Examination of Drone Usage in Estimating Hardwood Plantations Structural Metrics

Abstract

Planting hardwood trees on retired marginal agricultural land is one of the main strategies used to restore forested wetlands. Evaluating effectiveness of wetland restoration requires efficient monitoring to evaluate recovery trajectories and desired conditions. Recent advancements in unmanned aerial system (UAS) technologies have prompted wide-scale adoption of UAS platforms in providing a range of ecological data. In this study, we examined the use of UAS Structure from Motion (SfM) derived point clouds in estimating tree density, canopy height, and percent canopy cover for bottomland hardwood plantations within four wetland reserve easements. Using a local maxima approach for individual tree detection produced plantation level estimates with mean absolute errors of 150 trees per hectare, 0.5 m, and 18.4% for tree density, canopy height, and percent canopy cover, respectively. At the plot level, UAS-derived tree counts (r = 0.53, p < 0.01) and canopy height (r = 0.57, p < 0.01) were significantly correlated with ground-based estimates. We demonstrate that UAS-SfM is a viable method of assessing bottomland hardwood plantations for applications that require precision levels congruent with the mean absolute errors reported here. The accuracy of tree density estimates was reliant upon specific local maxima window parameters relative to stand conditions. Therefore, acquisition of leaf-off and leaf-on imagery may allow for better individual tree detection and subsequently more accurate tree density and other structural attributes.