## 5 Conclusions  
In this study on fragmentation, forest modelling was combined with remote-sensing measurements (lidar, radar) to investigate the changes in forest attributes at the footprint and landscape scales. Changes in biomass and productivity were especially observed in highly fragmented landscapes. Biomass values are particularly high in low-fragmented areas, while the net primary productivity values are higher in highly fragmented areas. The study shows that a high degree of forest fragmentation should be avoided in order to maintain large continuous forest areas, and thus the carbon sequestration potential of forests.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs16030501/s1](https://www.mdpi.com/article/10.3390/rs16030501/s1)]([https://www.mdpi.com/article/10.3390/rs16030501/s1](https://www.mdpi.com/article/10.3390/rs16030501/s1)), Figure S1: Frequency distribution of mean distance at footprint level with mean value, Figure S2: Density distribution of gross primary productivity and leaf area index for edge and core areas in the Amazon, Figure S3: Amazon map showing aboveground biomass in core areas based on combining GEDI measurements with forest modelling, Figure S4: Amazon map showing net primary productivity in core areas, Figure S5: Scatterplot of aboveground biomass, net primary productivity, gross primary productivity and leaf area index against distance to nearest non-forest area for forest in the Amazon (footprint level 25 m), Figure S6: Amazon map showing fraction of edge area in forests, Figure S7: Frequency distribution for mean gross primary productivity and leaf area index for forest in the Amazon (landscape scale, 10 \(\times\) 10 km\({}^{2}\)), classified according to landscape type (low fragmented, moderate fragmented, highly fragmented),Figure S8: Scatterplot showing the relationship between aboveground biomass and gross primary productivity for forest in the Amazon, Figure S9: Comparision of the three landscape types: low fragmented, moderate fragmented and highly fragmented with a satellite image, a radar image and an aboveground biomass map from this study.  
Conceptualization, A.H. and R.F.; methodology, L.B. and A.B.; software, L.B., A.B. and M.M.; validation, L.B., A.H. and R.F.; formal analysis, L.B., A.H., A.B., M.M. and R.F.; investigation, L.B., A.H. and R.F.; resources, L.B., A.H. and R.F.; writing--original draft preparation, L.B.; writing--review and editing, A.H., A.B., M.M. and R.F.; supervision, A.H. and R.F.; project administration, A.H. and R.F.; funding acquisition, A.H. and R.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Federal Ministry for Economic Affairs and Climate Action (BMWK), grant number 50 EE1911.  
Publicly available datasets were analyzed in this study. This data can be found here: [https://e4 ffl01.cr.usgs.gov/GEDI/](https://e4 ffl01.cr.usgs.gov/GEDI/) (accessed on 21 November 2021). The simulation data was generated with FORMIND. The forest model can be downloaded from [[https://formind.org/model/](https://formind.org/model/)]([https://formind.org/model/](https://formind.org/model/)) (accessed on 4 March 2023).  
We would like to thank the GEDI team led by [PERSON] for providing the GEDI data and for their feedback on our analyses. We also thank the TanDEM-X team for providing the forest cover maps. Furthermore, we would like to thank [PERSON] and [PERSON] for their support in comparing our results with other satellite data.  
The authors declare no conflict of interest.