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An assessment of correlation between vegetation parameters measured on the ground and endmember fractions from remotely sensed data of varying spatial
resolution
Seth Peterson
Department of Geography,
University of California, Santa Barbara
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Acknowledgements:
USFS - 4 years of funding
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1) Fire / fuel loads
2) SMA 3) Sample Endmember fractions
4) MESMA
5) Sample Endmember fraction / biomass correlations
Presentation Overview
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- Fuel loads have increased
- Urban encroachment into wildlands
- These processes may be different for different ecosystems (study sites are in 5 western states)
Why is fire important?
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- Massive amounts of ground-based sampling
- Small, well-designed ground-based studies to calibrate large area remotely sensed scenes
- Correlate different indices and products from
image processing techniques with ground-based data
How can we study fire fuel loads?
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Spectral Mixture Analysis (SMA)
-Expresses pixel values as mixtures of the scene components,
called endmembers (EMs)
-Typical EMs used are:
-green vegetation (GV -- e.g. green leaves)
-nonphotosynthetic vegetation (NPV -- e.g. bark, branches, litter)
-rocks, soils
-shade
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GV
Shade
NPV
Soil
Landsat TM imagery for MCAS Miramar with Endmember fraction images
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ADAR data, 1 m pixels Landsat TM data, 30 m pixels
The mixed pixel problem / Endmember analysis
GVSoil
NPV
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Feature space plots for the MCAS Miramar Landsat TM scene, with approximate EM locations
GVsoil
shadeBand 3
Ban
d 4
GV
soilNPV
shade
Band 4
Ban
d 7
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Multiple Endmember SMA (MESMA)
- Allows for flexibility in the number of EMs used to model each pixel- Allows for flexibility in the type of EMs used to model each pixel
-Modeled EM fractions will be most accurate when the fewest, most appropriate EMs are used to model each pixel
GV_1GV_2
soil_1
soil_2
NPV_1
NPV_2
shade_photo
shade_phyto
Band 4
Ban
d 7
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EM Fractions vs. time for stands of chamise chaparral
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Summary
Fire is a problem
Remote Sensing is one way to look at it