## 3 Results
### Accuracy of kNN Classification  
The classification was evaluated using the accuracy parameters OA, Kappa index, OE, CE, and cross-validation, as shown in Figure 6.  
In 2018, the accuracy decreases continuously as k increases, going from approximately 0.9899 with k = 5 to 0.9894 with k = 9. This indicates that in 2018, the increase in the number of neighbors had a negative impact on the model's performance, with k = 5 being the best performer. In 2023, there is a different trend, the accuracy peaks at k = 7, reaching approximately 0.9900, and then starts to decrease for higher values of k. This indicates that the choice of k = 7 was optimal in the 2023 scenario. Based on the classification defined by [PERSON] and [PERSON] [PERSON] and [PERSON] (2018), these results suggest that the classification was not only accurate, but also consistent and reliable. These results indicate good precision and agreement between the classification and the actual landscape. In this context, it is demonstrated that the model has precise classification potential, producing results that are very close to the actual or expected values. The narrow confidence interval suggests a high probability that the model's actual accuracy is within this range, which is indicative of consistent results ([PERSON], 2018). Figure 7 presents a comparison between the classification using the kNN algorithm and an RGB composite, with the PlanetScope bands. Figure 6: Comparison between accuracy (cross-validation) and the number of the training neighbors (k) for the years 2018 and 2023. Through Figure 7, it is possible to spatially identify the results obtained with the OA and Kappa index parameters; however, an analysis of OE and CE is necessary, as conducted in this study, to identify the presence of false positives and false negatives. Table 4 presents the verification results of these parameters. It can be observed in Table 4 that, in the \"Vegetation\" class, there was a slight improvement over time, with low commission and omission errors reflecting a high accuracy in detecting this class. In contrast, the omission and commission errors for \"Bare soil\" increased from 2018 to 2023, indicating a slight decline in detection accuracy and a moderate tendency toward overclassification. However, as with \"Vegetation\", the errors remained low. These results are consistent with previous studies [33], which suggest that soil and vegetation classes in the Caatinga tend to vary little compared to reference products. This behavior is associated with the consolidated use of these areas and the low anthropogenic interference in the Caatinga biome landscape [11]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Classes**} & \multicolumn{2}{c}{**2018**} & \multicolumn{2}{c}{**2023**} \\ \cline{2-5}  & **OE** & **CE** & **OE** & **CE** \\ \hline Water & 0 & 0 & 0 & 0 \\ Urban area & 22.10\% & 6.18\% & 24.67\% & 2.57\% \\ Bare soil & 0.6\% & 2.53\% & 0.37\% & 2.02\% \\ Vegetation & 0.01\% & 0 & 0.29\% & 0.19\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Omission Errors (OEs) and Commission Errors (CEs) for the years 2018 and 2023. Figure 7: Comparison between the classification using the kNN algorithm and an RGB composite, with the PlanetScope bands for the years 2018 and 2023: (**a**) sample area of the classified image for 2018; (**b**) sample area of the PlanetScope image (RGB) for the classified area in 2018; (**c**) sample area of the classified image for 2023; (**d**) sample area of the PlanetScope image (RGB) for the classified area in 2023. The \"Urban area\" class showed the highest error values, though with an improvement in omission error, suggesting greater effectiveness in detecting urban area over time (Table 4). On the other hand, the commission error increased considerably, indicating a greater tendency to misclassify other classes as urban area in 2023.