## 3 Results
### Random Forest (RF) Land Cover Classification  
The random forest classification algorithm was implemented with GEE in a cloud computing environment setting with the generation of 50 decision trees as a classifer parameter. This classifier was implemented on the Sentinel-2 optical image, the Sentinel-1 SAR image, and the optical/SAR images fused with the BR, HFM, GS and PC methods. Figure 6 shows the results obtained for each of the cases. Figure 6A shows the result of the classification process using the Sentinel-2 optical image. Unlike the case analyzed above, a finer delimitation can be observed between the different land covers of the study area, such as access roads and areas of bare soils. A remarkable similarity is observed in the classification of forest cover in relation to that  
Figure 6: Results of land cover classifications using RF algorithm. Classification of optical (**A**) and SAR images (**B**). Classification images fused by BR method (**C**), HFM method (**D**), GS method (**E**), PC method (**F**) and optical/SAR image stack (**G**) respectively. obtained by the SAR image. In this case, a greater area was observed in the classification of the cover associated with grassland and a reduction of low vegetation. Significant confusion can be observed in the classification of cover associated with oil palm areas with and forest areas, where the differentiation of these two coverages presented the most significant OE along with grassland cover. The result of the land cover classification using the SAR Sentinel-1 image is shown in Figure 6B. As can be seen, using only the SAR image, it is possible to discriminate between different types of coverages. However, there are classification errors between coverages associated with water bodies with bare soils, and with low vegetation zones with grassland and some wooded areas. Through the implementation of the adaptive median filter with a 5 \(\times\) 5 pixel moving kernel window and the reduction in \"salt and pepper\" noise, using the multitemporal speckle method, a high attenuation of the mottling produced by this effect was observed, noting a remarkable clarity in the differentiation of the different coverages present in the study area. A high level of precision in identifying the oil palm cover and its differentiation from the forest cover was evidenced. The results obtained in the classifications of the fused optical/SAR images are detailed below. In the case of the BR method, shown in Figure 6C, grassland, bare soil, and low vegetation coverages performed similarly to the optical image. However, a notable difference was observed in oil palm and forest cover classification. In this case, the oil palm cover was classified better, avoiding the CE and OE of the forest mentioned above. The resulting oil palm classification was similar to that evidenced using only the SAR image. The fused image, despite having the information provided by the SAR image, did not show any noise caused by the speckle effect. In the case of the classification of the image fused by the HFM method, shown in Figure 6D, a very high similarity was found compared with the result obtained from the optical image. This is because, as mentioned above in the results obtained by this fusion method, the correlations between the values of the fused image and the original image were very high. A difference in the previous case was evidenced in the oil palm coverage. A more significant similarity to the result obtained using only the optical image is appreciated, demonstrating a lower contribution of the SAR image. In this case, a lower saturation is observed in the classification of oil palm coverage when compared with the one obtained via the optical image. However, there are still areas with significant errors in classifying this coverage with forest areas.