## 1 Introduction
### Motivation  
The need to automate the classification of satellite images raises the question of developing tools that enable automated image processing, with reduced workloads. This is aimed at building a structured workflow for remote sensing data processing. The existing types of classification methods, such as clustering, maximal likelihood, or k-means have revealed the importance of the optimization of image-processing algorithms. In this regard, scripting techniques in GRASS GIS ensure the level of automation and accelerate the speed of the classification process. Another question concerns the lack of similar studies. To the best of our knowledge, no existing studies have investigated the use of the GRASS GIS scripts for the classification of satellite images for Chotts Melrhir and Merouane in Algeria. Most of the existing studies investigating chotts in Algeria are limited to geochemical analyses [31; 32] or geological evaluations of saline brine environments in terms of their potential for lithium resource exploration [33; 34]. This is because salt pan brines in desert regions of the Sahara are known to be rich sources of lithium [35], which has garnered attention in the field of geochemical research. Nevertheless, processing remote sensingdata for investigating environmental changes in water levels of saline lakes through GIS presents more advanced and complex approaches, due to the task of image interpretation. These include the use of algorithms for image clustering and classification, using the spectral reflectance of pixels in the imagery. Existing mapping techniques used in cartographic works on chotts in Algeria and environmental monitoring use other software approaches [36; 37; 38]. More examples include geological studies using traditional methods of GIS for visualization [39; 40; 41]. Most general methods of landscape monitoring in saline Saharan environments operate via specific identification approaches, where surface features of arid environments are assessed based on geomorphological analysis and modeling [42]. For instance, risk assessment methods utilizing information on the concentration of metals in water were implemented at the Ramsar site of Chott Merouane [43]. The existing approaches rarely consider these methods, which makes the classification of remote sensing data via GRASS GIS an actual process for environmental monitoring in the African Sahara region. With satellite images, the shape and extent of land cover types for the evaluation of desertification can be accurately applied [44]. More complex cases arise with partially eroded soil, which can also be detected in images where the masks for the erosion areas and surrounding backgrounds should be defined [45]. Moreover, the fluctuations in the lake levels detected by the time series analysis on satellite images should include extensive data processing to derive information on land cover types. To fill in the existing gaps, this paper contributes to this problem by presenting a GRASS GIS scripting framework for the automatic classification of satellite images. The study's objective is to identify and detect changes in the level of the saline lake over a span of several years. The GRASS GIS-based framework has several advantages. These include the open-source availability of the software, which enables one to freely use it for educational or research purposes; it provides extended functionality with a wide variety of geospatial processing tools; it offers a flexible mode with a graphical user interface (GUI) and command-line mode, enabling scripting. Moreover, similar to a standard GIS, GRASS GIS supports both raster and vector processing tools, satellite image analysis, and cartographic production. Such features of the GRASS GIS make it a beneficial tool for remote sensing data analysis. For instance, it is able to classify land cover types based on remote sensing data by using clustering data partitions and the maximum-likelihood discriminant analysis classifier.