Accuracy of small footprint airborne LiDAR in its predictions of tropical moist forest stand structure

We predict stand basal area (BA) from small footprint LiDAR data in 129 one-ha tropical forest plots across four sites in French Guiana and encompassing a great diversity of forest structures resulting from natural (soil and geological substrate) and anthropogenic effects (unlogged and logged forests). We use predictors extracted from the Canopy Height Model to compare models of varying complexity: single or multiple regressions and nested models that predict BA by independent estimates of stem density and quadratic mean diameter. Direct multiple regression was the most accurate, giving a 9.6% Root Mean Squared Error of Prediction (RMSEP). The magnitude of the various errors introduced during the data collection stage is evaluated and their contribution to MSEP is analyzed. It was found that these errors accounted for less than 10% of model MSEP, suggesting that there is considerable scope for model improvement. Although site-specific models showed lower MSEP than global models, stratification by site may not be the optimal solution. The key to future improvement would appear to lie in a stratification that captures variations in relations between LiDAR and forest structure.

► We used ALS over 129 one-ha tropical forest plots across four sites in French Guiana.
► Statistics from the Canopy Height Model were used in multiple regression models.
► Root Mean Square Error of Prediction (RMSEP) of basal area was 9.6%.
► Errors affecting data used in model building are account for less than 10% of RMSEP.
► Most of MSEP stems from local variation in LiDAR to forest structure relations.