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VAccess Team Meeting

VAccess Team Meeting. First Meeting of VAccess Team 19 th Floor 301 East Byrd Street Virginia Economic Development Partnership Richmond, Virginia July 9, 2001. GMU ODU JMU VT UVA W&M VSGC Hampton. VAccess: A Virtual Remote Sensing Information Access Center

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VAccess Team Meeting

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  1. VAccess Team Meeting First Meeting of VAccess Team 19th Floor 301 East Byrd Street Virginia Economic Development Partnership Richmond, Virginia July 9, 2001 GMU ODU JMU VT UVA W&M VSGC Hampton

  2. VAccess: A Virtual Remote Sensing Information Access Center for Regional Applications in the Commonwealth of Virginia Menas Kafatos CEOSR GMU ODU JMU VT UVA W&M VSGC Hampton • CEOSR URL: http://www.ceosr.gmu.edu • VAccess URL: http://www.VAccess.gmu.edu July, 2001

  3. VA julyVAccess Discussions - July 9, 200110th 1 1:00PM Introduction to VAccess Menas Kafatos Introductions, Overview, Status of VAccess 1:15PM Global EO Data for Regional Applications James McManus 1:25PM H S I Technology, Algorithms and Applications Richard Gomez 1:35PM Environmental Scenarios George Taylor 1:45PM Infrastructure, GIS & Other Tools Ruixin Yang 1:55PM VAccess Process Hank Wolf 2:05PM Landscape Epedemiology Tom Allen 2:25PM Visualization Testbed James Barnes 2:45PM Advanced Analysis Techniques for RS Data Pat McCormick 3:05PM Break GMU ODU JMU VT UVA W&M VSGC Hampton

  4. VAccess Discussions - July 9, 2001 3:20PM Virginia Space Grant Consortium Mary Sandy 3:45PM Interactive Internet GIS/RS Tutorial James Perry 4:05PM Natural Resources Applications Randy Wynne 4:25PM IR Atmospheric Sensor Gaby Laufer 4:45PM Summary: Action Items, TAC Meeting Plans, Schedule Menas Kafatos 5:00PM End of Meeting 5:30PM Optional Dinner Discussions of Any Open Issues GMU ODU JMU VT UVA W&M VSGC Hampton

  5. Earth, Space, Remote Sensing, Data Systems in CEOSR • 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 VAccess GMU ODU JMU VT UVA W&M VSGC Hampton

  6. VAccess:Virtual Remote Sensing Information Access Center: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 GMU ODU JMU VT UVA W&M VSGC Hampton

  7. 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 • VIMS (William & Mary) • VT GMU ODU JMU VT UVA W&M VSGC Hampton

  8. State of Virginia and the Use of Remote Sensing Data GMU ODU JMU VT UVA W&M VSGC Hampton

  9. Proposed Initial VAccess Data Sets for Prototyping Applications • Vegetation Products (agriculture & forestry) • AVHRR data from NDVI, LAI, ect. • MODIS 250m, 500m, 1000m • Pollution runoff-related products (Chesapeake Bay, ect.) • EO-1 (HSI); AVIRIS (HSI); Landsat • LU/LC Products • EO-1(HSI); AVIRIS (HSI); Landsat • Merged Products • SAR & HSI • HSI & visible (on Orion sounding rocket- possibly for the future) • Ocean Products • (possibly) SST data from AVHRR • Sea WiFS (selected products) • Littoral regions (NEMO HSI –future?) • Natural Hazards (hurricanes, fires, ect.) • TRMM • GOES • High Resolution, Commercial, Remote Sensing Data • TBD (in consultation with the Advisory Committee and the NASA Data Buy program) • SPOT (from VDEP and other state agencies) • Ikonos (NASA Data Buy Program) • Ground Data • Variety of GIS and other products for complementing RS data

  10. The Utility of AVHRR and MODIS Time-series Data in Remote Sensing Application Studies James McManus GMU July 9, 2001

  11. Introduction The purpose of the talk is to explain how VAccess can utilize data from the • NOAA’s Advanced Very High Resolution Radiometer (AVHRR) and • NASA’s Moderate Resolution Imaging Spectrometer (MODIS) In remote sensing application studies I will also explain the strengths of this type of data, in land surface applications, relative to higher resolution satellite data.

