NIMS Multiscale Sensing

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NIMS Multiscale Sensing
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Acknowledgements

• Student researchers
• Kathy Kong
• Steve Liu
• Richard Pon
• Tom Harmon
• Multiscale sensing for primary questions in
  science and resource management
• Michael Hamilton
• Greg Pottie
• Phil Rundel
• CENS Team
• NASA FSRC Program
• Dr. Larry Freudinger, NASA Dryden

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Progress in Scaling Problems

• Embedded networked sensing progress
• Energy aware systems
• High capability processing
• Mobile sensors
• High performance networking
• New methods improve scaling of operations
• Actual measurements reveal new problems
• Extreme spatiotemporal range associated with
  phenomena

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Ultimate Scaling Problem

• Objective
• Create a region-wide information base for
  decision support and science
• Desired approach
• Exploit
• Limited number of low cost, rapidly deployed
  sensors
• Global remote sensing
• Measure
• Limited number of sites
• Long standing problem.
• But, an entirely new technology is available
  adding to available methods

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Specific ExampleMicroclimate Characterization
Challenge

• Objective
• Develop fundamental model relating microclimate
  variables to growth
• Challenge
• Achieving required spatiotemporal sampling rate
  for dynamic processes
• Limitation fundamental to sensing physics
• Replication requirements
• Wide area conclusions from constrained sensing
  resources

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Microclimate Characterization Challenge

• Objective
• Develop fundamental model relating microclimate
  variables to growth
• Challenge
• Achieving required spatiotemporal sampling rate
  for dynamic processes
• Limitation fundamental to sensing physics
• Replication requirements
• Wide area conclusions from constrained sensing
  resources

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Microclimate Characterization Challenge

• Objective
• Develop fundamental model relating microclimate
  variables to growth
• Challenge
• Achieving required spatiotemporal sampling rate
  for dynamic processes
• Limitation fundamental to sensing physics
• Replication requirements
• Wide area conclusions from constrained sensing
  resources

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Microclimate Characterization Challenge

• Objective
• Develop fundamental model relating microclimate
  variables to growth
• Challenge
• Achieving required spatiotemporal sampling rate
  for dynamic processes
• Limitation fundamental to sensing physics
• Replication requirements
• Wide area conclusions from constrained sensing
  resources

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Microclimate Characterization Challenge

• Objective
• Develop fundamental model relating microclimate
  variables to growth
• Challenge
• Achieving required spatiotemporal sampling rate
  for dynamic processes
• Limitation fundamental to sensing physics
• Replication requirements
• Infer wide area phenomena from limited
  distributed sensing resources

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Adaptive Sampling Core
Fusion
Map
Sensor
Sensor
Decision
Phenomenon
Sensor
Sensor
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Consider Driving Phenomena
Wide Area Context
Sun Illumination Angle
Fusion
Sky Conditions
Map
Sensor
Sensor
Decision
Phenomenon
Canopy Structure
Sensor
Sensor
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Multilevel Sensing Now Possible
Wide Area Context
Fusion
Mobile Sensor
Sun Illumination Angle
Global Sensor
Sky Conditions
Map
Sensor
Sensor
Decision
Phenomenon
Canopy Structure
Sensor
Sensor
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Multilevel Sensing Now Possible
Wide Area Context
Fusion
Mobile Sensor
Sun Illumination Angle
Global Sensor
Sky Conditions
Map
Sensor
Sensor
Decision
Phenomenon
Canopy Structure
Sensor
Sensor
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Remote Sensing Resources

• Satellite
• Multispectral imaging
• Aircraft
• Multispectral imaging
• High resolution
• Frequent schedule

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Multilevel Sensor Active Fusion
Remote Sensor
Wide Area Context
Fusion
Mobile Sensor
Sun Illumination Angle
Global Sensor
Sky Conditions
Map
Sensor
Sensor
Decision
Phenomenon
Canopy Structure
Sensor
Sensor
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Multiscale Fusion for Solar Radiation Mapping

• Goal
• Identify proper sparse sensor distributions that
  accurately identify each context layer
• Active verification
• Fusion
• Local area fixed sensing
• Local area mobile sensing
• Local high resolution imaging
• Local low resolution imaging
• Global sensors
• Remote sensing
• Exploit adaptive sampling
• High throughput measurements

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Contexts
Time
Sun Angle
Seasonal Canopy Structure
Wide Area Context
Wide Area Sensing
Sky Condition
Global PAR
Local Area Context
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Multiscale Sensor Selection
Select Sensor Subset
Identify Wide Area Context
Identify Local Area Contexts
Fusion Predict Local Area Values
map
Estimate Uncertainty Using Ground Truth
exit
uncertainty lt threshold
uncertainty gt threshold
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Objectives

• Implement, deploy, verify
• CLEANER program
• Ecosytems research
• Utility Driven Optimization
• Compute expected utility based on actual
  measurements of cost in time and benefit in
  sensing certainty.
• Optimal training
• Reduce training time according to utility
  measures
• Optimal Verification
• Verification of sensing uncertainty
• Establish standard procedures
• Sensor selection
• Training
• Estimation
• Verification
• Operation in multiple environments
• Watersheds
• Ecosystems
• Address the nvironmental replication challenge

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View of NIMS Vertical Sensor NodeSpatiotemporal
Solar Radiation Mapping
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View of NIMS Vertical Sensor NodeSpatiotemporal
Solar Radiation Mapping
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View of NIMS Vertical Sensor NodeSpatiotemporal
Solar Radiation Mapping
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View of NIMS Vertical Sensor NodeSpatiotemporal
Solar Radiation Mapping
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View of NIMS Vertical Sensor NodeSpatiotemporal
Solar Radiation Mapping