## 3 Materials and Methods
### Scripting in GRASS GIS
The qualitative evaluation of the water level coverage in Chotts Melrhir and Merouane on the satellite images for each Landsat scene is based on the number of land cover classes as landscape segments; see Figure 6. The GRASS GIS framework of image classification was developed, considering the principle of confidence. Consequently, it was adapted to the recognition of the dominant land cover classes by selecting landscape patches with homogeneous spectral reflectances of pixels within each cluster group. In order to evaluate the classification results objectively, we tested all implemented GRASS GIS algorithms on nine classified images to conduct a comparative analysis of the classification results with different probability values of pixels that belong to identical land cover classes in different years. This is shown in the grayscale plots for each pair of images in Figures 6-9. The numerical pairwise comparison of each pair of images was performed using a quantitative evaluation of the output raster map holding reject threshold results. This was performed by the chi-squared test and is shown as the grayscale monochrome images for the maps of all corresponding years. Using this contingency test for each pair of images, the data were verified by the statistical hypothesis test adopted to the large sample sizes of each 16-bit Landsat scene. Hence, for each image, the test evaluated the independence of the two categorical variables corresponding to the two dimensions of land cover classes. The land cover types are automatically detected and visualized in Figure 6 for the years 2014 and 2015, in Figure 7 for the years 2016 and 2017, in Figure 8 for the years 2018 and 2019, in Figure 9 for the years 2020 and 2021, and in Figure 10 for the year 2022. The classification succeeded for images representing nine Landsat scenes. The percentage of pixel images with rejection probability values and pixel classification confidence levels was computed based on the reject threshold level. The statistical data on the classification

Figure 6: Classification of the Landsat 8–9 OLI/TIRS Algeria images with pixels classified into 10 classes, and rejection probability values with pixel classification confidence levels: (**a**) 2014 map, (**b**) 2014 rejected classes, (**c**) 2015 map, (**d**) 2015 rejected classes. results for processed scenes obtained from the GRASS GIS 'i.maxlik' is summarized in the monochrome images, to the right of the classification maps, Figures 6-10. The comparison of the classified images in 2016 (Figure 7a) and 2017 (Figure 7c) shows the visible increase of the salt areas in the northern part of the study area during one calendar year, Figure 7. Such fluctuations show that significant changes in the hydrology reflect the sensibility of a chott as a closed hydrological system to regional changes in rainfall patterns, as also noted earlier [107]. The solutions evolve to include halite, potassium, and magnesium salts that are visible on the satellite images covering chotts for several years (for a comparison). For instance, when comparing the distribution of salt areas between 2018 (Figure 8a) and 2019 (Figure 8c), the water level increase in Chott Melrhir is visible in the latter image. Moreover, the structure of the chotts is complicated by the occasional presence of sparse vegetation downstream of the depression where the palm grove gives way to the salty soils,

Figure 7: Classification of the Landsat 8–9 OLI/TIRS images of Algeria with pixels classified into 10 classes, and rejection probability values with pixel classification confidence levels: (**a**) 2016 map, (**b**) 2016 rejected classes, (**c**) 2017 map, (**d**) 2017 rejected classes. known as sabkha. During the dry periods in the summer, the salt in the chotts in Algeria is concentrated by evaporation, and the pH increases along with the increase in the level of carbonate calcium. During wet periods or rains, calcium carbonate precipitates, the pH decreases, while the residual alkalinity becomes negative [108].