###### Abstract  
Blowdown events are a major natural disturbance in the central Amazon Forest, but their impact and subsequent vegetation recovery have been poorly understood. This study aimed to track post-disturbance regeneration after blowdown events in the Amazon Forest. We analyzed 45 blowdown sites identified after September 2020 at Amazonas, Mato Grosso, and Colombia jurisdictions using Landsat-8 and PlanetScope NICFI satellite imagery. Non-photosynthetic vegetation (NPV), green vegetation (GV), and shade fractions were calculated for each image and sensor using spectral mixture analysis in Google Earth Engine. The results showed that PlanetScope NICFI data provided more regular and higher-spatial-resolution observations of blowdown areas than Landsat-8, allowing for more accurate characterization of post-disturbance vegetation recovery. Specifically, NICFI data indicated that just four months after the blowdown event, nearly half of \(\Delta\)NPV, which represents the difference between the NPV after blowdown and the NPV before blowdown, had disappeared. \(\Delta\)NPV and GV values recovered to pre-blowdown levels after approximately 15 months of regeneration. Our findings highlight that the precise timing of blowdown detection has huge implications on quantification of the magnitude of damage. Landsat data may miss important changes in signal due to the difficulty of obtaining regular monthly observations. These findings provide valuable insights into vegetation recovery dynamics following blowdown events. blowdowns; tropical forests; spectral mixture model; Google Earth Engine; PlanetScope NICFI 27 April 2023  
1  
D.S.:P.:S.P.:S. the end of the century. The authors also show that the area of the Amazon Impacted by extreme storms that trigger large windthrows will likely increase by about 50%. In the more pristine central Amazon, the percentage of tree cover loss in area due to natural disturbance (e.g., windfalls, river meandering, drought effect, and natural fires promoted by lightning) has increased compared to other disturbances (e.g., anthropic fires, forest logging, and agro-industrial clearing for cropland) [3; 4]. In the past decades, some severe windstorms have proven their ability to alter forest landscapes, showing that wind is one of the main natural disturbances causing tree loss in the central Amazon [5; 6; 7; 8]. Quantifying the intensity of blowdowns and subsequent forest recovery has been an important topic in wind disturbance studies. Before the use of remote sensing, most studies on forest disturbance damage were based on traditional repeated field surveys [9; 10]. Although surveys provide detailed and accurate local information on blowdown damage and vegetation recovery, their conduction in remote areas of the Amazon is time-consuming and expensive. Furthermore, the implementation of field surveys cannot occur well after the disturbance occurrence, affecting the quality of the investigation [11; 12; 13]. Another commonly used traditional approach is based on aerial surveys. It may use aerial photographs or lidar point clouds to map tree mortality over large areas based on canopy damage, which may provide practical details on the mechanisms of tree death [14; 15; 16; 17; 18; 19]. However, the lack of systematic data collection by these systems and substantial uncertainties associated with light conditions, flight height, viewing angles, and terrain affect the mapping accuracy when using aerial photographs [20; 21; 22]. In addition to the prohibiting cost of lidar data acquisition, these factors make these approaches not feasible for blowdown damage and recovery assessment. In contrast, satellite sensors allow for repeated data collection at a broader spatial and temporal scale. In this context, studies have used optical remote-sensing imagery to observe blowdown events, making progress in assessing blowdown size distribution and frequency [5; 6; 23; 24; 25]. Much progress has also been made in modeling tree mortality and carbon loss due to blowdowns [5; 26; 27; 28].