Shrub detection using disparate airborne laser scanning acquisitions over varied forest cover types

We explore the possibility of extending the national forest inventory-based point data of understory presence using region-wide, disparate lidar data for the southeastern USA. For this, we developed a simple inferential model that helps to understand the basic underlying relationships and associations between lidar predictor metrics and forest understory shrub presence over a wide range of forest types and topographic conditions. The model (a least absolute shrinkage and selection operator-based logistic regression model) had fair predictive performance (accuracy=62%, kappa=0.23). Hence, we were able to propose a set of biophysically meaningful predictor variables that represent understory (4), canopy (3), topographic conditions (1), and sensor characteristics (1). The single most important predictor variable was the understory layer canopy density, which is the ratio of lidar returns in the understory to those near the ground. Hence, we demonstrate that the interplay of several factors affects understory vegetation condition. Overall, our work highlights the potential value of using lidar to characterize understory conditions.