<![CDATA[Identification of land use and land cover in forest areas can be challenging due to various land cover types within a forest can be similar, making it hard to differentiate between them using remote sensing techniques. We hypothesized that random forest classification (RF) would outperform maximum likelihood (ML) in the classification of land use and land cover (LULC) in forest areas compared to maximum likelihood (ML). To verify this hypothesis, we conducted a comparative analysis, assessing the accuracy of RF and ML in the classification of LULC within the Taman Negara Endau-Rompin (TNER) region, utilizing Landsat 8 imagery. An accuracy assessment demonstrated that the RF classifier (overall accuracy: 87% kappa coefficient: 0.778, performed better than ML classifying land cover (overall accuracy 77% with kappa accuracy: 0.473) Our results suggest that both methods are able to classify land cover of forest area, but RF is more accurate than ML. From the classification result of RF classification, we calculate the land cover changes of TNER from 2013 to 2022. results showed that there are small changes of forest area were found in TNER. The total forest area decreases from 163250.089 ha to 144765.46 ha during 2013 to 2022. This finding suggests that the effectiveness of the protected area in mitigating deforestation in its surrounding regions may be somewhat limited, as indicated by the observed minor changes.]]>
