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VAccess: A Virtual Remote Sensing Center for Virginia

VAccess: A Virtual Remote Sensing Center for Virginia. Menas Kafatos CEOSR CEOSR URL: http://www.ceosr.gmu.edu. April, 2001. Earth, Space, Remote Sensing, Data Systems. CEOSR is involved in several space-related interdisciplinary areas Space Sciences Astrophysics Solar Physics

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VAccess: A Virtual Remote Sensing Center for Virginia

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  1. VAccess: A Virtual Remote Sensing Center for Virginia • Menas Kafatos • CEOSR • CEOSR URL: http://www.ceosr.gmu.edu April, 2001

  2. Earth, Space, Remote Sensing, Data Systems • CEOSR is involved in several space-related interdisciplinary areas • Space Sciences • Astrophysics • Solar Physics • Earth Observing & Earth Sciences • Data Information Systems (S-I ESIP Project & Federation) • Satellite Missions • Aeronomy of Ice in the Mesosphere (AIM) (Phase A:Polar mesospheric Clouds) • IMAGE (Imaging the Ionosphere; on common platform with GIFTS) • ARGOS (RAD Hard Computing) • Remote Sensing for Regional Applications • Hyperspectral • Virtual RS Center for Virginia

  3. Current representative graduate student Earth science, RS & data information areas • Data Management and Knowledge Discovery Approach in On-line Earth Science Data Information System Design (Ph.D. thesis, summer 1998) • Hyperspectral Imaging Spectrometer Data Mining Using Genetic Algorithms • Hyperspectral Studies of Virginia Wetlands and Coastal Areas • North American Regional Vegetation Studies from 1982 to 1992 • Tropical Forest Biomass Density on Barro Colorado Island, Panama • Remote Sensing of Vegetation in South Vietnam and Effects of Defoliants • Lower Tropospheric and Sea Surface Temperature Differences as Related to Hurricane Development in the Atlantic Ocean • Interdisciplinary Studies of Climate Changes from Interannual to Millenial Phenomena Highlighting Cryolithohydroatmospheric Processes and the El Nino Southern Oscillation • Model of Hypothesized Dimethylsulfide-Temperature Regulation in Remote Oceans • Remote Sensing of the Neutral Density Medium in the Upper Atmosphere • Remote Sensing on the MSX Experiment and the Ozone Hole • Image Registration, Parallel Architectures and Rain Data • Remote Sensing of Oil Spills in the Red Sea • Remote Sensing and floods in Bangladesh

  4. CEOSR Themes, Projects and Relationships • Key to Chart • GCDC: Global Change • Data Center • DAAC: Distributed Active • Archive Center • TSDIS: TRMM Science • Data Information System • SSD: Space Sciences Div. • RSD Remote Sensing Div. • Research • Institutional • Links NASA GCDC VIRGINIA Coop Agreement SCS CEOSR VAcees DAAC TSDIS GMU ISE, AES, GES, Biology ESPP CHARM SIESIP Regional Projects COLA DSWA www.siesip.gmu.edu Coop Agreement GSFC Code 600 Space Sciences Directorate GSFC Code 900 Earth Sciences Directorate NRL SSD RSD Earth Science, Data Information Astrophysics

  5. RS: Leveraging Earth Observing Research Activities • Leverages existing grants & cooperative agreements in Earth & space science with national labs and NASA Headquarters (estimated > $4M for FY2001) • Has substantial student interest (many from industry) • Couples with State and No. VA focus & emphasis in Information Technology and Space • Closely ties to strengths in other related areas at COLA and SCS (Climate Dynamics, Atmospheric Science) & collaborative efforts with other GMU units (CAS, IT&E) • Leverages GMU expertise & strengths

  6. INFORMATION TECHNOLOGY STRATEGY • Development of science scenarios which drive the content-based searching to serve particular user communities • Web accessibility • Content-based browsing • Integration of tools accessibility with data set accessibility to allow meaningful, user-specified queries • Integration of freely/easily accessible visualization/ data mining and analysis tools with relational data base management system

  7. VAccess:Virtual Remote Sensing Center of Excellence:Providing RS Data & Information Products for Regional Applications in Virginia • A STATE-WIDE, SATELLITE-DERIVED AND OTHER ENVIRONMENTAL DATA, & INFORMATION PRODUCTS, • FOR • LOCAL, REGIONAL & STATE NEEDS WITH USER-DETERMINED NEED FOR STUDIES, INFORMATION, & SOLUTIONS • AN ALLIANCE BETWEEN 6 UNIVERSITIES LED BY CEOSR Initial Funding FY 2001: $1M • Prototyping an operational alliance of academia, State interests, NASA & the commercial sector

