Modeling stand-level forest attributes using lidar and Common Stand Exam data

This study focuses on the development of a lidar-based methodology that recreates stand-level inventory results from Common Stand Exams (CSE). CSE protocols are the U.S. Forest Service’s approach to measuring forest stocking and volume on public lands. Stand-level statistics of lidar-derived height metrics, individual tree height, and tree density were generated for 105 stands on the Sam Houston National Forest in Montgomery County, Texas, US. When comparing traditionally acquired CSE data versus lidar-based analysis, we successfully modelled linear relationship of stand-level pine basal area (BA) (R2 = 0.40), trees per acre (TPA) of pine (R2 = 0.61), and pine volume (R2 = 0.58). Similar studies often compare lidar-based metrics to individual plot results, whereas our workflow demonstrated reasonable extraction of stand-level metrics from an established forestry protocol. While lidar-based approaches might not be appropriate for every forest management objective, our results demonstrate that they have the potential to be leveraged in scenarios where relatively coarse results are acceptable. This could represent significant time and cost efficiency for forest managers who are confronted with challenging deadlines, fiscal limitations, and harsh environmental conditions.