The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
37 
These regions were visited during our first field study on 
September, 2007 (Figure 3). 
Figure 3: A view from a homogeneous region on the lake. 
Current Status of the Site: 
Xj = digital number attributed to the pixel location 
indexj 
i = target pixel location index 
Let’s define a set A, which contains the location indices of 
pixels which are within the chessboard distance d to the target 
pixel location i. For example if we choose all eight neighboring 
pixels (d = /), this means A has nine elements including 
location index i. So; 
If jeA;W,j = 1 
Otherwise, Wy = 0 
Therefore; Equation (1) gives the ratio of the sum of the 
weighted DNs within the set of A to the sum of the DNs for the 
entire image (Wulder and Boots, 1998). 
In the context of application to remote sensing digital images, a 
standard version of G, (d) is used by calculation of Z score 
standardized form * . 
TUBITAK UZAY will be organizing field campaigns at Tuz 
Gold during summer period each year. Site will be temporarily 
instrumented during campaigns. The maintenance of the site is 
funded by TUBITAK and the first campaign is planned to be 
held during August 2008. 
(Bannari et al., 2005) used the following standardized formula 
obtained from Z score formula, to evaluate the homogeneity in 
Lunar Lake Playa, Nevada calibration test site. 
4. HOMOGENEITY ANALYSIS 
As mentioned previously, spatial homogeneity is one of the 
important criteria, having influence on the accuracy of the 
absolute calibration results. Homogeneity of the area, which can 
change over time, affects the site selection and also usability of 
the area. Therefore, a special focus on homogeneity will be 
given in this section. 
For homogeneity analysis, the spatial autocorrelation, the 
degree of dependence between the pixels digital numbers 
associated with the pixel coordinate is used (Bannari et al., 
2005). The clustering of similar digital numbers denotes 
positive autocorrelation while neighborhood of dissimilar 
values denotes negative autocorrelation. Global or local 
statistics are used to measure spatial autocorrelation. Getis 
statistics (Getis and Ord, 1992) is used as a local indicator 
which is an example of the local indicators of spatial 
association (LISA) (Bannari et al., 2005). 
Gi (d) = 
j 
s[W i *(n-W i *)/(«-!)]' 
(2) 
where w* = '£w ij (c1) 
(n-(x) 2 ) 
The Getis Statistics, explained above, is conducted on Tuz Golii 
calibration test site for homogeneity analysis using the NIR 
band of MODIS (LPDAAC, 2007) image taken on 20.07.2007 
and d is taken as unity. 
The statistics G*(d) for some chessboard distance (Gonzalez, 
2002) d is defined as (Wulder and Boots, 1998): 
'L w vW x j 
G/(</) = ^=- 
L x j 
(i) 
The resultant image showing Tuz Golti homogeneity, calculated 
using Getis Statistics, is given in Figure 4. According to this 
statistics, if the target pixel and its neighborhood pixels have 
similar high values, Getis statistics gives a high value. If the 
target pixel and its neighborhood pixels have similar low values 
Getis statistics gives a low value (Wulder and Boots, 1998). 
Therefore; from the results, it is clear that there is a large 
where w.. (¿/) = a spatial weight matrix 
’ Z score standardization is: 
X-jU 
T 
of x and T is standard deviation of x. 
where JU is the mean