UAV-derived Estimates of Vertical and Horizontal Structure across Forest Density Gradients

Restoring forest ecosystems and predicting forest response to projected climate change have become an increasingly high priority for land managers. Restoration and management goals require accurate, quantitative estimates of vertical and horizontal forest structure, which has relied upon either field-based measurements, manned airborne, or satellite remote sensing datasets. We use unmanned aerial vehicle (UAV) image-derived structure from motion (SfM) models and high resolution multispectral and thermal orthoimagery to: 1) quantify vertical and horizontal forest structure at both fine- (< 4 ha) and mid-scales (4-400 ha) across a forest density gradient, and 2) quantify horizontal structure, health, and survival rates in a genetics experimental garden also with a density gradient. In both cases, we find that UAV multispectral and thermal image-derived SfM model estimates of individual tree height and canopy diameter are most accurate in low-density conditions, with accuracies degrading significantly in high-density conditions. In addition, UAV thermal images demonstrate significant differences in tree health and survival rates among various populations and genotypes within a single species.  Mid-scale estimates of canopy cover and forest density follow a similar pattern across the density gradient, demonstrating the effectiveness of UAV image-derived estimates in low to medium-density conditions as well as the challenges associated with high-density conditions.