## 3 Materials and Methods
### NDVI Calculation  
The NDVI is a key vegetation indicator to describe vegetation dynamics and vigour. It is effectively applied to environmental management and monitoring the responses of tropical rainforests to climate change and anthropogenic activities. Therefore, the use of the NDVI is crucial to reveal the health of the terrestrial ecosystems in central Africa, which are threatened by the deforestation. As a robust traditional estimation method to determine vegetation coverage, NDVI is deservedly applied in a variety of the remote sensing studies due to its effectiveness and reliability. The visualization of the NDVI is indispensable in areas where fieldwork and ground point collection is hardly accessible, such as the tropical African regions of central Congo. Therefore, we adapted the algorithm of the NDVI to extract the information on vegetation for knowledge on plant coverage in the three target areas in the Congo Basin. The NDVI has been generated using composite band arithmetic operations by the ratio of difference and the sum of the reflected NIR and visible red wavelength. The NDVI approach is grounded in the calculation of ratios in the reflectance between the red and NIR bands of the image (Bands 4 and 5) as arithmetical variables using the existing formula \(\ NDVI=(NIR-Red)/(NIR+Red)\). The NDVI computation has been performed using multi-band manipulation by R library terra, as per Listing 5. ```
1#Setupworkingdirectory
2 setwd(\"Users/pollimelemkova/Documents/R/S3_UC_k_means/Basoko_LCOS_L1 TP_177059_20131216_20200912_02_T1\")
3#Importingdata
4 filename<-paste0('LCOS_LITP_177059_20131216_20200912_02_T1_B', 1:7, '.tif')
5 filename<-landsat<-rast(Filenames)
6 landsat<-function(img,k,i){
7 bl<-zim[[k]]
8 li<-img[[i]]
9 vi<-(bk-bi)/(bk+bi)
10 return(vi)
11]
12}
13#plottingthe NDVI.ForLandsatNIR=5,red=4. 14 rol<-vilandsat,5,4)
15 colors<-brebrever.pal(10, 'R4 vlGn')
16 plot(ndvi,col=colors,fontmain=1,main=\"NDVIforLandsat-8 OLI/TIRSClimageLCOS_LITP_177059_20131216_20200912_02_T1:Basoko,CongoDC(2013)\",exex.main=0.95,axes=FALSE,legend=FALSE)
```  
Listing 5: R code using library terra used for calculation the NDVI; here a case of Basoko town (2013) with the same principle applied for all other images  
Figure 9: Kisangani city (Tshop Province) and region in the junction of the Tshopo and Lindi tributaries into the Congo River: raw Landsat-8 OLI/TIRS scene for years 2013, 2015 and 2022 and the classified outputs by k-means clustering. Mapping: RStudio. Source: authors. (**a**) True-colour composite image, Kisangani, 2013. (**b**) Classified scene using k-means clustering: 2013. (**c**) Classified scene using k-means clustering: 2015. (**d**) Classified scene using k-means clustering: 2022. 18 legend(, x = \"topleft\", inset = 0.13, legend=(\"0.1\", \"0.2\", \"0.3\", \"0.4\", \"0.5\", \"0.6\", \"0.7\", \"0.8\", \"0.9\", \"1.0\"), fill = colors, title = \"Classes\", horiz = FALSE, by = \"n\", text.font=3, ncol=1) ```  
The calculation of the NDVI enabled us to exploit the information derived from spectral reflectance regarding vegetation coverage. This was enabled due to the parameters of the ratios of NIR/red spectral bands, which lessen the influence from the ground relief, rocks and soils. Thus, the methodological advantage of the NDVI consists of the reinforcement of the slight differences and nuances in the spectral reflectance characteristics of vegetation. This enables us to better distinguish forest-covered areas, discriminate various vegetation classes, and discern them from all other land cover types. Using pairwise representations of the 9-year time series of Landsat OLI/TIRS (2013 to 2022), we calculated the NDVI for the three target regions along the Congo river: Bumba, Basoko and Kisangani. Here we computed the values of the NDVI for each couple of images, respectively, to show the dynamics of the vegetation growth in middle Congo, as shown in Figure 10. The NDVI is computed for each pixel, with brighter green hues indicating more dense and healthy plant canopy, and contrariwise.