  8. Vaccess: Virtual Remote Sensing Center of Excellence:Providing RS Data & Information Products for Regional Applications in Virginia • Partners • GMU • JMU • ODU • Hampton • Virginia Space Grant Consortium • UVA • VT

  9. State of Virginia and the Use of Remote Sensing Data

  10. Virginia Access to Remote Sensing Data - Concept and ExamplesFigure 1 Special Capability Users Community Server Graduate Courses Certificate Courses Distance Learning Course Materials Instructor List Schedule Sites Topography Maps Road Maps Demographic Data Education & Training Low-Cost Regional Data Virginia’s Virtual Remote Sensing Data Information System Application DataBases Wetlands Data Land Classifications Vegetation Collaboration Support Algorithms Statistical Tools Protocol Data Metadata Files HSI Signature Library Landsat 7 AVHRR MODIS ASTER TRMM SeaWIFS GOES SSM/I NextRad Datasets: Satellite & Other Vegetation Structural Materials Roadway Materials Sources – AVIRIS, EOS-1, In Situ Synthetic Aperture Radar Statewide Application Licenses Vendor MOUs DEM Surface Objects Foliage Penetration Images

  11. VAccess Support Staff Services Virginia’s Virtual Remote Sensing Data Information System Staff Services • Task: Virginia-wide Data Access • & Software Licensing Goals • Minimize cost to obtain/buy • Data from diverse sources • Minimize cost to obtain • state-wide software licenses for • Academia • Approach: Form small group from • Industry & academia to determine • Ways to achieve goals • Benefits: User access to more data • At lower cost; • Providers gain more users along • with product/tool new ideas • Outreach • - Partners and Alliances • Web page(s) Development • Brochure Preparation • Project Management • Coordination • Planning • Integrated Budgeting • Project Reporting • Performance Metrics • PODAR – perform other duties as required Statewide Application Licenses

  12. Manage and execute HSI projects and programs for the GMU/CEOSR • Provide research support for other GMU departments and other research partners • Conduct R&D in support of these programs • Manage and execute remote sensing programs for CEOSR • Develop and maintain capability for responding to local, state, region, and national emergencies • Support VA, region, and national hyperspectral imagery initiatives

  13. Hyperspectral Sensing An Enabling Mature Technology

  14. Hyperspectral Technology Applications • Agriculture and Forestry • vegetation type identification, assessment of vegetative stress, crop yield, resource monitoring • Geology • mapping of minerals and rock types for mineral and hydrocarbon exploration • Environmental • detection of spills, baseline studies, land use planning • Marine and inland waters • mapping of shoreline materials, bathymetry, water quality • Civil • Transportation corridors, city planning

  15. Figure 1. The Warrenton-Fauquier Airport based Piper platform is shown with the SAR installed.

  16. Figure 2. SAR image and topographic retrieval using WINSAR.

  17. Reconfiguration of PALDaily data into Tiled Regions NOAA/NASA 8-km Pathfinder AVHRR Land (PAL) Data Set, used in the Production of Vegetation Dynamics Data Products: LAI, fPAR, Land Cover Change... AVHRR Channel 1 & 2 and NDVI (Respectively) Daily Time-series of Egypt 1981-1994

  18. Customized MODIS Data Applications for V Access 1. Because the MODIS senses all the earth’s surface in 36 spectral bands spanning the visible (0.415 µm) to infrared (14.235µm) spectrum with at nadir spatial resolution of 1 km, 500 m and 250 m, MODIS remote sensing data are of interest not only to land and ocean scientists but also to atmospheric and environmental scientists. 2. Native MODIS data files are stored in HDF-EOS (Hierarchical Data Format – Earth Observing System), a file format that does not currently have wide support. 3. MODIS land product imagery is in a new map projection called the Integerized Sinusoidal (ISIN) projection which is not supported by most existing software packages. 4. MODIS dataset sizes are too big to process by users. 5. Customized (subsetted, data format converted, reprojected and GIS compatible) MODIS datasets are very important for most local users. 6. V-Access will provide customized MODIS Level-1B (MOD02), Surface reflectance (MOD09), and NDVI/EVI (MOD13) as starting points.

