## 4 Results and Discussion  
### Land Use and Land Cover Assessment and Change Detection  
The area and percentage of the LULC data in 2014 and 2019 are summarized in Table 7, and the spatial distribution of LULC is mapped in Figure 6. Results indicate that three LULC types are dominant on the Island. These include perennial trees and orchards, urban and built-up areas, and evergreen forest, which covered 37.7%, 24.0%, and 15.5%, respectively, in 2014 and 35.3%, 27.1%, and 14.2%, respectively, in 2019. The results of the accuracy assessment of the LULC map in 2019 are reported in Table 8. The overall accuracy and the Kappa hat coefficient of the LULC map in 2019 are 96.06% and 95.15%, respectively. The producer's accuracy (PA), which represents omission error, varies from 87.50% to 100%, and the significant omission error occurs in idle land, with a value of 12.5%. At the same time, the user's accuracy (UA), which represents commission error, varies from 87.50% to 100%, and the significant commission error occurs in the paddy field with a value of 12.5%. According to Fitzpatrick-Lins, a Kappa hat coefficient of more than 80% represents a strong agreement (high accuracy) between the classified and reference \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Station Code**} & \multicolumn{2}{c}{**Flow (cms) in Different Criteria**} \\ \cline{2-3}  & **Normal (70\%)** & **Surveillance (70–90\%)** & **Critical (90\%)** \\ \hline X.191 & \textgreater{}0.50 & 0.50–0.20 & 0.24 \\ \hline Source: Southern Region Irrigation Hydrology Center, Royal Irrigation Department [51]. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The criteria of minimum water from the flow duration curve. Figure 5: The flow duration curve of X.191 station (Satree Phuket School). Source: Southern Region Irrigation Hydrology Center, Royal Irrigation Department [51]. maps [52]. Additionally, an overall accuracy of more than 85% for the LULC map in 2019 is regarded as an acceptable result [53]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**LULC Type**} & \multicolumn{2}{c}{**LULC Data in 2014**} & \multicolumn{2}{c}{**LULC Data in 2019**} \\ \cline{3-6}  & & **Area in km\({}^{2}\)** & **Percent** & **Area in km\({}^{2}\)** & **Percent** \\ \hline
1 & Urban and built-up area & 125.32 & 24.00 & 141.64 & 27.13 \\
2 & Paddy field & 2.15 & 0.41 & 0.15 & 0.03 \\
3 & Field crop and horticulture & 1.46 & 0.28 & 3.43 & 0.66 \\
4 & Perennial trees and orchards & 196.63 & 37.66 & 184.39 & 35.32 \\
5 & Aquaculture & 8.74 & 1.67 & 8.56 & 1.64 \\
6 & Idle land & 34.67 & 6.64 & 39.74 & 7.61 \\
7 & Evergreen forest & 80.86 & 15.49 & 74.16 & 14.20 \\
8 & Mangrove forest & 25.15 & 4.82 & 24.72 & 4.73 \\
9 & Scrub forest & 27.08 & 5.19 & 27.00 & 5.17 \\
10 & Waterbody & 14.56 & 2.79 & 14.81 & 2.84 \\
11 & Miscellaneous land & 5.43 & 1.04 & 3.46 & 0.66 \\ \hline  & Total & 522.05 & 100 & 522.05 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Area and percentage of LULC data in 2014 and 2019. Figure 6: Spatial distribution of LULC classification: (a) LULC in 2014; (b) LULC in 2019. Moreover, the transition area matrix of LULC change between 2014 and 2019 is presented in Table 9. These results indicate that the increase in urban and built-up areas in 2019 was due to the conversion of perennial trees and orchards and idle land in 2014. In addition, the increase in perennial tree and orchard areas in 2019 was due to the conversion of evergreen and scrub forests in 2014. Similarly, the increase in idle land area in 2019 was due to the conversion of perennial trees and orchards in 2014. In contrast, perennial trees and orchards in 2014 were converted into idle land, field crops and horticulture, scrub forest, evergreen forest, miscellaneous land, and water bodies in 2019. Additionally, idle land in 2014 was converted into urban and built-up areas, perennial trees and orchards, and scrub forest in 2019.