - protecting coral reef environments KArang TanimBAR Pulau Nukaha
    71. 76. Similar bottom types in different geomorphological units are finally manual fused to obtaine 17 meaningful classes. Introduction Field survey Hyperspectral data Classification - MNF - Geomorfology - Endmembers - Benthic cover map - Labelling - Accuracy Conclusion Acknowledgments
    72. 77. Introduction Field survey Hyperspectral data Classification - MNF - Geomorfology - Endmembers - Benthic cover map - Labelling - Accuracy Conclusion Acknowledgments Coral Group 6: Fore reef / ± -2 m ↔ ± -7 m Hard coral on calcified rock, minor soft coral. Acropora sp.; Typical: Acropora palifera Coral Group 7: Lagoon / ± -2 m ↔ ± -9 m Patch coral. Different species of hard and soft coral. Coral Group 10: Fore reef /± -7 m ↔ ± -15 m Soft coral on sandy bottom, minor hard coral. Sarcophyton, sp.; Gorgonians Algae Group 1: Back reef / ± -2 m Calcified rock covered with turf algae. Sparse macro algae are present.
    73. 78. Stress detection of Heavy-metal Contaminated Trees : The Maatheide track The area is contaminated by different Zn, Pb, Cu & Cd –rich minerals. N Former location of the zinc factory Mol Lommel J.Vangronsveld et al., 1995 10-70 Cd 1000 Cu 1700 Pb 10000 Zn Concentration (mg/kg) Heavy metal
    74. 79. Visualization of EGFN along the track Location of the former zinc factory. In the neighborhood of the zinc factory the pine trees show high stress levels. Going further east or west stress level decreases. X
    75. 80. Some conclusions and trends
    76. 81. VITO is active in all the phases of a disaster Response Prevention and Monitoring Preparedness Monitoring Crisis Alert Post Disaster Reconstruction Recovery but not always on an operationnal basis
    77. 82. End-users are local, regional, national and international authorities <ul><li>Flemish fire brigades, municipalities </li></ul><ul><li>Antwerp harbour, private industry </li></ul><ul><li>Belgian Federal Police and Civil Protection </li></ul><ul><li>French and Dutch fire brigades (OSIRIS, Miramap) </li></ul><ul><li>China </li></ul><ul><li>African partners (GMFS, AIDA) </li></ul><ul><li>… </li></ul>
    78. 83. The “perfect” sensor system do not exist <ul><li>Only combination of systems provides adequate information, especially for real-time issues </li></ul><ul><li>Interoperability is therefore a key issue </li></ul><ul><li>Selection of information is crucial (avoid data overflow) </li></ul><ul><li>Understanding the end-user needs is not always obvious but is crucial </li></ul><ul><li>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 </li></ul><ul><li>During demos, the proposed solution should enhance the performances but not disturb the operationnal system </li></ul>
    79. 84. Some trends are <ul><li>Standardization (e.g. OGC for geographic information) </li></ul><ul><li>Development of smart sensors </li></ul><ul><li>Direct involvment of the population </li></ul><ul><li>Miniaturization and cost lowering </li></ul><ul><li>Development of sensor networks/constellations </li></ul><ul><li>Higher resolution => more data </li></ul><ul><li>Data fusion (all type) will increase </li></ul><ul><li>Processing will be more and more automated </li></ul><ul><li>Data processing in limited specialized centres -> communication is very important </li></ul><ul><li>Concerning RS, UAV’s will play a major role during the next decade </li></ul>
    80. 85. Unmanned Aerial Vehicles (UAVs) From EURO UVS report Micro Flying Robot - Japan Helios (Aerovironment/NASA, USA ) Sanswire (USA) TU Delft de Delfly Micro
