View angle effects on canopy reflectance and spectral mixture analysis of coniferous forests using AVIRIS

The dependence of vegetation reflectance on sun and sensor geometry can potentially provide information on canopy properties, but also may be a source of unmodelled systematic error in single-angle remote sensing measurements. In this study, we investigated the angular variability of reflectance measurements from the NASA Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and the resulting impact on spectral mixture analysis (SMA) using both full-range (400-2500 nm) and shortwave-infrared wavelengths (2080-2280 nm; AutoSWIR ). The study was conducted in coniferous forests in Central Oregon using five AVIRIS overpasses to generate multiple view angle measurements. Canopy reflectance was highly anisotropic, with the strength of the angular signal controlled by species type, canopy cover and soil reflectance. Canopy cover estimates from full-range SMA averaged only slight decreases (∼6% relative) toward the retro-solar direction for 16 field plots in the study region. AutoSWIR was even less influenced by view angle, exhibiting changes only for large differences in view angle. In addition, AutoSWIR 's ability to accommodate endmember variability led to stronger agreement with field cover values than full-range SMA. The results suggest that while view angle can significantly affect reflectance measurements from AVIRIS, the consequent variability in vegetation cover estimates from SMA and AutoSWIR is low.

[1]  T. Eck,et al.  Characterization of the reflectance anisotropy of three boreal forest canopies in spring-summer , 1999 .

[2]  Jessica A. Faust,et al.  Imaging Spectroscopy and the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) , 1998 .

[3]  Warren B. Cohen,et al.  Empirical methods to compensate for a view-angle-dependent brightness gradient in AVIRIS imagery☆ , 1997 .

[4]  A. Strahler,et al.  Geometric-Optical Modeling of a Conifer Forest Canopy , 1985, IEEE Transactions on Geoscience and Remote Sensing.

[5]  J. Kleman,et al.  Directional reflectance factor distributions for two forest canopies , 1987 .

[6]  Alan R. Gillespie,et al.  Vegetation in deserts. I - A regional measure of abundance from multispectral images. II - Environmental influences on regional abundance , 1990 .

[7]  N. Goel Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data , 1988 .

[8]  Bernard Pinty,et al.  The effect of surface anisotropy and viewing geometry on the estimation of NDVI from AVHRR , 1995 .

[9]  P. Bicheron,et al.  Enhanced discrimination of boreal forest covers with directional reflectances from the airborne polarization and directionality of Earth reflectances (POLDER) instrument , 1997 .

[10]  D. Roberts,et al.  Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data , 1993 .

[11]  John B. Adams,et al.  SPECTRAL MIXTURE ANALYSIS - NEW STRATEGIES FOR THE ANALYSIS OF MULTISPECTRAL DATA , 1994 .

[12]  J. Mustard,et al.  A semianalytical approach to the calibration of AVIRIS data to reflectance over water application in a temperate estuary , 2001 .

[13]  J. Muller,et al.  New directions in earth observing: Scientific applications of multiangle remote sensing , 1999 .

[14]  J. Privette,et al.  Impact of Tissue, Canopy, and Landscape Factors on the Hyperspectral Reflectance Variability of Arid Ecosystems , 2000 .

[15]  L. F. Johnson Multiple view zenith angle observations of reflectance from ponderosa pine stands , 1994 .

[16]  Brian Curtiss,et al.  A method for manual endmember selection and spectral unmixing , 1996 .

[17]  R. Myneni,et al.  A review on the theory of photon transport in leaf canopies , 1989 .

[18]  S. Sandmeier,et al.  Structure Analysis and Classification of Boreal Forests Using Airborne Hyperspectral Brdf Data from Asas Imagery and Processing Techniques Have Also Been Used Potential for Combining Both High Spectral Resolution And , 2022 .

[19]  Alan H. Strahler,et al.  Modeling bidirectional radiance measurements collected by the advanced Solid-State Array Spectroradiometer (ASAS) over oregon transect conifer forests☆ , 1994 .

[20]  A. Goetz,et al.  Software for the derivation of scaled surface reflectances from AVIRIS data , 1992 .

[21]  Jean-Philippe Gastellu-Etchegorry,et al.  Modeling BRF and Radiation Regime of Boreal and Tropical Forests: I. BRF , 1999 .

[22]  Thomas F. Eck,et al.  Reflectance anisotropy for a spruce-hemlock forest canopy , 1994 .

[23]  B. Hapke,et al.  The cause of the hot spot in vegetation canopies and soils: Shadow-hiding versus coherent backscatter , 1996 .

[24]  Ranga B. Myneni,et al.  Estimation of global leaf area index and absorbed par using radiative transfer models , 1997, IEEE Trans. Geosci. Remote. Sens..

[25]  D. Hodáňová,et al.  Leaf Optical Properties , 1985 .

[26]  James R. Irons,et al.  Bidirectional reflectance of selected BOREAS sites from multiangle airborne data , 1997 .

[27]  David B. Lobell,et al.  Subpixel canopy cover estimation of coniferous forests in Oregon using SWIR imaging spectrometry , 2001 .

[28]  F. Baret,et al.  Modeling Spectral and Bidirectional Soil Reflectance , 1992 .

[29]  Didier Tanré,et al.  Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview , 1997, IEEE Trans. Geosci. Remote. Sens..

[30]  G. Asner Biophysical and Biochemical Sources of Variability in Canopy Reflectance , 1998 .

[31]  D. Lobell,et al.  A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation , 2000 .

[32]  Darrel L. Williams,et al.  Multispectral bidirectional reflectance of northern forest canopies with the advanced solid-state array spectroradiometer (ASAS)☆ , 1994 .

[33]  J. Privette,et al.  Effects of orbital drift on advanced very high resolution radiometer products: Normalized difference vegetation index and sea surface temperature , 1995 .

[34]  Christopher B. Field,et al.  Combining satellite data and biogeochemical models to estimate global effects of human‐induced land cover change on carbon emissions and primary productivity , 1999 .

[35]  Gregory P. Asner,et al.  Estimating vegetation structural effects on carbon uptake using satellite data fusion and inverse modeling , 1998 .