## 2 Materials and Methods
### Materials and Datasets  
The data used were the FB shapefiles, provided by ICNF, and the Sentinel-2 images. The Sentinel-2 images are Level 1C (L1C) products, since Level 2A (L2A) products only became available since March of 2018. The L1C imagery is Top-of-Atmospher radiance of each band, while L2A has an additional correction corresponding to the Bottom-of-Atmospher radiance. From the available Sentinel-2, observations only bands with a spatial resolution of 10m and 20m were used. Images with 60m spatial resolution were not used, due to the small width of an FB. The bands analysed were: B02, B03, B04, B05, B07, B08, B8A, B11 and B12. Besides the spectral bands, several vegetation and other spectral indices were calculated; (Table 1): normalized difference moist index (NDMI), normalized difference vegetation index (NDVI), ratio vegetation index (RVI), normalized multi-band drought index (NMDI [18]), normalized difference index (NDI), excess of green (ExG), excess of red (ExR), excess of green minus excess of red (exgr) and modified excess of green (MExG) [19]. The NDVI and NDMI indices have several applications for change detection in the vegetation [20; 21]. Additionally, NDI highlights the distinction between vegetation and soil land cover types [22]. The visible spectrum indices ExG, ExR, ExGR and MExG also evidence good results in the separation of vegetation from the background [23; 24]. Figure 3: Example of a maintenance operation on an FB (red) in Serra dos Candeeiros, as seen by Sentinel-2 Band04 images. (**a**) before operation (08-05-2017); (**b**) after operation (04-07-2017). The vegetation has a typical dynamic throughout the whole year. Due to phenology, the spectral reflectance of the vegetation is not constant. There are some trends that can be observed in the different seasons of the year, particularly in the summer and in the winter. The influence of these cyclical effects in vegetation areas is shown in Figure 4. Note that Portugal has a Mediterranean climate, which implies four well defined seasons. This enhances phenology dynamics, a drawback for the detection of the maintenance operations. This is because a change detection methodology looks for variations in data, so it is important to understand if the disturbance occurs due to phenology or a maintenance operation. In Figure 5 are presented two vegetation indices time-series where the two kinds of transitions are shown. It may also be concluded that there are indices more robust to the phenology, for instance, in Figure 5b with ExG, the operation is more evident. Due to cloudiness and snow (Fundao and Seia) some of the observations were not clear, winter months being the most affected ones. In Table 2, the number of clear observations per study area and year, number of FBs and the months defined for the operations are shown. From the study areas, a total of 14 FB and 9 VEG areas were defined. The maintenance operation or implementation dates were given by ICNF and confirmed by visual inspection of Sentinel-2 imagery. \begin{table}
\begin{tabular}{c c} \hline \hline
**Index** & **Equation** \\ \hline Normalized Difference Moisture Index (NDMI) & \(\frac{\text{B08}-\text{B11}}{\text{B08}+\text{B11}}\) \\ Normalized Difference Vegetation Index (NDVI) & \(\frac{\text{B08}-\text{B04}}{\text{B08}+\text{B04}}\) \\ Ratio Vegetation Index (RVI) & \(\frac{\text{B04}}{\text{B08}}\) \\ Normalized Multi-Band Drought Index (NMDI) & \(\frac{\text{B8A}-\text{B11}-\text{B12}}{\text{B8A}+\text{B11}-\text{B12}}\) \\ Normalized Difference Index (NDI) & \(128\times\left(\frac{\text{B03}-\text{B04}}{\text{B03}+\text{B04}}+1\right)\) \\ Excess of Green (ExG) & \(2\times\text{B03}-\text{B04}-\text{B02}\) \\ Excess of Red (ExR) & \(1.3\times\text{B04}-\text{B03}\) \\ Excess of Green minus Excess of Red (ExGR) & ExG – ExR \\ Modified Excess of Green (MExG) & \(0.441\times\text{B04}-0.811\times\text{B03}+0.383\times\text{B02}+18.78745\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Spectral indices equations.