## 3 Method
### Modeling Land-Use Changes and Assessing Landscape Characteristics  
The land-use/land-cover (LU/LC) map produced by the Land Development Department in 2016 [42] was used as a baseline for modeling. The original 24 LU/LC classes were generalized to 11 classes, namely (1) paddy fields, (2) maize area, (3) cash crops, (4) perennial trees (fruits and rubber), (5) evergreen forest, (6) deciduous forest, (7) forest plantation, (8) settlement and infrastructure, (9) miscellaneous land uses (others), (10) water body, and (11) shifting cultivation. Paddy is irrigated rice; cash crops include all annual economic crops such as cassava, sugar cane but exclude maize. Perennial trees include fruit trees and rubber plantations, while forest plantation is a mono-tree species plantation such as teak, eucalyptus. Water consists of streams, rivers, lakes, and dams. In addition, shifting cultivation or swidden cultivation is slash and burn forest clearing for planting crops then moving on when soils are depleted (Table 1). The Dyna-CLUE model (Conversion of Land Use and its Effects Modeling Framework) [45] was selected to predict land-use patterns because the model has been used successfully for allocating future land demands at both local [46; 47] and regional levels [48], as well as for dealing with multiple interacting variables [31]. In addition, this model is a spatially explicit and enables to deal with multiple interacting variables [32; 35] and restriction policies. The model requires four inputs: (1) future land-use requirements, (2) characteristics of current land use, (3) spatial policies and restrictions, and (4) land-use type-specific conversion rules. Land-use requirements represent the aggregate land demands of future scenarios (i.e., trends, market-based and conservation). The location preferences of the different LU/LC classes were determined by using \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{LU/LC} & \multicolumn{2}{c}{**1977**} & \multicolumn{2}{c}{**2009**} & \multicolumn{2}{c}{**2016**} & \multicolumn{2}{c}{**Change 2009-2016**} \\ \cline{2-10}  & **(ha)** & **\%** & **(ha)** & **\%** & **(ha)** & **\%** & **ha** & **\%** & **Annual Rate** \\ \hline Paddy 1 & 52,540 & 4.32 & 37,604 & 3.1 & 38,860 & 3.2 & 1256 & 3.34 & 0.47 \\ Maize & 5590 & 0.46 & 136,708 & 11.2 & 212,720 & 17.5 & 76,012 & 55,60 & 6.52 \\ Cash crops 2 & 2310 & 0.19 & 2116 & 0.2 & 5352 & 0.4 & 3236 & 152.93 & 14.18 \\ Perennial trees 3 & 4014 & 0.34 & 35,256 & 2.9 & 75,008 & 6.2 & 39,752 & 112.75 & 11.39 \\ Evergreen forest & 108,037 & 8.91 & 105,892 & 8.7 & 97,436 & 8.0 & \(-\)8,456 & \(-\)7,985 & \(-\)1.18 \\ Dediduous forest & 1,020,080 & 83.87 & 744,436 & 61.2 & 656,072 & 53.9 & \(-\)88,244 & \(-\)11.86 & \(-\)1.79 \\ Forest plantation 4 & 240 & 0.02 & 25,164 & 2.1 & 19,388 & 1.6 & \(-\)5,776 & \(-\)22.95 & \(-\)3.66 \\ Settlement & 11,310 & 0.93 & 20,520 & 1.7 & 21,896 & 1.8 & 1376 & 6.71 & 0.93 \\ Others 5 & n/a & - & 6876 & 0.6 & 3876 & 0.3 & \(-\)3000 & \(-\)43.63 & \(-\)7.86 \\ Water 6 & 4140 & 0.34 & 7440 & 0.6 & 8316 & 0.7 & 876 & 11.77 & 1.60 \\ Shifting cultivation 7 & 7420 & 0.61 & 94,368 & 7.8 & 77,336 & 6.4 & \(-\)17,032 & \(-\)18.05 & \(-\)2.80 \\ \hline Total & 1,216,260 & 100.00 & 1,216,260 & 100.0 & 1,216,260 & 100.0 & 0 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Extent of land-use/land-cover classes in 1977, 2009 and 2016.  
logistic regression, identifying the relationship between a particular LU/LC type (dependent variable) and a set of independent variables (drivers). \[\text{Logit(p_{i})}=\text{ln (p_{i})}/(\text{1-p_{i})}=\beta 0+\beta_{1}X_{1}+ \beta_{2}X_{2}+\dots+\beta_{n}X_{n} \tag{2}\]  
where p1 is the probability of the occurrence of a particular LU/LC type and the Xi parameters are the independent variables [45]. \(\beta\)i is the estimated coefficient of each independent variable in the logistic regression.