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G.P.Asner and K.B.Heidebrecht

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. Objective – comparison of multi- and hyper-spectral observations to

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G.P.Asner and K.B.Heidebrecht

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  1. Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: comparing multispectral and hyperspectral observations G.P.Asner and K.B.Heidebrecht

  2. Introduction • Objective – comparison of multi- and hyper-spectral observations to decompose remotely sensed data • Why Important? – Study of impacts of climate variability and land use on vegetation cover • Difficulties – small individual canopies - phenological changes - separation of NPV and bare soil in NDVI • Approaches – correlation of NDVI - Spectral Mixture Analysis

  3. SMA • 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

  4. Data used – Image based • Landsat TM – commonly available • Terra ASTER – dense 5-channel sampling at SWIR2 • Terra MODIS – available daily 15-channel sampling of visible and NIR

  5. The land under research • 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

  6. Measurements • ADC camera for grassland • Ikonos camera for shrubland • Areas 8ha each, with 300m N-S transect established using GPS • Field Spectroradiometer - 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

  7. Model and Analysis • 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 divided spectral reflectance values by reflectance at first wavelength mathematically inappropriate for linear SMA

  8. 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

  9. 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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