## 4 Discussion
This study distinguished itself through its unique emphasis on the SVM algorithm and binary classification for forest detection. In contrast, other studies, like those by [PERSON] et al. (2019) [99] and [PERSON] et al. (2015) [100], employed different methodologies. [PERSON] et al. 's study utilised a two-stage convolutional neural network (TS-CNN) for oil palm tree detection in a large-scale study area in Malaysia. Their approach achieved a high average F1-score of 94.99% in their study area. However, their method required very high-resolution images. It did not consider the features of the plantation region, which may have limited its applicability in different contexts or regions with lower-resolution images. On the other hand, using airborne imaging spectroscopy data, [PERSON] et al. 's study focused on identifying individuals belonging to three specific canopy tree species amidst a varied assortment of tree and liana species on Barro Colorado Island, Panama. The researchers employed binary SVM and biased SVM techniques to evaluate their effectiveness in distinguishing pixels associated with a particular focal species. Their methodology demonstrated excellent precision in identifying the focal species, with pixel-level producer accuracies ranging from 94% to 97% for the three species in focus. Furthermore, field validation of the predicted crown objects confirmed user accuracies ranging from 94% to 100%. However, their study was limited to three focal species and required high-resolution imaging spectroscopy data. Furthermore, the study by [PERSON] et al. [101] offered a different perspective on using machine learning in environmental studies. Their research focused on mapping forest canopy cover (FCC) in Mediterranean oak forests using Sentinel-1 and Sentinel-2 time series. They employed Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Tree (CART) machine learning models. Their results showed that SVM outperformed RF and CART in terms of accuracy, irrespective of data density and integration. However, their study was focused on FCC mapping, which, while related, was a different task from binary classification for forest detection. Furthermore, their study required the integration of Sentinel-1 and Sentinel-2 spectral-temporal metrics, which may have limited its applicability in regions with different satellite coverage or data availability. Alternatively, another study focused on land use/land cover (LULC) mapping using satellite time series [102]. The authors used the Random Forest classifier to produce accurate LULC maps, similar to the approach used in this paper. However, their study focused on LULC mapping, a task different from binary classification for forest detection. Moreover, their study required spectral-temporal metrics from satellite time series, which may have limited its applicability in regions with different satellite coverage or data avail ability. Furthermore, a comparison of earlier studies incorporating vegetation indices as supplementary bands to multispectral satellite bands in machine learning applications will be conducted. Vegetation indices' effectiveness in distinguishing between different vegetation types, as demonstrated in [PERSON]'s (2016) research [18], was echoed in the present study's application to forest detection. This research, however, extended the application of these indices to a larger scale, highlighting their potential in real-world scenarios. The automatic segmentation method for vegetation detection in precision agriculture proposed by [PERSON] (2022) [19] shared methodological similarities with the present study, particularly in using vegetation indices. The focus, however, diverged, as this research was centred on forest detection. The employed machine learning algorithm also differed with the present study utilising support vector machines (SVM), which was proven effective in the given context. Contrasting with the [PERSON] et al.