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Sensor Web Strategies

Sensor Web Strategies. Karen Moe Sensor Web Task Team NASA Earth Science Technology Office February 25, 2008 CEOS WGISS-25 Sanya, China. Earth Observation Sensor Web. Sensor Web Task Team (SWTT) strategies and expected outcomes Sensor web operational concepts

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Sensor Web Strategies

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  1. Sensor WebStrategies Karen MoeSensor Web Task TeamNASA Earth Science Technology Office February 25, 2008 CEOS WGISS-25 Sanya, China 1

  2. Earth Observation Sensor Web • Sensor Web Task Team (SWTT) strategies and expected outcomes • Sensor web operational concepts • Concept development since WGISS-24 • Technology push • Technology pull • What we’ve learned so far… 2

  3. Sensor Web: A Service-Oriented Architecture Approach Sensor webs will be dynamically organized to • collect data, • extract information from it, • accept input from other sensor / forecast / tasking systems, • interact with the environment based on • what they detect or • are tasked to perform, and • communicate observations and results in real time. 3

  4. SWTT Sensor Web Strategy • Address GEOSS goals (science -> SBA) • Apply emerging sensor web technologies • Leverage international resources • EO data, models, in situ sensors, & satellites • Explore technology push & pull • Expected outcomes • Use Cases (featuring operational concepts) • Proof-of-concept prototypes • Lessons learned / implications for GEOSS 4

  5. Sensor Web Operational Concepts • Dynamically acquire & fuse data from models, satellite and in situ sensors • Validate data observations in near RT • Provide intelligent sensor control feedback to enable RT sensor tasking • Enable discovery and access to sensor web components and services 5

  6. SWTT Exploration Phase • Technology push • What sensor resources team members can bring and create plausible applications • How can sensor webs support virtual constellations • Technology pull • What do scientists need to better understand and forecast phenomena • What information do policy makers or disaster response teams need 6

  7. Sensor Web Use Cases Explored • Sensor web assisted Cal/Val for GRACE- CHAMP constellation • Put on hold due to lack of member resources to pursue • Flash flood monitoring use case builds on WGISS Grid technology demo • SWTT proposed phase 1 project presentation in WGISS-25 • Later phases will extend demo to show model feedback to EO-1 sensor tasking and provide resulting data and forecasts to SERVIR disaster management system, and Int’l Fed of the Red Cross (IFRC) global flood monitoring system 7

  8. Sensor Web Support to ACC • CEOS Atmospheric Composition Constellation (ACC) team discussed possible collaboration with SWTT • Smoke Trajectory Forecast: ACC wants to leverage relevant satellite and in situ sensors and evolve modeling approaches to overcome limitations of existing sensor assets to produce improved forecasts • Sensor Web for ACC builds on aerosol trajectory model and incorporates EO-1 sensor tasking 8

  9. CALIPSO - CloudSat Terra (MODIS) Aqua (MODIS) EO-1 Fire Sensor Web Evolution Sensor Web Support to ACC • CALIPSO (near RT aerosol data) and MODIS (vertical component data) augment model forecast • Produce a 3D smoke trajectory forecast product to international AirNow system • Compare predicted with actual smoke conditions using EO-1 imagery MODIS Active Fire Map EO-1 (ALI & Hyperion) Smoke Trajectory Forecast Model 9

  10. Underlying Sensor Web flow • Example sensor web themes in use cases, an emerging pattern • Routine event monitoring (in situ rain gauges, sentinel systems for fire, volcanos, etc) • Model predicts potential event (flood, smoke trajectory) • Event detection or model prediction triggers request for near RT sensor observation task • New observation data augments model for more accurate forecast • New observation and improved forecast feeds disaster management portal 10

  11. Reflections on SWTT Activities since WGISS-24 • Lessons learned on how we work as a team in support of CEOS • Discuss need for documenting SSWT • “Program” perspective • “Project” perspective • Use Case • Activity Flow Chart • Findings and Recommendations 11

  12. Reflections on SWTT (cont’d.) • “Program” perspective • A strategic view of the team activities • One over-arching “profile” of team, expected outcomes, relating activities to GEOSS • “Project” perspective • A short project plan describing the objectives of a selected application prototype • One each per application: flood, wildfire/smoke 12

