## 1 Introduction
Other approaches include continuous metrics to measure the degree of forest degradation [39]  
While the techniques of the remote sensing data acquisition and image processing by Geographic Information System (GIS) have demonstrated advances over recent decades [40,41], the algorithms of geospatial data modelling are not as straightforward regarding handling the data automatically. At the same time, the required automation is possible using the programming approaches. A large number of methods focused on forest monitoring by the remotely sensed data have been proposed over recent years [42, 43, 44, 45]. Popular cases include the use of multi-source data, such as a fusion of LiDAR, radar and optical data, very high-resolution satellite images and unmanned aerial vehicles (UAV) [47], as well as the application of Land Use/Land Cover (LULC) change [48], classification and clustering [49], and integration of the remote sensing data with reference maps for change detection [50]. Typically, methods of forest mapping are based on the use of Landsat imagery [51; 52; 53], though others utilize SAR [54; 55; 56; 57; 58] or Moderate Resolution Imaging Spectroradiometer (MODIS) scenes [59; 60] for land cover classification in forest areas. Compared with GIS, the use of programming languages for image processing presents new possibilities in mapping through the extended functionality of algorithms and has received much attention in the geoscience community [61; 62; 63; 64; 65]. As previous work shows [66; 67; 68], integrating spatial information from the satellite images using various perceptual tasks, such as object recognition, location, classification and interpretation, are necessary for disambiguating visually similar land cover classes of the Earth's surface representing the landscapes. The automatic recognition of the diverse land cover classes on various periods facilitates the recognition of the environmental changes. However, misclassifications remain in challenging situations with similar classes with human-based interpretation using supervised classification GIS for image analysis [69; 70; 71]. This requires us to recognise and classify all possible occurrences of pixels in the scenes automatically, which is a computationally effective task using programming algorithms. In this paper, we propose an application of R programming language [72] as a means to process and classify satellite images. Specifically, we presented the unsupervised classification of the Landsat-8 OLI/TIRS data and calculation of the Normalized Difference Vegetation Index (NDVI) based on RSToolbox, raster and terra packages. Motivated by recent reports regarding the environmental changes in rainfall tropical forests of central Africa [10; 73; 74; 75; 76; 77], we applied multi-temporal data covering identical selected regions along the middle Congo River Basin with a time span of 2013 to 2022. The practical aim is to demonstrate the dynamics of the landscapes within a decade using Landsat satellite images. Our methods apply k-means automated clustering and calculate the NDVI index using the R libraries RStoolbox [78], terra [79], and raster [80] and auxiliary graphical packages such as RColorBrewer [81] and viridis [82].