## 3 Materials and Methods
### Scripting in GRASS GIS
Such fluctuations affect the content of the evaporite (anhydrite) and soft sulfate minerals, such as gypsum and halite. Figure 9 shows the comparison of the image classification results between 2020 and 2021 with the visualized classified maps and the rejected classes. Exporting files to the rejection probability values for processing pixel classification confidence levels was performed using the 'i.maxlike' GRASS GIS module, which shows the probability of the correct classification of pixels for quality control. A higher salinity level in the Chott Melrhir in January 2020 (Figure 9a) compared to January 2021 (Figure 9c) shows a dry period and less atmospheric precipitation. Compared to 2020, in 2021, the salt lake was filled with water, which indicated intense rainfalls. Figure 8: 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**) 2018 map, (**b**) 2018 rejected classes, (**c**) 2019 map, (**d**) 2019 rejected classes. In contrast, the Chott Merouane demonstrated less variability in salinity, which can be explained by the topographic structure of the basin and better access to groundwater. The automatic recognition of salinity in both chotts and other land cover classes was achieved through the implementation of a maximum-likelihood discriminant analysis classifier in the GRASS GIS algorithm. This was performed using the empirical trials of pixel assignments into classes by clustering algorithms with the most appropriate parameters of the Algerian landscapes detected for the classification of satellite images. The proposed algorithm of GRASS GIS scripting presents a sequence of several modules, which enabled the detection of 10 land cover classes in the northeastern region of Algeria through quantitative evaluations of the landscapes. The comparison of land cover types was based on pixel values, which enabled assigning them into cluster groups representing land cover types. This presents a fully automated GRASS GIS-based workflow that utilizes spectral reflectance values of pixels extracted from satellite images for a parameter-defined analysis aimed at identifying the land cover features; see Figure 10.

Figure 9: 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**) 2020 map, (**b**) 2020 rejected classes, (**c**) 2021 map, (**d**) 2021 rejected classes. ## 5 Discussion

In this section, we discuss the relationships between the approaches to land cover classification by GIS. Despite the need for a standard approach to the classification system, none of the current methods has been accepted as the standard, and many approaches exist. Methods of automated image classification include k-means clustering [109], principal component analysis [110], hierarchical clustering [111], segmentation [112], object-based classification, and deep learning approaches [113]. Among these, clustering uses algorithms that group pixels with common characteristics into clusters that represent different land cover types. With this approach, the coplanarity constraint solution introduces an indeterminacy in the geometric positions of pixels within a given land cover class. Such ambiguity can be efficiently solved by using positional data of proposed classes that are predetermined and known based on land use data from FAO classification schemes. On the other hand, the second approach explicitly uses object-oriented segmentation algorithms to solve the ambiguity of landscape patches in cases involving similar land cover classes (e.g., sparse grassland and sparse shrubland) [114]. In this method, segments are arranged by grouping neighboring pixels together based on similarities in color and shape features. Note that the problem is the same as the deep learning process of semantic land object detection [115; 116; 117].