## 6 Conclusions  
Land-use and land-cover (LULC) classification and change detection between 2009 and 2019 was successfully conducted using a supervised classification with a support vector machine and post-classification comparison change detection algorithms. The overall accuracy and Kappa hat coefficient of the LULC maps in 2009 and 2019 were higher than 85%. Then, the land requirements under three scenarios--Scenario I (Trend of LULC evolution), Scenario II (Maximization of ecosystem service values), and Scenario III (Economic crop zonation)--were estimated based on their characteristics. Time-series LULC data between 2020 and 2029 were then effectively predicted using the CLUE-S model. After that, the actual LULC data in 2019, as base data, and the predicted LULC data under the three scenarios between 2020 and 2029 were used as significant inputs for ecosystem service assessment, in terms of sediment and nutrient export, using the SDR and NDR models. Finally, a suitable LULC allocation scenario was successfully identified in order to minimize sediment and nutrient export using the ecosystem services change index: the most suitable LULC allocation scenario to minimize sediment or/and nutrient export into Kwan Phayao was Scenario II (Maximization of ecosystem service value). In conclusion, the integration of remote sensing data with an advanced classification method (support vector machine classifier), GIS data with linear programming, and advanced geospatial models (CLUE-S model, SDR and NDR model) can be used as an efficient tool to assess ecosystem services at the watershed level--particularly sediment and nutrient (N and P) export--and can be further applied to identify a suitable LULC allocation scenario to minimize sediment and nutrient export into certain lakes. The results of the current study can serve as crucial information for land-use planners, land managers, and decision-makers in order to reduce sediment and nutrient export into Kwan Phayao in the future. Conceptualization, S.O. and J.K.; methodology, S.O. and J.K.; software, J.K.; validation, S.O. and J.K.; formal analysis, S.O. and J.K.; investigation, S.O. and J.K.; data curation, J.K.; writing--original draft preparation, J.K.; writing--review and editing, S.O. ; visualization, J.K.; supervision, S.O. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors would like to thank the Suranaree University of Technology for providing a scholarship to [PERSON] and the University of Phayao for supporting the facilities to undertake this research. Special thanks from the authors go to the anonymous reviewers for their valuable comments and suggestions that improved our manuscript from various perspectives. The authors declare no conflict of interest. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multicolumn{10}{c}{**LULC Type**} \\ \cline{2-13}  & **UR** & **PD** & **FC** & **RB** & **PO** & **FO** & **WB** & **RL** & **WL** & **ML** & **Total** \\ \hline Predicted & 36.94 & 210.78 & 21.80 & 28.46 & 98.68 & 416.34 & 33.70 & 13.57 & 29.72 & 1.36 & 891.35 \\ \hline Land & 36.92 & 210.86 & 21.84 & 28.45 & 98.74 & 416.32 & 33.37 & 13.63 & 29.85 & 1.36 & 891.35 \\ \hline Deviation value (\%) & 0.0002 & \(-0.0008\) & \(-0.0004\) & 0.0001 & \(-0.0006\) & 0.0002 & 0.0033 & \(-0.0006\) & \(-0.0013\) & 0.00 & 0.00 \\ \hline Deviation value (km\({}^{2}\)) & 0.02 & \(-0.08\) & \(-0.04\) & 0.01 & \(-0.06\) & 0.02 & 0.33 & \(-0.06\) & \(-0.13\) & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Deviation of estimated land requirements and predicted LULC data in 2029 under Scenario II.