## 1 Introduction  
### Background  
Detecting changes in the water levels of ephemeral salt lakes using remote sensing data is an important task in the hydrological analysis of arid regions. It forms the foundation for higher-level applications, such as environmental monitoring, water resource management, climate change studies, and the analysis of groundwater distribution. Located in Northern Africa's Maghreb region in the Sahara Desert, salt lakes (or \"chotts\") are subject to temporal water level fluctuations throughout the calendar year. Chotts remain dry for most of the year and are fed by water during the winter, occasional rains, and wadi. Monitoring changes in the water supplies of chotts is possible by using satellite image classification. This is based on evaluating spectral reflectances of objects that are visible on the surface [1]. Changes in lake contours are used as indicators for monitoring land cover changes and hydrological modeling through the variability of contours in ephemeral stream beds. Satellite images are key data sources in computational ecology and environmental monitoring. The various colors and brightness of the land cover classes visible on the images are determined by the spectral reflectance of the pixels and can be used for mapping the Earth's landscapes [2; 3; 4; 5; 6]. Using remote sensing data allows for the detection of heterogeneity in the Earth's landscapes [7], enabling the recording of geological processes [8; 9] and the analysis of environmental connections [10; 11]. Furthermore, spectral reflectances of land cover types identified on the satellite image may be used to detect soil salinity [12]. More applications of spaceborne data include geomorphological studies and terrain analyses [13; 14; 15]. Finally, remote sensing data are widely used in climate studies, including the mapping of evapotranspiration and droughts [16], and the characterization of soil layers [17]. Satellite images serve as data sources in many applications of Earth science, such as in mapping the distribution of dust in arid regions [18], assessing and mapping erosion [19], aquifer characterization [20], evaluating groundwater potential [21], agricultural monitoring of crops [22; 23; 24; 25], hazard risk assessments [26], and geological investigations of rock physics, tectonics, and hydrogeological simulations [27]. The analysis of satellite images can also be used for geophysical assessments in the desert areas of the African Sahara by integrating geological and spatial data modeling [28]. The goal of this study was to create a GRASS GIS framework for image classification by using scripts that yield good image-processing capabilities, and to create a series of maps based on the automated land cover classification. An important consideration for the use of satellite images for cartographic data analysis is that they need to be classified into maps of land cover types for the interpretation and analysis of landscapes. Manual classification is a time-consuming process involving many iterations, which might lead to errors and result in low accuracy. The problem is, therefore, focused on finding effective algorithms for the classification of satellite images. Landsat 8-9 OLI/TIRS images, which have been recorded since 2013 as a continuation of the Landsat data collection, form a valuable pool of Earth's geospatial data to evaluate land cover types and landscapes of the Earth. However, such datasets should be processed and classified by GIS to enable the use of satellite images in the environmental applications. At the same time, the use of commercial software (e.g., ArcGIS version 8.0) is subject to the availability of the license, which can lead to restrictions on its usage. In this regard, the use of the freely available open-source GRASS GIS for satellite image processing is advantageous. Moreover, it includes the Python API and libraries as extended functionalities. Therefore, this paper proposes using GRASS GIS as a free open-source GIS for image classification.