Emerging technologies for biodiversity assessment of changing tropical forests

Increasing anthropogenic pressure leads to habitat loss of tropical forests through deforestation and forest degradation. Tropical forest-dependent species are threatened with such disturbances that alter the complexity of their habitat. Measuring the structural configuration and diversity of tropical forest habitats will help explain the state of forest degradation and the resulting biodiversity dynamics. Biodiversity dynamics due to natural and anthropogenic disturbances are mainly monitored using conventional field survey approaches. However, these approaches often fall short at addressing complex disturbance factors and responses at different spatiotemporal scales. The integration of novel monitoring approaches such as satellite remote sensing, terrestrial LIght Detection and Ranging (LiDAR), and high-throughput DNA metabarcoding have the potential to improve the detection of subtle tropical forest disturbances and responses of species to changing tropical forests, which are largely unknown. This thesis’ aim is to investigate the application of emerging satellite remote sensing and in-situ measurements to assess the complex forest biodiversity dynamics in changing tropical forests. A particular focus is given to the use of terrestrial LiDAR and satellite remote sensing for deriving forest structure parameters that inform on the state of different tropical forest habitats. For this purpose, field plots were established in the UNESCO Kafa biosphere reserve (KBR), Ethiopia. The study has identified the complementarity between remote sensing and in-situ measurements, on the bases of the primary biodiversity attributes and the essential biodiversity variables; demonstrated that the impacts of disturbance on forest structure can be captured with terrestrial LiDAR measurements; assessed the sensitivity of satellite remote sensing derived parameters to field measured structural variables; and demonstrated that the influence of forest habitat conditions on leaf-litter-arthropod composition can be identified by linking forest structure parameters that are derived from remote sensing and conventional measurement with DNA metabarcoding diversity dataset. This thesis provides a scientific contribution to the exploration of integrating technological advancements in remote sensing and in-situ measurements to derive information that is essential for assessing forest biodiversity change.