  12. Polar-Orbiting Operational Environmental Satellites (POES) AVHRR and MODIS are remote sensing instruments flown on board what are commonly referred to as POES type satellites. POES are Sun-synchronous, polar orbiting, wide field of view, low resolution (250 m to 4-km) satellites that are capable of view the entire earth within a one or two day period Examples of POES Satellites are: • NOAA series began in 1979 with NOAA-6 and continues today with NOAA-16 • Defense Meteorological Satellite Program (DMSP), which began in the 1960’s with more modern instruments being deployed in the 1980’s to present. • European Remote Sensing Satellites (ERS), began in 1981 with ERS-1 and continuing with ERS-2, which was launched in 1995. • NASA’s Earth Observation System, began with the launch of Terra (EOS/AM-1) in December 1999 and which will continue with the launch of Aqua (EOS/PM-1) in 2001 • Other satellites include the FY series from china and SeaWiFS, as well as non sun-synchronous satellites such as the Tropical Rainfall Measuring Mission (TRMM)

  13. Purpose of POES POES satellites were originally designed for meteorological purposes. • POES daily global coverage enables the monitoring of clouds and other atmospheric meteorological variables that required diurnal data frequency. • POES data are used in conjunction with data from Geostationary Satellites (GEOS), which do not provide global coverage, to monitor the atmosphere. In the mid 1980’s data from the AVHRR instrument, flown on the NOAA series of satellites, began to be used for monitoring vegetation. • This was partially a reaction to the high cost of data from satellites such as LandSat and SPOT, which are specifically designed to study the land surface. • In contrast data from the NOAA series as well as NASA’s EOS series are free. • They also provided data at a temporal frequency and spatial coverage where Global and regional vegetation dynamic studies can be performed. • Compositing methods have been developed that remove cloud cover, enabling the continuous monitoring of vegetation and other land surface variables, such as temperature, on a bi-weekly bases.

  14. Instrument specifics • MODIS is flown on NASA, Terra & Aqua • launches 1999, 2001 • 705 km polar orbit, sun synchronous descending (10:30 a.m.) & ascending (1:30 p.m.), providing 1 to 2 day global coverage • Sensor Characteristics • 2300 km (cross track) and 2000 km (5 min. granule along track) • 36 spectral bands ranging from 0.41 to 14.385 µm • Spatial resolutions: • 250 m (bands 1 - 2) • 500 m (bands 3 - 7) • 1000 m (bands 8 - 36) AVHRR is flown on the NOAA series of satellite Launch date: 6/23/81 (NOAA-7), 12/12/84 (NOAA-9), 9/24/88 (NOAA-11), 12/30/94 (NOAA-14) Sun synchronous, near polar (98.8 degrees) at 833 km Ascending (14.30 (NOAA-7), 14.20 (NOAA-9), 13.30 (NOAA-11), 13.30 (NOAA-14) LST), providing 1 day global coverage Sensor Characteristics 2700-km (cross track) and 102 minutes orbit period 5 spectral bands ranging from 0.58 to 12.5 µm Spatial resolutions: 1.1 km for Local Area Coverage (LAC) and High Resolution Picture Transmission (HRPT) 4 km for Global Area Coverage (GAC)

  15. Utilization of AVHRR and MODIS data to Monitor Vegetation and Other Land Surface Variables • The +2000-km cross track swath of these instruments, compared to Landsat-7 ETM 185-km swath (16-day repeat cycle), enable data to be collected over the same region on a 1 or 2 day temporal frequency. • The data is also continually collected for the entire globe, compared to higher resolution satellite data, such as Landsat and SPOT, which selectively choose images. • As stated previously the higher temporal frequency of the data enables compositing methods to be used that remove cloud cover, resulting in the ability to produce cloud free land surface parameters on a bi-weekly temporal frequency. • This gives VAccess the opportunity to provide state wide land surface products, supplying information on the condition of vegetation as well as other environmental variables, on a bi-weekly bases. • This will provide base information to perform a wide variety of environmental studies.

