31 pattern in study area and it can be used to predict future land use pattern.
Furthermore, related to the uncertainty of land use pattern in the future, 90.83 overall accuracy and 86.00 Kappa accuracy values show that all of driving factors
capable to reduce the uncertainty because they capable to describe the land use behavior that will shape the future land use condition. The results are confirmed to
the statement of Pontius and Neeti 2009 where high agreement resulted from validation process indicates that the processes of land use change during the
calculation are stable trough the interval of validation and suitable to be used in simulation process. The result of Kappa measurement can be seen in the table
below.
Table 11. Results of KAPPA Analysis for Comparison between Land use map
between interpretation and simulation for year 2009.
Land Use User
Total User
Accuracy W
G E
S F
Pr o
d u
c e
r W
374 374
1.000 100.000
G 6971
832 76
1375 9254
0.753 75.330
E
830 42608
1419 315
45172 0.943
94.324
S 172
1407 12222
17 13818
0.884 88.450
F 1280
325 102
18593 20300
0.916 91.591
Total 374
9253 45172
13819 20300
88918
Producer Accuracy
1 0.75
0.94 0.88
0.92 100
75.34 94.32
88.44 91.60
Overall Accuracy 90.83
Kappa Accuracy 86.00
Figure 7. Land use map based on interpretation and simulation result year 2009
32
2.4.3. Scenario-based land use modeling by using CLUE-S model
Scenario-based land use modeling is used in this research to understand the phenomena of Upstream Cisadane Watershed dynamic, especially related to land
use change. In order to understand the dynamic of land use in recent and future time, the model will be used as a tool to link between present and future of land use
condition and the scenarios will be built to design different alternative conditions of land use. In order to design the plausible scenarios of highland use in the future,
the most important things are the understanding of relationship between land use and the driving factors and the understanding of pressures that caused by those
driving factors. In CLUE-S model, the demand of land use is specified on yearly basis before
the iteration and used as direct input for the allocation module. The allocation of land use in this framework is based on combination between empirical analysis,
spatial analysis and dynamic modeling in raster base iterative procedure. This procedure allows continuous interaction between macro scale demand and local
land use suitability as derived from logistic regression. Macro scale demand or regional demands influence the actual allocation change together with the local
highest probability derived from logistic regression. Based on the iteration variable, preliminary allocation is created by
considering conversion matrix see Table 6. and the total allocated area is calculated by using land use requirements from the regional demand. The iteration
process is repeated until all of demands are balance and correctly allocated. In this research, four combinations of land use change are designed. These
scenarios have been explained in the section 2.3.3.4, including baseline scenario and increasing population growth scenario. These scenarios are demonstrated to
verify the usability of scenario based modeling to support land use analysis in Upstream Cisadane. Each scenario and the results will be explained in the following
section.
