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Remote Sensing Technology for Scalable Information Networks. Douglas G. Goodin Kansas State University Geoffrey M. Henebry University of Nebraska - Lincoln. What is the role of remote sensing in ecological research?. Ecological Remote Sensing enables recurrent observation….

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remote sensing technology for scalable information networks

Remote Sensing Technology for Scalable Information Networks

Douglas G. Goodin

Kansas State University

Geoffrey M. Henebry

University of Nebraska - Lincoln

slide2

What is the role of remote sensing

in ecological research?

Ecological Remote Sensing enables recurrent observation…

slide4

…at multiple spatial scales…

Konza

Konza Prairie – 4 m resolution

Konza Prairie – 1000 m resolution

remote sensing technology is
Remote Sensing Technology is…
  • Hardware – sensors, computers, storage, distribution networks
  • Software – commercial, public domain,

user-created

  • “Wetware”– scientists, data managers
what are the elements of remote sensing technology from an ecological perspective
What arethe Elements of Remote Sensing Technology (from an ecological perspective)?
  • Orbital, airborne, near-ground sensor systems
  • Ranges of spatial, temporal, & spectral resolutions
  • System for data acquisition, processing, distribution, & archiving
  • Algorithms to retrieve biogeophysical variables
  • Theory for interpretation & prediction
slide11

Landsat

  • US – Private/Gov’t
  • Moderate spatial resolution
  • 1972-Present
slide12

IKONOS

  • US – Private
  • 1999 – present
  • Very fine spatial resolution (1-4m)
slide13

NOAA – Polar Orbiter

  • US Government
  • Coarse spatial resolution, global coverage
  • 1982 - Present
slide14

RADARSAT

  • Canada – Gov’t/private
  • Imaging radar
  • 1996 - Present
slide15

Terra/EO-1

“Next-Generation” – Earth Observation

  • Multi-instrument platform
  • Multispectral, hyperspectral

Coordinated observation

With Landsat - 7

slide16

Aircraft Sensing Systems

  • Flexible mission planning
  • Selectable spatial resolution
  • High cost (?)
slide17

AVIRIS

  • US Gov’t (NASA)
  • Hyperspectral (224 bands)
  • Multiple Aircraft (ER-2, Twin Otter)
slide18

Other Aircraft Systems

  • Multiple (light) aircraft platforms
  • (Relatively) modest cost
  • Researcher control!
slide19

Close Range Remote Sensing

  • A wide variety of multi/hyper

spectral instruments

  • Not just “ground truth”
  • Researcher control
what are the elements of remote sensing technology from an ecological perspective21
What are the Elements of Remote Sensing Technology (from an Ecological perspective)?
  • Orbital, airborne, near-ground sensor systems
  • Ranges of spatial, temporal, & spectral resolutions
  • System for data acquisition, processing, distribution, & archiving
  • Algorithms to retrieve biogeophysical variables
  • Theory for interpretation & prediction
slide23

Spatial Resolution

Coarse

Moderate

Fine

slide24

Spectral Resolution

Panchromatic: 1 spectral band - very broad

Multispectral: 4-10 spectral bands - broad

Superspectral: 10-30 spectral bands - variable

Hyperspectral: >30 spectral bands - narrow

The challenge of hyperspectra is to reduce dense, voluminous, redundant data into a compact, effective suite of superspectral bands and indices for retrieval of biogeophysical fields.

what are the elements of remote sensing technology from an ecological perspective25
What are the Elements of Remote Sensing Technology (from an Ecological perspective)?
  • Orbital, airborne, near-ground sensor systems
  • Ranges of spatial, temporal, & spectral resolutions
  • System for data acquisition, processing, distribution, & archiving
  • Algorithms to retrieve biogeophysical variables
  • Theory for interpretation & prediction
slide26

Data Handling System - Hardware

Acquisition

Distribution/Storage

Processing

slide27

Data analysis system – linkages are critical

Researchers/

Groups

Archiving/Distribution

what are the elements of remote sensing technology from an ecological perspective29
What are the Elements of Remote Sensing Technology (from an Ecological perspective)?
  • Orbital, airborne, near-ground sensor systems
  • Ranges of spatial, temporal, & spectral resolutions
  • System for data acquisition, processing, distribution, & archiving
  • Algorithms to retrieve biogeophysical variables
  • Theory for interpretation & prediction
slide30

Retrieval of Biogeophysical Quantities & Indices

R = òòf(,) sin cos d d

T = [BT*(es)-1].25

NDVI = (rNIR - rRed)/(rNIR + rRed)

EVI =2.5*(rNIR-rRed)/(L+rNIR+C1*rRed-C2*rBlue)

s0 = [(S(i=1..N)xi2)/N] * [(C/k) * (sin a)/(sin aref)]

slide31

Calibration to derive physical quantities: an engineering problem

  • Does the instrument give the correct physical data?
  • Is the instrument’s range & sensitivity appropriate for the application?
  • Cross-sensor calibration
slide32

Calibration to derive ecological quantities: a scientific problem

  • Can the sensor data yield ecologically relevant relationships?
  • NOT ground “truth” – ground level observation RESCALING
  • Empirical relationships are site & time specific but reflectance, emission, and backscattering are interactions not intrinsic properties of observable entities
slide33

Calibration to derive ecological quantities: a scientific problem

  • Top-down vs. bottom-up modeling perspectives
  • Model invertibility
  • Model robustness
what are the elements of remote sensing technology from an ecological perspective36
What are the Elements of Remote Sensing Technology (from an Ecological perspective)?
  • Orbital, airborne, near-ground sensor systems
  • Ranges of spatial, temporal, & spectral resolutions
  • System for data acquisition, processing, distribution, & archiving
  • Algorithms to retrieve biogeophysical variables
  • Theory for interpretation & prediction
slide37

To enable ecological forecasting,

we need monitoring strategies for

change detection:perceiving the differences

change quantification: measuring the magnitudes of the differences

change assessment: determining whether the differences are significant

change attribution: identifying or inferring the proximate cause of the change

slide38

Observations

Retrieval of

biogeophysical

variables

Ground segment

Acquisition, processing,

storage, & archiving

Information for

Ecological

Forecasting

Ecological

Questions &

Hypotheses

Change attribution

Change assessment

Assimilation of current

observational datastreams

Change detection

Change quantification

Spatio-Spectral-

Temporal

analysis

Definitions of nominal

trajectories and

estimates of uncertainty

slide40

ACKNOWLEDGMENTS

DGG acknowledges support from NASA EPSCoR subcontract 12860.

GMH acknowledges support from NSF #9696229/0196445 & #0131937.

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