CEOSR

CSI GMU

VAccess: A Virtual Remote Sensing Center for Virginia

Menas KafatosCEOSR

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

•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

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

CEOSR

CSI GMU

CEOSR Themes, Projects and Relationships

NASAGCDC

DAAC TSDIS

SIESIP COLA

VIRGINIA

www.siesip.gmu.edu

VAcees

NRLSSDRSD

Coop Agreement

Coop Agreement

AstrophysicsEarth Science, DataInformation

GSFCCode 600Space SciencesDirectorate

GSFCCode 900Earth SciencesDirectorate

RegionalProjects

CHARM

CEOSR

SCS

GMUISE, AES,GES, BiologyESPP

DSWA

Key to Chart•GCDC: Global ChangeData Center•DAAC: Distributed ActiveArchive Center•TSDIS: TRMM ScienceData Information System•SSD: Space Sciences Div.•RSD Remote Sensing Div. Research

Institutional Links

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

CEOSR

CSI GMU

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

CEOSR

CSI GMU

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

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

State of Virginia and the Use of Remote Sensing Data

F lood s :- F lash & S u rg eS torm sH u rrican esA b n orm a l T id esW ild firesD rou g h tsU VL ig h tn in g

P o llu tion- A g ricu ltu re C h em ica ls- W as te P rod u c tsS p ills- O il- C h em ica ls- ToxicsF u m es- A u to E xh au s tL an d R esou rce M ism an ag em en t- E xcess ive R u n o ff- W aterw ay C log s- S iltU rb an G row th & C on s tru c tion

C oas tsR iver C ou rsesC itiesF arm in g A reas

P rep ared n ess

A ssessm en t

M itig a tion

P rovid ers - O rb ita l S c ien ces

A g ricu ltu ra l In te res ts

L an d C over

F ores try

W ater U tiliza tion

L an d P lan n in g

H ig h w ay P lan n in g

N atu ra l H azard s M an -M ad e E ven ts- P lan n ed

- A cc id en ta l- N e fa riou s

R eg ion s In te res ts& V iew p o in ts

E n viron m en ta l Issu es

Virginia Access to Remote Sensing Data - Concept and Examples

Figure 1

Virginia’sVirtual RemoteSensing DataInformation

System

Education&

Training

StatewideApplication

Licenses

CollaborationSupport

Datasets:Satellite & Other

HSISignatureLibrary

ApplicationDataBases

Low-CostRegional

Data

VegetationStructural Materials Roadway MaterialsSources – AVIRIS, EOS-1, In Situ

Landsat 7AVHRRMODISASTERTRMMSeaWIFSGOESSSM/INextRad

AlgorithmsStatistical ToolsProtocol DataMetadata Files

Graduate CoursesCertificate CoursesDistance LearningCourse MaterialsInstructor ListScheduleSites

Wetlands DataLand ClassificationsVegetation

Topography MapsRoad MapsDemographic Data

CommunityServer

Special CapabilityUsers

SyntheticAperture

Radar

DEMSurface ObjectsFoliage Penetration Images

Vendor MOUs

VAccess Support Staff ServicesVirginia’s

Virtual RemoteSensing DataInformation

System

StatewideApplication

Licenses

Task: Virginia-wide Data Access & Software Licensing Goals-Minimize cost to obtain/buyData from diverse sources-Minimize cost to obtain state-wide software licenses forAcademia

Approach: Form small group fromIndustry & academia to determineWays to achieve goals

Benefits: User access to more data At lower cost;Providers gain more users alongwith product/tool new ideas

Staff Services

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

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

S p e c tra l R e se a rch D iv is ionT e a m L e a d er

In s tru m e n ta tio n D iv is ionT e a m L e a d er

A p p lica tio n s D iv is ionT e a m L e a d er

H yp e rsp e c tra l Im a g e ry L a b o ra to ryD ire c to r

State of CEOSR 2000

Hyperspectral Sensing Hyperspectral Sensing An Enabling Mature TechnologyAn Enabling Mature Technology

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

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

Figure 2. SAR image and topographic retrieval using WINSAR.

