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Environmental Knowledge for Disaster Risk Management Challenges in Integrating Geospatial Technologies

Environmental Knowledge for Disaster Risk Management Challenges in Integrating Geospatial Technologies. P. S. Roy (psr@iirs.gov.in). Indian Institute of Remote Sensing ISRO, Dept. of Space, Govt. of India Dehradun. We Live in Two Environments. Natural. Manmade. Managed. Self-Regulated.

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Environmental Knowledge for Disaster Risk Management Challenges in Integrating Geospatial Technologies

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  1. Environmental Knowledge for Disaster Risk Management Challenges in Integrating Geospatial Technologies P. S. Roy (psr@iirs.gov.in) Indian Institute of Remote Sensing ISRO, Dept. of Space, Govt. of India Dehradun

  2. We Live in Two Environments Natural Manmade Managed Self-Regulated And they are increasingly in conflict..

  3. Major Issues & Challenges CO2 (ppm) (1950 : 29; 2009 : 380; 2050 : 480) Deforestation (mha/yr) – ( 1950 : 15 ; 2007 : 13; 2050 : 10 ) Per capita forest present & future (ha) (1950: 1.13; 2007: 0.62; 2050: 0.35) Species loss - Present : 40-200 / day; 5-20%; lost during last century Projected : 2050 : 15-35%; Ozone Depletion (DU) (1950 : 160 ; 2009 : 110; 2050: 220 ) Atmosphere Atmospheric loading Loss of ozone layer Green house effect Global Mean Temp (oC) (1950 : 13.96 ; 2009: 14.59; 2050:16.30) Bio-resources Deforestation Loss of species Loss of critical habitats Climatology Global warming Extreme Weather Food Security Water Security Enhanced Natural Disasters Social & Health Security Snow & Glaciers Melting of Mountain Glacier Loss of Polar Ice cap Average glacier Thickness change (cm / yr) 1990 – 40 ; 2050– 95; Physical Resources Land dynamics Unequal Water availability Soil degradation Biodiversity loss Ocean Rise in sea level Destruction of coral reefs Ocean Circulation Global freshwater withdrawals 1990 – 3500 km3 ; 2000 – 4430 km3 Agriculture water use : 70%, Expected rise by 18% by 2050 to maintain agriculture production ; 26% global wetland lost Sea level rise (1950 : 2cm ; 2009 : 9cm; 2050: 16cm ) 27 % of coral reef destructed

  4. FLOOD MAP DROUGHT MAP DAMAGE MAP HAZARD ZONES RISK MAP Knowledge Building for Disaster Risk Management Data Data (Observation) for Information [Process] Information Information for Decision [Model] DSS/SDSS Decision for Action [AI] Measurement, Monitoring, Modeling, Planning, Decision Making, Management Expert System

  5. How Can Geospatial Technologies Help in Knowledge Building? • EO Systems (spaceborne & airborne) • SENSORS with various SPATIAL, TEMPORAL SCALES, EM REGIONS • GEOPHYSICAL PRODUCTS from RS • GIS • Link, view, analyse all geo-linked data • GPS • LOCATIONS, MOBILE MAPPING • (INTEGRATION) : Sensor-Web, Web-GIS, Crowdsourcing GI, • Mobile Mapping, Spatial/ Non-spatial Databases, AWS, etc. • APPLICATION :Modelling & Decision Making with knowledge

  6. Earth Observation From Space (High Res.) Local Multi-resolution Capability (Low Res.) Regional

  7. EARTH OBSERVATION FROM SPACE Time-Series & All-weather Observations 11.04.2008 (pre-flood) 20.08.2008 (post-flood) 24.08.2008 27.08.2008 22.08.2008 23.08.2008 10.09.2008 05.09.2008 03.09.2008 29.08.2008

