## 4 Discussion  
Monitoring of the forest is a fundamental task in fire prevention. Not only is it important to characterize the land cover, but also to understand what is changing and why it is happening, or to validate the execution of land management operations. As is known and verified during this study, the phenology is a major concern in change detection problems, since it represents transitions in the time-series without occurring any modification in the coverage. Additionally, when earth observation satellites such as Sentinel-2 are used, the variations in the luminosity add more background noise to the data. Another concern about using optical satellites is its sensitivity to adverse atmospheric  
Figure 14: TCI observations before and after maintenance operations. 1-Seia; 2-Fundão; 3-Serta; 4-Serra dos Candeeiros; 5-Marisol.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Number of Cases** & **Wrong Detections** \\ \hline
0\%–25\% & 3 & 0 \\
25\%–50\% & 4 & 0 \\
50\%–75\% & 2 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of the incomplete operations detection.  
conditions, which leads to greater periods without available observations. Finally, a methodology to detect changes in any kind of forest needs to handle the different behaviour of vegetation types.  
The results from the feature selection revealed that the better indices for the detection were based on the visible spectrum, namely ExR and ExR, which is understandable, since an operation is easily visible in TCI observations. Additionally, a further analysis of the time-series shows that these indices are more robust of the seasonal effects. This showed the importance of exploring other regions from the electromagnetic spectrum in remote sensing.  
Compared with previous works, one of the achievements of this work was to achieve a monthly timestep for the change detection. This is important, since in [12; 16; 17], several observations were needed, and by using Landsat more than one month was needed to detect changes. In [13], annual composites were used, which only enabled the creation of annual change maps and in [15], the used observations were spaced by more than one month. The drawback was that the use of less data led to the occurrence of false positives (due to less confirmation steps) and the imposition of a month timestep obliged to estimate month values whenever there were no usable observations. In this work the error is approximately the same as in [12; 13; 15; 16; 17], but was tested in geographically separated areas, with different kinds of vegetation and during two full years, representing a more uncontrolled environment. In [10], the composites were generated in specific seasons of the year, avoiding the phenology effects. The proposed ANN was trained to detect only a specific kind of change, without discriminating changes as in [15; 16; 17].  
The two steps for the noise reduction (object-based analysis and the application of noise filters) improved the quality of data and reduced false detections. Additionally, for filtering, two approaches were presented. The mean filter revealed more robustness to the false positives, although less ability to identify the desired events than the median filter. This is verified by the lower recall and higher precision of the mean approach. Although these methods helped to separate the phenological trends from the maintenance operations, misclassifications are still occurring. Since, in this case, it is very important to avoid the false positives, the mean filter was found to be a better solution.  
The results in the test datasets were also promising, since in Marisol the expected operations were detected. Additionally, the results in the incomplete FBs test verified that this methodology could distinguish a complete operation from an incomplete. Although there is not a classification of the quality of the operation, if an operation is detected, it is known with good certainty that it was a correct operation.