## 3 Data and Methods
### Using MODIS NDVI Time Series to Track Vegetation Phenology  
[PERSON] et al. 2015 [23] showed that MODIS NDVI time series may be used to distinguish between ephemeral herbaceous understory vegetation and evergreen woody vegetation in Mediterranean environments. Using time series of the MOD13Q1 product, they demonstrated that the baseline of the NDVI time series corresponds to the signal coming from the trees, while the seasonal component of the time series mostly corresponds to the ephemeral herbaceous vegetation in the understory of the forests [23; 37].  
We used the technique proposed by [23] to find the relevant dates in which the woody vegetation mostly contributed to the VI signal. For that purpose, the NDVI derived from the 3-m and 10-m images (Planet and Sentinel-2A, respectively) were aggregated to the MODIS-pixel size and compared to the longer NDVI time series from MODIS (MOD13Q1) in the study area (Figure 2). Comparison was done using the Google Earth Engine platform after adding a constant value of 0.16 to the NDVI time series from Planet, due to a known shift towards lower values from Planet satellites [30].  
The MOD13Q1 product is based on a single day maximum value criteria and is considered to be of a high quality and very reliable for vegetation monitoring [38]. We used a robust, locally-weighted regression and smoothing scatterplots (lowess) technique, as in [51; 52], to reduce noise and uncertainties originating from cloud contamination and sensor artefacts (see green line in Figure 2).  
Figure 2 shows the seasonal growth cycle of the vegetation in the MODIS-NDVI time series. The minimum value at the beginning of the season corresponds to the signal coming from the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Formulation** & **Reference** \\ \hline NDVI 1 & NDVI = \(\frac{\rho(\lambda_{\text{NM}})-\rho(\lambda_{\text{Rad}})}{\rho(\lambda_{ \text{NM}})+\rho(\lambda_{\text{Rad}})}\) & [48] \\ GNDVI 2 & GNDVI = \(\frac{\rho(\lambda_{\text{NM}})-\rho(\lambda_{\text{Comm}})}{\rho(\lambda_{ \text{Num}})+\rho(\lambda_{\text{Comm}})}\) & [49] \\ GCC 3 & GCC = \(\frac{\rho(\lambda_{\text{Comm}})}{\rho(\lambda_{\text{Comm}})+\rho(\lambda_{ \text{Num}})+\rho(\lambda_{\text{Red}})}\) & [50] \\ \hline \hline \end{tabular} 1 NDVI is the normalized difference vegetation index; 2 GNDVI is the green NDVI; 3 GCC is the green chromatic coordinate index.  
\end{table}
Table 2: Formulation of satellite-based vegetation indices used in this study.  
trees (evergreen woody vegetation), while the seasonal signal of NDVI corresponds mainly to the ephemeral herbaceous vegetation growth and senescence. The fire of November 2016 is evident in the NDVI time series as a sharp decrease clearly seen from the NDVI time series derived from the three satellites (Figure 2).  
We used Figure 2 to select four dates that better represented the signal from the woody vegetation prior to the fire of November 2016 (Table 3). This, in order to avoid mixed-pixel signal from the herbaceous vegetation and soil background [53]. VIs calculated from the four images (dates) were used to derive stand density and woody biomass maps through calibration with plot-based stand density and tree diameter at breast height (DBH) measurements (Section 3.4). The image with the VI that best correlated with the aforementioned biophysical parameters was chosen to derive stand density and woody biomass maps. It was also used with the first available image following the fire to derive burn severity map. In the case of Sentinel-2A, this was ca. a month and a half after the fire, while first Planet image was available 11 days after the fire (Table 3).