## 4 Discussion
The reason is that the continuous cloudless images make PlanetScope NICFI data easier to identify the true maximum change. For instance, we had an average of 12.9 PlanetScope NICFI images available in 15 months of analysis. In contrast, Landsat-8 had a much lower number of cloud-free images (average 5.1 in 15 months). The first available post-blowdown image of Landsat-8 was found on average after \(\sim\)1.1 months; therefore, recovery is already in place and the maximum change can be lost. As we already presented, our analyses show that \(\Delta\)NPV decreases by 28.47% in the first month post-disturbance. For example, in a study of tree mortality inthe Amazon Forest following a squall line event in 2005 [27], the first image captured after the blowdown occurred 6 months later, potentially underestimating tree mortality due to rapid growth of pioneer species that covered the dead trunks and branches, decreasing the NPV fraction [25; 27; 51]. According to our analyses, after six post-disturbance months, the \(\Delta\)NPV signal may have recovered an average of around 60%. Therefore, while models relating \(\Delta\)NPV to tree loss may work well if calibrated locally for specific sets of images, they would not be easily applied to other areas if imagery is collected with a different time since disturbance. Additionally, the spatial resolution of the satellite sensor used for mapping blowdowns can affect the accuracy of the estimates. In particular, we observed that Landsat-8 OLI tends to overestimate the area of disturbance, especially when using a lower threshold (\(\Delta\)NPV \(>\) 0.3) to capture more subtle changes. This overestimation may be due to the coarser resolution of Landsat-8 data compared to PlanetScope NICFI. However, when a more conservative or higher threshold (\(\Delta\)NPV \(>\) 0.5) was used, therefore mapping only the more severely damaged areas, the area mapped by Landsat-8 and PlanetScope NICFI was similar. It is important to consider these differences in mapping blowdowns for accurate estimation of the extent of disturbance and its impact on ecosystem dynamics. Further research is needed to investigate the effect of spatial resolution on other ecosystem disturbances and to determine the appropriate threshold values for different satellite sensors. Similarly, the use of high-spatial-and-temporal-resolution data is important for further estimating the loss of biomass and carbon in forest ecosystems caused by wind disturbances. Previous studies have focused on the loss of carbon and biomass caused by blowdowns [6; 25; 54]. However, using open optical imaging data such as Landsat-8 and Sentinel-2 to identify forest disturbances may inaccurately estimate tree mortality rates and the loss of biomass and carbon. In contrast, higher-spatial-and-temporal-resolution data can detect smaller blowdowns and potentially obtain more usable images closer to the most severe disturbance, enabling more accurate estimation of these damages. Against the backdrop of global warming, windthrow density in the Amazon region may further increase [2], which could result in more blowdown events in the future. With the goal of carbon neutrality in mind, the increase in blowdowns will undoubtedly increase the pressure on carbon sequestration in the Amazon Forest. Accurately measuring the loss of biomass and carbon after blowdowns in the future could be helpful for forest conservation policies and carbon offset programs. For all the 45 blowdowns, we show that the spectral response of vegetation recovers on average after about 15 months since disturbance (Figures 7 and 8). It is expected that the small gaps recover faster than the large ones, which, although not explicitly quantified, can be seen in the example of Figure 9. This is in line with previous studies estimating recovery time to be around one year [25; 27; 29].