# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
## 3 Results
### Comparison with Copernicus Global Land Cover and ESA WorldCover  
An additional method used to validate our land cover products involves their comparison with other datasets, such as the 2015 Copernicus Global Land Cover and the 2020 WorldCover by ESA (Figure 12). The comparison was conducted on a pixel-by-pixel basis, scrutinising all pixels within the Valencian Community. In the case of the comparison with Copernicus' product, our classification was initially rescaled to the same resolution of Copernicus Global Land Cover (100 m), and then the classes from Copernicus matching of those created by us were selected to generate a confusion matrix (Figure 13). This comparison revealed a significant number of points where Copernicus classifies areas as urban, which in our classification belong to Sparse and Dense Vegetation. Furthermore, the multitude of vegetation classes in Copernicus results in our Sparse Vegetation values corresponding to different classes defined by them, although Dense Vegetation aligns for the most part. In this case, the percentage of urban area corresponds to 6.91%, slightly higher than the value obtained in our land cover (3.74%), attributable to the factors discussed earlier. Figure 11: Maps illustrating the expansion of Built-up Areas class in Castellón (**top**), Valencia (**centre**), and Alicante (**bottom**) between 1985 and 2020. The Built-up Areas class shows a rise of 110.37% (an additional 56.59 km\({}^{2}\)) in Castellón, a 70.47% increase (adding 143.30 km\({}^{2}\)) in Valencia, and a remarkable growth of 157.68% (an addition of 100.20 km\({}^{2}\)) in Alicante. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **1984** & **1985** & **1990** & **1995** & **2000** & **2005** & **2010** & **2015** & **2020** & **2022** \\ \hline \multicolumn{1}{c}{**Overall**} & & & & & & & & & & & \\
**Accuracy (\%)** & 94.16 & 93.85 & 91.72 & 93.37 & 92.18 & 92.96 & 94.52 & 95.02 & 94.90 & 94.81 \\
**Kappa (\%)** & 90.67 & 90.03 & 87.54 & 89.48 & 87.13 & 89.03 & 90.79 & 91.90 & 90.78 & 91.79 \\ \hline \end{tabular}
\end{table} 
Table 5: OA and Kappa coefficient results of the Holdout Validation for each year. The validation was performed while allocating 70% of the dataset for training and reserving 30% for testing purposes. The OA achieved was 0.73, accompanied by a Kappa coefficient of 0.41 and producer and consumer accuracies of 0.75 and 0.73, respectively. Regarding the WorldCover provided by ESA, it was rescaled from the original resolution of 10 m to match the resolution of our product (30 m). The WorldCover comprises multiple classes (Figure 12), which were grouped according to their closest resemblance to ours. The confusion matrix obtained through the pixel-by-pixel comparison (Figure 14)  
Figure 13: Confusion matrix comparing the 2023 land cover map calculated with the Random Forest algorithm and the one provided by Copernicus. Classes 80 and 200 correspond to Water Bodies class; class 50 represents Built-up Areas class; classes 20, 90, and 111–126 pertain to Dense Vegetation; classes 30 and 40 represent Sparse Vegetation; and class 60 represents the Other class. Figure 12: Global Land Cover provided by Copernicus for the year 2015 (**left**) and WorldCover v100 2020 provided by ESA (**right**). suggests that the ESA urban class sometimes corresponds to our vegetation pixels. This response, similar to that obtained for the Copernicus product, possibly has similar reasons. It is worth noting that no class equivalent to our Other class was identified in WorldCover. This discrepancy arises from their inclusion of bare soil within the Sparse Vegetation class, leading most of our points in the Other class to be categorised as vegetation classes in WorldCover. In this instance, the urban area percentage is 4.31%, more closely aligning with the value obtained in our land cover (3.91%). For our vegetation classes, a substantial alignment is observed with the various classes they define.