Assessing the accuracy of GEDI for mapping resilience in the Amazon rainforest along a gradient of disturbance to recovery

Understanding the resilience of tropical vegetation, its ability to recover from disturbance, is fundamental to assess future responses to environmental and climatic fluctuations. The Amazon rainforest has been identified as a potential tipping element in the Earth’s climate system and there is mounting concern over its persistent degradation. Extreme climate events and continued logging, forest fire and fragmentation threaten the Amazon’s structural integrity and its role as a carbon sink, with remotely sensed data providing observational evidence of resilience loss since the early 2000s. Fragmentation and degradation of tropical forest is suggested to slow recovery from perturbations, ensuing a potential to destabilise the rainforest and cause widespread transition from forest to savanna-like ecosystem state.

Remotely sensed LiDAR data provides a structural blueprint of forest canopy. The Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR characterises a new era of large-scale forest height quantification, with capabilities to further understand forest structure, and therefore forest response to perturbation across the entire Amazon. Although GEDI’s capabilities have been realised in boreal forest early disturbance monitoring, and to assess growth rates of tropical secondary forest, research thus far is yet to assess its ability to identify tropical forest of various degradation and recovery including logged, burned and fragmented over increasing timescales of recovery. Forest degraded by burning is characterised by different structure than selectively logged, or edge forest, and validating the ability of GEDI to represent these states is essential for identifying alternative forest states.  

Here, we investigate the potential of the GEDI LiDAR mission to map tropical forest along a gradient of degradation to recovery. A combination of ground data, MapBiomas secondary forest and burned area products are utilised to classify perturbed forest. We then assess the correspondence of GEDI waveform metrics including relative height and canopy cover, extracted from 2A and 2B products using the newly developed R package ‘chewie’, with airborne LiDAR across the Brazilian Amazon. This research will inform further tropical forest alternative-state study, whilst the assessment of GEDI’s structural capability to represent degraded forest types provides valuable information for forest restoration status to support post-degradation management strategies.