A Comparison of Linear-Mode and Single-Photon Airborne LiDAR in Species-Specific Forest Inventories

Abstract

Single-photon airborne light detection and ranging (LiDAR) systems provide high-density data from high flight altitudes. We compared single-photon and linear-mode airborne LiDAR for the prediction of species-specific volumes in boreal coniferous-dominated forests. The LiDAR data sets were acquired at different flight altitudes using Leica SPL100 (single-photon, 17 points · m⁻²), Riegl VQ-1560i (linear-mode, 11 points · m⁻²), and Leica ALS60 (linear-mode, 0.6 points · m⁻²) LiDAR systems. Volumes were predicted at the plot-level using Gaussian process regression with predictor variables extracted from the LiDAR data sets and aerial images. Our findings showed that the Leica SPL100 produced a greater mean root-mean-squared error (RMSE) value (41.7 m³ · ha⁻¹) than the Leica ALS60 (39.3 m³ · ha⁻¹) in the prediction of species-specific volumes. Correspondingly, the Riegl VQ-1560i (mean RMSE = 33.0 m³ · ha⁻¹) outperformed both the Leica ALS60 and the Leica SPL100. We found that the cumulative distributions of the first echo heights > 1.3 m were rather similar among the data sets, whereas the last echo distributions showed larger differences. We conclude that the Leica SPL100 data set is suitable for area-based LiDAR inventory by tree species although the prediction errors are greater than with data obtained using the modern linear-mode LiDAR, such as Riegl VQ-1560i.