## 2 Materials and Methods
### Analysis  
Monthly Landsat-8 and PlanetScope NICFI time series were analyzed for each blowdown. This time series started from the three months of available cloud-free images before the blowdown gap was first visible until the gap was not observed at all, or until May 2022. If there were clouds obscuring the blowdown gaps, the image was excluded from the analysis. To observe the vegetation signal recovery process, sample points and polygons were set for the forest gap areas. For samples, the mean NPV, GV, and shade fractions for all Landsat and PlanetScope NICFI time series images were then calculated, along with a 95% confidence interval (CI) around their mean values of three pre-blowdown images. In this way, the chronological changes in the signals and fractions of two satellite data were prepared, and comparisons were performed of the completeness of the change trajectory.  
For the image with each blowdown first visible and all images afterwards, the difference in the NPV of the sample pixels between them and the pre-blowdown images (i.e., \(\Delta\)NPV) was calculated. \(\Delta\)NPV\({}_{\text{first}}\) thus refers to the difference between the average NPV of the first post-blowdown image and of the three pre-blowdown images (\(NPV_{pre-mean}\)). This step allowed us to assess the magnitude of \(\Delta\)NPV right after the blowdown, that is, the signal closer to the \"true maximum change\", and to determine when the blowdown signals reached pre-disturbance levels. The higher the value of \(\Delta\)NPV\({}_{\text{first}}\), the more severe the damage to the forest [27]. In addition, it is also important to consider that blowdown-affected areas may suffer from delayed tree mortality and browning after the first visible image is acquired, so \(\Delta\)NPV may increase compared to \(\Delta\)NPV\({}_{\text{first}}\). To find the maximum \(\Delta\)NPV, we also search the \(NPV_{max}\) for each blowdown, which is the maximum NPV value of the blowdown among all months. The difference between \(NPV_{max}\) and \(NPV_{pre-mean}\) is the maximum \(\Delta\)NPV.  
We then recorded the \(\Delta\)NPV change after blowdowns. Instead of recording from the first month the blowdown was found, because the recovery of each blowdown can differ significantly, and starting from the most damaged month (highest \(\Delta\)NPV) provides a more accurate representation of the recovery process, the average of \(\Delta\)NPV for all blowdowns and 95% CI were then calculated for each time step, recording the decline process from their maximum value (i.e., 1) to pre-blowdown level.  
Similarly, to reflect the process of GV fraction recovery to pre-blowdown levels, the ratio was calculated:  
\[GV_{i}\ Ratio=\frac{GV_{i}}{GV_{pre-mean}}, \tag{2}\]  
where \(GV_{i}\) is the average GV value of the \(i\)th month from also the month when the blowdown had highest NPV (\(i\) = 0); \(GV_{pre-mean}\) is the average GV of the sample pixels of the three pre-blowdown images. The average and its 95% CI of the GV ratio was then calculated for all blowdowns, recording its increase to 1 from the month of the \(\Delta\)NPV maximum. When this ratio is greater than or equal to 1, the GV fraction has reached or exceeded the average pre-blowdown GV. This calculation is not the same as NPV because a simple ratio can reflect blowdowns' GV changes.