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A detailed understanding of the spatial distribution of forest understory is important but difficult. LiDAR remote sensing has been\uddeveloping as a promising additional instrument to the conventional field work towards automated forest inventory. Unfortunately,\udunderstory (up to 50% of the top-tree height) in mixed and multilayered forests is often ignored due to a difficult observation\udscenario and limitation of the tree detection algorithm. Currently, the full-waveform (FWF) LiDAR with high penetration ability\udagainst overstory crowns can give us new hope to resolve the forest understory. Former approach based on 3D segmentation\udconfirmed that the tree detection rates in both middle and lower forest layers are still low. Therefore, detecting sub-dominant and\udsuppressed trees cannot be regarded as fully solved. In this work, we aim to improve the performance of the FWF laser scanner for\udthe mapping of forest understory. The paper is to develop an enhanced methodology for detecting 3D individual trees by partitioning\udpoint clouds of airborne LiDAR. After extracting 3D coordinates of the laser beam echoes, the pulse intensity and width by\udwaveform decomposition, the newly developed approach resolves 3D single trees are by an integrated approach, which delineates\udtree crowns by applying normalized cuts segmentation to the graph structure of local dense modes in point clouds constructed by\udmean shift clustering. In the context of our strategy, the mean shift clusters approximate primitives of (sub) single trees in LiDAR\uddata and allow to define more significant features to reflect geometric and reflectional characteristics towards the single tree level.\udThe developed methodology can be regarded as an object-based point cloud analysis approach for tree detection and is applied to\uddatasets captured with the Riegl LMS-Q560 laser scanner at a point density of 25 points/m<sup>2</sup> in the Bavarian Forest National Park,\udGermany, respectively under leaf-on and leaf-off conditions. The experiments lead to a detection rate of up to 67% for trees in the\udmiddle height layer and up to 53% for trees in the lower forest layer. It corresponds to an overall improvement in the detection rate\udof nearly 25% for forest understory compared to that obtained by the former method by extracting individual trees using normalized\udcuts segmentation solely

isprs annals of the photogrammetry, remote sensing and spatial information sciences

Enhanced detection of 3D individual trees in forested areas using airborne full-waveform LiDAR data by combining normalized cuts with spatial density clustering