## 4 Discussion  
The three-dimensional structure of forests is an essential component for assessing changes in forest biomass due to human activities or natural disturbances. Structural data are also a key component in assessing forest habitat quality and biodiversity at local and regional scales [35], and assessing forests' capacity to provide ecosystem services such as water regulation and soil protection [75]. Remote sensing holds a relevant part in forest monitoring, as disturbance maps can be predicted using automatic procedures and optical sensors images. Accordingly, in this contribution, we used Landsat BAP composites [55] and the 3I3D algorithm [19] to predict almost 2 million forest disturbances that occurred in Italy in the period 1985-2019. An in-depth performance evaluation of the 3I3D algorithm is presented in [PERSON] et al. [19; 20]. Commission errors reported in this paper are overestimated because 3I3D predicts several forest disturbance, while the reference dataset includes just forest harvestings. Similarly, as previously demonstrated [15; 56], most omission errors coincided with forest disturbance correctly predicted, but with slightly smaller boundaries. To overcome this issue, a reference dataset of sample points may be preferred to polygons [20]. Either way, as expected, Landsat data provided crucial information on forest disturbances across the whole of Italy over a historical period of about four decades (Figure 4). The key years 1985 and 1990--characterized by very intensive frost events [76]--and 2017--characterized by several forest fires [77; 78]--were correctly predicted as the years with the largest forest disturbance areas (Figure 5). A well-known limitation of optical sensors is that they do not accurately capture biomass changes since they can measure spectral properties but not 3D forest structure. In this work, we demonstrated that the new GEDI mission and, most importantly, the very new GEDI AGBD product, provide global information that can consistently advance our understanding of forests and forest biomass changes. Accordingly, we found good correspondence (r\({}^{2}\) = 0.75) between the groups of GEDI and ALS data in Italian forests. These results suggest that GEDI--even if not available wall-to-wall--provides data that may complement incomplete ALS datasets. GEDI data--with more than 6 million pulses acquired in 2019 and 2020--provided new information for 3% of Italian forests. In particular, the regions with the larger percentage of forests for which AGBD data were available were Calabria, Sicily, Campania, and Molisie, with more than 4.0% of the forest area covered. Even for regions with the largest forest area, such as Tuscany and Sardinia, the percentage of forests with AGBD data were 2.7% and 2.2%, respectively. On the other hand, the regions with the smaller availability of ABGD data (<2.0%) were Umbria and Marche (Figure 2). Such data coverage is far from providing wall-to-wall information. However, combining GEDI pulses at different spatial scales--as is usually done with forest inventory data--may provide aggregated statistics of different forest variables, such as AGBD and forest height [79]. Future studies may further explore the usage of GEDI data in such directions since the percentage of forests covered by GEDI will still increase day by day, as long as GEDI continues to operate. We showed that GEDI provides new data for quantifying biomass changes due to forest disturbances. Our results indicate that the AGBD product correctly captured the expected biomass increment due to post-disturbance forest recovery (Figure 6). Fluctuations of AGBD values over time were recorded as expected due to harvestings in different forest species or forests of different ages, and thus, with different biomass. Results also indicate the possible limitations of the GEDI sensor.