Exploring the relation between remotely sensed vertical canopy structure and tree species diversity in Gabon

Mapping tree species diversity is increasingly important in the face of environmental change and biodiversity conservation. We explore a potential way of mapping this diversity by relating forest structure to tree species diversity in Gabon. First, we test the relation between canopy height, as a proxy for niche volume, and tree species diversity. Then, we test the relation between vertical canopy structure, as a proxy for vertical niche occupation, and tree species diversity. We use large footprint full-waveform airborne lidar data collected across four study sites in Gabon (Lopé, Mabounié, Mondah, and Rabi) in combination with in situ estimates of species richness (S) and Shannon diversity (H′). Linear models using canopy height explained 44% and 43% of the variation in S and H′ at the 0.25 ha resolution. Linear models using canopy height and the plant area volume density profile explained 71% of this variation. We demonstrate applications of these models by mapping S and H′ in Mondah using a simulated GEDI-TanDEM-X fusion height product, across the four sites using wall-to-wall airborne lidar data products, and across and between the study sites using ICESat lidar waveforms. The modeling results are encouraging in the context of developing pan-tropical structure diversity models applicable to data from current and upcoming spaceborne remote sensing missions

Distinguishing vegetation types with airborne waveform lidar data in a tropical forest-savanna mosaic : a case study in Lopé National Park, Gabon

Tropical forest vegetation structure is highly variable, both vertically and horizontally, and provides habitat to a large diversity of species. The forest-savanna mosaic in the northern part of Lope National Park, Gabon, has a large and complex variation in vegetation structure along a successional gradient. The goal of this research is to assess whether large footprint full-waveform lidar data can be used to distinguish successional vegetation types based on their vertical structure in this area. Eleven vegetation metrics were derived from the lidar waveforms: canopy height, canopy fractional cover, total Plant Area Index (PAI) and vertical profile of PAI. The PAI profiles from airborne waveform lidar showed good agreement with those from Terrestrial Laser Scanning, sampled at eight field plots across different vegetation types (r(2) = 0.95, RMSE = 0.63, bias = 0.41). The agreement further strengthened our confidence that lidar waveforms can be used to distinguish between the five vegetation types, within the limits of the sampled structure, because TLS was known to provide distinct PAI profiles for these vegetation types. We then employed a Random Forest model, trained with 193 locations of known vegetation type, to classify the entire study area into five successional vegetation types (classification accuracy = 81.3%). The resulting predictive map revealed the overall spatial pattern of vegetation types across the study area. Our results suggest that lidar-derived vegetation profiles can provide valuable information on vegetation type and successional stage. This, in turn, can further help to improve habitat and biodiversity conservation and forest management activities