## 5 Conclusions  
In this study, we demonstrated methods of geospatial information processing and mapping by R that outperform existing traditional software due to the automation of data computation and the functionality of the high-level language. The approaches of map algebra utilise scripts to process remotely sensed data using interactive steps that are completely different from the built-in functions of the traditional software since they use the syntax of R rather than the GUI interface. The scripting method of the satellite image processing is presented in this paper with a case study of three cities of the D.R.C. located along the Congo River in central Africa. We present a method based on the R programming language that offers an integration of several libraries and applies algorithms for band arithmetics, classification, and computing the NDVI indices by unsupervised clustering. A special focus is given to monitoring vegetation in the tropical rainforests of Congo. Empirically, we showed that this R approach can determine the complexity of landscapes through the classification of pixels, grouping them into 10 land cover classes using selected functions of RStoolbox as operators for band calculations. Moreover, we performed experiments on cartographic data processing with R using a series of Landsat-8 OLI/TIRS images and compared landscape dynamics. Weobserved land cover changes from 2013 to 2022, which illustrate the deforestation in tropical regions of Congo, as reflected in the NDVI for the three target locations: Bumba, Basoko and Kisangani. The proposed method of programming for satellite image classification is not a replacement for the state-of-the-art methods of remote sensing data processing but an extended methodological approach. The existing solutions based on GIS can be used as quality and conceptually sound methods of image processing in remote sensing, given proper care in supervised classification. A variety of efficient cases on image processing exist, as reported in many studies. Importantly, the automation of image classification is still a challenging problem due to the presence of complex background noise on the image scene and the restricted functionality of the conventional tools in many GIS. In contrast to the traditional software, R supports deep learning analysis through several packages that enhance data processing and enable semantic segmentation. The examples include the 'deepnet' toolkit and its dependent library 'deepr', which enhances the training or predicting process or fine-tunes machine learning methods with 'RcppDL'. As such, deep learning with R can be used with the aim of simplifying the workflow of satellite image pre- and post-processing, as well as optimising the operations and abstraction of objects during classification, which is required by a variety of remote sensing applications. A method of automatic image processing using R scripts is demonstrated for image classification based on the k-means clustering algorithm in the RStoolbox library and band algebra for NDVI computations by the terra package. We consider the algorithms of R by computing the raster data and visualising changes in the current forest stage on the built maps. The experimental results on the vegetation index for the three different datasets taken in the years 2013 and 2022 use attributes of various land cover classes to visualise the changes in the land cover types of the forest regions of central Congo. A scripting approach to image clustering shows that programming by R is able to divide the image scene on the defined number of classes using the unsupervised k-means algorithm of RStoolbox. We demonstrate a set of images based on high-level classification of the Landsat-8 OLI-TIRS raster imagery. Based on the analysis of these images, we confirm that the distribution of the vegetation in rainforest regions of central Africa experience changes. The driving factors are diverse and include both social and climatic processes, which affect tropical forests and contribute to deforestation.