3. DATA AND METHODOLOGY
3.1 Data
In this study, SPOT 4 XS (20 m) image acquired on July 24,
2003 and SPOT 5 MS (10 m) acquired on August 14, 2007
were used (SPOT Image Copyright 2007, CNES). 1/5000 scaled
aerial photographs and SPOT 5 Pan-sharpen (2.5 m) data were
used for ground truth and accuracy assessment.
3.2 Pre-processing
The aim of remotely sensed data change detection is to compare
the differences in the spatial representation of reflectance values
of two points in time (Green et al, 1994; Yool and Flores,
2007). The preliminary requisite for accurate change detection
is precise geometric and atmospheric correction of the multi-
temporal images. In the study, all images were geometrically
corrected using first order polynomials and Ground Control
Points (GCPs) collected from rectified SPOT 5 PAN data and
1/5000 scaled maps to eliminate geometric distortions, and to
define images in a common Universal Transverse Mercator
(UTM) coordinate system. The digital numbers of the images
were transformed to radiance values according to the reference
information from image header file. Radiance values were
converted to at-satellite reflectance based on the method
presented by Vermote et al., 1997. Dark Object Subtraction
(DOS) atmospheric correction method applied to multi temporal
images to remove haze caused by atmospheric phenomena
(Chavez, 1988).
3.3 Change Vector Analysis
Change vector analysis (CVA) is a spectral differencing
technique, the primary utility of which is the detection of all
changes present in the input multispectral data (Malila, 1980).
The method was first used to characterize magnitude and
direction variations of a vector in an n-dimensional spectral
space defined by the axes of bands, transforms, or spectral
features from a multi-temporal dataset (Malila, 1980). It is
flexible enough to be effective when using diverse types of
sensor data and radiometric change applications. CVA avoids
spatial-spectral errors associated with post-classification
comparison (Chen et al., 2003).
3.4 Description of Tasselled Cap Transformation Features
Brightness, greenness and wetness are the components of
Tasseled Cap Transformation and descriptions of the
components are given in Table 1 (Chris and Kauth, 1986;
Lillesand and Kiefer, 1987). These components were used in
mCVA to determine the dynamics of LULC changes that
occurred in the selected region.
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3.5 Gram Schmidt Transformation and Tasselled Cap
Parameters (Kauth -Thomas) Extraction
Gram Schmidt Transformation (GST) is a mathematical tool
that performs a rotational transformation in n-dimensional
space, resulting in a series of orthogonal axes, each of which is
a linear combination of the original axes. Three Tasseled Cap
parameters are named Brightness, Greenness and Wetness were
extracted from SPOT 5 MS data by using GST based on Ivits et
al, 2008. Samples of bare soil, green vegetation, and water
were selected to derive the TC coefficients.
3.6 Change Vector Analysis Based on TC Features
After image pre-processing, CVA was applied using derived TC
coefficients to reveal the dynamics of land use/land cover
(LULC) changes in Terkos Water Basin, Istanbul. The TC
coefficients derived for SPOT 5 (Bektas Balcik, 2010) (Table 2)
were used in a linear combination to transform 2003 dated
SPOT 4 and 2007 dated SPOT 5 images into brightness,
greenness and wetness features.
SPOT 5 Green Red Near Short Wave
Infrared Infrared
Brightness | 0.201 0.397 0.548 0.707
Greenness | -0.180 -0.330 | 0.832 -0.408
Wetness 0.388 0.573 0.013 -0.724
Component | Description
Brightness It is weighted sum of all bands, aligned in the
direction of the principal variation in soil
reflectance.
Greenness It represents the contrast between the near-
infrared and visible bands (orthogonal to
brightness)
Wetness It relates to canopy and soil moisture
(orthogonal to brightness and greenness)
Table 1. Description of TC features
Table 2. TC coefficients for SPOT 5 data at satellite reflectance
In this study, an extended CVA experiment was implemented
based on the spherical geometry approach introduced by (Allen
and Kupfer, 2000) to measure vector longitude and colatitude.
This method adds a new colatitude measurement to improve the
change detection capability. A model in Erdas Imagine
Modeler tool was created to calculate the results of extended
CVA. The detailed description of the model is given in Figure 2
and the model is produced based on Flores and Yool, 2007.
CC E
(Rhoj
Figure 2. Graphical model script of the extended change vector
analysis algorithm for SPOT 4 XS (2003) and SPOT 5 XS
(2007) data.
This method derives the information about both the amounts
and types of changes in the data. The output of the model
includes the magnitude and three direction images.
