Assessment of Forest Structure Estimation from Terrestrial LiDAR in Fire-Affected Areas

This study evaluated the performance of terrestrial LiDAR (TLS) for post-fire forest inventory across two large wildfires in Spain as a function of burn severity. We analyzed tree-level diameter at breast height (DBH), plot-level above-ground biomass (AGB), and the influence of burn severity on return intensity. DBH of segmented trees was accurately retrieved across severities, with overall accuracies of 92.1%, 95.0%, and 94.4% and RMSE of 1.19, 0.94, and 0.93 cm in unburned, moderate, and severe plots, respectively (rRMSE = 7.97%, 6.46%, 6.94%). AGB showed lower agreement, with accuracies of 93%, 88%, and 74%. After adjusting by quadrant-level biomass consumption, mean post-fire AGB values were 76.29, 65.07, and 32.90 Mg ha−1, with mean absolute errors of 4.55, 6.38, and 6.11 Mg ha−1. Return intensity decreased with burn severity, reducing the number of returns by 14.9% in moderately burned and 54.3% in severely burned plots. These results support the use of TLS for post-fire forest inventory in low-to-moderate severity conditions. However, in high-severity plots, return intensity reduction limited tree segmentation and DBH extraction, introducing uncertainty in plot-level AGB estimation.