Abstract
Abstract Accurate quantification and mapping of forest productivity are critical to understanding and managing forest ecosystems. Local LiDAR or photogrammetric surveys have been used to obtain reliable estimates of canopy heights, yet these acquisitions can entail substantial expenses. Therefore, we developed models using freely available US Geological survey (USGS) LiDAR data for prediction of dominant height to map site index across loblolly pine (Pinus taeda L.) plantations in the southeastern US. We used 2017–2020 national USGS 3D Elevation Program LiDAR acquisitions and explored how different height percentiles, grid output resolutions, time difference between LiDAR and ground acquisitions, tree height, and dominant height definition affected the proposed model. We built the dominant height models using 1301 ground plots. The final regression model was constructed with the 95th percentile of the height distribution of the first returns above-ground and had values of R2 = 0.89, RMSE = 1.55 m, and RRMSE = 7.66 per cent at a 20-m pixel grid, yet all the examined percentile-resolution combinations were acceptable. No effect evidence was found for time difference when the flight was less than 4 months in advance or after the ground measurement, and it was also found independent of pulse density when this variable was lower than 9.5 pulses m−2. Using the recorded age of the plantations, we assessed the error propagation when translating dominant height to site index in two site index models, obtaining an RRMSE lower than 10 per cent in both. We found that USGS LiDAR acquisitions can be reliably used to map dominant height at a large scale, and consequently used to map forest productivity when age is known. This ability adds more value to a tool proven widely applicable in time and space and offers a great opportunity for stakeholders in different fields of use.
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