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Sensor Web 2.0: Connecting Earth’s
Sensors via the InternetJune 25, 2008
Sensors are everywhere! Space air and
ground!
Disasters occur everywhere also!
P l ff t d b t l di t i 2007 f I t ti l E Di t D t b (
Types of disasters 2004-2008
S I t ti l F d ti f R d C /R d C t
Enable Rapid Deployment of Existing Sensors– Desirable Features
• Theme-based tasking
• User customized data products
• Rapid electronic delivery of data products
• Discoverable workflows (recipes) to create these data products
• Workflows reusable
• Network of sensors is easily scalable
• Security
• Open standards
• Leverage Internet
Past and OngoingDemonstrations/Collaborations
• OGC Interoperability Pilots– OWS-4– OWS-5– Empire Challenge with DIA
• GEOSS/CEOS– Architecture Implementation Pilot– Red Cross Flood Early Warning System
• SERVIR emergency response– Panama – Cathalac– Kenya - RCRMD
• Southern California Fire Sensor Web demos– Summer 2007– Summer 2008
• Collaborations with– Cloud screening - Kolitz– ASF – John Dolan– Lightning Early Warning – Prasana B.– UAVSAR – Y Lou– SWAMO – K. Witt
Sensor Web 2.0 Vision
L1G
SOS
WFS
SPS
SAS
SOS
WFS
SPS
SAS
Sensor Plann
Service (SP
Sensor Ale
Service (SA
Sensor
Observatio
Service (SO
Web Feat
Service (W
SensorML
Capabilitie
Document
Satellite Data Nod
EO-1
Satellite
EO-1
Satellite
In-s
itu
Sen
so
r D
ata
No
de
UA
V S
en
so
r D
ata
No
de
SensorML
SensorML
Sate
llit
e s
en
so
r
da
ta p
rod
uc
t
Web
Processing
Service
(WPS)
Web
Coverage
Service
(WCS)
Web
Coordinate
Transformation
Service
(WCTS)
Service
ChainWorkflow
Engine
SensorML
Capabilities
Documents
Data
Processing Node
Internet
• User queries
• Status
• Science Products
OpenID 2.0 Enab
Use Really Simple
Syndication (RSS)
feeds for notifications
where appropriate
Key Architecture Features
• OGC standards– Sensor Web Enablement standards
• Web 2.0– RSS
– News Readers
– Web Broswers
• Rest-ful approach versus SOAP/WSDL approach for SOA– Simpler
– Supports mashups
– Point and click access
1
WILDFIRE Sensor on
UAS - Flight Corridor
MODIS Active Fire Map
ICS209 National Fire Database
Decision Support
System - Cross correlate
possible fire targets
within flight path of
UAS
- Get feasible assets
- Get feasible task
allocations
- Allocate tasks to
various assets
Sensor Pla
Service (S
WILDFIRE o
Get feasibilities &
Task
Sensor Plan
Service (SP
Get
feasibilities
& TaskMODIS Image from
Terra/Aqua
Get
feasibilities
& Task
Sensor Plann
Service (SP
Internet
EO-1 Satelli
Hyperion &
Terra Satellite
ASTER
Ambrosia, Sullivan/
Mandl/GSFChien/JPL
Kolitz, Abramson/DraperCappelaere/Vightel
Web Feature
Service (WFS)
Quayle/Remote SensingApplication Center (RSAC) - ForestService
National Interagency Fire Center (NIFC)
Ambrosia, Sullivan/ARC
Air Force Weather Agency
Gl b l Cl d P di ti
Remote Automated
Weather Stations
(RAWS)
Get
feasibilities
& Task
Composite Sensor Web Demo
1
Sensor Planning
Service (SPS)
EO-1
Satellite
Sensor Observation
Service (SOS)
Cappelaere/Vightel
Level 0
Science
Process
Tran, Chien/JPL
Tran, Chien/JPL
Alexander/GSFCTran, Chien/JPL
Web Processing
Service (WPS)
Tran, Chien/JPLLevel 1G or Level 0.5
Pixels and
heat values
Web Coverage Servic
–Transformation
(WCS-T)
JPEG
Zhao/GMU
Community
Map Builder
client (on
eo1.geobliki.com)Web Map
Publish composite JPEG
map with data overlay
(map with fire locations)
EO-1 Fire Sensor Web Workflow• Potential savings
