摘要： Advances in remote sensing technology since the mid-2000s have drastically increased the potential to acquire traditional forest inventory variables as well as information about the effects of forest canopy structure on radiative transfer, and its implications for tree and ecosystem physiology. Light detection and ranging (LiDAR) has provided means to record in high-resolution (cm-scale), the three-dimensional (3D) structure of the canopy, including height and leaf area density and in certain cases even information related to the branching architecture of trees (Wulder et al., 2012b; Dubayah and Drake, 2000). LiDAR data are currently being acquired from airborne and ground-based platforms with pulse repetition frequencies over 500,000 pulses per second. The many billions of returned laser pulses that reflect off the ground surface and other objects, such as trees, shrubs, and rocks, captured by the detector and for which the exact 3D positions are determined based on accurate measurements of the instrument’s position and orientation, are accessible in easy-to-use tabular data files, and can be easily displayed on modern graphics hardware through a handful of command line expressions or even user-friendly software packages, often available free of charge to academic and noncommercial users (e.g. SPDlib.org, see Bunting et al., 2013a,b; cloudcompare.org; rapidlasso.com). Over the past decades, the combined effort of a broad community of academic researchers and corporations has delivered a range of algorithms for the processing of these data, such as the extraction of terrain models and surface shapes, including stems and tree crowns, or wall-to-wall forest inventory attributes, including but not limited to basal area, merchantable timber volume, biomass, tree stocking density, and tree height. An overview of these algorithms and some of the accuracies by which forest attributes can be extracted from the data can be found in review articles and in the introductions of many research papers. A query on the Scopus search engine for academic research papers shows over 2000 hits on the topic of “LiDAR” and “forestry” alone (Disney, 2016), indicating both the great success of the technology in acquiring forest inventory information and the perceived value of the technology to the field.