## 3 Results
### Land Use and Land Cover Change through LULC Transition Matrix  
The last part of the analysis of these results includes the generation of the LULC transition matrix (Table 15), which indicates the transitions of each of the classes with respect to each other, for instance, how much area of the dunes converted to low vegetation density areas [69]. This matrix summarizes the changes already validated by the precision matrix but in terms of actual surface area. One disadvantage of this analysis is that it does not consider what happened throughout the two isolated years. However, it brings up an excellent tool for studies where LULC changes want to be predicted for future scenarios along with the possible drivers forcing those changes. The main diagonal contains the reference areas of the classes between the analyzed years. Columns named loss and gain represent the area lost or gained, respectively, for a particular class. Total gain or loss are calculated by adding up columns or rows of the reference area for each class. Particularly, this study selected the ends of the period, the years 2015 and 2021. The analysis of this matrix is straightforward. For instance, the water bodies gained 0.24 km\({}^{2}\) but lost 0.0297 km\({}^{2}\). Gains were associated with transitions of wetland (0.22 km\({}^{2}\)), shrubland (0.0063 km\({}^{2}\)), mixed forest (0.0009 km\({}^{2}\)) and bare surfaces (0.0081 km\({}^{2}\)) to water bodies. On the other hand, losses are due to a mixture of transitions, including sparse vegetation (0.009 km\({}^{2}\)), bare surfaces (0.009 km\({}^{2}\)) and wetlands (0.00117 km\({}^{2}\)). All these transitions occurred between the two selected classifications.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Measure** & **2015** & **2016** & **2017** & **2018** & **2019** & **2020** & **2021** \\ \hline UA & 96.48 & 96.13 & 96.46 & 97.11 & 97.33 & 96.82 & 95.13 \\ PA & 94.45 & 96.97 & 97.05 & 97.38 & 94.57 & 97.59 & 90.41 \\ Overall precision & 96.18 & 96.64 & 96.28 & 97.22 & 97.59 & 97.03 & 95.19 \\ \(K\) & 0.95 & 0.96 & 0.96 & 0.97 & 0.97 & 0.95 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Overall precision and Kappa coefficient (\(K\)) for class-based classification.  
For the sake of simplicity, only the most significant changes are discussed here. Residential land use grew only 0.88 km\({}^{2}\) during the last six years, which reflects a very small growth in comparison with that of industrial cities in Mexico, which has been characterized as about 5% per year [80], while Coatzacoalcos only grew 1% per year. Commercial areas reduced by 0.16 km\({}^{2}\), resulting in abandoned areas due to the previous mentioned socio-economic issues. One of the prominent changes was in areas of mixed forest, which gained 0.52 km\({}^{2}\) and lost 0.43 km\({}^{2}\), indicating reforestation practices and more sustainable urbanization development in some new residential, industrial and commercial areas but some activities of deforestation. Swamps, on the other hand, lost 2.09 km\({}^{2}\). This loss warns us of the possible future loss of ecological services provided by wetlands and their habitat, which are essential for biochemical processes and water purification because some of the non-point pollution in the city is contained by them. Lastly, vegetation densities fluctuated the most among them, namely, grassland and sparse vegetation density areas transitioned to bare surfaces. The latter gained 9.12 km\({}^{2}\), whereas the former two lost 2.48 and 4.66 km\({}^{2}\), respectively, which represents 80% of the bare surfaces gain.