## 1 Introduction  
Vegetation is an essential component of ecosystems that connects the atmospheric, hydrological, and pedological processes [1]. Environmental preservation and conservation heavily depend on economic stability and human and political resources. In the past two decades, the cost of evaluation procedures has significantly reduced due to the public availability of open satellite data. Now, we can obtain crucial information on deforestation and degradation by employing remote sensing techniques to analyse this data [2; 3; 4; 5]. Earth observation (EO) data, which include satellite, aerial, or ground-based observations, and geospatial data are crucial for monitoring changes in forest ecosystems, especially for identifying vegetation degradation [6; 7]. Monitoring land use and land cover change is vital to the ecosystem. Remote sensing offers excellent potential for monitoring landscape change caused by natural cycles and human activity [8]. One of the crucial applications of remote sensing in environmental resource management and decision-making is the detection and quantitative evaluation of vegetation patterns. This technology is pivotal in assessing the ecosystem and identifying vegetation patterns and structural shifts. Such assessments and identifications are principal when evaluating and monitoring natural resources. Remote sensing in forest detection has been a significant research and development topic. Remote sensing technologies provide a powerful tool for monitoring and managing forests on a large scale, offering the ability to detect changes in forest cover and health over time. [PERSON] et al. (2020) provide an overview of optical remote sensing technologies used in early fire warning systems, highlighting the importance of these technologies in mitigating the impacts of natural hazards such as large-scale forest fires [9]. Similarly, [PERSON] et al. (2018) discuss the Operational Remote Sensing (ORS) program, which leverages Landsat and MODIS data to detect forest disturbances across the United States. The ORS program supplements traditional Insect and Disease Survey (IDS) data with imagery-derived forest disturbance data, demonstrating the potential of remote sensing in forest health monitoring [10]. Furthermore, [PERSON] et al. (2018) present a novel approach to individual tree-level forest inventory using airborne LiDAR (Light Detection And Ranging) remote sensing. Their research underscores the potential of remote sensing technologies in providing detailed, high-resolution data for forest management [11]. This study's primary challenge is identifying forest cover. In this case, forest detection is simplified to a classification issue involving categorising the input data set into two classes, \"forest\" and \"not forest\". This binary classification is suitable for inventorying forests or creating thematic masks for topographic maps when it is essential to identify forest cover without distinguishing forest types. In terms of binary classification, this approach offers several advantages. Binary classification simplifies the problem by focusing on the distinction between two classes, which can lead to more accurate and efficient models. It is beneficial when classes are imbalanced, allowing the model to focus on the minority class. Furthermore, binary classification models are often easier to interpret and understand, making them more practical for decision-making processes [12]. Forest detection becomes challenging and complex in such instances, especially in regions with high biodiversity, i.e., a wide range of forest ecosystems. The challenge of categorising data under such specific conditions is distinctively novel. The research investigated the feasibility of augmenting the initial array of input variables, including Sentinel-2 bands and vegetation indices, for executing the machine learning protocol. Satellite imagery serves as the input data for forest identification and is the most prevalent data source for forest inventory, particularly for categorising extensive regions.