  19. Customized MODIS Data Infrastructure for V Access Near real time MODIS data from GSFC DB MODIS data on ECS Subscription Subsetting software Subsampling software Reprojection software Mapping software GIS Conversion software Visualization software VAccess MODIS Processing Toolkits Customized MODIS Datasets in V-Access Database Web Access by users ftp VAccess Users

  20. Hydrology and Forest Fire • Objective: Provide regional moisture information and assessment of fire potential • Potential Users: EOF, DOA, EPA • Approach: develop RS and in situ data set to estimate basin scale water budget; develop fire model • Output: Soil moisture and ET maps from Landsat/MW sensors, GOES/MW rainfall, Land Surface temperature, vegetation/surface type from AVHRR and MW sensors, fire product; basin water budget, fire potential model • Validation/ancillary data: NOAA surface gauge rainfall and temperature, River runoff, DOF Historical fire reports

  21. Natural hazard Monitoring, Prediction and Assessment Objectives: Improved regional monitoring, assessment and prediction of natural hazards such asHurricane, snowstorm, freezing rain, flash flood Potential users: VDOT, DOA, DOT, EPA, FEMA Approach: examine RS and in situ data for extreme cases to determine model output statistics (MOS) bias Output: Merged GOES/MW rain/snow, model bias, soil moisture, flash flood potential, flood area assessment, aerosol Validation/ancillary data: NEXRAD, surface type, surface precipitation, wind, temp and upper air sounding data, NCEP and regional model model output statistics (MOS), weather related traffic accident reports, air pollution data, historical flood data

  22. Proposed VIRGINIA ACCESS Center Architecture2001Figure Industry User Student or Educational User GMU User Partner User INet Client Side Middleware for Search and Browse Tailored Data Bases By Discipline By Geographic Area By Community Order via INet INet Server Side Processor(s) Foreign GMU Partners NASA NOAA Satellite Down Link For Tailored Databases

  23. GOES Ground Station Data Storage Filer Data Storage AVHRR Ground Station Application Servers(Labs) Production Area(engine) Partner’s Data Set ARCINFO ENVI DB Server Coding Area Partner Alpha Partner Beta Web Host Users

  24. Virginia Access to Remote Sensing Data - Roles of GIS Topography Maps Road Maps Demographic Data Distance Learning Support Course Materials Instructor List Schedule (modules on integrating GIS/RS analysis) Low-Cost Regional Data Virginia’s Virtual Remote Sensing Data Information System These data are mostly in GIS formats. GIS can provide an integrated environment to bring together these data and RS data Algorithms Statistical Tools Protocol Data Metadata Files (spatial analysis and statistical capabilities in GIS) Collaboration Support Satellite Datasets Application DataBases Wetlands Data Land Classifications Vegetation Landsat 7 AVHRR MODIS ASTER TRMM NextRad (some RS data are available in GIS formats) Synthetic Aperture Radar Statewide Application Licenses (ESRI GIS sofware Licenses) DEM Surface Objects Foliage Penetration Images (DEM and topo data are handled efficient by raster-based GIS)

  25. Data Analysis and Visualization Tools • ENVI/IDL • GIS (ArcView/Arc/Info) • Splus • Training on Tools • Local usage • Regional applications/Scientific research • Integrate tools with data for access through the Internet • General system setup • Setup for specific research work • Knowledge Discovery & Data Mining • Content-based search • Knowledge discovery from RS data and other Earth science data • Web-based Tools • Data access, leverage existing tools • ·VDADC • ·SIESIP/GDS • ·DIAL • ·WMT prototype (International standard) • Metadata access • ·Metadata ingesting/creating • ·DBMS • ·XML technology (DIMES)

  26. Use of Metadata ServerExample: Interface with GrADS/DODS Server User/Scientist General User Call out MetadataBrowse/Search GrADSClient DODS URL Client workstation GrADS/ DODSServer Metadata(XML)Server Remote systems

  27. The Future: Distributed Client-Server Architecture Clients Metadata request/result (XML) Data request/ result (DODS) DIMES: Distributed Metadata Server DIMES Register DIMES DIMES Ingest Tool Box Ingest Tool Box ... ... To be developed Server (DODS, GrADS/DODS) Server (DODS, GrADS/DODS) DATA DATA Super Data Server Super Data Server

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