  13. Reflections on SWTT (cont’d.) • Use Case • A detailed discussion of a specific application summarizing actions, actors, resources • Activity Flow Chart • A very detailed diagram of the source and sink of each step of the prototype demo • Methodology presented by M. Burnett • “An Approach for Repeatable Sensor Web Construction” 13

  14. Reflections on SWTT (cont’d.) • Findings and Recommendations • Summarize our findings on what worked, what didn’t work, other approaches to try • Describe experience (pros, cons) with • Standards • Processes • Tools • Make recommendations • CEOS • GEOSS • Standards bodies (ISO, OGC, others) 14

  15. Back-up Charts • Overview of use case progression since WGISS-24 • WGISS-24 sensor web technology challenges 15

  16. Sensor Web Use Cases Explored • Sensor web assisted Cal/Val for GRACE- CHAMP constellation, involving taskable weather balloons, associate with GPS water vapor profiles • CHAMP-GRACE constellation used to profile water vapor • Task in situ weather balloons • Implement web services to discover and task applicable weather balloons • Fuse data products and identify mismatches where calibration is needed 16

  17. Building on Grid Technology Demo • Flash flood monitoring: Rain gauge input to forecast model detects potential flood condition. Improve flood model forecasts by discovering and supplying recently acquired applicable satellite data • Rain gauge sensors on Zambezi River (Mozambique seasonal flood) • NASA data identified via ECHO services • NASU model forecasts flood conditions 17

  18. Model Feedback to Sensor Tasking • Flash flood monitoring: Model flood forecast triggers EO-1 tasking event. Resulting image delivered directly to first responders • NASU model forecasts flood triggers EO-1 • EO-1 acquires current image • Model forecast accuracy is improved • Satellite image also delivered directly to SERVIR, a disaster response system initially developed for Central and South America, now being applied to African events 18

  19. Sensor Web Extension to IFRC • Flash flood monitoring: Int’l Fed of Red Cross & Red Crescent (IFRC) approached NASA about incorporating satellite data to improve existing and planned global flood monitoring of 200 sites world wide • Team from Geneva is providing operational user insight to use case • IFRC has disaster response planning system and staff interested in improved information available from use of NASU model and RT sensor tasking in EO-1 19

  20. Sensor Web Features and Benefits • Some Features: • Targeted observations through dynamic tasking • Incorporate feedback to adapt autonomous operations (e.g., weather forecasts) • Ready access to data and information • Some Benefits: • Improved resource use and reuse through reconfiguration of assets • Improved cost effectiveness through autonomous operations • Rapid response to evolving, transient phenomena • Improved data quality and science value by comparing sensor data from the same event • Derived from the NASA ESTO Sensor Web Meeting Feb 2007 20

  21. 1. Technical Challenges • In the collection and analysis of information from heterogeneous nodes • There is a lack of uniform operations and standard representation for sensor data • There exists inadequate means for resource reallocation and resource sharing • Deployment and usage of resources is usually tightly coupled with the specific location, application, and devices employed 21

  22. 2. Technical Challenges • Publishing and discovering sensor resources • Create a publicly accessible infrastructure for publishing heterogeneous sensor resources and complex applications • Discover and use sensor resources • Sensor data fusion • Sensor data has different data models and formats and different spatial and temporal resolutions, • Fusion -> higher spatial coverage and temporal resolution 22

  23. 3. Technical Challenges • Context-based information extraction • End users have insufficient technical expertise and time to extract information from sensor data • Users require different views of the data according to needs and context • Data can be filtered, summarized, transformed • Features can be extracted -> higher level features -> information -> application/decision making • Same data can be reused for different applications 23

  24. WGISS Sensor Web Discussion • Identify Collaboration Opportunity • Standards-based proof-of-concept sensor web demo • Applied to significant GEO objective (e.g., Virtual Constellation?); identify GEO “champion” user(s) • Mature standards, capture lessons learned • Develop processes, toolkits to improve usability • Leverage NASA Earth Science Sensor Web technology investments and prototypes • Provide feedback to standards bodies, e.g. • OGC SensorML, Mike Botts/UAH • OGC SWE, Liping Di/GMU, Stefan Falke/NG, others • Other standards? • Formally recommend proven standards to GEOSS 24

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