Spectral unmixing of vegetation, soil and
dry carbon cover in arid regions:
comparing multispectral and
hyperspectral observations
G.P.Asner and K.B.Heidebrecht
Introduction
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Objective –
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Why Important? –
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Difficulties –
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Approaches – correlation of NDVI
comparison of multi- and hyper-spectral observations to
decompose remotely sensed data
Study of impacts of
climate variability and land
use on vegetation cover
small individual canopies
- phenological changes
- separation of NPV and bare soil in NDVI
- Spectral Mixture Analysis
SMA
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Assumes linear combination
Two methods of reflectance
coefficient selection
Image-based
 reflectances used that are likely to exist in the area
lack of pure pixels
Spectral Libraries
 data readily collected
lack of generability and scalability
Data used – Image based
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Landsat TM – commonly available
Terra ASTER – dense 5-channel sampling at
SWIR2
 Terra MODIS – available daily
15-channel sampling of
visible and NIR
The land under research
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
Chihuahuan Desert, New Mexico
- 210mm ppt per year
- Long-term ecological
research site
- mainly grassland and
shrub
Requirements
- low species diversity
- strong differences of PV
and NPV between sites
- nearly constant soil type
- few soil crusts
Measurements
ADC camera for grassland
 Ikonos camera for shrubland
 Areas 8ha each, with 300m N-S
transect established using GPS
 Field Spectroradiometer
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- measurements every 5m along transects
- all canopies within 5m of sampling pts measured
- conversion to reflectance using calibration panel
 AVIRIS sensor – NASA ER-2 aircraft altitude 20km
- pixels 19m x 19m
Model and Analysis
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Auto MCU
- Fully automated Monte Carlo based
derivation of uncertainty of cover fractions
-
Code carried out on field spectra and
sub-sampled to satellite channels
 Algorithms – tied
SWIR2 PV, NPV, soil spectra ‘tied’ at 2.03μm
Less dependent on biomass, architecture, biochemistry
- division
wavelength
divided spectral reflectance values by reflectance at first
mathematically inappropriate for linear SMA
Results
Landsat TM convolved data - little difference between
shrubland and grassland sites
MODIS and most of AVIRIS - spectrally indistinguishable
ASTER - some differences
AVIRIS
– finds negative PV fractions
- bare soil overestimated by ~20%
- NPV fractions good
Tied SWIR2 – showed consistent accuracy
- corroborated by previous work
Future
Important to continue this
research for ecological
monitoring
 Further research into the
use of instruments such as
AVIRIS (i.e. high SNR
in SWIR2)
for use in SMA methods
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