## 3 Results
### Macro-Class-Based LULC Classification
This decrease in population and the variation within estimated in this study have socio-economic drivers. The city of Coatzacoalcos was the second most dangerous city in 2019 [72]. This fact  
Figure 4: Maps of the macro-class-based classification for the study period (2015-2021). Units: meters. Projection: WGS84 / UTM Zone 15N. provoked serious economic and social issues due to extortion, racketeering and kidnapping. People moved to nearby cities to feel safe, and big companies closed their services; as a result, unemployment rate grew and investments in the city slowed down, which, in turn, decelerated urbanization and industry development. Bare soil also seems to increase visually in Figure 4 and is confirmed in Table 7. Bare soil variations are linked to a decrease in vegetation. These two variables are highly dependent on water availability, droughts and weather fluctuations. We can see that, in 2015, the area of study was 25.67% of bare soil, while, in 2021, it increased up to 26.37%. On the other hand, vegetation presented the inverse behavior, being 26% and 24.96% in 2015 and 2021, respectively. Analyzing the water availability, which is proportional to the precipitation, one can see in Figure 6 that rainfall tended to fluctuate from a minimum of 1107.6 mm in 2015 to a maximum of 1730.90 mm in 2017. This fluctuation, in general, tells us that, of the last three years of the period of analysis studied here, precipitation reduced drastically in 2019, which can explain the cause of the bare soil increment. Additionally, Figure 7 shows that although minimum temperature tended to be steady from 2013 to 2021, mean temperature and maximum temperature increased during the study period. For instance, mean temperature was 26.60 \({}^{\circ}\)C in 2018, whereas the maximum temperature increased from 28.20 to 30.20 \({}^{\circ}\)C in 2018 and 2020, respectively. Increments in temperature increases soil evaporation and plant transpiration. If the latter is limited due to lack of water, water stress occurs in plants and they do not reach maturity, which might explain changes in pixel color and the move from vegetation to bare soil. It can be seen that the use of remote sensing for LULC evolution can be related and explained with inland information. This not only promotes the use of remote sensing but also validates its implementation. All the macro-class-based classification showed average user accuracy (UA) and producer accuracy (PA) higher than 90% (Table 8). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Macro-Class** & **2015** & **2016** & **2017** & **2018** & **2019** & **2020** & **2021** \\ \hline Water & 28.45 & 28.44 & 28.50 & 28.47 & 28.43 & 28.44 & 28.44 \\ Built-up & 19.88 & 19.92 & 19.99 & 20.03 & 20.12 & 20.15 & 20.21 \\ Vegetation & 26.00 & 25.98 & 25.74 & 25.56 & 25.46 & 25.18 & 24.96 \\ Bare soil & 25.67 & 25.66 & 25.77 & 25.94 & 25.99 & 26.24 & 26.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Percentage area estimated for the macro-class-based classification. Figure 5: Last six five-year censuses in Coatzacoolcos, Veracruz. Source: National Institute of Statistics and Geography (INEG) [73]. This means that both producers and users of these maps can rely on this classification with at least 90% confidence. These values are comparable to the ones found in [47,48]. The mean overall precision for the study period is 94.81%. The Kappa coefficients for all the studied years are located in an excellent range, as they all are higher than 0.81, according to [PERSON] and [PERSON] [66]. Although this classification was appropriate for the proposed macro-classes, a more refined algorithm was implemented to understand more about the evolution of the LULC of Coatzacoalcos, as it is necessary for decision making purposes.