# Uncovering Vegetation Changes in the Urban-Rural Interface through Semi-Automatic Methods
## 3 Results and Discussion
The model developed by [26] registered a general accuracy higher than 90%, but because it is a model based on pre-established limits according to the spectral characteristics of the area analysed, this accuracy can change according to the study area. Concerning the models for detecting changes applied in Portugal, one can find the works of [42] to estimate the increase in vegetation biomass after fires in the north of the country, where the overall accuracy of 79.6% was recorded and of [31], in the search for vegetation cuts around the road network which yielded an accuracy of 79% for an annual temporal cut-off and 98% for a monthly temporal cut-off. The relatively low values associated with precision, recall and F1-score metrics obtained in our model are associated with the small number of true positive events found. This is related to false positive and false negative events. There are some possible explanations of why we found these false events. False positives are associated with: (i) when the cleansing of the vegetation was carried out after the field visit and audit carried out by the GNR. This can be attributed to the standards of items 5, 6, 12, and 13 of Article 15 of Decree-of-Law 124/2016, which say that in the case of fuel management is not done by 30 April the municipality council will be able to proceed itself with the management of fuel of the area; and (ii) the model may find statistically significant differences that are not related to the removal of vegetation per se. This can result from the natural reflectance of vegetation that is not constant and undergoes variations throughout the year. [PERSON] [34] presented intra-annual variation of the NDVI in confers, broad-leaved species and grassland in the north, centre and south of Italy, it is observed that the behaviour of the spectral signal was like that presented by [43] when analysing the annual variation of vegetation in the central part of mainland Portugal. Both studies, it is verified that there is a natural decay in the value of the NDVI in some types of vegetation in the period between January and September and this decay can be identified as alteration, despite the 60-day time window used to minimise these seasonal effects. Additionally, small signal noises or constant observations with the presence of clouds may interfere with the prediction of the model. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{3}{c}{**Statistical Metrics**} & **Matrix** \\ \hline  & **(C)** & **(NC)** & **(C)** & **(NC)** \\ \hline Precision & 36.36\% \(\pm\) 1,90\% * & 71.43\% \(\pm\) 1.78\% * & \multirow{2}{*}{(C)} & \multirow{2}{*}{20} & \multirow{2}{*}{35} \\ Recall & 22.73\% \(\pm\) 1,65\% * & 82.93\% \(\pm\) 1.48\% * & & \\ F1-score & 27.97\% \(\pm\) 1,77\% * & 76.75\% \(\pm\) 1.66\% * & & \\ Overall accuracy & 64.85\% \(\pm\) 1.88\% * & & & \\ \hline \hline \end{tabular}  
* Confidence Interval; C—Change; NC—Not Change.  
\end{table} 
Table 3: Validation with \(p\)-value \(\leq\) 0.0005. False negatives are associated with some characteristics of how fuel management should be carried out, as described in the Annex to Decree-of-Law 124/2016, which may not be captured by Sentinel-2. For instance, regarding isolated tree elements, which may be close to buildings, the spatial resolution of the data used limits the precise identification of the tree, making it difficult to identify this type of fuel management using Sentinel-2. Additionally, the removal of branches near the soil or cut the vegetation below the trees may not significantly interfere in the canopy of the vegetation fragment. This indicates that the variation of the NDVI may be small to be detected by the model. Regarding the grassland stratum [28] shows that the decrease in the NDVI after cutting the high grass is in the order of 0.2, but it recovers 0.1 in the period between 8 and 10 days  
Figure 3: Changes detected in: (**A**) Figueiro dos Vinhos, (**B**) Ilhavo, (**C**) Miranda do Corvo, (**D**) Penela, (**E**) Cantanhede, with \(p\)-value \(\leq\) 0.0005.  
after the cut.