Evaluating the Potential of Full-waveform Lidar for Mapping Pan-Tropical Tree Species Richness

Aim: Mapping tree species richness across the tropics is of great interest for effective conservation and biodiversity management. In this study, we evaluated the potential of full-waveform lidar data for mapping tree species richness across the tropics by relating measurements of vertical canopy structure, as a proxy for the occupation of vertical niche space, to tree species richness. Location: Tropics Time period: Present Major taxa studied: Trees Methods: First, we evaluated the characteristics of vertical canopy structure across 15 study sites using (simulated) large-footprint full-waveform lidar data (22 m diameter) and related these findings to in-situ tree species information. Then, we developed structure-richness models at the local (within 25-50 ha plots), regional (biogeographic regions), and pan-tropical scale at three spatial resolutions (1.0, 0.25 and 0.0625 ha) using Poisson regression. Results: The results showed a weak structure-richness relationship at the local scale. At the regional scale (within a biogeographical region) a stronger relationship between canopy structure and tree species richness across different tropical forest types was found, for example across Central Africa and in South America (R2 ranging from 0.44-0.56, RMSD ranging between 23-61%). Modelling the relationship pan-tropically, across four continents, 39% of the variation in tree species richness could be explained with canopy structure alone (R2 = 0.39 and RMSD = 43%, 0.25 ha resolution). Main Conclusions: Our results may serve as a basis for the future development of a set of structure-richness models to map high resolution tree species richness using vertical canopy structure information from the Global Ecosystem Dynamics Investigation (GEDI). The value of this effort would be enhanced by access to a larger set of field reference data for all tropical regions. Future research could also support the use of GEDI data in frameworks using environmental and spectral information for modelling tree species richness across the tropics