  8. Depends on • Phase of the disaster • Type of the disaster • Extent and severity • …. Spatial Vs. Temporal Resolutions – Trade-offs High temporal resolution Large swath Medium temporal resolution Large swath Low temporal resolution Limited swath Low spatial resolution Global level information Low temporal resolution Very limited swath Coarse spatial resolution Regional level information Medium spatial resolution Local level information Global to Local High spatial resolution Location specific information

  9. Location Based Services and Mobile GIS • Mobile Mapping – Integration of 4 Technologies • Lightweight hardware • GPS • Telecommunications • GIS

  10. Automatic Weather Stations • Special sensors for • measuring soil moisture • Data transmission through • communication satellites • Consistency in data recording • Enhanced frequency of • coverage • Coverage of inaccessible • areas, all weather and all • time operations • Affordable alternatives • to get detailed weather • information like rainfall, • humidity, temperature, • etc.

  11. Geo-spatial Analysis Using GIS Village Infrastructure Water Resources Soil Depth to Water Table Village boundaries Transport network Settlements Drainage system Canal network Census Information PRA/RRA data • SIMPLE QUERY • SPATIAL QUERRYING • SINGLE LAYER OPERATION • MULTIPLE- LAYER OPERATIONS • SPATIAL MODELING • SURFACE ANALYSIS • NETWORK ANALYSIS • POINT PATTERN ANALYSIS • GRID ANALYSIS Land use Integrated Geo-spatial Analysis Cadastre

  12. EM Conceptual Framework • Survey of India • Forest survey of India • Public Works • Department • Indian Railways • Ministry of shipping and • surface transport • Department of Space • Land Records • department • Central Water • Commission • Ministry of Defense • Indian Meteorological • Department • National Hydrographic • Organization • Ministry of agriculture • Geological Survey of • India • Ministry of Home Affairs • …….. • National Informatics Centre • Ministry of Industry • Public Works Department • Office of Registrar General of India • Bureau of Economics and statistics • Central Water Commission • Indian Meteorological Department • Ministry of agriculture • Ministry of Home Affairs • Ministry of Health • ……. Spatial Data Non-Spatial Data INPUTS NDEM EM Data Server Expert System Shell Decision Analysis Spatial Analysis Authorized user community Information system Spatial Output Decision Maker Output Statistical Report DSS/ SDSS Decision Outcome Request Other GUI Response

  13. Non spatial data IDRN link Sensitive Areas Converting Knowledge base into DSS / SDSS • Health facility • Hospitals • Diagnostics centers • Medical shops • Doctors/Medical staff information • Blood banks • Eye banks • Surgical instrument shops • Infrastructure • Admin boundary • Roads • Rail and Railway station • Police station • Airports/helipads • Settlement • Drainage/surface water bodies Census Population Density Income level occupation • DEM • Relief shelter locations • Education centers • Fire stations • Forensic Lab • Mining areas • Industry locations Input Input Utility Power lines pipelines communication network Data server Input Evaluation & validation INPUTS No Yes EXPERT SYSTEM SHELL Database Manag. System Logical operators Multi-criteria Spatial Modeling Decision Outcome Statistical Report Output Spatial Output Request DECISION MAKER SDSS SOFTWARE

  14. Mapping Indicators of Climate Change using Space inputs Climate Change Research Initiatives • Glacial Retreat in Himalaya • Change in Polar Ice Cover • Upward Shift in Timberline & Vegetation in Alpine zone • Bleaching of Coral Reefs • Desertification • Disasters - Flood, Drought • GHGs & Other Gases - Variability of atmospheric CO/ CO2/ NO2/ CH4 • Biomass burning/ forest fire • Terrestrial Carbon • Atmospheric Aerosols & Trace gases Monitoring the Agents of Climate Change • Impact on Food Security • Hydrology • Coastal Zone • Ocean Productivity • Land Surface Changes in Regional Climate • Simulations over India • Role of Indian Ocean in Climate variability Modeling the impact of climate change

