## 3 Results  
### Land Use and Land Cover Mapping Results  
In the prepared LULC maps (Figure 6), the yellow colour represents the agricultural area, the green colour represents the forest area, battleship grey represents barren land, and red represents the urban area. From the Landsat-8 imagery, the KD-Tree classifier detected the true positives for the agriculture class, e.g., 45 out of 50 with a user accuracy of 90% and a producer accuracy of 79% (Figure 7A and Table 4). For Sentinel-2A imagery, the highest true positives were classified by the K-NN algorithm, e.g., 47 out of 50 for the agriculture class. The RF and K-NN in Landsat-8 and KD-Tree and RF in Sentinel-2A recorded true positives for agriculture classes ranging within 38-45 out of 50 (Figure 7A,D). Interestingly, the highest and lowest true positives for the agriculture class were recorded by the K-NN algorithm. This implies that the performance of classification may be improved by using a finer resolution and more refined imagery [49; 50]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**Classes**} & **User** & **Producer** & **User** & **Producer** \\  & & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** & **Accuracy (\%)** \\ \hline \hline \multirow{4}{*}{KD-Tree} & \multicolumn{4}{c}{Sentinel-2A} & Landsat-8 \\ \cline{2-5}  & Agriculture & 80 & 93 & 90 & 79 \\ \cline{2-5}  & Barren Land & 98 & 80 & 84 & 82 \\ \cline{2-5}  & Forest & 78 & 85 & 64 & 86 \\ \cline{2-5}  & Urban & 86 & 86 & 96 & 87 \\ \hline \multirow{4}{*}{RF} & Agriculture & 88 & 94 & 86 & 81 \\ \cline{2-5}  & Barren Land & 84 & 91 & 94 & 87 \\ \cline{2-5}  & Forest & 94 & 84 & 78 & 92 \\ \cline{2-5}  & Urban & 100 & 98 & 84 & 82 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: User and producer accuracies for LULC types used in this study. Figure 6: Remote sensing indices based LULC map prepared based on two satellites’ data. Land allocation was divided into four classes including agriculture, urban, forest and barren land. For the barren land class, the highest true positives were recorded by the KD-Tree classifier with the Sentinel-2A imagery, i.e., 49 out of 50 [51] with a user accuracy of 98% and a producer accuracy of 80% (Figure 7D and Table 4). However, the performance of the KD-Tree classifier with Landsat-8 imagery for the barren land class recorded relatively lower true positives, e.g., 42 out of 50. Similarly, the highest true positives for the urban forest \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classifier** & **Classes** & \begin{tabular}{c} **User** \\ **Accuracy (\%)** \\ \end{tabular} & \begin{tabular}{c} **Producer** \\ **Accuracy (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **User** \\ **Accuracy (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Producer** \\ **Accuracy (\%)** \\ \end{tabular} \\ \hline \hline \multirow{4}{*}{K-NN} & Agriculture & 94 & 89 & 76 & 79 \\ \cline{2-6}  & Barren Land & 86 & 91 & 80 & 82 \\ \cline{2-6}  & Forest & 80 & 80 & 72 & 84 \\ \cline{2-6}  & Urban & 86 & 86 & 96 & 80 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 7: Error matrix showing correct and incorrect training vectors of the evaluation samples by each ML algorithm. The error matrices for Landsat-8 data are shown in Figures (**A**–**C**). The error matrices for Sentinel-2A data are shown in Figures (**D**–**F**). Table 4 shows the producer and user accuracy calculated based on the error matrices. class were recorded by the random forest classifier with Sentinel-2A imagery. However, a relatively lower number of true positives was recorded for the forest class with Landsat-8 imagery, e.g., 32, 39, and 36 for the KD-Tree, RF, and K-NN classifiers, respectively (Figure 7A-C). For the urban class, the highest average true positives were recorded for both satellite images. For the urban class, the RF algorithm with the Sentinel-2A satellite achieved the highest possible user accuracy (100%) compared to all other satellite-algorithm comparisons (Table 4).