The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
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Identifying expansive soils and quantifying their potential 
expansiveness is crucial to ensure proper site selection, 
environmentally compatible and economically feasible 
designing and construction especially of lightly loaded 
structures. However, common geotechnical practices of 
characterizing expansive soils need dense sampling, thus are 
costly, labour intensive, time consuming and difficult to get a 
continuous representation of soil masses in space. 
A great deal of effort has gone into investigating methods that 
can be supporting or alternate tools of estimating soil properties. 
Advances in remote sensing techniques have enabled 
discrimination of clay minerals that cause swelling and 
shrinkage in soils and mapping their abundances (Chabrillat et 
ah, 2002; Goetz et ah, 2001; Kariuki et ah, 2003; Kariuki et ah, 
2004; Van der Meer, 1999). Van der Meer (1999) reported 
possibility of mapping clay soils from remotely sensed data 
based on the dependence of spectral signatures on soil 
constituent minerals. Goetz et ah, (2001) established 
relationships between short wave infrared (SWIR) 1800 - 2400 
nm spectral bands and soil swelling potential classes of Seed et 
ah, (1962). Chabrillant et ah, (2002) identified and mapped 
exposed clay minerals (the three most important clay minerals 
with respect of soil expansion; smectite, illite, kaolinite) from 
airborne remote sensing images based on diagnostic absorption 
bands in the SWIR spectral region. Kariuki et ah (2004) 
proposed models that made use of spectral parameters from 
selected single wavelength regions. They established a one-to- 
one link between engineering parameters and absorption feature 
parameters (position, depth, width, asymmetry and area of 
absorption band) at -1400 nm, -1900 nm and -2200 nm 
wavelengths. 
In this study we developed new empirical models for estimating 
specific engineering parameters of expansive soils from their 
respective reflectance spectra. A multivariate calibration 
method, partial least squares regression (PLSR) analysis, 
making use of all absorption feature parameters calculated from 
three wavelength regions (~ 1400 nm, -1900 nm and -2200 nm) 
was employed. Resulting models provide numerical estimates 
of engineering parameters that can be directly used in practical 
engineering applications. 
2. MATERIALS AND METHODS 
2.1 STUDY AREA 
The study area is located in the eastern part of Addis Ababa city 
(Figure 2). Climate is cool to temperate with a mean annual 
temperature of 16 °C, and a mean annual rainfall of 1200 to 
1600 millimetres (EMA, 1988). Elevation ranges from 2700 
meters to 2300 meters above sea level. 
2.2 SAMPLING AND LABORATORY ANALYSIS 
Disturbed soil samples were collected through a stratified 
random sampling technique. Stratification was done through 
combining information on lithology and topography of the 
study area. 
Engineering parameters that are commonly used for 
identification of and indirect estimation of soil expansiveness; 
consistency limits (liquid limits (LL), plastic limits (PL) and 
plasticity indices (PI)) following the standard test procedures of 
AASHTO specifications (AASHTO, 2002) T89 and T90; cation 
exchange capacity (CEC) using methylene blue adsorption test 
‘spot’ method (Verhoef, 1992) and free swell (FS) in 
accordance with the methods and procedures demonstrated by 
Head (Head, 1994) were measured in a soil mechanics 
laboratory. 
Soil reflectance spectra were acquired using ASD fieldspec full 
range spectrometer (http://www.asdi.com) that covers the 350 
to 2500 nm wavelength region of the electromagnetic spectrum. 
Figure 2. Location map of the study area with names of places 
and distribution of sampling points. 
2.3 MULTIVARIATE (PARTIAL LEAST SQUARES) 
REGRESSION ANALYSIS 
Partial least squares regression (PLSR) deals with prediction of 
set of dependent (y) variables from set of independent (x) 
variables. PLSR is particularly important when dealing with 
large number of variables that express common information 
(Brereton, 2000; Wold et al., 2001; Yeniay and Goktas, 2002). 
Though multiple linear regression (MLR) analysis can be 
employed to explore relationships between a number of 
predictors and response variables, with an increase in number of 
predictors it will not perform well due to multicollinearity 
problems. MLR assumes x variables as linearly independent 
and require smaller number of x variables than the number of 
observations. Significant predictors should also be well known 
in MLR (Brereton, 2000). Another multivariate approach, 
principal component regression (PCR) analysis decomposes set 
of predictors into eigen vectors and scores to overcome 
collinearity. After achieving optimal projection of x variables in 
few principal components, it regress them against the responses 
in a separate step. Unlike PCR, PLSR decomposes both 
predictors and responses simultaneously to capture their 
common variation, which will be projected into a small number 
of mutually independent factors. Decomposition and regression 
is a single step, through fewer principal components than that 
required by PCR. Hence PLSR reduces the impact of irrelevant 
x variations in the calibration modeling by balancing the 
information in the x and y spaces (Martens and Naes, 1989; 
Wold et al., 2001). More information on the differences of the 
three multivariate calibration methods and their algorithms can