Applied sciences Article  
R Libraries for Remote Sensing Data Classification by K-Means Clustering and NDVI Computation in Congo River Basin, DRC  
[PERSON]*\({}^{\copyright}\) and [PERSON]\({}^{\copyright}\)  
Laboratory of Image Synthesis and Analysis (LISA), Ecole Polytechnique de Bruxelles (EPB, Brussels Faculty of Engineering), Universite Libre de Bruxelles (ULB), Building L, Campus de Solbosch, ULB--LISA CP165/57, Avenue Franklin D. Roosevelt 50, B-1050 Brussels, Belgium  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +32-471-860-459  
**Featured Application: Libraries of the R programming language, including \(R\)_Rstoolbox, raster and terra,_ were used to classify Landsat OLI-TIRS satellite images by k-means clustering and NDVI computation for comparative vegetation mapping of tropical rainforests for the period of 2013-2022 in the three target areas (Bumba, Basoko, Kisangani) of the middle Congo River Basin, central Africa, D.R.C.**  
**Abstract:** In this paper, an image analysis framework is formulated for Landsat-8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) scenes using the R programming language. The libraries of R are shown to be effective in remote sensing data processing tasks, such as classification using k-means clustering and computing the Normalized Difference Vegetation Index (NDVI). The data are processed using an integration of the RStoolbox, terra, raster, rgdal and auxiliary packages of R. The proposed approach to image processing using R is designed to exploit the parameters of image bands as cues to detect land cover types and vegetation parameters corresponding to the spectral reflectance of the objects represented on the Earth's surface. Our method is effective at processing the time series of the images taken at various periods to monitor the landscape dynamics in the middle part of the Congo River basin, Democratic Republic of the Congo (DRC). Whereas previous approaches primarily used Geographic Information System (GIS) software, we proposed to explicitly use the scripting methods for satellite image analysis by applying the extended functionality of R. The application of scripts for geospatial data is an effective and robust method compared with the traditional approaches due to its high automation and machine-based graphical processing. The algorithms of the R libraries are adjusted to spatial operations, such as projections and transformations, object topology, classification and map algebra. The data include Landsat-8 OLI-TIRS covering the three regions along the Congo river, Bumba, Basoko and Kisangani, for the years 2013, 2015 and 2022. We also validate the performance of graphical data handling for cartographic visualization using R libraries for visualising changes in land cover types by k-means clustering and calculation of the NDVI for vegetation analysis.  
**Keywords:** image processing; remote sensing; Landsat; R language; programming; cartography; mapping; data visualization; NDVI; Africa  
**PACS:** 91.10.Da; 91.10.Jf; 91.10.Sp; 91.10.Xa; 96.25.Vt; 91.10.Fc; 95.40.+s; 95.75.Qr; 95.75.Rs; 42.68.Wt  
**MSC:** 86A30; 86-08; 86A99; 86A04  
**JEL Classification:** Y91; Q20; Q24; Q23; Q3; Q01; R11; O44; O13; Q5; Q51; Q55; N57; C6; C61