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

Acknowledgements:

USFS - 4 years of funding

1) Fire / fuel loads

2) SMA 3) Sample Endmember fractions

4) MESMA

5) Sample Endmember fraction / biomass correlations

Presentation Overview

- 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?

- 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?

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

GV

Shade

NPV

Soil

Landsat TM imagery for MCAS Miramar with Endmember fraction images

ADAR data, 1 m pixels Landsat TM data, 30 m pixels

The mixed pixel problem / Endmember analysis

GVSoil

NPV

Feature space plots for the MCAS Miramar Landsat TM scene, with approximate EM locations

GVsoil

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soilNPV

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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

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EM Fractions vs. time for stands of chamise chaparral

Summary

Fire is a problem

Remote Sensing is one way to look at it