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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

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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…


…at vast but variable spatial extents…


…at multiple spatial scales…

Konza

Konza Prairie – 4 m resolution

Konza Prairie – 1000 m resolution


…and provides regional context

*Konza


Elements of Remote Sensing


Remote Sensing Technology is…

  • Hardware – sensors, computers, storage, distribution networks

  • Software – commercial, public domain,

    user-created

  • “Wetware”– scientists, data managers


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


Types of Earth Observing Sensors


Orbital Remote Sensing Systems


Landsat

  • US – Private/Gov’t

  • Moderate spatial resolution

  • 1972-Present


IKONOS

  • US – Private

  • 1999 – present

  • Very fine spatial resolution (1-4m)


NOAA – Polar Orbiter

  • US Government

  • Coarse spatial resolution, global coverage

  • 1982 - Present


RADARSAT

  • Canada – Gov’t/private

  • Imaging radar

  • 1996 - Present


Terra/EO-1

“Next-Generation” – Earth Observation

  • Multi-instrument platform

  • Multispectral, hyperspectral

Coordinated observation

With Landsat - 7


Aircraft Sensing Systems

  • Flexible mission planning

  • Selectable spatial resolution

  • High cost (?)


AVIRIS

  • US Gov’t (NASA)

  • Hyperspectral (224 bands)

  • Multiple Aircraft (ER-2, Twin Otter)


Other Aircraft Systems

  • Multiple (light) aircraft platforms

  • (Relatively) modest cost

  • Researcher control!


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 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


Types of Earth Observing Sensors


Spatial Resolution

Coarse

Moderate

Fine


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 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


Data Handling System - Hardware

Acquisition

Distribution/Storage

Processing


Data analysis system – linkages are critical

Researchers/

Groups

Archiving/Distribution


The MODIS system

An example


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


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)]


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


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


Calibration to derive ecological quantities: a scientific problem

  • Top-down vs. bottom-up modeling perspectives

  • Model invertibility

  • Model robustness


Empirical Model – Top down


Analytical Models – Bottom up


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


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


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


ACKNOWLEDGMENTS

DGG acknowledges support from NASA EPSCoR subcontract 12860.

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


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