## 1 Introduction  
The Amazon rainforest is the largest intact tropical rainforest on Earth and stores a huge amount of carbon [1]. Yet, it is still considered a carbon sink [2, 3], although first studies show a negative trend due to ongoing deforestation [3]. Deforestation not only leads to habitat loss and high carbon emissions, it also leads to a fragmented landscape of forests [4, 5]. In South America, deforestation has been observed at a rate of 0.3% since 2000 [5; 6; 7]. Therefore, the forests in these fragmented landscapes are partially degraded [8]. The main cause of forest degradation is through forest edge effects driven by forest fragmentation [9]. The microclimate at the forest edge changes and thus forest dynamics are altered. The negative consequences of forest fragmentation in the first 100-300 m from the edge are losses in species diversity, increased tree mortality, less biomass and increased CO\({}_{2}\) emissions due to mortality and degradation [10; 11; 12].  
Ongoing deforestation causes more forest edges, and therefore increased CO\({}_{2}\) emissions [1; 4]. Model projections revealed that by 2100, 50% of the tropical forest area will be at the forest edge, resulting in additional carbon emissions of up to 500 million tons of carbon per year [5].  
Since forest fragmentation has been accelerating for many decades, the edge effects should be visible in large parts of the Amazon. Previous studies estimated biomass changes in forests for the whole tropics at a coarse spatial resolution of 500 m [13]. They found that biomass was reduced in the first 500 m by ca. 25%. One explanation for this decline could be the changes in forest dynamics at forest edges, but this is difficult to assess due to the coarse resolution of the used satellite products.  
To sum up, changes in the forest edges were observed either locally (with empirical data or lidar campaigns) or globally with low spatial resolution (500 m-1000 m). Since the forest edge effects only occur in a range of 100 m to 300 m, it is clear that a resolution of 500 m is not sufficient for such analysis. Furthermore, the use of passive optical satellite data are not necessarily appropriate for measurements in the dense vegetation of the Amazon and detecting forest degradation [14]. Active systems such as lidar are better suited for this, such as the lidar satellite mission GEDI [14].  
By linking such lidar data with an individual-based forest model, various forest properties can be directly examined at the footprint level of the lidar measurement (here, 25 m), such as biomass and productivity [3; 15; 16]. In addition, radar measurements make it possible to distinguish between areas where the forest is currently still present and where the forest has previously been lost [17]. Such forest/non-forest maps (here from TanDEM-X with 50 m resolution) help to assess the current state of forest fragmentation [17]. By combining the radar-derived forest maps and high-resolution lidar remote sensing data with an individual-based forest model, the following questions can be answered:  
(1) How does forest biomass and productivity differ in the edge and core area of forest stands throughout the whole Amazon?  
(2) How strong is the effect of fragmentation on forest biomass and productivity for forest landscapes?  
To answer these questions, the forest model FORMIND is used. Lidar data from the GEDI mission for the entire Amazon rainforest was integrated into this forest model. To determine the degree of fragmentation, we evaluated forest/non-forest maps from the TanDEM-X mission.  
We suspect considerable differences in biomass and productivity depending on whether a forest stand is located on the edge or in the intact core area of a forest area. This should also lead to considerably lower biomass and productivity values in highly fragmented landscapes.