## 4 Discussion
### Relationship between Forest Dynamics and Structure  
In this study, it was found that the individual-based forest model in combination with the GEDI data was well suited to investigate the vertical and horizontal structure of the forest. Thereby, hidden relationships between dynamics and structure became evident. As [PERSON] et al. [12] already mentioned, a combination of remote sensing data with forest models provides a better understanding of forest growth and the impact of canopy height complexity [12].  
Here, it was found that forests with medium aboveground biomass and a heterogeneous horizontal structure have lower productivity than forests with a homogeneous horizontal structure. Heterogeneous canopy height structure probably results from a mixture of old and young forest patches (or disturbed and undisturbed), consisting of large trees shading smaller trees and some young high-productivity trees. Homogeneous canopy height structures are composed of many medium-aged trees, which are associated with high GPP [42].  
The extensive dataset of the GEDI mission made it possible to generate a detailed forest height map. Due to the high resolution of the lidar data, spatial patterns could be recognized. Between both the Rio Negro and Solimoes rivers, as well as in the transitional forests from the southern part of the Amazon to the Arc of Deforestation, medium tree heights from 20 to 35 m could be seen. Likewise, the significantly higher tree areas in the northeast and western Amazon were clearly visible with tree heights from 30 to 45 m. These results were also noted in the research of [PERSON] et al. [43]. In [43], the structure of the forest was described and studied using canopy height. The canopy height (RH90) from the ICESat mission was used in [43] to show the distribution of tall trees, potential gradients, and large spatial variability. The results are therefore comparable, although the extensive dataset of the GEDI mission made it possible to resolve significantly more lidar waveforms per km\({}^{2}\), which could lead to more accurate results.  
[PERSON] et al. [44] used ground plots to calculate the aboveground biomass and, consequently, total carbon stock in the Amazon. They compared results from their ground plot with remote sensing results generated in [11; 43]. [PERSON] et al. [44] criticized the mismatches between ground plots and remote sensing maps. There are severe under- or overestimations of aboveground biomass. They [44] argued that interpretation from remote sensing data using single relationships between tree canopy height and aboveground biomass led to large, spatially correlated errors. This challenge was addressed in this study, and we determined the forest parameters, such as the AGB, GPP, and NEE, of the Amazon by linking over 110 million GEDI waveforms with the forest model FORMIND. For this, we did not just use a single metric describing the tree canopy height; instead we used the complete GEDI waveform at each GEDI shot position to determine the forest state. In our study, the entire information from the waveform was used, which substantially improved the description of the current forest state and thus reduced ambiguities (see, e.g., [19]).  
For future investigations, in order to follow up on this analysis and to examine the Amazon-wide forest structure and parameters in more detail, it makes sense to also analyze the horizontal structure of the forest at a high resolution. There are already radar missions that can detect the horizontal forest structure from wall to wall (e.g., TanDEM-X and BIOMASS) [45; 46]. By linking GEDI data, radar data, and forest models, it is possible to conduct a detailed investigation of forest structure in order to explore new, yet unknown forest parameter connections.