  16. A simple example of a land surface product that can be produced on a bi-weekly bases is the Normalized Difference Vegetation Index (NDVI) • NDVI is derived from the red and near infrared channels on • satellite instruments such as AVHRR and MODIS NDVI = Rch2 - Rch1/Rch2 + Rch1 where Rch1 is the land surface reflectance in the visible wavelengths (580 to 680 nanometers) and Rch2 is the land surface reflectance in the infrared wavelengths (725 to 1000 nanometers) • NDVI is Widely Used for Monitoring Global Vegetation • Dynamics having been Applied to: 1) Studies of the Global Carbon Cycle 2) Modeling the Hydrological Cycle 3) Crop monitoring 4) UN’s Famine Early Warning System 5) Producing a wide variety of other vegetation products including: Net Primary Production (NPP) Leaf Area Index (LAI)

  17. Example of NDVI Image Derived from AVHRR 10-day Composite AVHRR NDVI Image of Virginia, July 1-10, 1992

  18. AVHRR VS. MODIS • Both AVHRR and MODIS can be used to produce land surface variables such as: • Surface Temperature, Land Cover, Thermal Anomalies/Fire, Leaf Area Index, Net Primary Production and Vegetation Cover • MODIS is a more advanced instrument than AVHRR, and as a result can produce more accurate products. • However it currently has less than two years of data available, this limits its use in vegetation dynamic studies. • AVHRR has +20 years of data, stretching over multiple satellites • Efforts such as the NOAA/NASA Pathfinder project have produced calibrated data sets over this entire time period, providing an extremely valuable historical record of the environment. • The historical record also permits the development of anomaly products, which compare the entire 20 year time period with a specific time, showing anomalies from the mean.

  19. Comparison Between MODIS and AVHRR The MODIS 250m-resolution multi-spectral observations clearly discriminate different types of vegetation and urban areas in this image. The subsets show MODIS near-infrared band 2 (859nm) at 250m resolution (top right) and the corresponding NOAA14 AVHRR 1km band 2 (bottom right) over the Choptank River and the Cambridge area, in the Delmarva Peninsula. The improved spatial resolution of MODIS data over the heritage AVHRR data is apparent.

  20. AVHRR Products Three variations of AVHRR products will be produced 1) Products produced from the NOAA/NASA Pathfinder AVHRR LandPAL 8-km data set, covering the time period from 1981 to the present. • The PAL data set has been calibrated over the entire temporal range of AVHRR and mapped to a standard projection. • The daily data has been reconfigured into regional time-series files that will allow new compositing methods to be utilized, improving cloud removal, resulting in more accurate vegetation parameters such as LAI. 2) Products produced, from level-1b data at the original 4-km GAC resolution, covering a shorter time period. 3) Prototype products produced from HRPT data collected at GMU The products will focus on vegetation and include NDVI, LAI, Land Cover Change and fraction of Absorbed Photosynthetically Active radiation (fAPAR) Experimental products including Land Surface Temperature, Vegetation Anomalies and Net Primary Production (NPP) will also be explored.

  21. MODIS Products A wide variety of high level products are currently being produced from MODIS data including: Surface Temperature, Land Cover, Thermal Anomalies/Fire, Leaf Area Index, Net Primary Production and Vegetation Cover These products will be acquired for VAccess and technical issues such as map re-projection will be dealt with. Standard MODIS products that may be useful in monitoring atmospheric pollution and the Chesapeake bay will also be examined. Data obtained through MODIS’s Direct Broadcast system will be aquired.

  22. Conclusion Producing and acquiring land surface data sets derived from POES satellites, will enable VAccess to provide state wide products, for the Commonwealth of Virginia, on a bi-weekly bases. By doing this VAccess will provide base products that can be utilized in a wide variety of Environmental studies and monitoring efforts including: 1) Forest and Agricultural monitoring 2) Non-point Pollution runoff Monitoring 3) Air Quality studies 4) Wetland inventories 5) ...

