## 3 Materials and Methods
### Scripting in GRASS GIS
While this study used nine satellite images from Landsat 8-9 OLI/TIRS, which were classified using scripting methods, the extended studies may include other spaceborne data, such as Sentinel-2A images from MODIS temperature data. Compared to the existing proprietary software, the presented GRASS GIS-based framework is free and available and can be utilized for educational purposes in studies and research. This study demonstrates the high automation of image processing using scripts, which reduces potential errors and minimizes workflow routines due to machine-based data processing in the classification of remote sensing data. A significant advantage of the presented method compared to the previous methods is that it only requires one script to run the process, instead of having to select various separate procedures in the GIS menu. The effectiveness of GRASS GIS is especially useful for the multi-temporal analysis of long-term environmental trends and modeling. In particular, automated geospatial data processing is applicable for the analysis of desertification, which can be performed using a time series of satellite images. Satellite image classification is central to many ecological applications, including the analysis of land cover changes, landscape monitoring, recognition of vegetation patterns, and risk assessment. Such applications heavily depend on accurate and automated classifications of satellite images. The archives of the Landsat mission, beginning from the 1970s, can serve as reliable data sources for the tasks, which require comparisons between old and new datasets. Classified images provide information for further analysis in environmental monitoring, and enable the acquisition of the geometry and the extent of the landscapes (in order to obtain information about changes over time through the comparison of several images). Therefore, the classification of several Landsat OLI/TIRS images as time series is an actual task in environmental studies. The GRASS GIS approach demonstrated in this study enables the conversion of bitmap raster images into the maps of land cover changes,based on the spectral reflectance of the pixels corresponding to land cover types and objects on the Earth. ## 6 Conclusions

This article presents an advanced GRASS GIS-based algorithm that was developed to accurately and automatically classify bitmap images into the maps of land cover types in northern Algeria, based on the cell spectral reflectances in imagery data. While different software types for satellite image processing exist, scripting methods of GRASS GIS remain the most accurate. This is due to the high levels of automation and machine-based data processing that minimize human-induced errors and maximize the speed of the workflow. The objective of the presented article was to classify the Landsat OLI/TIRS satellite images covering a span of nine years (2014-2022), obtained from USGS sources. The implemented image analysis methods are presented along with the given image-processing examples in the GRASS GIS application. The identified levels of lakes were accurately discriminated from other land cover types and sand areas of the Sahara Desert (Grand Erg Oriental). The maintained geometric shapes and topologies of classes and automated recognition of pixel colors are presented by the GRASS GIS. In this way, this work addressed the challenges of satellite image classification using GRASS GIS. A sequence of several modules was used to support the environmental analysis of the changes in the salt lakes of Algeria using scripts. The functionality of the GRASS GIS syntax was used for the automated classification of Landsat 8-9 OLI/TIRS data as the developed workflow presented in this paper. The modules included 'i.maxlik', which is used for classifying the cell spectral reflectances in imagery data, 'i.segment', which is to identify segment objects from imagery data, and 'i.cluster', which is to define spectral signatures. Auxiliary graphical modules were also used, for instance, 'i.landsat.toar', and others.