Nonlinear Spectral Mixture Effects for Photosynthetic/Non-photosynthetic Vegetation Cover Estimates of Typical Desert Vegetation in Western China

Desert vegetation plays
significant roles in securing the ecological integrity of oasis ecosystems in
western China. Timely monitoring of photosynthetic/non-photosynthetic desert vegetation
cover is necessary to guide management practices on land desertification and
research into the mechanisms driving vegetation recession. In this study,
nonlinear spectral mixture effects for photosynthetic/non-photosynthetic
vegetation cover estimates are investigated through comparing the performance
of linear and nonlinear spectral mixture models with different endmembers
applied to field spectral measurements of two types of typical desert
vegetation, namely, Nitraria
shrubs and Haloxylon. The
main results were as follows. (1) The
correct selection of endmembers is important for improving the accuracy of
vegetation cover estimates, and in particular, shadow endmembers cannot be
neglected. (2) For both the Nitraria
shrubs and Haloxylon, the Kernel-based Nonlinear Spectral Mixture
Model (KNSMM) with nonlinear parameters
was the best unmixing model. In consideration of the computational complexity and accuracy requirements, the Linear
Spectral Mixture Model (LSMM) could be adopted for Nitraria shrubs plots, but this will result in significant errors for the Haloxylon
plots since the nonlinear spectral mixture effects were more
obvious for this vegetation type. (3) The
vegetation canopy structure (planophile
or erectophile) determines the strength of the nonlinear spectral mixture
effects. Therefore, no
matter for Nitraria shrubs or Haloxylon, the non-linear spectral mixing effects
between the photosynthetic / non-photosynthetic vegetation and the bare soil do
exist, and its strength is dependent on the three-dimensional structure of the
vegetation canopy. The choice of linear or nonlinear spectral mixture models is
up to the consideration of computational complexity and the accuracy requirement.