  23. VAccess HSI Project GMU/SCS/CEOSR Dr. Richard B. Gomez Hyperspectral Imagery (HSI)Technology

  24. HyperspectralImagery • Data of the same scene collected simultaneously from hundreds of spectral bands, and registered on a single format. • A spectral band is a portion of the electromagnetic spectrum over which a sensor detects and measures scene reflections or emissions.

  25. Reflected and Emitted Energy UV BLUE GREEN RED NIR SWIR MWIR LWIR What you see is not whatyou get!

  26. Pushbroom Hyperspectral Sensing Pixel Spectrum Flight Line Intensity Single Pixel Wavelength Spatial Pixels Spectral Bands Single Sensor Frame Series of Sensor Frames

  27. AISA Hyperspectral System Airborne Hyperspectral Systems

  28. Data Space Representations • Image Space - Geographic Orientation • Spectral Signatures - Physical Basis for Response • N-Dimensional Space - For Use in Pattern Analysis

  29. Oil Spill Program Objectives A well-managed oil spill response for the Patuxent River in the Chesapeake Bay area serves to: • Protect human life • Develop mitigation processes • Identify vulnerable coastal locations before a spill happens (reduces the environmental consequences of both spills and cleanup efforts) • Establish protection priorities and identify cleanup strategies

  30. Dr. George Taylor

  31. Remote Sensing and the Environmental Sciences • Goal: Demonstrate and encourage the application of remote sensing technology to pressing and emerging issues in the environmental sciences and policy • Multiple Media • Upland landscapes (e.g., agriculture, forestry, brownfields) • Rivers, Streams and Reservoirs • Estuaries and Wetlands • Bay and Near-Coastal Waters • Atmosphere (air quality) • Integrated and regional systems (e.g., urban-suburban-rural systems with multiple landscape types)

  32. Premiere Issues in the Environmental Sciences • Wetland ecology and management • Contaminants (organic and inorganic) in soil, surface water, subsurface, and plant/animal • Restoration/remediation of contaminated sites • Air quality (e.g., nitrogen, ozone, PM) • Stress detection and management in managed (e.g., forests) and more natural stands of vegetation • Invasive species monitoring and management • Ecological risk assessment and management

  33. Demonstration Scenarios • Wetland ecology and management • Atmospheric nitrogen deposition and eutrophication in the Chesapeake Bay • Monitoring contaminants in terrestrial landscapes • Stress detection in plant canopies

  34. Ruixin Yang

  35. 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

  36. VAccess Hardware Architecture GIS Lab Application Servers DB Server VPN Solution VPN Solution Programming Mail Server Data Sets Filer Temp Data Storage FTP Server Web Server Partner Alpha Partner Beta AVHRR Ground Station Key GMU-Partners Software Hardware

  37. Software and IT components • 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/specific) • 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)

  38. VAccess System Architecture Industry User Partner User Student or Educational User GMU User INet Client Side Middleware for Search and Browse Local User Local user 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

  39. Virginia Access to Remote Sensing Data - Roles of GIS These data are Mostly in GIS Formats. GIS can provide an Integrated environment to Bring together These data & RS data. Spatial Analysis & statistical Capabilities in GIS Community Server Collaboration Infrastructure Lo-Cost Regional Data Prototyping Applications for VIRGINIA ACCESS Application DataBases Education & Training Modules on Integrating GIS/RS analysis HSI Signature Library Global RS Datasets Some RS data Are available In GIS formats Radars: SAR NextRad Key GMU Non-GMU DEM and Topo data Are handled Efficiently by Raster-based GIS People Process HW/SW Data HW/SW

