## 1 Introduction
For these reasons, forests' 3D structure is usually measured using ALS sensors [27; 28; 29], implying large survey and processing costs. As a result, updated information related to tree height is often not available wall-to-wall and over large areas but is constrained in both time and space. Consequently, estimates of the magnitude and distribution of aboveground carbon in Earth's forests remain uncertain. On the other hand, knowledge of forest carbon content at a global scale is critical for forest management in support of climate mitigation actions. Consistent and large-scale monitoring of forest height is essential for estimating forest-related carbon emissions, analyzing forest degradation, and quantifying the effectiveness of forest restoration initiatives. The Global Ecosystem Dynamics Investigation (GEDI) sensor offers unique opportunities to improve forest biomass mapping. It is the first space-based mission specifically conceived for retrieving the vertical structure of vegetation. GEDI was launched at the International Space Station in late 2018 and has been collecting unique data on vegetation structure since April 2019. GEDI products are provided at different preprocessing levels that allows the derivation of a variety of forest products, such as foliar canopy profiles, leaf area index (LAI), sub-canopy topography, and canopy height. GEDI promises to advance our understanding of forests. In one of the most groundbreaking works regarding the GEDI sensor, canopy height data were used together with Landsat imagery to create a 30 m spatial resolution global forest canopy height map for the year 2019 with RMSE ranging between 6.6 m--estimated using validation GEDI data--and 9 m--estimated using available lidar data [30]. [PERSON] et al. [31] integrated GEDI measurements with optical satellite observations, to produce maps at a 1 km resolution of US canopy cover (r\({}^{2}\) = 0.79; RMSE = 0.09), plant area index (r\({}^{2}\) = 0.76; RMSE = 0.41), foliage height diversity (r\({}^{2}\) = 0.83; RMSE = 0.25), and tree height (r\({}^{2}\) = 0.8; RMSE = 3.35 m). [PERSON] et al. [32] presented a different usage of the GEDI data by predicting multi-layer fuel load in the Brazilian tropical savanna. On the other hand, most of the research focuses on simulated data despite the few studies we mentioned using GEDI data. For example, simulated data were used to compare biomass estimates from GEDI, NISAR, and ICESat-2 over California (Sonoma County), indicating greater precision in GEDI, with RMSEs of 57%, 75%, and 89%, respectively [33]. In the same study area, also using simulated data, [PERSON] et al. [34] applied a multi-sensor (GEDI, ICESat-2, and NISAR) fusion approach to producing more accurate (RMSE of 52%) and spatially-complete aboveground biomass maps [33]. While the potential of the GEDI sensor still needs to be further explored, an additional GEDI product was recently released (Level 4) consisting of aboveground biomass density (AGBD) estimates for the period 18 April 2019 to 2 September 2020 [35; 36]. To simplify the usage of GEDI AGBD data, an R package (GEDI4R) was recently developed [37] that provides functions for downloading, reading, clipping, exporting, and visualizing GEDI Level 4A data. Although GEDI Level 4 data is of great interest, to date, no studies exploiting these data have been developed. In particular, while one of the principal objectives for which the GEDI sensor was conceived is to \"_provide answers to how deforestation has contributed to atmospheric CO\({}_{2}\) concentrations_\" ([[https://gedi.umd.edu/](https://gedi.umd.edu/)]([https://gedi.umd.edu/](https://gedi.umd.edu/)), accessed on 5 January 2022), currently, no study has analyzed the capability of GEDI to monitor biomass changes due to forest disturbances. This study aims to assess GEDI AGBD data across Italy and its use to study the rate of biomass increase following forest disturbances, which is crucial considering that forest disturbances are expected to increase due to climate change [2; 38].