– Originally, USGS charged $20task EO-1 and perform L0 andata processing for customer($1250 now)
– Potential cost reduced to und$100 since tasking EO-1, L0 aL1G data processing automat
Cappelaere/Vightel
Level 1G
Process
Classify hot pixels
Level 0.5
Process
L. Di, Zhao/GMU
BPEL Workflow
Engine
1
2
34
5
1
Sensor Planning
Service (SPS)
AMS on UAS
Web Map
Service (WMS)
Sullivan/AMES
Ambrosia, Sullivan/AMES
KML Files
(PNG Graphic
Web Coverage
Service (WCS)
GoogleEarth
Service
Level 1G Terrain
Corrected and
Georectified
GeoTiff 1-3 bands
Kolitz, Abramson/Draper
Air Force Weather Agency
Global Cloud Predictions
UAS Fire Sensor Web Workflow
GIS – Geographical
Information System
Sullivan/AMES
Updated
flight plan
Global cloud
predicts
Kolitz, Abramson/Draper
Event Notification
Service
OASIS CAP 1
Notifications
over SMTP
Tasking mess
to EO1 SPS
1
Future Smoke Prediction Model with AutoTasking for EO-1 and UAS
Sensor Pla
Service (
WILDFIRE on UAS
Web Feature
Service (WFS)
MODIS Active Fire Map
Web Feature
Service (WFS)
WILDFIRE on UAS
EO-1 Sate
Hyperion
ICS209 National Fire
DatabaseQuayle/Remote SensingApplication Center (RSAC) - ForestService
National Interagency Fire Center (NIFC)
Fire ID and
general fire
locationSensor Planning
Service (SPS)
Refined fire
location
Refined fire
location
2007 Table of
Graphics Markup
Language
S. Falke/NGC
Web Processing
Service (WPS)
Web Processing
Service (WPS)
2008 Smoke Mod
Automatic tasking
requests based on
model analysis
Ambrosia, Sullivan/AMES
Ambrosia Sullivan/AMES
Mandl/GSFCChien/JPL
1
Prototype Smoke Prediction ModelOutputs by S. Falke
Accomplishment for Year 2 Thus Far (1 of 5)Sensor Web Services Established
JPL SPS
EO-1 Hyperion
EO-1 ALI
Geobliki SPS
EO-1 Hyperion
EO-1 ALI
JPL SOS
EO-1 Hyperion L0
EO-1 HyperionL0.5EO-1 HyperionL1REO-1 HyperionL1GEO-1 ALI L0
EO-1 ALI L1R
EO-1 ALI L1G
JPL WPS
Thermal classifier
Burn Index
Composite BrowseImageFluvial classifier
Cloud classifier
Sulfur classifier
SWIL classifier
Fire fuel load classifier(various, future)
Geobliki WPS
Vegetation Index (future)
Burn scar
Water classifier (future)
Rhodamine dye (future)
Snow & Ice (future)
Geobliki SOS
EO-1 Hyperion L0
EO-1 HyperionL0.5EO-1 HyperionL1REO-1 HyperionL1GEO-1 ALI L0
EO-1 ALI L1R
EO-1 ALI L1G
Geobliki WMS
Fire maps
KML transform forGoogle Earth
Geobliki WfCS
WfXML workflowengine
Accomplishment for Year 2 Thus Far (2 of 5)Sensor Web Services Established
Draper WPS
AFWA Cloud Cover
GMU WCS
Hot Pixels
AMES WCS
Ikhana UAS hotpixels
WVHTF WfCS
Sensor Workflow Engine
Northrop Grumman
WPSSmoke Model
ASTER SPS
ASTER
AMES SPS
Ikhana UAS WildfireInstrument
AMES WMS
Ikhana UAS WildfireImages & Firelocation maps
KML transform forGoogle Earth
GMU WCS-T
Transform HotPixels for Mapproduction
GMU WfCS
BPEL engine toexecute workflow
ASTER SPS
ASTER
MODIS WFS
MODIS Hot Pixels
Earth Science
Gateway CSW
NASA data
Global Change
Management
Directory CSW
SPOT-5 SPS
SPOT-5
Accomplishment for Year 2 Thus Far (3 of 5)Sensor Web Services Established
JPL SAS/WNS
MODVolc Alert
CVO Mt. St. HelensalertsMEVO Alerts
Geobliki SAS/WNS/OPS
EO-1 Hyperion/ALIproducts ready for pick upUser subscribes toproducts they areinterested and thenreceive SMS, IM or TwitteralertsEO-1 Hyperion/ALItasking complete,notification via SMS, IM orTwitter
SAS – Sensor Alert Service (pub/sub)WNS – Web Notification ServiceOPS – OGC Publish/Subscribe
Prototype web service thatGeorectified Advanced LandImager ontomap with clouds removed.