  40. Hank Wolf

  41. State of Virginia and the Use of Remote Sensing Data GMU ODU JMU VT UVA W&M VSGC Hampton

  42. Technical Advisory Committee Advise re: High-Level Priorities, Plans, Needs, & Emphasis Areas VAccess Process Overview • Application Scenario Examples • Nitrogen, Contaminants & Vegetation Stress • Water Quality & Wetland Assessment • Agriculture & Forestry Resource Management • Oil Spill Analysis and Mitigation • Natural Hazard Monitoring & Prediction • Analysis Techniques for Virginia Hazards • Landscape Epidemiology • = Mosquito-borne Illnesses • RS Data Sets • H S I - SAR • MODIS - AVHRR • LandSat - MISR • IKONOS • Other NASA Data • Buy Products Subset & Apply To • Building Infrastructure • Center Architecture • Functional Architecture • Data Analysis/Access • GIS • HSI Library/Access • Direct Broadcast Reception • a. User Education & Awareness • -RS Algorithms, Tools, H S I • -Data Visualization Test Bed • -GIS/RS Tutorial • Natural Resources Tutorial b. Future Workforce Training Hardware: IR Atmospheric Sensors Receiving Stations Software: Tools Training Selected Prototypes User Feedback

  43. Proposed Significant Project Activity Process Subcontracts with VAccess Team Contract with SSC P.I. Activity Baseline Priority Activity Listing Planned; Active; Completed Technical Advisory Committee Ranked Selection Criteria: - Regulatory; - Programmatic; - Decision Support; - Legislative Factfinding PI Approval Emphasis Areas & Priorities Proposed Activity Plan Objectives; Design Expected Results; Schedule; Costs; Metrics Map of RS Data To TAC Priorities VAccess Team Scenarios’ Inputs

  44. Technical Advisory Committee Priority Definition; Emphasis Area Criteria; Data/Products Validation P.I. VIRGINIA ACCESS Project Component Relationships Research & Applications: Goals & Objectives Data Needs Interfaces Expected Outputs Approved Activity Education & Training Data: Earth Observing, Regional & High Resolution RS Subsets Data Attributes Data Files Storage Sites Access Techniques Design Requirements Implementation Concepts Access: Protocols Installation Requirements Access Requirements Hardware/Software Standards: Data Access/Catalog FTP Sites Distributed Access & Analysis Data Search Prototype(s) Stakeholder Feedback

  45. Emphasis Areas & Priorities will Drive Implementation Completion Virginia Access to Remote Sensing Data - Concept and Examples Special Capability Users Community Server Algorithms Statistical Tools Protocol Data Metadata Files Collaboration Infrastructure Topography Maps Road Maps Demographic Data Low-Cost Regional Data Prototyping Applications for VIRGINIA ACCESS Application DataBases Education & Training Wetlands Data Land Classifications Vegetation Graduate Courses Certificate Courses Distance Learning Course Materials Instructor List Schedule Sites HSI Signature Library Global RS Datasets Vegetation Structural Materials Roadway Materials Sources – AVIRIS, EO-1, In Situ Landsat 7 AVHRR MODIS ASTER TRMM SeaWiFS GOES MISR SSM/I Radars: SAR NextRad DEM Surface Objects Foliage Penetration Images Prototype Examples For TAC Input GMU Non-GMU Key Edu HW/SW Data

  46. VAccess & Innovation Pipeline Concept Number Hours Concept Creation 100 1 Concept Refinement 15 5 Proof of Concept 4 40 Prototype Development 2 500 Transfer to Provider 1 TBD VAccess Keep the Innovation Pipeline Full Keep Users Involved Keep the Science & Technology Real Keep Nurturing the Later Steps VAccess, Commonwealth Innovation Engine

  47. VAccess First Year Phases Start Up and Activity Processes Data Sub Setting Scenario Refinement Education & Training Infrastructure Evolution Prototype Refinement & User Requirement Validation

  48. GMU ODU JMU VT UVA W&M VSGC Hampton

  49. VAccess Team Projects ODU RS Applications in Landscape Epidemiology JMU Visualization Test Bed & Software for Shenandoah Valley Hampton Advanced Analysis Techniques for RS Data VSGC Leveraging a State-wide Network VIMS Development of an Interactive I-Net GIS/RS Tutorial VT Natural resources Applications of RS & Related Geospatial Information Technologies UVA Deployment of an IR Atmospheric Sensor

  50. Thomas Allen

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