## 3 Materials and Methods
### Scripting in GRASS GIS
It uses the maximum likelihood discriminant analysis classifier as a core algorithm approach to classify the cells according to their spectral reflectances in satellite imagery. The main approach of this algorithm is that it applies the cluster means and covariance matrices generated during previous steps in a signature file by the 'i.cluster' module. They serve to determine the categories of spectral classes. The determination is performed according to the degree of suitability of each cell in the image to a given class based on the distance to the cluster's centroids. The technical implementation was performed using Listing 6. ```
1#Dimension=0```
1d.monwx0
2d.rast.legL8.2014_cluster_classes
3d.rast.legL8.2014_cluster_reject
```

**Listing 7** GRASS GIS code for mapping the results of image classification and rejection probability classes. To make the classification by 'i.maxlik' more accurate, a thresholding procedure was applied by the k-means clustering. This was implemented using the 'i.cluster' modules aimed at separating the target image classes, using the distance from the centroids. The identification of segments (objects) from the imagery data was performed by the 'i.segment' module of GRASS GIS using Listing 8:

```
1i.segmentgroup=L8_2014 output=L8_2014_seg.14 threshold=0.4 memory=1000
```

**Listing 8** GRASS GIS code for image segmentation by 'i.segment' algorithm clustering. This workflow of the GRASS GIS software briefly outlined above was used in the classification of satellite images. The sequence of the utilized modules aimed to classify pixels and sign them into clusters based on their features. Furthermore, this enabled the discrimination of spectral properties of the land cover objects using signature files and generated classification maps according to the assigned land cover classes. The landscape categories are recognized, based on the contrast in the digital numbers (DNs) of the pixels, which are assigned to land cover classes and mapped using color palettes. These solutions have advanced features and a high level of automation, which enabled the processing of a time series of the nine images. Once the images were classified in the GRASS GIS, land cover classes were assigned to interpret the variety of landscapes in the target region of northeastern Algeria. The land cover classes indicate the dominant feature classes of the landscapes identified on the images, which have specific characteristics and vegetation patterns that are unique for that given spot on the image. These include the following types derived from the FAO and adopted with generalization for the study area: (1) Bare areas (desert sands); (2) salt hardpans; (3) water bodies (Chotts Melrhir and Merouane); (4) non-consolidated soils (5) consolidated soils; (6) sparse grassland; (7) sparse shrubland; (8) sparse trees (9) sparse vegetation; (10) grassland; (11) broadleaved shrubland. The land cover classes were used as representative areas on the classified maps to compare and distinguish landscape dynamics over nine years. They also permitted the evaluation of lake shrinkages, the extent of salt areas, and the extent of the patches within the visualized landscapes. ## 4 Results

In this section, we evaluate the results and evaluate the performances of the classification algorithms on the nine images. The evaluation is performed using pixel classification confidence levels and rejection probability values. The process of classification has a flaw related to the assignment of pixels to the classes using their spectral signatures. The allocation of pixels may be different between years due to different nuances in spectral signatures caused by the wetland brightness, greenness, and other remote sensing characteristics caused by the level of chlorophyll in leaves. Moreover, the presence of sand dust and dunes on the surface cause selected pixels to be assigned to neighboring classes, such as non-consolidated and consolidated soils.