###### Abstract
Both reforestation and afforestation are changes in land use, although only aforested land has never had forest before. In the process of reforestation, the land is transformed from a non-forest to a forest area by means of planned sowing on the ground that had previously been used for other purposes [7]. There were more than 3.95 billion hectares of forest cover globally in 2005. Among them, 36 percent was natural forest, 53 percent was modified, 7 percent was semi-natural, and 3 percent was plantation [10]. Pakistan is Asia's second-highest deforestation hotspot (4.6 percent annually) after Indonesia, according to the World Resources Institute. As a result, the country's forest resources are being overexploited [11]. Natural resources management, environmental conservation, and other fields of study rely heavily on Earth Observation satellite data with high to moderate resolution. In the early 1970s, the emergence of geographic information systems (GIS) and remote sensing (RS) became crucial for studying global environmental studies [12]. The temporal and geographical change in resources over time and land use and land cover (LULC) changes can be well analyzed using satellite data and GIS techniques [13]. This provides a powerful tool for assessing ecosystem stress, detecting changes in forest cover, and making informed management and planning decisions [14]. However, the quality of satellite images can be adversely affected by factors such as terrain impact and atmospheric influence, which can lead to increased inaccuracy in land cover analysis. In the evaluation and assessment of land cover, different corrections such as atmospheric radiometric and topographic corrections are critical when using multi-sensor, multi-temporal, and multi-spectral images. The Landsat imagery archive (Landsat 1-8 from 1972 to 2020) is one of the most frequently used products for many uses in agriculture and ecology, such as multi-spectral and time series satellite image classifications and mapping of natural resources management. Various vegetation indexes, such as NDVI, VCP, and SAVI, have the potential for a more precise result of classifications and the assessment of vegetation cover with digital elevation models [15]. Maximum likelihood [16], random forest (RF), deep learning [17; 18], artificial neural networks (ANN), conventional neural networks [19], and fuzzy classification [20] are some of the classification techniques used for land use and land cover (LULC) mapping [21]. Among these classifiers, support vector machine [22] and random forest [23; 24] have gained attention and evolved into two excellent choices in LULC mapping due to their improved accuracy and efficiency, and comparatively low computational process [25]. Although deep learning and deep transfer learning approaches have been efficient computational strategies in machine learning in recent years, they require a large amount of data and restrict intricate calculations in the cloud platform. Random forest is a classification and regression tree-based technique. However, in land cover classification, the random forest is predominantly used with NDVI [26] and multispectral and hyperspectral images [27]. In order to more precisely map land cover from satellite images, the random forest (RF) approach was subsequently proposed and implemented [28]. The process of feature generation is a crucial one in many machine learning applications. The mean decline in accuracy (MDA) and the mean drop in the Gini index (MGI) are two measures of the variables' relative importance that are considered by RF algorithms. The method is put to use in order to identify the most essential features among a large number of candidates in order to construct reliable learning models [29]. Therefore, knowing which spectral, spatial, and temporal characteristics are most important in the classification method and process is as important as getting more precise maps.