## 3 Materials and Methods
### Data
```
1 library(rgdal)
2 library(raster)
3#Setupworkingdirectory
4 setwd('Users/polimalemenkova/Documents/Rf/S3_UC_k_means/Basoko_LC08_L1 TP_177059_20131216_22020912_22_T1\")
5#Importingdata:LandsatOLLI/TIRSimageforBasoko,Congo(2013):
6 Landatsat_Basoko2013=list.files('Ivers/polimalemenkova/Documents/Rf/S3_UC_k_means/Basoko_LC08_L1 TP_177059_20131216_20200912_02_T1\")
7#Printinglist
8 list.files()
9#ReadingsinfilesspatialGridDataFrameobjects:
10 BasokoBand1\(\leftarrow\)readDL('L0S_L1 TP_177059_20131216_20200912_02_T1_B1.T1F')
11 BasokoBand2\(\leftarrow\)readDL('L0S_L1 TP_177059_20131216_20200912_02_T1_B2.T1F')
12 BasokoBand4\(\leftarrow\)readDL('L0S_L1 TP_177059_20131216_20200912_02_T1_B3.T1F')
13 BokoBand5\(\leftarrow\)readDL('L0S_L1 TP_177059_20131216_20200912_02_T1_B4.T1F')
14 BasokoBand5\(\leftarrow\)readDL('L0S_L1 TP_177059_20131216_20200912_02_T1_B5.T1F')
15 BasokoBand6\(\leftarrow\)readDL('L0S_L1 TP_177059_20131216_20200912_02_T1_B6.T1F')
16 BasokoBand7\(\leftarrow\)readDL('L0S_L1 TP_177059_20131216_20200912_02_T1_B7.T1F')
17#Visualizingandcheckingtheclassofrandombands
18 plot(BasokoBand1)
19 res(BasokoBand1)
20 class(BasokoBand5)
21##[1]\"SpatialGridDataFrame\"
22##[1]\"Spatial_\"package\"
23##[1]\"sp\"
24 slotNames(BasokoBand5)
25##[1]\"data\"grid\"bbox\"proj4 string\"
26##[1]\"sp\"
27##[1]\"sp\"
28##[1]\"sp\"
30##[1]\"sp\"
31##[1]\"sp\"
32##[1]\"sp\" \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Parameters** & **Basoko** & **Basoko** & **Bumba** & **Bumba** & **Kisangani** & **Kisangani** & **Kisangani** \\
**(2013)** & **(2022)** & **(2013)** & **(2022)** & **(2013)** & **(2015)** & **(2022)** \\ \hline Date & 16 Decembember & 8 February & 23 December & 30 January & 13 April 2013 & 13 January & 17 February \\  & ber 2013 & 2022 & ber 2013 & 2022 & & 2015 & 2022 \\ \hline TWRS P. & 177 & 177 & 178 & 178 & 176 & 176 & 176 \\ WRS Row & 59 & 59 & 58 & 58 & 60 & 60 & 60 \\ WRS Path & 177 & 177 & 178 & 178 & 176 & 176 & 176 \\ Cloudiness & 0.00 & 0.00 & 0.04 & 0.00 & 5.85 & 1.36 & 0.48 \\ UTM Zone & 34 & 34 & 34 & 34 & 35 & 35 & 35 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main technical characteristics of the Landsat-8 OLI/TIRS imagery. * # NA's :17221239
* Check the topology of the class
* str(BasokoBand5@grid)
* Formal class'GridTopology' [package \"sp\"] with 3 slots
* #.0 _collecture.offset:_ Named num [1:21] 696300 43500
* attr(*, \"names\") = chr [1:2]

* #. @ cellsize : num [1:2] 30 30
* #. @ cells.dim : int [1:2] 7591 7741
* # Check spatial dimension
```
* BaskoBand5@[EMAIL_ADDRESS]
* # x
* # 696300 43500
* BaskoBand5@grid@cellsize
* # [11] 30 30
* BaskoBand5@bbox
* #.min max
* # x 696255 924015
* # y 43485 275715
* BaskoBand5@proj4 string
* # generating RasterLayers of Landsat bands
* BaskoB1 < raster(\"LCOs_LITP_177059_20131216_202000912_02_T1_B1,TIF\")
* Basko  
The sensor instruments of Landsat-8 OLI-TIRS record more channels of data than are actually needed for vegetation applications. For instance, to calculate the NDVI, we only need the Red and NIR bands (4 and 5, respectively). However, combining the bands in various RGB triplets enables us to highlight diverse objects on the surface to discriminate and capture information on a wider variety of objects. Thus, the composition of red, green and blue colours creates images with varied hue, saturation and intensity representations owing to the pixel brightness as the spectral reflectance of the objects and surfaces on the Earth. Therefore, the bands were mixed to form various colour composites and inspect the land cover types. The selected and most representative band composites for the three main studied regions were plotted as natural and false colour composites, respectively, which were generated using the 'raster' library. A relevant code snippet is given for the town of Basoko and was applied similarly for the rest of the Landsat-8 OLI-TIRS scenes with changed parameters, as per Listing 2.