Stand validation of lidar forest inventory modeling for a managed southern pine forest

We evaluated area-based approaches (ABAs) to light detection and ranging (lidar) predictions of plot- and stand-level forest attributes (tree count, height, basal area, volume, aboveground biomass, broadleaf/conifer, and diameter at breast height -- diameter). ABA methods included post-stratification (PS), ordinary least squares (OLSs) regression, k nearest neighbors (kNN), and random forest (RF). This study was conducted on the Savannah River Site in South Carolina, USA. Plot- and stand-level predictions were validated against fixed-radius 0.04 ha (0.1 acre) plots in 49 approximate to 2.0 ha (5 acre) stands. Our findings demonstrate that lidar can be incorporated operationally into forest inventory systems to provide stand-level inferences for a wide range of forest attributes. Volume predictions for specific diameter classes, however, often fared poorly (root mean squared error (RMSE) > 100%) for the methods we explored, especially for larger (less common) diameter trees. Stand-level results were consistently better than pixel-level results (10-200+ percentage points). kNN and RF performed similarly and better than OLS and PS, but RF was the most robust to model configurations, while kNN has practical advantages such as simultaneous predictions of many attributes.

Automated summary

We evaluated several area-based approaches to predict forest attributes using lidar data, including post-stratification, ordinary least squares (OLS) regression, k nearest neighbors (kNN), and random forest (RF). The study was conducted in South Carolina, USA. The results showed that lidar can effectively provide stand-level inferences for a wide range of forest attributes, although volume predictions for specific diameter classes were often inaccurate, especially for larger diameter trees. kNN and RF performed similarly and better than OLS and PS, but RF was more robust while kNN had practical advantages in simultaneous predictions of multiple attributes.