Hierarchical integration of individual tree and area-based approaches for savanna biomass uncertainty estimation from airborne LiDAR

Understanding the role that the vast north Australian savannas play in the continental carbon cycle requires reliable quantification of their carbon stock at landscape and regional scales. LiDAR remote sensing has proven efficient and accurate for the fine-scale estimation of above-ground tree biomass (AGB) and carbon stocks in many ecosystems, but tropical savanna remain under studied. We utilized a two-phase LiDAR analysis procedure which integrates both individual tree detection (ITC) and area-based approaches (ABA) to better understand how the uncertainty of biomass estimation varies with scale. We used estimations from individual tree LiDAR measurements as training/reference data, and then applied these data to develop allometric equations related to LIDAR metrics. We found that LiDAR individual tree heights were strongly correlated with field-estimated AGB (R² = 0.754, RMSE = 90 kg), and that 63% of individual trees crowns (ITC) could be accurately delineated with a canopy maxima approach. Area-based biomass estimation (ABA), which incorporated errors from the ITC steps, identified the quadratic mean of canopy height (QMCH) as the best single independent variable for different plot sample sizes (e.g. for 4 ha plots: R² = 0.86, RMSE = 3.4 Mg ha^{− 1}; and 1 ha plots: R² = 0.83, RMSE = 4.0 Mg ha^{− 1}). Our results show how ITC and ABA approached can be integrated to understand how biomass uncertainty varies with scale across broad landscapes. Understanding these scaling relationships is critical for operationalizing regional savanna inventories, monitoring and mapping.