Comparison of small-footprint discrete return and full waveform airborne lidar data for estimating multiple forest variables

• Published in: Remote sensing of environment 2016-02, Vol.173, p.214-223
• Main Authors: Sumnall, Matthew J., Hill, Ross A., Hinsley, Shelley A.
• Format: Article
• Language: English
• Subjects: Airborne laser scanning; Area-based regression; Forest inventory; Remote sensing
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2015.07.027

The quantification of forest ecosystems is important for a variety of purposes, including the assessment of wildlife habitat, nutrient cycles, timber yield and fire propagation. This research assesses the estimation of forest structure, composition and deadwood variables from small-footprint airborne lidar data, both discrete return (DR) and full waveform (FW), acquired under leaf-on and leaf-off conditions. The field site, in the New Forest, UK, includes managed plantation and ancient, semi-natural, coniferous and deciduous woodland. Point clouds were rendered from the FW data through Gaussian decomposition. An area-based regression approach (using Akaike Information Criterion analysis) was employed, separately for the DR and FW data, to model 23 field-measured forest variables. A combination of plot-level height, intensity/amplitude and echo-width variables (the latter for FW lidar only) generated from both leaf-on and leaf-off point cloud data were utilised, together with individual tree crown (ITC) metrics from rasterised leaf-on height data. Statistically significant predictive models (p