## 4 Discussion
In particular, in Figure 6A, two low AGBD values are visible corresponding with 16 and 17 YSLD, which coincides with a well-known drought event that occurred in 2003 in Italy and particularly affected copplice forests in Tuscany [80], to which the harvest dataset refers. The AGBD decrease captured by GEDI following 15 YSLD may be caused by a decrease in the vegetation photosynthetic activity and canopy condition related to the drought event, which may have resulted in a smaller amount of light reflected by the canopy resulting in the underestimation of the AGBD. This hypothesis is consistent with recent studies highlighting a major underestimationof dry forests' carbon stocks through multi-sensor biomass mapping [81]. Considering all forest disturbances predicted across Italy, fluctuations are expected because 3I3D also predicts disturbances that do not imply biomass changes, the occurrence of which change significantly among different years (Figure 5, left panel). For these reasons, in this study - because of the lack of more detailed information on different forest disturbances - it was not possible to define a clear relationship between YSLD and AGBD. On the other hand, future studies combining GEDI and forest disturbance products may define a benchmark of biomass increase following different kinds of forest disturbance occurring in different forest types. Our results indicate that the GEDI sensor can provide data related to biomass changes due to forest disturbances, information that promises to advance our understanding of such events. For example, some of the disturbances we predicted using 3I3D (e.g., snow avalanches or abiotic disturbances) do not imply a significant loss of AGBD immediately after the disturbance, as is instead the case with logging and wind damage (Figure 7). For these reasons, GEDI may represent a game-changer not just for biomass monitoring, but also for forest disturbance characterization. Our results also indicate that the maximum AGBD is reached at about 15 YSLD in harvests (Figure 6A) and other forest disturbances (Figure 6B). This peak appears earlier than expected. Such saturation may be related to the predominant forest coppice management. Coppice forests are characterized by a rapid biomass increase within the first 15 years after the disturbance [74]. Furthermore, according to forest regulation, standing trees that maintain forest cover are also released. This makes it difficult for the GEDI sensor to measure AGBD variations once the tree cover is re-established, even if it is fragmented [43; 44]. Finally, an in-depth evaluation of AGBD to forest disturbance would require automatic forest disturbance characterization using remote sensing imagery [82]. On the other hand, it remains challenging to identify the disturbance type automatically [38], and--despite several attempts [2; 56; 59; 83]--optical sensors do not allow good accuracies yet. This is especially true for Italian-managed forests, where natural disturbances are often superimposed by a management signal. An additional issue is that a precise, clear, and unique definition of forest disturbance is missing. For example, while FAO defines forest degradation as \"the reduction of the capacity of a forest to provide goods and services\", it is more than challenging to match this definition with forest disturbance products that can be derived with remotely-sensed data. On the other hand, such products are often the only ones available over large and remote regions of the globe. Furthermore, most ecologists tend to exclude natural disturbances from those which cause forest degradation [84], since natural disturbances can be considered part of forests' life cycle. However, such alterations have the potential to profoundly impact the ability of forests to provide ecosystem services to society over the years. Similarly, climate-driven change in disturbance regimes could exceed the capacity of forests to regenerate.