23 =
� ∑ �
�� �
�=
− ∑ �
�+
, �
+� �
�=
� − ∑ �
�+
, �
+� �
�=
Where :
� = Total number of sites in the matrix, � = Number of rows in the matrix
�
��
=
Number in row i and column i �
�+
=
Total for row i, and �
+�
= Total for column i.
Kappa can be used to determine if the values contained in an error matrix represent a result significantly better than random Jensen 1996.
2.4. Result Discussion
This section will mainly discuss the results of overall analysis, including analysis of land use changes, examination of driving factors in logistic regression
and land use scenario development by using relevant variables in the CLUE-S model. The first section explains the result of identifying, quantifying and trend
analysis of land use change. The second section of this chapter mainly demonstrates the use of relevant variables logistic regression analysis in development scenarios
of future land use. The model used will depict the effect of land use change to the future land use. All of sections of this chapter will be directly followed by
discussion to provide clear explanation about all of the findings of this research.
2.4.1. Land Use Pattern
2.4.1.1. Land Use Maps Year 1991 and 2009
According to the classification processes, there are five land uses that could be identified from all of images: water, grassland, estate, settlement and forest.
Description of each land uses are explained in Table 4 and the location and distribution of land use could be seen in the figure below.
24 Figure 4.
Land Use Map Year 1991 and Year 2009
2.4.1.2. Land Use Change Analysis
The land coveruse change patterns from one land coveruse to another are presented in the following table and the maps can be seen in Figure 5.
Table 9. Matrix of land use change year 1991-2009
Land Use 1991
2009 Change
1991 - 2009 Percentage
Annual Average Change
Water 93.50
93.50 0.00
0.00 0.00
Grassland 2209.25
2313.50 104.25
4.72 0.26
Estate 12040.25
11293.00 -747.25
-6.21 -0.34
Settlement 3055.25
3454.50 399.25
13.07 0.73
Forest 4831.25
5075.00 243.75
5.05 0.28
Figure 5. Land use change map year 1991-2009
25 Based on the findings about land use changes in year 1991 and 2009 above,
it can be noted that urban settlement area experienced the most significant development followed by forest and grassland. However estates tend to decrease
and river is stable during the 1991-2009 period.
2.4.1.3. The Trends of Land Use Change during 1991-2009
According to the change detection for the land use classification year 1991 and 2009, it can be seen that settlement, forest and grassland area are increasing
over time, whereas estate tend to decrease and river remains constant. The trends of land use changes of the area during 1991-2009 are presented in following figure.
Figure 6. Trends of land use change year 1991-2009
2.4.2. Analysis of driving factors of land use changes
2.4.2.1. Logistic regression results
The results of logistic regression between land use and independent variables are presented in this section. Each land use has independent variables or driving
factors that influence to its pattern. In logistic regression analysis, five classes of land use are included in the regression calculation.
The selection of the significant and non-significant independent variables is based on a enter procedure see 2.3.3.3.. The variables, which have coefficient
values below 0.01 significant thresholds, are categorized as significant and the
