Nonlinear spectral mixture effects for photosynthetic/non-photosynthetic vegetation cover estimates of typical desert vegetation in western China.

Cuicui Ji,Xiaosong Li,Zhihai Gao,Huaidong Wei,Yonghong Jia,Daniel Capella Zanotta

doi:10.1371/journal.pone.0189292

Abstract

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.

Highlights

Arid and semiarid regions occupy 41% of the world’s whole land area, where the ecosystem are prone to desertification due to the irrational use of natural resources and climate change [1]
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
According to the characteristics of different Nonlinear Spectral Mixture Model (NSMM) [39], this study proposes the use of a bilinear spectral mixture model (BSMM), which is relatively simple to use and yields results with physical meaning, and a kernel-based NSMM

Summary

Introduction

Arid and semiarid regions occupy 41% of the world’s whole land area, where the ecosystem are prone to desertification due to the irrational use of natural resources and climate change [1]. In western China, oases are the basis of human life and social economic development, supporting more than 95% of the population, they cover less than 5% of the total area of arid regions[2]. Desert vegetation between oasis and desert is very important, which functions as a shelter against drifting sand, and a major food source for sheep, goats and camels[3]. In order to preserve the desert vegetation as a valuable resource and to maintain its sand-fixing and food-providing function, it has to be protected[4]. A sustainable management of desert vegetation requires accurate and timely information on vegetation cover at large scale[5]

Methods

Results

Discussion

Conclusion