## 1 Introduction  
Forest ecosystems cover approximately one-third of Earth's land surface [1]; they supply water, provide livelihoods, mitigate climate change, and are essential for sustainable food production [2, 3]. Forests also represent a strong regulating element in global biogeochemical cycles and the climate system [4], sequestering up to 60% of the human-made carbon emissions in the atmosphere [5]. On the other hand, forests nowadays must also cope with the anthropogenic intensification of stressors that affect their condition, either directly through logging and clearing or indirectly through climate change, pollution, and invasive species [6]. Natural disturbances, such as fires, insect outbreaks, and windthrows are an integral part of ecosystem dynamics in forests around the globe. Usually, they occur as relatively rare events and form characteristic regimes of typical disturbance frequency, sizes, and severity over extended spatial and temporal scales [7]. However, forest disturbance regimes are increasing over time due to climate change. In particular, extreme drought events, wind storms, and forest fires represent the most relevant changes in Mediterranean forest ecosystems [8]. Remote sensing (RS) data have a long history in mapping forest disturbances. Optical sensors, in particular, have been widely used to automatically map forest disturbancesacross the globe at different spatial scales through several different approaches, including (1) bi-temporal analyses of image pairs [9; 10; 11], (2) time series segmentation [12; 13], and (3) near-real-time monitoring of forest disturbances [14; 15]. Results indicate that simple bi-temporal methods are not suitable for large-scale monitoring because they depend too much on data availability and imply calibrations that are difficult to generalize [4]. Similarly, time-series segmentation approaches--even if independent of data availability--require a set of parameters as input that should be calibrated explicitly for each ecosystem [16; 17; 18]. The accuracy of time-series segmentation methods also decreases for near-past detection applications [19]. Near real-time monitoring using optical remote sensing imagery instead is a relatively novel topic [2; 20] and it is useful for quick assessment of relevant/extensive damages [21]. However, the achievable products often result in multiple inaccuracies, as the greater the promptness with which products are provided, the greater the expected number of errors in the maps [14]. To overcome these issues, an algorithm specifically conceived for Mediterranean ecosystems was recently developed (3I3D), which resulted particularly accurate in Tuscany [19], compared to more traditional approaches such as the Landrendr [12], the Global Forest Change map [22], and the Two Thresholds Method [21], which are three of the most sophisticated forest disturbance prediction algorithms. 3I3D was also recently used to map forest disturbance across Italy in 2018, and considerable accuracy was confirmed [20] as all the events in the reference dataset were detected - even with slightly smaller areas than the real ones. While optical sensors are useful for mapping forest disturbances, they are less useful for predicting tree heights. Some studies have tried to predict the three-dimensional (3D) structure of forests from Landsat images [23] or integrated optical data with synthetic aperture radar (SAR) data (such as ALOS-PASAR and Sentinel-1) [24]. Many studies have utilized modeling approaches to extend airborne laser scanning (ALS) data to communicate methods, outcomes, and accuracies and offer guidance on linking ALS metrics and ALS-derived forest attributes with broad-area predictors [25]. However, while the generation of spatially exhaustive lidar data would be useful for forest management purposes, lidar data predicted using satellite imagery are insufficiently accurate or reliable to drive decision-makers [26].