AVHRR Channel 1 & 2 and NDVI (Respectively) Daily Time-series of Egypt 1981-1994

Reconfiguration of PALDaily data into Tiled Regions

NOAA/NASA 8-kmPathfinder AVHRR Land (PAL)Data Set, used in the Production

of VegetationDynamics Data Products:

LAI, fPAR, Land Cover Change...

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), Surfacereflectance (MOD09), and NDVI/EVI (MOD13) as starting points.

Customized MODIS Data Infrastructure for V Access

Subscription

Subsetting softwareSubsampling softwareReprojection software

Mapping softwareGIS Conversion software

Visualization software

Customized MODISDatasets in

V-Access DatabaseWeb Access

by users

Near real time MODIS

data from GSFC DB

ftpVAccess

Users

VAccessMODIS ProcessingToolkits

MODIS data on ECS

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

Natural hazard Monitoring, Prediction and Assessment

Objectives: Improved regional monitoring, assessment and prediction of natural hazards such as Hurricane, snowstorm, freezing rain, flash flood

Potential users: VDOT, DOA, DOT, EPA, FEMAApproach: 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

Proposed VIRGINIA ACCESS Center Architecture2001

Figure

INetClient Side

INetServer Side

Middleware for Search and Browse

Processor(s)NOAA

GMUUser

PartnerUser

Student or Educational

User

Tailored Data BasesBy Discipline

By Geographic AreaBy Community

SatelliteDown Link

Order via INet

GMU Partners NASAForeign

For Tailored Databases

IndustryUser

GOES Ground Station

AVHRR Ground Station

FilerData

StorageData

Storage

ApplicationServers(Labs)

Web Host

Users

ARCINFO ENVI

ProductionArea(engine)

Partner’sData Set

DBServer

CodingArea

PartnerAlpha

PartnerBeta

Virginia Access to Remote Sensing Data - Roles of GIS

Virginia’sVirtual RemoteSensing DataInformation

System

DistanceLearningSupport

StatewideApplication

Licenses(ESRI GIS

sofwareLicenses)

CollaborationSupport

Satellite Datasets

ApplicationDataBases

Low-CostRegional

Data

Landsat 7AVHRRMODISASTERTRMMNextRad(some RSdata areavailablein GIS formats)

AlgorithmsStatistical ToolsProtocol DataMetadata Files(spatial analysisand statisticalcapabilitiesin GIS)

Course MaterialsInstructor ListSchedule(modules on integratingGIS/RS analysis)

Wetlands DataLand ClassificationsVegetation

Topography MapsRoad MapsDemographic Data

SyntheticAperture

Radar

DEMSurface ObjectsFoliage Penetration Images(DEM and topo dataare handled efficientby raster-based GIS)

These data are mostly in GISformats. GIS can provide anintegrated environment tobring together these data and RS data

Data Analysis and Visualization ToolsENVI/IDLGIS (ArcView/Arc/Info)Splus

Training on ToolsLocal usageRegional applications/Scientific researchIntegrate tools with data for access through the Internet1. General system setup2. Setup for specific research work

• Knowledge Discovery & Data Mining1. Content-based search 2. Knowledge discovery from RS data and other Earth science data

• Web-based Tools1. Data access, leverage existing tools2.         VDADC3.         SIESIP/GDS4.         DIAL5.         WMT prototype (International standard)

    Metadata access6.         Metadata ingesting/creating7.         DBMS8.         XML technology (DIMES)

Use of Metadata ServerExample: Interface with GrADS/DODS Server

GrADSClient

User/Scientist

GrADS/DODSServer

MetadataBrowse/Search

Call out

DODS URL

Metadata(XML)Server

Remote systems

Client workstation

General User

The Future: Distributed Client-Server Architecture

Clients

DATA

Super Data Server

DIMESIngest Tool Box

Metadatarequest/result(XML)

...

Data request/result (DODS)DIMES Register

Server (DODS,

GrADS/DODS)

DATA

Super Data Server

DIMESIngest Tool Box

Server (DODS,

GrADS/DODS)

To be developed

...

DIMES: DistributedMetadata Server