33
2.4.2.1. Baseline scenario
In this scenario, it is assumed that land use change in future is the continuity of land use change in the past time. Land use change in the past time has been
calculated in the land use pattern section. In this model, the initial land use is land use map year 1991 created from image interpretation. The demand of land use in
the future until the year 2030 is the continuity of the demand of land use change 2001-2009. Moreover, with no
spatial policies applied, it’s mean that all of areas inside the study area are possible to change to another land use. Simulated maps for
selected years 2000, 2010, 2020, and 2030 can be seen in the following figure.
Figure 8. Simulated land use maps with baseline scenario
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2.4.2.2. Scenario 2
In this scenario, the demand of the land use is still the same with baseline. The difference, in this scenario spatial policies is apply where forest area based on
forest designation map from Ministry of Forestry is not allowed to change to other land use. Simulated maps based on this scenario in selected years 2000, 2010, 2020,
and 2030 can be seen in the following figure.
Figure 9. Simulated land use maps with Scenario 2.
2.4.2.3. Scenario 3
In this scenario, the demand of the land use is doubled compared with baseline. It’s mean that the demand for each land use also doubled from baseline.
Spatial policies are not applied in this scenario. Simulated maps based on this
35 scenario in selected years 2000, 2010, 2020, and 2030 can be seen in the following
figure.
Figure 10. Simulated land use maps with Scenario 3
2.4.2.4. Scenario 4
In this scenario, the demand of the land use is doubled compared with baseline, same with Scenario 3. The difference, spatial policies are applied in this
scenario. Simulated maps based on this scenario in selected years 2000, 2010, 2020, and 2030 can be seen in the following figure.
36 Figure 11.
Simulated land use maps with Scenario 4
2.4.2.5. Scenario-base land use modeling interpretation
Based on the results of land use scenario modeling using CLUE-S framework, it can be seen that the combination between empirical analysis, rule-based modeling
and cellular automata is valuable in depicting future land use distribution and can be use to answer how does the
future land use ‘might be’ occurs in the study area. It means that it can provide possibilities of future land use configuration before it
happens. However, due to the uncertainty in the future and the complexity of land use and the drivers, this scenario-based modeling is not only addressed to make
predictions but more in improving understanding about land use behavior on upstream watershed zones based on the influencing factors and what might happen
in the future based on these known underlying factors. In the planning process, the
37 understanding about land use behavior as well as future land use distribution is
important for planners and managers to justify a spatial planning decision and avoid inappropriate decision Pontius and Neeti, 2009. Therefore, in order to provide an
appropriate explanation, the critical discussion in this research will be conducted by considering the findings and its appropriateness with some literatures.
According to the change detection for the land use classification year 1991, simulated year 2000, year 2010, year 2020 and 2030, it can be seen that settlement,
forest and grassland area are increasing over time, whereas estate tend to decrease and water remains constant. The trends of land use changes of the area during 1991
– 2030 are presented in following figure.
Figure 12. Trends of land use change based on Scenario 1.
Figure 13. Trends of land use change based on Scenario 2.
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