# Uncovering Vegetation Changes in the Urban-Rural Interface through Semi-Automatic Methods
## 3 Results and Discussion  
For the \(p\)-value limit of 0.05, the overall accuracy was 53.24% with a confidence interval of 1.97%. The accuracy was higher for the detection of no-changes events, 74.29%, with a confidence interval of 1.72% while in the detection of changes it was 33.99% with a confidence interval of 1.87%. The recall was higher in the detection of changes, 59.09%, with a confidence interval of 1.94%, for the not-changes was obtained 50.70% with a confidence interval of 1.97%. The F1-score showed better results for no-changes events, 60.29%, with a confidence interval of 1.93%, while for the events of changes it was 43.15% with a confidence interval of 1.95% (Table 2). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Statistical Metrics** & & **Matrix** \\ \hline  & **(C)** & **(NC)** & **(C)** & **(NC)** \\ \hline Precision & 33.99\% \(\times\) 1.87\% * & 74.29\% \(\times\) 1.72\% * & \multirow{3}{*}{(C)} & \multirow{3}{*}{52} & \multirow{3}{*}{101} \\ Recall & 59.09\% \(\times\) 1.94\% * & 50.73\% \(\times\) 1.97\% * & & \\ F1-Score & 43.15\% \(\times\) 1.95\% * & 60.29\% \(\times\) 1.93\% * & & \\ Overall Accuracy & 53.24\% \(\pm\) 1.97\% * & & & \\ \hline \hline \end{tabular}  
* Confidence Interval; C—Change; NC—Not Change.  
\end{table} 
Table 2: Validation with \(p\)-value \(\leq\) 0.05. Figure 2 show the change maps with this \(p\)-value limit for all municipalities. The same maps can be found in large format in (Supplementary Materials Figures S1-S5). For the model with the \(p\)-value limit of 0.0005, the overall accuracy was 64.85% with a confidence interval of 1.88%. The model showed a higher predictive capacity to detect no-changes in all verified metrics, as well as the associated confidence intervals were also lower. The accuracy obtained in the no-changes events was 71.43% with a confidence interval of 1.78% and for the events of changes, it was 36.36% with a confidence interval of 1.90%. The recall of 82.93% obtained for the detection of no-changes, the best value obtained by the model, presented a confidence interval of 1.48%, however, to detect changes the recall value obtained was the worst of the model, 22.73%, with a confidence interval of 1.65%. The F1-score was 76.75% in the no-changes events and 27.97% in the events of changes. The confidence intervals associated with these values were 1.66% and 1.77% respectively. Figure 2: Changes detected in: (**A**) Figueiró dos Vinhos, (**B**) Iflavo, (**C**) Miranda do Corvo, (**D**) Penela, (**E**) Cantanhede, with \(p\)-value \(\leq\) 0.05. The rational of using two \(p\)-value limits to identify the changes was to improve the predictive capacity of the model and reduce false positive events. The results obtained by lowering the significance threshold from 0.05 to 0.0005 (Tables 2 and 3) corroborated this hypothesis, showing an increase of approximately 11% in the overall accuracy of the model, from 53.24% to 64.85%. The results presented by [31] also recorded this same behaviour of increasing accuracy as the \(p\)-value threshold decreased. The calculation of the confidence interval shows the variation rate that may exist within the results, i.e., their reliability. The model presented low confidence interval rates, indicating that the results obtained may vary little above or below from that obtained. Figure 3 show the change maps with \(p\)-value limit 0.0005 for all municipalities. The same maps can be found in large format in (Supplementary Materials Figures S6-S10). The 64.85% of the overall accuracy obtained by the developed model is low when compared to other models developed to detect changes in vegetation. Change detection algorithms such as LandTrendr [27] or VCT [25] showed higher overall accuracy. For LandTrendr the authors reported accuracy above 85%, while for VCT is stated an accuracy higher than 75%.