  15. National Database to address Environmental Issues & their Web Dissemination ISRO & Multi-institutional Initiatives Forest & Vegetation Soils Geomorphology Wetlands Land Degradation Land Use /Land Cover • AWiFS -1 : 250000 • LISS III - 1 : 50000 NR Census Layers * User Projects

  16. Some Solutions: Sensor Web • Theinteroperability framework for accessing and utilizing • sensors and sensor systems in a space-time context • via Internet and Web protocols • A set of web-based services may be used to maintain • a registry of available sensors. • Thesameweb technology standards for describing the • sensors’ outputs, platforms, locations, and control • parameters, thus ensuring interoperability..

  17. Move from 2-D description to 4-D interaction and beyond? • Past • 2-D flat map displays • User as observer from 2-D description to 4-D interaction • Future • Effective 3-D visualization • 4-D incorporation of time: “The time has come for time.” • Via agent-based modeling / cellular automata? Or how? • agents (e.g. vehicles, fires or people) interacting over time in a raster (cell)-based environment according to established rules • 5, 6 and 7-D incorporation of touch (pressure, texture, temperature), sound and smell into modeling/simulation environment) • User as participant • Users (researchers, professionals, the public) interact with the model; • Participatory GIS: the public as the planner.

  18. Adapting Advanced Methods for Knowledge Discovery from large databases Data Mining Expert System Artifical Intelligence Operational Database Knowledge Discovery in Database (KDD) Process Data Cleaning Data Preparation Training Set Data Mining Data Warehouse Operational Database Extracted Pattern Verification & Evaluation

  19. Distributed Geoprocessing, Spatial Analysis and Modelling Operational Data-1 User Defined Products Geo-visualizations Operational Data-2 Information Display Platform Independent Solution for Geo-spatial analysis DSS/SDSS Outputs Operational Data-3 Outcomes from Expert System Operational Data-n Many more…

  20. Distributed Geoprocessing contd.. An Example of Drought Assessment and Early Warning System RS Satellite Systems Soil Moisture measurement & Changes Receiving Stations NDVI Space Based Inputs Real-time data Drought Monitor & Early Warning System Meteorological Data Ground Observation Network Real-t ime data Real time data

  21. Using Crowdsourcing & VGI Technologies in Disaster Applications • New mechanism for voluntarily producing & disseminating geographic • information using ICT/web-based mapping services (Goodchild, 2007). • Especially useful in disaster/ emergency applications where real-time • updated information (in case of a disaster) is required or where spatial • information is not adequate. • Examples – Wikimapia, OpenStreetMap, Google MyMaps, etc. • Success Stories – Haiti Earthquake of 2010, Wild Fires of Sanata Barbara • (USA) in 2007-09, etc. Challenge • Integration of crowdsourced • & authoritative data? • Data quality? • Credibility of contributor?

  22. Indian EO Missions - The Near Future Oceansat-3 Ku Band Scatterometer Resourcesat-3 LISS-3 WS GHGSAT Spectrometer (being planned) 2012-16 RISAT 3/4 LX SAR RISAT-1 C-band SAR 2011-12 Resourcesat - 2 R LISS III, LSS IV , AWiFS INSAT-3D VHRR, Sounder Resourcesat – 2 LISS III, LSS IV , AWiFS Geo HR Imager 50m resolution Scatsat Ku Band Scatterometer IMS-ATM Being Planned SARAL Ka band Altimeter Cartosat- 2C/ 2D 80 cm res. MEGHA-TROPIQUES SAPHIR, SCARAB & MADRAS ISTAG MAGIS, MAVELI, MAPI Cartosat- 3 30 cm res.

  23. Integrate what we know in to a knowledge system We Need Better Ways to… • Represent • Understand • patterns, • relations, • processes • Manage • Communicate

  24. Thank you.. www.iirs.gov.in On Mission for transferring technology through education, research & capacity building…..

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