## 2 Materials and Methods
### Land Use and Land Cover Change Detection  
This study employed Landsat imagery captured by Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) sensors, each possessing a spatial resolution of 30 m. The imageries selected covered the temporal periods of 1980, 1990, 2000, 2010, and 2020. These images were harnessed to detect Land Use and Land Cover (LULC) changes, as outlined in Table 1. Landsat data files were gathered from the website of the US Geological Survey ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)), accessed on 1 March 2021).  
For the identification of land use classes, very high-resolution images accessible through Google Earth were employed. This identification process involved photo interpretation guided by a previously agreed legend. To ensure uniformity and consistency, a five-class legend was established, drawing inspiration from the Land Cover Classification System (LCCS) introduced by the FAO (Food and Agriculture Organization) in 2014. The study focused its investigation on five primary LULC classes: forests, rangeland, cropland, water bodies, and urban areas.  
In addition to the imagery analysis, supplementary data sources such as maps, field surveys, databases, and insights from domain experts were also leveraged. These complementary resources were utilized to enhance and refine the classification process, contributing to a comprehensive and robust understanding of the LULC dynamics within the study area [29]. To prevent discrepancies caused by seasonal variations in vegetation distribution throughout the year, summer images were selected for each year, which also made it easier to differentiate rainfed croplands from natural vegetation cover.  
The classification process was carried out using the Support Vector Machine (SVM) method, a robust machine learning algorithm known for its effectiveness in classification and regression analysis. The integration of SVM within OTB (Orfeo ToolBox) facilitated the accurate delineation of land use categories. SVM has been widely used in a variety of fields, such as image classification, text classification, and bioinformatics due to its high accuracy and robustness [29].  
The assessment of classification accuracy involved analyzing the confusion matrix to ascertain the total number of pixels that were correctly classified. The Kappa coefficient, or  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Years** & **1980** & **1990** & **2000** & **2010** & **2020** \\ \hline Cropland & 73.0 & 79.0 & 81.3 & 78.0 & 83.7 \\ Forest & 96.0 & 98.0 & 91.4 & 84.1 & 94.9 \\ Rangeland & 73.8 & 99.0 & 99.2 & 93.7 & 99.3 \\ Urban areas & 62.5 & 72.3 & 77.6 & 84.2 & 82.9 \\ Water bodies & 95.0 & 92.0 & 93.0 & 94.0 & 98.0 \\ \hline
**Accuracy (\%)** & **80.1** & **88.1** & **88.5** & **86.8** & **91.8** \\
**Kappa index** & **0.68** & **0.8** & **0.8** & **0.77** & **0.85** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification accuracy.  
[PERSON]'s Kappa, was utilized to assess the extent of error reduction achieved as a result of the classification process [26].  
The Kappa coefficient is a statistical metric employed to assess the level of agreement between raters when dealing with categorical data. This measure assesses the agreement level between two or more evaluators or raters who classify items into distinct categories. The Kappa coefficient (1) was computed following the methodology outlined by [PERSON] [26], where  
\[K=\frac{P_{\mathit{eb}}-P_{e}}{1-P_{e}} \tag{1}\]  
\(P_{\mathit{eb}}\): observed agreement,  
\(P_{e}\): expected agreement.  
The Kappa coefficient is bounded within the range from \(-1\) to \(1\). A score of \(1\) signifies complete consensus among the raters, \(0\) indicates agreement no more significant than random chance, and \(-1\) signifies complete discordance among the raters.