## 3 Results  
### Classification Accuracy and Mangrove Cover Changes  
Using the SVM classifier, seven land use/land cover classes, including forest, oil palm, mangrove, rubber, urban, water, and \"others\", were classified. The class \"others\" includes orchard, shrub, bush, farm, and aquaculture. Table 3 reports the accuracy of the classification from the two satellite images, including the producer accuracy (PA), user accuracy (UA), overall accuracy (OA) and the kappa coefficient (KC). The overall accuracy of both images is ~90%, with a KC of 0.85 and 0.88 for the 2000 and 2019 classifications, respectively. Both the Landsat 7 and Sentinel-2A classification results showed a high accuracy of around 90%, which may be because of their finer spatial resolution and multi spectral bands, and is also due to the machine learning technique of SVM. On the other hand, the pan-sharpened Landsat image also maintained high classification accuracy when more bands (more information) were considered during the classification process. Z-statistics were computed for both classification results, where both values were higher than 1.96, indicating that the classification is meaningful and significant and the classification results are better than random classification. Because the general land use land cover changes are similar to the previous findings in Kanniah, Sheikhdi, Cracknell, Goh, Tan, Ho, and Rasli [32], we omitted the land use maps in this study and focused only on the mangrove cover in the following results and discussions. In this study, we further found an expansion of the urban area, especially at the eastern part of Sungai Pulai, where the new Nusajaya township is located, and in the central part of IM, where the Kulai-Senai township has expanded due to increased population. Rubber conversion to oil palm plantation and urban area is still found (in 2019 image) surrounding the Sultan Iskandar Reservoir in the eastern part of IM, showing similar results to [32] in their 2014 satellite data. The use of the SVM classifier showed great capability by clearly distinguishing mangroves from forests and water bodies, which often can be easily misclassified. Both the user and producer accuracies are high for mangrove classification in the 2000 and 2019 images (Table 3). From the classification results, the loss of mangrove area is noticeable, particularly where it has been converted to urban area and oil palm plantations. Interestingly, the conversion of mangrove to aquaculture land use (classified as \"others\") was found at multiple locations, which showed the recent development and expansion of the economically valuable aquaculture activities in the IM region (Table 4 and Figure 4b). The performance of SVM classification in this study supports the accuracy of mangrove loss and \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Landsat 7 (2000)**} & \multicolumn{2}{c}{**Sentinel-2A (2019)**} \\ \cline{2-5}  & **PA (\%)** & **UA (\%)** & **PA (\%)** & **UA (\%)** \\ \hline
**Forest** & 89.25 & 92.45 & 78.22 & 87.93 \\
**Mangrove** & 97.52 & 90.64 & 87.85 & 99.70 \\
**Oil Palm** & 88.66 & 78.35 & 73.02 & 84.47 \\
**Rubber** & 43.17 & 78.15 & 96.40 & 53.49 \\
**Urban** & 64.56 & 93.12 & 95.44 & 93.88 \\
**Water Bodies** & 98.56 & 99.57 & 92.55 & 98.17 \\
**Others** & 46.32 & 19.19 & 91.42 & 62.67 \\ \hline
**OA** & & 89.96\% & & 90.51\% \\
**KC** & 0.85 & & 0.88 \\
**Z-statistic** & 271.37 & & 109.35 \\ \hline \hline \end{tabular}  
* The definition of land cover types is given in Table A1.  
\end{table} 
Table 3: Distribution of land use area and the producer accuracy (PA), user accuracy (UA), overall accuracy (OA), and kappa coefficient (KC) for respective class *. gain detection (Table 4 and Figure 4), as well as clearly identifying fragmented mangrove patches (Table 6). Based on the classification results, the total mangrove areas in 2000 and 2019 were 13,612.25 ha and 10,704.96 ha, respectively.