    81. 86. Altitude Satellites HAPs Manned systems Small UAVs
    82. 87. The Pegasus system
    83. 88. <ul><li>Platform: Mercator-1 </li></ul><ul><ul><li>32 kg, 18 m wingspan </li></ul></ul><ul><ul><li>Rapid launch possible </li></ul></ul><ul><ul><li>Solar cells + batteries </li></ul></ul><ul><ul><li>Flying attitude : night 14 km - day 18 km </li></ul></ul><ul><ul><li>Ground launch or using balloon </li></ul></ul><ul><ul><li>Operationnal speed ~ 20 m/s </li></ul></ul><ul><ul><li>Endurance: weeks to months </li></ul></ul><ul><ul><li>Data downlink (170 Km LOS) </li></ul></ul><ul><li>Ground control station </li></ul><ul><ul><li>Mobile (container) </li></ul></ul><ul><ul><li>Control of UAV </li></ul></ul><ul><ul><li>Reception telemetry </li></ul></ul><ul><ul><li>Programming trajectories </li></ul></ul><ul><ul><li>Reception payload data + archiving buffer </li></ul></ul><ul><ul><li>Transmission to VITO-CDPC </li></ul></ul><ul><ul><li>Automatic flight planning </li></ul></ul>Pegasus is a project <ul><li>Payload: Medusa (ESA contract) </li></ul><ul><ul><li>High resolution RGB + PAN camera (GSD 30 cm) </li></ul></ul><ul><ul><li>2kg, 100 cm length, 11 cm frontal diameter </li></ul></ul><ul><ul><li>CMOS sensor 10.000 x 1.200 pixels </li></ul></ul><ul><ul><li>Up to 1 image every 2 seconds </li></ul></ul><ul><ul><li>Max. data rate downlinked : 20 Mbits/sec </li></ul></ul><ul><ul><li>Equipped with IMU, GPS, transmittor-receptor, CDHU, … </li></ul></ul><ul><ul><li>Work: only during day </li></ul></ul><ul><li>Central Data Processing Center (at VITO) </li></ul><ul><ul><li>Software for processing raw data into information </li></ul></ul><ul><ul><li>HDF5 self decsribing format (NASA) </li></ul></ul><ul><ul><li>Up to level 4 data on request </li></ul></ul><ul><ul><li>Archiving of all data (database) </li></ul></ul><ul><ul><li>Actually ~60 TB available </li></ul></ul><ul><ul><li>Parallel processing </li></ul></ul>
    84. 89. Pegasus status <ul><li>Platform: Mercator-1 </li></ul><ul><ul><li>Preliminary tests successful abroad (world record) </li></ul></ul><ul><ul><li>Scale model tested in Helchteren in June 06 </li></ul></ul><ul><ul><li>Problem of batteries solved </li></ul></ul><ul><ul><li>Ready to fly - wait at Verhaert Space premises </li></ul></ul><ul><li>Payload: Medusa (ESA contract) </li></ul><ul><ul><li>Phase B achieved </li></ul></ul><ul><ul><li>Phase C/D started </li></ul></ul><ul><ul><li>Payload tested and ready for fly : expected for 03/08 </li></ul></ul><ul><li>Ground control station </li></ul><ul><ul><li>Most part of hardware available </li></ul></ul><ul><ul><li>Software still under development at Verhaert </li></ul></ul><ul><ul><li>Integration will be done soon by VITO </li></ul></ul><ul><ul><li>Customisation for OSIRIS (done by VITO) </li></ul></ul><ul><li>Central Data Processing Center (at VITO) </li></ul><ul><ul><li>Software under development </li></ul></ul><ul><ul><li>Some test already performed </li></ul></ul><ul><ul><li>Next tests (real-time) during Flanders day (22/04) </li></ul></ul>
    85. 90. 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 ~ swath
    86. 92. Update ~ 1 min Coverage ~ 72 km² Update ~ 25 min Coverage ~ 314 km² Pegasus with GSD = 1 m Swath = 10 km Update ~ 4.7 hours Coverage ~ 2500 km² 50 km R ~ 0.5 swath R ~ swath 50 km
    87. 93. Pegasus 1m
    88. 94. Zephyr : endurance WR (54+33 hours)
    89. 95. Zephyr in White Sand http:// www.newscientist.com/blog/technology/labels/UAVs.html
    90. 96. Thank you for your attention http://www.vito.be

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