## 1 Introduction  
Satellite image processing is a fundamental problem in remote sensing data analysis and is a key step towards the automatic visualization of the Earth's landscapes. The significance of the satellite images for visualising the Earth is that they incorporate rich information about the land cover types, which can be derived from the interpretation of the pixels. The brightness of pixels in the satellite images correspond to the spectral reflectance of various surfaces and objects: built-up urban areas, bare soil, deserts, forest and various vegetation types, water areas, etc. Since the absorption of solar radiation varies in these objects, their spectral reflection is different. Such fundamental properties of optics of the materials form a basis for the environmental applications of satellite imagery generated by the sensors onboard the satellites [1]. The differences in spectral signatures of the surfaces are detectable in bands, or channels, of the satellite imagery and digital numbers (DNs) of the pixels in bitmap images, which can be used for land cover mapping [2]. Satellite images processed by remote sensing tools are often used to visualise landscape dynamics as a way to detect land cover changes by exploiting the similarities and differences in pixels of the images from Landsat [3,4] or Sentinel [5, 6, 7] or their integration [8,9]. Some of the methods of image processing use time series analysis using fine spatial resolution imagery [10,11], topological data extraction by spectral analysis derived from Synthetic Aperture Radar (SAR) [12,13], photogrammetry or Light Detection and Ranging (LiDAR) processing to analyse forest canopy by collecting 3D data [14]. Additionally, image processing may use a supervised classification approach, which includes automated extraction of the training samples and reference maps [15], regression tree analysis for canopy height modelling [16], or statistical methods to analyse forest degradation [17]. The spectral bands that comprise the satellite imagery consist of a matrix of pixels. Different intensities of the wavelengths of light of the pixels are visible on the image as colours of various hues and shadowing. The Landsat OLI/TIRS consists of 11 bands, including spectral bands: ultra violet (UV), visual, near-infrared (NIR), short-wave infrared (SWIR) and thermal infrared (TIR). Red and NIR bands, which correspond to channels 4 and 5, are specifically useful for vegetation analysis and forest mapping, as has been demonstrated in related works [18, 19, 20]. This is explained by the chlorophyll reflectance of plants in the red/NIR zones, which can be used as an arithmetic combination of bands 4-5 for computing the Normalized Difference Vegetation Index (NDVI) [21] or other vegetation indices [22,23] as indicators of vegetation health, vigour, and plant phenology, as reported earlier [24, 25, 26, 27]. The modelling, reconstruction and prognosis of tropical forest degradation in Africa poses a challenge for both environmental research as well as socio-economic applications, since deforestation affects the current land use policies and agro-industrial development of central African countries [28,29]. In addition to climate factors, selected human activities, such as timber production, road expansion, logging and manufacturing, contribute to deforestation [30, 31, 32]. Another factor includes the high consumption of wood by local population through agriculture and woodcutting [33]. At the same time, deforestation and the unbalanced functioning of forest ecosystems have wider effects and environmental consequences including land degradation, desertification, the loss of wildlife habitat and soil erosion [34]. Analysis of deforestation is possible by comparing forest areas on older maps with those detected on the remote sensing data [35, 36, 37] or landscape dynamics evaluated by time series of images [38].