ACCURACY ASSESSMENT OF LINEAR SPECTRAL MIXTURE MODEL(LSMM) IN 
ESTIMATING VEGETATION ABUNDANCE IN MOUNTAIN AREA 
WANG Tianxing\CHEN Songlin',MA Ya 2 
(1 College of Geographical Sciences ,Fujian Normal University , Fuzhou 350007 ,China; 
2 Guangzhou Institute of Geochemistry, Chinese Academy of Sciences Guangzhou 510640,China) 
KEYWORDS: Linear Spectral Mixture Model —LSMM; Terrain Undulation; Correction; Vegetation Abundance; NDVI; MVI; 
Regression Analysis 
ABSTRACT: 
Linear Spectral Mixture Model—LSMM which is prevailing presently is one of pixel unmixing models,the unmixing-accuracy of 
which is restricted by kinds of factors,but now the research about LSMM is mostly focused on appraisement of linear hypothesis 
relating to itself and techniques used to select endmembers, nevertheless, the environment conditions of study area which could sway 
the unmixing-accuracy such as atmosphere reflectance or scatteration and terrain undulation are not studied.This paper probes 
emphatically into the accuracy uncertainty of LSMM resulting from the terrain undulation with reference to unmixing vegetation 
abundance under LSMM. ASTER data set was chosen and the C terrain correction was applied to it. Based on this, vegetation 
abundances were extracted from both pre-C corrected and post-C terrain illumination corrected ASTER using LSMM, then the 
regression analysis between vegetation abundance and vegetation indices(NDVI and MVI) was further conducted to assess the 
unmixing accuracy which quantitatively measure the impact of terrain illumination on LSMM. The results indicate that terrain 
undulation could dramatically constrain the application of LSMM in inversion of vegetation abundance. A improved unmixing 
accuracy of 17.6% and 18.6% for R2 was achieved in regression against to NDVI and MVI respectively because of the removing 
terrain undulation by C correction method. Especially, effective removal or minimization of terrain effects is essential in 
mountainous areas. This study can also provide new theory basis for LSMM applications in mountainous areas. Though we took 
vegetation abundance as a case study, it should be envisioned that the similar result for other endmember types (water, barren soil, 
impervious area and so on ) could be achieve because of the same impact mechanism of terrain undulation and the identical unmixing 
procedure with LSMM. 
1. INTRODUCTION 
Information of land surface targets observed by remote sensing 
is measured spatially by pixels. Because of the the heterogeneity 
of ground features and relatively coarse spatial resolution of the 
satellite borne imagery characterized by 
TM,ETM+,MODIS,NOAA/AVHRR and so on., it is common 
that mixture spectra are generated when the pixel is occupied by 
more than one land-cover class (Ichoku &Kamieli, 1996). The 
effective information interpreted from mixture pixels is limit for 
quantitatively analyzing the characteristics of the targets. So a 
large number of sub-pixel models are developed, such as Linear 
or non-linear Spectral Mixture Model, Probabilistic model, 
Geometric-optical 
2. TEST SITE AND DATA 
Considering the computational burden and representation, we 
choose the rectangle-shaped suburb area of Fuzhou(26° 10 ' N, 
116 ° 21 ' E at center) where the land covers are 
abundant(Fig. 1). The site is covered by cropland, water, forest 
vegetation which is dominant, urban cover which mostly locate 
at SW of the site and bare soils. The terrain condition within the 
area is relatively undulate with elevation from 50m to 1000m, it 
is appropriate to investigate the impact of terrain undulation on 
LSMM. 
As one of the recent developments in remote sensing 
technology , Advanced Spacebome Thermal Emission and 
Reflection Radiometer (ASTER) provide substantial 
improvements over the traditional multispectral sensor, such as 
Landsat thematic mapper (TM), in spatial, spectral and 
radiometric resolutions, and has become a vital data source for 
earth science researchers (Fang Qiu et al,2006). ASTER is a 
multispectral scanner that produces images of high spatial 
resolution launched on July, 1999 aboard on the first platform 
of NASA's Earth Observing System -Terra. The instrument 
has three bands in the visible and near-infrared (VNIR) spectral 
range (0.5-0.9 pm) with 15-m spatial resolution, six bands in the 
shortwave infrared (SWIR) spectral range (1.6-2.4 pm) with 
30-m spatial resolution, and five bands in the thermal-infrared 
(TIR) spectral range (8-12 pm), with 90-m resolution (Kahle et 
al.,1991 ;Abrams,2000). An additional backward-viewing 
telescope with a single band duplicating VNIR band 3 could 
provide the capability for same-orbit stereogram metric data at 
15m spatial resolution. 
Another appealing aspect of ASTER data is the open 
availability of its data and even the on-demand standard 
products for research use are at very low cost (Rowan & Mars, 
2003). 
With the advantage of ASTER data described above, we 
collected ASTER data in Fuzhou area attempting to make use of 
the improved spatial and spectral information of ASTER data. 
WANG Tianxing, E-mail: watixi@163. com. TEL: 13459194134.