## 2 Materials and Methods
### Identification of Urban LULC and Change Detection  
Time series datasets, which were of different types and had different production stages, were examined for urban fabric, and changes, developments, or differences in cities could be observed due to the dynamic structures of cities [32]. The urbanization level of Gaziantep was extracted using satellite images for the 8-year period. The overall rate of variation between classes ranged from 0.04% to 13.7%, while the highest change was in the arable land class and the lowest change occurred in the water class for the 8-year period. In addition, the rate of changes in LULC classes are shown in Figure 5 over four years. It was clear that although changes in LULC classes had decreased in some classes and regions, they had not increased steadily from 2010 to 2018. The reasons for this change may be different classification processes and a change in the area of the classes. For instance,  
Figure 3: Representation of revised area for Urban Atlas 2010 and 2015. Figure 2: The original Urban Atlas nomenclature and the Urban Atlas nomenclature for the land use/cover classes used in the study. there were changes within the classes in the urban fabric class, forest class, and green areas class. When the direction of change was examined regionally, the most significant change was observed in the construction area class (code 13100). Particularly, an increase in the construction area class (code 13100) was noticeable north and west of the settlement area. There was also an increase in the industrial, commercial, public, military, and private class (code 12000) in the northern and eastern regions. Also, construction areas were observed to be on agricultural and vacant lands. On the other hand, the urban fabric class (code 11000) increased in the southern and western regions of the city. The city was seen to expand towards the south and west in terms of settlement, while changes are occurring in the northern part in terms of industry. Figure 4: Representation of the Gaziantep Urban Atlas for 2010, 2012, 2015, and 2018. Figure 5: Representation of LULC ratio for each year. A change detection analysis was conducted on classified images to identify changes within classes and determine which classes were undergoing changes in relation to each other. In this context, the Semi-Automatic Classification Plugin (SCP) tool, which facilitates the post-processing of classified images by comparing classes and conducting change analysis, resulting in the generation of change images, was employed within the QGIS 2.18 software [44]. Changes in classes were calculated over consecutive two- and three-year periods. Figure 6 shows the LULC changes in each class for the Urban Atlas 2010, 2012, 2015, and 2015. It was observed that there were changes in the direction of increase in general in the classes. After 2015, serious changes were observed in the construction area class (code 13100) and arable land class (code 20000), and these changes included an increase of 4.686 ha and a decrease of 7.265 ha, respectively. Change detection can also be seen in Figure 4 with visual interpretation. The new urban fabric and commercial areas started to form around the settlement center and commercial areas. When the values of class area were also examined, the highest change occurred between 2015 and 2018 in the arable land class (code 20000) and construction areas class (code 13100), with 7% and 11%, respectively. The least change occurred in the water class (code 50000), and these changes were between 0.002% and 0.007%. Also, the changes in the mine extraction and dump sites class (code 13000) and green urban areas, sports and leisure facilities class (code 14000) were similar, and these changes corresponded to approximately 0.2% and 0.4%. The urban fabric class (code 11000) and industrial, commercial, public, military, and private class (code 12000) areas were increased, except between 2012 and 2015.