Fractional Vegetation Cover Estimation Based on an Improved Selective Endmember Spectral Mixture Model

Ying Li,Hong Wang,Xiao Bing Li,Ke Lu

doi:10.1371/journal.pone.0124608

Abstract

Vegetation is an important part of ecosystem and estimation of fractional vegetation cover is of significant meaning to monitoring of vegetation growth in a certain region. With Landsat TM images and HJ-1B images as data source, an improved selective endmember linear spectral mixture model (SELSMM) was put forward in this research to estimate the fractional vegetation cover in Huangfuchuan watershed in China. We compared the result with the vegetation coverage estimated with linear spectral mixture model (LSMM) and conducted accuracy test on the two results with field survey data to study the effectiveness of different models in estimation of vegetation coverage. Results indicated that: (1) the RMSE of the estimation result of SELSMM based on TM images is the lowest, which is 0.044. The RMSEs of the estimation results of LSMM based on TM images, SELSMM based on HJ-1B images and LSMM based on HJ-1B images are respectively 0.052, 0.077 and 0.082, which are all higher than that of SELSMM based on TM images; (2) the R2 of SELSMM based on TM images, LSMM based on TM images, SELSMM based on HJ-1B images and LSMM based on HJ-1B images are respectively 0.668, 0.531, 0.342 and 0.336. Among these models, SELSMM based on TM images has the highest estimation accuracy and also the highest correlation with measured vegetation coverage. Of the two methods tested, SELSMM is superior to LSMM in estimation of vegetation coverage and it is also better at unmixing mixed pixels of TM images than pixels of HJ-1B images. So, the SELSMM based on TM images is comparatively accurate and reliable in the research of regional fractional vegetation cover estimation.

Highlights

Vegetation is the comprehensive result of the long-term interaction of landform, hydrology, soil, climate variability and human activities and its distribution, composition and development are closely related with environment condition, especially climate condition [1,2,3]
Taken the response value of a reference endmember as the spectral contribution value of the reference endmember to actual pixel so that it can participate in unmixing of mixed pixel, we developed a new selective endmember linear spectral mixture model (SELSMM)
The study incorporated three sections: 1) select endmembers from images which have been processed with Pixel Pure Index (PPI) method to ensure that the pixels participating in spectral unmixing are purer; 2) extract vegetation coverage information in study site from TM image and HJ-1B image with improved SELSMM and compare it with the estimation result of LSMM; and 3) quantify the accuracy of the results estimated with different models based on the two kinds of images using field measured coverage in the same period

Summary

Introduction

Vegetation is the comprehensive result of the long-term interaction of landform, hydrology, soil, climate variability and human activities and its distribution, composition and development are closely related with environment condition, especially climate condition [1,2,3]. As an important parameter reflecting horizontal coverage degree of vegetation on land surface, PLOS ONE | DOI:10.1371/journal.pone.0124608 April 23, 2015

Methods

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Discussion

Conclusion