## 2 Materials and Methods
### Methodology
#### 2.2.2 LULC Classification Using the Semi-Automatic Classification Plugin (SCP)  
The SCP version 6.2.9 [36] for QGIS was employed to perform the classification process. In a comparison of different open source remote sensing applications, the SCP plug-in for QGIS has been highlighted as particularly suitable for image pre-processing and classification [37]. The plugin allows the creation of regions of interest (ROI) as training input to define land cover classes and to edit results. The classification was based on macro-classes (MC), which are the general categories selected for defining each land cover class. The MC were defined after analyzing the most prominent urban landscape features found in the study areas.  
Thus, those features can be summarized in three categories. Natural elements included any waterbody, such as streams or reservoirs, vegetation, farming land, and bare land. Any street, path, or building depicted a as roof, was classified as man-made infrastructure. Non-covered constructions, such as graveyards or stadiums, were also included in this category. In the third category, clouds, shadows, and mobile elements such as vehicles or pedestrians, were ranked as special features not desired in the final LULC classification. Consequently, 'Buildings,' 'Paved,' 'Unpaved,' 'Bare land,' 'High vegetation,' 'Low vegetation,' 'Shadows,' 'Clouds,' and 'Water' were defined as MCs, since they are easy to differentiate but at the same time provide a sufficient level of detail to assess the degree of urbanization and ecological status. Only mobile elements were excluded at the initial stage of the classification because other undesired features (i.e., clouds and shadows) were considered in the post-processing. This exclusion was done because the heterogeneity of their colors led to false classifications, and because they could be easily edited.  
Green areas were sorted into high and low vegetation groups. The first one consists of single trees, shrubs, and forests, while the second one included public and private gardens, pasture zones, grass, and crops. Regarding waterbodies, they were not considered in the initial classification but at the post-processing phase because the dark color projected from the water led to misclassifying them as shadows. Streets were split into paved and unpaved elements, while any eroded areas, such as unpaved pedestrian paths or uncultivated lots, were classified bare land. Although unpaved streets can be defined conceptually as bare land, they were still considered separately, since the post-processing phase allowed us to redefine those zones. Finally, clouds were also assigned as a new land cover class. Thus, nine categories represent the final classes, but only eight were considered at the initial stage.  
Following an iterative process, ROIs were added to a training input, as the results improved the visually based on the selection of MC. Each ROI was set up with a maximum ROI pixel-width equal to 200, a maximum distance between pixels equal to 10, and a minimum ROI pixel area equal to 10. The maximum likelihood algorithm was employed in the classification to estimate whether any pixel belonged to an MC. Once a satisfactory output was achieved, an LULC raster was generated, hereafter named high resolution classification (HRC).