Interactive Simulation Awareness Workshop Universitry of California – San Diego 25 May 2010 Vince Ambrosia NASA - Ames Research Center 870 NASA INNOVATIVE SOLUTIONS FOR AIRBORNE FIRE MONITORING: UAS, INTELLIGENT SENSORS, AND DATA VISUALIZATION SYTEMS

Build It and They Will Come NASA Science Serving Society

Turning Swords Into Plowshares Adaptation of UAVs to Support Civilian Use NASA Ikhana with Sensor Pod under- wing mount NASA Ikhana UAV The Ikhana is a derivative of the Predator B (MQ-9) Reaper UAV, designed as a NASA science and research platform.Ikhana is a Native American Choctaw-nation word meaning:Intelligence, Conscience or Aware Length: 36 feet, Wingspan: 66 feet Operations: ~50K ft; Endurance: ~24-hours Speed: kts Payload: 2400 lbs of instruments Max T/O weight: 10,500 pounds C&C and sensor telemetry: C- band (local) & Ku-band (global) Autonomous Modular Sensor (AMS)

AMS Wildfire Sensor Two environmental enclosures (data disks & GPS; and power supplies & controllers) Scan Head Data System Enclosure Sensor System: AMS Wildfire Instrument Band Wavelength m (TM1) (TM2) (TM3) (TM4) (TM5) (high gain) (TM7) (high gain) (VIIRS M12) (high gain) (VIIRS M15) (high gain) (TM5) (low gain) (TM7) (low gain) (VIIRS M12) (low gain) (VIIRS M15) (low gain) Total Field of View: 42.5 or 85.9 degrees (selectable) IFOV: 1.25 mrad or 2.5mrad ( ) Spatial Resolution: 3 – 50 meters (variable)

Automated On-Board Processing Develop automated on-board product-generation processes for different collection scenarios: Day / Night fire imagery; R/T Hot Spot (Fire) Algorithms; R/T BAER and Post-Fire NBR Algorithms Hot Spot Detection Night BAER Normalized Burn Ratio NASA Science Serving Society Station Fire, CA Day

Collaborative Decision Environment supporting UAV wildfire monitoring missions Objectives: Integrate varied decision support data sets in one application Disseminate flight status and near-real-time sensor data Collaborate between remote users Client Apps: Google Earth, Openlayer, Jabber, QuickTime, Twitter CDE used by mission and incident management teams during 2007, 2008 and wildfire seasons. San Diego EOC, Oct 2007 Flight plan and MODIS fire data, Oct. 24, 2007 Group IMStatus updatesStreaming Video Rice Fire 10/24-25/2007 WMS viewer Basin Fire, 7/8/2008

Goals –Mission planning –Situational awareness –Data visualization –Collaboration among distributed team Users –Mission Development Team –Data Products Users COLLABORATIVE DECISION ENVIRONMENT Real-Time Information Sharing and Visualization

Sensor Data Visualization Camera placemarks appear when images are available Click on icon to display thumbnail image Download image from server Hot Pixel detections are available on separate layer Adjust 3D view of data draped on terrain Procedure for viewing sensor data in CDE

Web-Map Services Data Access An enhanced browser-based viewer allows fire images and flight tracks to be displayed on a user-selectable base-map layer. Currently available base map layers include road, satellite and hybrid maps from Yahoo, Google and OpenStreetMap. Microsoft's "Bing" base maps will also be incorporated in the final version (2010). NASA Science Serving Society

WSFM Flights 2007 August 16 (10 Hrs) CA August (16 hours); CA, NV, UT, ID, MT, WY September 7-8 (20 hrs) CA, OR, WA Sept 27(10 hrs) CA BAER Imagery Moonlight Fire (CA) Castle Rock Fire (ID)

So. CA Missions October 2007 ~1350 nm / mission ~9 hour missions October 24 October 25 October 26 October 28

SoCA Firestorms Mission Summary Poomacha / Witch Fires Grass Valley & Slide Fire Santiago Fire CDE In-Use at San Diego County EOC

2008 No CA Firestorm Missions Cub Complex Piute Fire Basin Fire

2009 Station Fire Mission BAER Imagery Flight Plan & Route Sensor Swath Visualization

Using Instruments Together: SensorWeb Early DaysBaby Steps Manual coordination of acquisitions (telephone calls and Post-It notes). Handcrafting of images and derived products. Filled critical need, in above case (Oct So. California wildfires). Use of GOES sounder to determine which of two alternative targets is likely to be cloud free. EO-1 added onboard classification algorithms. Multiple images collected fire seasons. The Teenage Years Coordination of multiple assets (MODIS, EO-1, ASTER and Ikhana UAS). USAF cloud predicts. OGC-compliant data and processing interfaces. Bi-directional tasking and flight planning ( ). THE FUTURE: Automated on-demand tasking and processing of multiple, heterogeneous self-registered assets including orbital, airborne and in situ sensors. Optimization of the tasking plan, post-processing, and delivery of the data are transparent to the end-user. Flight hardware segment (IPM) capable of conducting complex and custom image processing onboard with near real-time distribution to the end user via Direct Broadcast. Classifier Toolbox for ease of use.

cFE/SWAMO SensorWeb 3G in NASA SensorWeb Extend Access and Standards into Flight Software Users GMSEC SCL/ASE cFE/SWAMO I N T E R N E T ST-5 FlatSat Earth Observing 1 (EO-1) Midshipman Space Technology Application Research (MidSTAR) Terra and Aqua Alaska Ground Station Naval Academy Ground Station HyspIRI Dryden igloo Control Centers NASA/AMES Predator Ikhana Berkeley Ground Station WCPS

Next Steps Continued support to the Nation in 2010 & 11 with wildfire imaging capabilities on NASA and other agency (USFS) platforms. Transfer NASA- (and industry)-derived capabilities to operational entities Collaborate with International community to share and transfer capabilities. NASA Science Serving Society

Accessing the Wildfire CDE Step 1: Add Network Link Step 2: Enter URL: