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Remote Sensing Technology for Remote Sensing Technology for Scalable Information NetworksScalable Information Networks
Douglas G. GoodinKansas State University
Geoffrey M. HenebryUniversity of Nebraska - Lincoln
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Ecological Remote Sensing enables recurrent observation…
What is the role of remote sensing in ecological research?
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…at vast but variable spatial extents…
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…at multiple spatial scales…
Konza Prairie – 4 m resolution Konza Prairie – 1000 m resolution
Konza
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…and provides regional context
*Konza
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Elements of Remote Sensing
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Remote Sensing Technology is…
Hardware – sensors, computers, storage, distribution networks
Software – commercial, public domain,
user-created
“Wetware”– scientists, data managers
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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
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Observed
Phenomenon
Spectral Region
Biogeophysical Variables
Representative
Sensors
Ranges of Resolutions
Solar Reflectance
Visible,
Near-IR,
Mid-IR
Albedo
fPAR
Land Cover
NPP
AVHRR SeaWiFS
MODIS MERIS
TM/ETM+ ALI IKONOS
AVIRIS MASTER
1 m – 1 km
<1 d – 18 d
1–228 bands
Terrestrial Emission
Mid-IR,
Thermal-IR,
Microwaves
Surface temperature
Surface moisture
SMMR SSM/I
AVHRR MODIS
ASTER TIMS
25 m - 25 km
<1 d – 3 d
1 – 50+ bands
Anthropogenic Radiation
RADAR,
LIDAR,
[SONAR]
Surface roughness
Soil moisture
Terrain
RADARSAT ASAR
JERS SIR-C
VCL LVIS
8 m – 150 m
18 d
<10 bands
Types of Earth Observing Sensors
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Orbital Remote Sensing Systems
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Landsat
US – Private/Gov’t
Moderate spatial resolution
1972-Present
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IKONOS
US – Private
1999 – present
Very fine spatial resolution (1-4m)
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NOAA – Polar Orbiter
US Government
Coarse spatial resolution, global coverage
1982 - Present
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RADARSAT
Canada – Gov’t/private
Imaging radar
1996 - Present
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Terra/EO-1“Next-Generation” – Earth Observation
• Multi-instrument platform
• Multispectral, hyperspectral
Coordinated observationWith Landsat - 7
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Aircraft Sensing Systems
• Flexible mission planning• Selectable spatial resolution• High cost (?)
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AVIRIS
• US Gov’t (NASA)
• Hyperspectral (224 bands)
• Multiple Aircraft (ER-2, Twin Otter)
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Other Aircraft Systems
•Multiple (light) aircraft platforms
•(Relatively) modest cost
•Researcher control!
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Close Range Remote Sensing
•A wide variety of multi/hyper spectral instruments
•Not just “ground truth”
•Researcher control
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TheData
Pyramid
Coordinated Observation at Multiple Scales
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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
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Observed
Phenomenon
Spectral Region
Biogeophysical Variables
Representative
Sensors
Ranges of Resolutions
Solar Reflectance
Visible,
Near-IR,
Mid-IR
Albedo
fPAR
Land Cover
NPP
AVHRR SeaWiFS
MODIS MERIS
TM/ETM+ ALI IKONOS
AVIRIS MASTER
1 m – 1 km
<1 d – 18 d
1–228 bands
Terrestrial Emission
Mid-IR,
Thermal-IR,
Microwaves
Surface temperature
Surface moisture
SMMR SSM/I
AVHRR MODIS
ASTER TIMS
25 m - 25 km
<1 d – 3 d
1 – 50+ bands
Anthropogenic Radiation
RADAR,
LIDAR,
[SONAR]
Surface roughness
Soil moisture
Terrain
RADARSAT ASAR
JERS SIR-C
VCL LVIS
8 m – 150 m
18 d
<10 bands
Types of Earth Observing Sensors
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Spatial Resolution
Coarse FineModerate
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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.
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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
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Acquisition
Processing
Distribution/Storage
Data Handling System - Hardware
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Data analysis system – linkages are critical
Archiving/Distribution
Researchers/Groups
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The MODIS systemAn example
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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
![Page 30: Remote Sensing Technology for Scalable Information Networks Douglas G. Goodin Kansas State University Geoffrey M. Henebry University of Nebraska - Lincoln](https://reader036.vdocuments.us/reader036/viewer/2022070406/56649ddf5503460f94ad86c2/html5/thumbnails/30.jpg)
NDVI = (NIR - Red)/(NIR + Red)
R = f(,) sin cos d d
0 = [((i=1..N)xi2)/N] * [(C/k) * (sin )/(sin ref)]
Retrieval of Biogeophysical Quantities & Indices
EVI =2.5*(NIR-Red)/(L+NIR+C1*Red-C2*Blue)
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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
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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
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Calibration to derive ecological quantities: a scientific problem
Top-down vs. bottom-up modeling perspectives
Model invertibility
Model robustness
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(4) June 1998 sampling
NDVI = 0.1226(ln{total aboveground biomass}) - 0.3171
r2 = 0.6075
0
0.1
0.2
0.3
0.4
0.5
0.6
0 1 2 3 4 5 6 7 8
total aboveground biomass ln(g/m2)
ND
VI
Moss-Annual
Not Moss-Annual
Linear (Not Moss-Annual)
Empirical Model – Top down
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Analytical Models – Bottom up
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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
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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
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Observations
Ground segmentAcquisition, processing,
storage, & archiving
Ground segmentAcquisition, processing,
storage, & archiving
Retrieval of biogeophysical variables
Spatio-Spectral-Temporal
analysisDefinitions of nominal trajectories and
estimates of uncertainty
Assimilation of current observational datastreams
Change detection Change quantification
Change attribution Change assessment
Ecological Questions &Hypotheses
Information for Ecological Forecasting
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Tuning the macroscope of remote sensing to support ecological inference requires an integrated and sustained
approach to technology & theory
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ACKNOWLEDGMENTS
DGG acknowledges support from NASA EPSCoR subcontract 12860.
GMH acknowledges support from NSF #9696229/0196445 & #0131937.