## 3 Materials and Methods
### LULC Classification and Change Detection  
We downloaded Landsat 5 TM data in 2009 and Landsat OLI data in 2019 from the USGS website (www.earthexplorer.usgs.gov: accessed date 1 May 2019), which were applied to classify LULC data by conducting supervised classification using the support vector machine (SVM) algorithm of the EnMap-Box software. Two training areas for ten LULC types were separately prepared in order to define an optimal hyperplane for LULC classification using SVM. The scaled reflectance data of Landsat data (visible, NIR, and SWIR bands) and additional bands, Normalized Difference Vegetation Index (NDVI) [28], Normalized Difference Moisture Index (NDMI) [29], Soil-Adjusted Vegetation Index (SAVI) [30], Modified Normalized Difference Wetness Index (MNDWI) [31], and DEM were applied to classify the LULC map in 2009 and 2019. The optimized model parameters were provided by a grid search: namely, Gaussian radial basis function kernel, \begin{table}
\begin{tabular}{l l l} \hline \hline
**Data** & **Data Collection** & **Source** \\ \hline \multirow{8}{*}{GIS} & Land-use data in 2009 & LDD \\  & Land-use data in 2015 & LDD \\  & Watershed boundary & DEQP \\ \cline{2-3}  & SRTM DEM & USGS \\  & Soil drainage & LDD \\  & Stream network & DEQP \\  & Waterbody & DEQP \\  & Villages & LDD \\  & Road network & LDD \\  & Fault line & DMR \\  & Income per capita at subdistrict level & NSO \\  & Population density at subdistrict level & DOPA \\  & Soil series map & [22] \\  & Geology unit map & [22] \\  & Agri-Map & [23] \\ \hline \multirow{4}{*}{Remote Sensing} & Landsat 5 TM, Date 27 February 2009 & USGS \\  & Landsat 8 OLI, Date 23 February 2019 & USGS \\  & Google Image 2009 and 2010 & Google Earth \\ \hline \multirow{8}{*}{Secondary data} & Annual and monthly rainfall between 2011 and & TMD \\  & 2019 & TMD \\  & C-factor and P-factor & [22] \\  & Predicted rainfall between 2020 and 2029 & [24] \\  & Nitrogen/Phosphorus loading & [25] \\  & Nitrogen retention coefficients & [25] \\  & Phosphorus retention coefficients & [25] \\  & Total suspended solids/Total phosphorus/Total & PCD \\  & nitrogenbetween 2011 and 2018 & Annual surface runoff between 2011 and 2018 & RID \\ \hline \multirow{8}{*}{Tools and models} & EnMap-Box software & EnMAP \\  & Linear Programming with Simplex method & What’s Best in Excel \\ \cline{1-1}  & CLUE-S model & [26] \\ \cline{1-1}  & SDR and NDR models of InvEST software & NatCap \\ \cline{1-1}  & Ecosystem Services Change Index (ESCI) & [27] \\ \hline \hline \end{tabular} Note: DEQP: Department of Environmental Quality Promotion; DMR: Department of Mineral Resources; DOPA: Department of Provincial Administration; EnMAP: Environmental Mapping and Analysis Program; LDD: Land Development Department; MRC: Mekong River Commission; NatCap: Natural Capital Project; NCAR: National Center for Atmospheric Research; NSO: National Statistical Office of Thailand; PCD: Pollution Control Department; RID: Royal Irrigation Department; TMD: Thai Meteorological Department; USGS: United States Geological Survey.  
\end{table} 
Table 2: List of data collection for data analysis in this study. which required the variables gamma (\(\gamma\)), which defines the width of the Gaussian, and the regularization parameter (C), which controls the trade-off between the maximization of the margin between the training data vectors and the decision boundary plus the penalization of training errors [32]. The standard land-use classification of the LDD--including (1) urban and built-up area, (2) paddy field, (3) field crop, (4) para rubber, (5) perennial trees and orchards, (6) forest land, (7) water body, (8) rangeland, (9) wetland, and (10) miscellaneous land--was modified to classify the LULC type and to extract the coefficient value of each LULC type for ecosystem service value (ESV) evaluation (Table 3).