Advanced full-waveform lidar data echo detection: Assessing quality of derived terrain and tree height models in an alpine coniferous forest

Abstract

Small footprint full-waveform airborne lidar systems offer large opportunities for improved forest characterization. To take advantage of full-waveform information, this paper presents a new processing method based on the decomposition of waveforms into a sum of parametric functions. The method consists of an enhanced peak detection algorithm combined with advanced echo modelling including Gaussian and generalized Gaussian models. The study focuses on the qualification of the extracted geometric information. Resulting 3D point clouds were compared to the point cloud provided by the operator. 40 to 60% additional points were detected mainly in the lower part of the canopy and in the low vegetation. Their contribution to Digital Terrain Models (DTMs) and Canopy Height Models (CHMs) was then analysed. The quality of DTMs and CHM-based heights was assessed using field measurements on black pine plots under various topographic and stand characteristics. Results showed only slight improvements, up to 5 cm bias and standard deviation reduction. However both tree crowns and undergrowth were more densely sampled thanks to the detection of weak and overlapping echoes, opening up opportunities to study the detailed structure of forest stands.