## 1 Introduction
Examining the content within satellite imagery presents an additional concern, primarily due to the heterogeneity of materials in the images and the substantial volume of data. It is imperativeto employ sophisticated and robust technologies to effectively manage such a complex data set, especially in categorisation tasks. In addressing the classification problem associated with forest detection using satellite imagery, this research harnessed the power of artificial intelligence and machine learning. The development of Machine Learning (ML) requires the determination of all essential metrics for the decision-making process. The mechanisms of machine learning generate models to enhance the metrics. In order to ensure the development of an effective solution for any decision-making process, it is crucial to carefully select and consider the metrics used throughout the conceptual phases. This is necessary because the metrics are essential in decision-making, and their selection can significantly impact the outcome [13]. ML's purpose is to anticipate future occurrences or situations unknown to the computer. It belongs to the subfield of artificial intelligence (AI) that synthesises the underlying correlations between data and information via the systematic application of algorithms [14]. In 1959, [PERSON] defined ML as \"the field of study that allows computers to learn without being explicitly programmed\". He stated that training computers to learn from experience would someday obviate the need for a significant portion of this comprehensive programming work [15]. The increasing prevalence of ML can be attributed to its ability to describe underlying connections within massive data arrays, thereby solving challenges in big data analytics, behavioural pattern identification, and information evolution. In addition, ML systems may be taught to classify the changing circumstances of a process to represent changes in operational behaviour. As knowledge evolves under the impact of new ideas and technologies, ML systems may detect disruptions to old models and redesign and retrain themselves to adapt to and coevolve with the new information [16; 17]. Using vegetation indices and multispectral bands in machine learning models has proven to be a powerful tool in various applications. For instance, researchers have successfully used vegetation indices derived from light reflectance properties of plants to distinguish soybean from weeds, demonstrating the potential of these indices as decision-support tools for weed identification [18]. In precision agriculture, an automatic segmentation method combining vegetation indices with a Discriminative Common Vector Approach classification algorithm has outperformed traditional methods, facilitating sustainable production [19]. Furthermore, a machine learning model using the extreme gradient boosting method is developed to predict vegetation growth throughout the growing season in China, highlighting the potential of these techniques for monitoring vegetation dynamics and crop growth [20]. Lastly, the selection of suitable Sentinel-2 bands and vegetation index for crop classification using artificial neural networks has been discussed, underscoring the importance of these parameters in enhancing classification accuracy [21]. Mentioned articles demonstrate the potential of incorporating vegetation indices and multispectral channels in machine learning models for improved vegetation detection. This study aims to leverage the power of remote sensing technologies, specifically focusing on the Support Vector Machines (SVM) algorithm for forest detection and classification.