WPS ALI Geo-
rectificationAutomatic geo-rectificationof ALI images
Accomplishment for Year 2 Thus Far (3 of 5)Sensor Web Services Established
Prototype web service that performs atmosphericcorrection on each Hyperionimage.
WPS Hyperion
Atmospheric Correction
Decision support systemdiscovers what othersensors are available toinput intoMODTRAN/FLAASH toget best atmosphericcorrected Hyperion imageL1R-AC
Accomplishment for Year 2 Thus Far (5 of 5)Sensor Web Services Established
Workflow Chaining Service
EO-1
GeoBliki
OPS
WfCS
GeoBPMS
Workflow Chaining ServiceWf-XML-R specificationdeveloped with OGC and WFMC
GeoBPMS = Workflow Engine(WfCS) + Multi-CriteriaDecision Support System
GeoBPMS – Geographical Business Processing Management ServiceOPS - OGC Publish-SubscribeWfMC – Workflow Management Coalition
Status:WfCS: 90% CompleteDSS: 50%OPS: 50%Clouds: NOAA 95% Draper 0%
Cloud
Coverage
Service
Multi-Criteria Decision Support System
Mission
Science
Office
(MSO)
Sensor Web
Long Term Campaigns, Surveys...20-25 scenes per day
Short Term Campaigns, Emergencies...1-2 scenes per day
PriorityReplacement
Event though sensor web requests are infrequent, wecan not unilaterally bump all science requests. Some
are more important than others.
How Multi-criteria Decision SupportSystem will be used
MSOSensor Web
Long Term Campaigns, Sur20-25 scenes per da
Short Term Campaigns, Emergencies...1-2 scenes per day
PriorityReplacement
New Campaign New Campa
Joint Review BoardRequest
Weight
Request
Weight
Criteria
MSOSensor Web
Long Term Campaigns, Survey20-25 scenes per day
Short Term Campaigns, Emergencies...1-2 scenes per day
PriorityReplacement
Campaign Weight
User Role
Weather Forecast
NOAA
Global
Forecast
Model
DraperForecast
Model
Priority
Security for Open Web Services
MSOSensorWeb
Long Term Campaigns, Surve20-25 scenes per day
Short Term Campaigns, Emergencies...1-2 scenes per day
User AuthenticationProfile Exchange
RolesOpenIDProvider
Security
OpenID
Provider
GeoBPMSEO-1
GeoBliki
WPS
Security
OpenID
Provider
GeoBPMSEO-1
GeoBliki
WPS
Computer
Application
Empire Challenge
Screenshots
Screenshots
Screenshots
High Level Architecture for Fall 2007 FireSensor Web Demo
• Emergency
• Discover availabsensor assets ovInternet
• Wizard assemblepossible workflow
• Workflow enginecontrols creationmulti-sensorproducts, procesand delivery to udesktop
Geo-Emergency
First responderTheme Based Tasking Request
Theme:
Loc:
Priority:
Witch Fire (SoCal) Oct 23, 2007EO-1 Fire Sensor Web Image
Published in CNN- Popular Science
Result: Efficient / timely use of as
Wizard
Workflow Engine
Sensor Web 2.0: Connecting the Earth’s Sensors viathe Internet 3 minute movie
Established Collaboration with InternationalFederation of Red Cross/Red Crescent
Flood Early Warning System
• Developing prototype Flood Sensor Web Early Warning System
• Selected area of interest in Africa and Asia– Underserved
– Population at greatest risk with least resources
– Greatest potential to save lives
• Question- How to augment workflow to enable earlierdecisions and save lives
Type of disasters 2004-2008
Disasters by region 2004-2008
Established Collaboration with InternationalFederation of Red Cross/Red Crescent
-Timeline of Myanmar Red Cross Effort
Established Collaboration with InternationalFederation of Red Cross/Red CrescentTrying to augment their workflow to enable earlier decisions
