## 3 Materials and Methods  
### Data  
The GRASS GIS-based image classification workflow is based on the Landsat 8-9 dataset containing nine satellite images (Figure 3). The images cover the region of Chott Melrhir and Chott Merouane in Algeria. The major metadata are listed in Table 1.  
All of the images were selected during the January period for two reasons. First, the water levels in the chotts fluctuate, with the highest levels occurring during the wet periods (i.e., spring and winter), when the lakes are filled by water from occasional rainwater or groundwater sources. In contrast, during the summer and autumn periods, the majority of lakes are completely dry due to the highest temperature and evaporation rates in the Sahara Desert. Therefore, to detect changes on the images, it is necessary to select the \begin{table}
\begin{tabular}{l c c c l} \hline \hline
**Date** & **Spacecraft** & **Landsat Product ID** & **Scene ID** \\ \hline
1 January 2014 & 8 & LC08\_L2 SP\_193036\_20140101\_20200912\_02\_T1 & LC81930362014001 LGN01 \\
4 January 2015 & 8 & LC08\_L2 SP\_193036\_20150104\_20200910\_02\_T1 & LC81930362015004 LGN01 \\
7 January 2016 & 8 & LC08\_L2 SP\_193036\_20160107\_20200907\_02\_T1 & LC81930362016007\_LGN02 \\
25 January 2017 & 8 & LC08\_L2 SP\_193036\_20170125\_20201013\_02\_T1 & LC8193036201725 LN02 \\
28 January 2018 & 8 & LC08\_L2 SP\_193036\_20180128\_20200902\_T1 & LC819303620180218 LGN00 \\
15 January 2019 & 8 & LC08\_L2 SP\_193036\_20190115\_20200830\_02\_T1 & LC81930362019015 LGN00 \\
2 January 2020 & 8 & LC08\_L2 SP\_193036\_20200102\_2000824\_02\_T1 & LC81930362020002 LGN00 \\
4 January 2021 & 8 & LC08\_L2 SP\_193036\_20210104\_20210309\_02\_T1 & LC81930362021004 LGN00 \\
15 January 2022 & 9 & LC09\_L2 SP\_193036\_20220115\_20220118\_02\_T1 & LC91930362022015 LGN00 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Satellite images used in this study: Landsat-8 OLI/TIRS collected from the USGS. wet period in the calendar year. The second reason is related to the quality of the data covering target areas related to the cloudiness of the images. Many scenes from Landsat 8-9 OLI/TIRS were checked, and only those with low cloud coverage (below 10%) were selected for classification. Such conditions corresponded to the images taken on January during the time period of 2014-2022, Figure 3.  
The data were obtained from the USGS EarthExplorer repository (from the January period, spanning from 2014 to 2022). The Landsat OLI/TIRS data include spectral bands in visible and NIR ranges. The atmospheric correction was performed using an example code that applies top-of-atmosphere reflectance using the GRASS GIS module \"i.landsat.toar \". It enables calibrating the DN of the pixels on Landsat images using data on top-of-atmosphere reflectance and converting the updated image. The procedure was performed using the code in Listing 1:  
Figure 3: Landsat 8–9 OLI/TIRS images in natural RGB colors: lake changes over a period of 9 years (2014–2022). ```
1.1.landsat.toarinput=LB_2014.output=LB_2014.tour.sensor=0110 method=dos1 date -2014-01-01 sun_elevation=28.79978934 product_date=2014-01-01 gain=HHLHLHLHL',
```  
The series of Landsat images exhibited high-quality scenes as input data sources: distinct colors, contrasting and clearly recognized areas corresponding to various land cover classes, no defects, faults, or technical noise on the imagery, distinguishable landscape patterns and continuity, identical coverage of the scene over the years, and the availability of multi-spectral bands. The images were processed, analyzed, and stored in a working directory for further processing by the GRASS GIS. Additional mapping was performed using the Generic Mapping Tools (GMT) scripting toolset [102], following the existing technical cartographic workflow [103; 104].