## 3 Materials and Methods
### Scripting in GRASS GIS
Several approaches have been proposed to solve the problem with a single image, e.g., a composite pattern of multiple bands [118], or as a time series analysis with the assumption of dynamically developed landscape structures [119]. Our solution provides an alternative method for land cover change detection based on image classification using scripts within the GRASS GIS framework. The presented application of geospatial data processing using GRASS GIS for the environmental monitoring of Saharan deserts and saline ephemeral lakes was used for the two selected chotts, i.e., Melrhir and Merouane, located in Algeria. The demonstrated and explained workflow includes the collection of images from the USGS EarthExplorer repository, dataset storage, organization, analysis, and image processing using the GRASS GIS. Creating the spectral signatures determined the categories of the land cover classes to which image pixels were assigned during the classification process. The higher-level image interpretation process includes the identification and recognition of pixels and their assignments to these land cover classes based on the threshold defined using the chi-square test. This solution is based on the technical application of scripts instead of using several different image-processing phases in the existing classification workflow methods,

Figure 10: 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)** 2022 map, **(b)** 2022 rejected classes. which include the procedures of image preprocessing, training samples, classification, and reclassification with merging and assigning classes. Detecting the land cover classes on the images strongly depends on the contrast of the digital numbers (DNs) of the pixels compared to those of neighboring classes. In the cases of clear, cloud-free images and distinct landscape contours, detection can be performed smoothly. However, high cloud coverage on the image, or noise, can seriously affect the inspected scenes and worsen the recognition and identification of land cover classes. Therefore, the images were selected with minimal cloudiness and other noise. The second issue concerns specific characteristics of the Landsat 8-9 OLI/TIRS scenes, which have 30 m resolution in multi-spectral channels. This limits the identification of land objects to a spatial scale of 30 m. More detailed landscape patterns are ignored due to the restrictions of resolution and image sharpness. In contrast to the existing state-of-the-art GIS (e.g., commercial ArcGIS, or ERDAS Imagine), GRASS GIS offers a free and open-source approach to satellite image processing. Moreover, it accurately detects variations in the pixel reflectances which enable the identification of land cover classes. The assignment of pixels into land cover classes and discriminating signatures of the pixels are performed during clustering, which involves computing the covariance matrices calculated by the 'i.cluster' module. The results of the image classification performed by GRASS GIS, compared to the existing methods, demonstrate a robust and automated classification of remote sensing data, such as Landsat 8-9 OLI/TIRS. Such an approach worked accurately, according to the resolution of the original image (30 m), which is applicable to all images from the evaluated time series. For future applications, the demonstrated GRASS GIS approach for image processing will be useful for big data processing. Possible cases will include the multi-temporal interpretation of landscapes as spatially and temporally varying vegetation patterns, as responses to climate forcing [120; 121; 122]. For these purposes, future applications of the presented GRASS GIS algorithms will foresee the use of the presented workflow for the automatic classification of other regions of the African Sahara that experience changes over decades due to climate effects.