Vision -Theme-Based Flood Product Generation for IF
From portal select
desired theme(s) and
area of interest
Wizard picks
appropriate
workflow for
desired result
Wizard
Mozambique
Disaster Management
Information System (DMIS)
Workflows
Estimated rainfall
accumulation and flood
prediction model
Flood Model
Selected workflow
automatically activates
needed assets and models
Baseline water level, flood
waters and predicted flooding
Select sensor and get details
2 MayColumbia Univ IRIAverage climatic rainfallas compared to currentPredicted rainfall. Thus lookingfor rainfall anomalies as Possible early flood warning.
NARGIS TRMM Animation of Rainfall Progression( t i t ti d & li k t i )
NARGIS TRMM Animation of Flash Flood Potential( t i t ti d & li k t i )
Red - deep
Yellow - me
Green - me
Blue - shallo
Burma May 5, 2008
15 km resolution
1. Real-time flood estimate using global
hydrological model and satellite rainfall
estimate - Adler
Water Depth Classifier True c Advanced Land Imager 30m May 5, 2008
These two data productsare only approximately1/8 of entire image avai
Inundation Map from Dartmouth Flood
Observatory (using MODIS data) May 5, 2008
1 km resolution
2. MODIS used to validate
flood locations with direct
observation
3. EO-1 Advanced Land Imager
automatically triggered and pointed
to get more water depth details in
area of interest.
4. Future experiment will
be to substitute
predicted rainfall versus
real time rainfall estimate
into Adler model to
obtain predicted flood
warning and
automatically task EO-1
in area of interest and
create MODIS and EO-1
data products
International Charter for Disaster Management
• The International Charter aims at providing a unified system of spacedata acquisition and delivery to those affected by natural or man-madedisasters through Authorized Users. Each member agency hascommitted resources to support the provisions of the Charter and thuis helping to mitigate the effects of disasters on human life andproperty.
• Members– ESA ERS, Envisat (Europe)
– CNES SPOT, Formasat (France)
– CSA Radarsat (Canada)
– ISRO IRS (India)
– NOAA POES, GOES (US)
– CONAE SAC-C (Argentina)
– JAXA ALOS (Japan)
– USGS Landsat, Quickbird (2 ft res), GeoEye-1 (2 ft res) (US)
– DMC ALSAT-1 (Algeria), NigeriaSat, Bilsat (Turkey), UK-DMC, Topsat
– CNSA FY, SJ, ZY satellite series (China)
Radarsat (3 m) – May 7, 2008 Myanmar
Quickbird Image (2 ft res) – May 5, 2008 Myanmar
Future Work
• Correlate Red Cross workflow with available images, measurements andmodels
• Establish one workflow to demonstrate early decision/warniong due toflood sensor web
• Show decision save lives or property
• Leverage demonstration to get ministers of various nations to fundexpansion.
• Sample decision– Detect whether flood water is fresh or salty water
– If fresh water then send water purifiers valued at $500K to $1 million
– If salty water then send water
– Problem – have not identified how to classify water as fresh or salty
• Looking for other similar decision scenarios
SensorWebSensorWeb
Linking Sensors, Products & PeopleLinking Sensors, Products & People
For Science, Humanitarian Assistance and Disaster Relief ApplicationsFor Science, Humanitarian Assistance and Disaster Relief Applications
