Airborne LiDAR Remote Sensing for Individual Tree Forest Inventory Using Trunk Detection-Aided Mean Shift Clustering Techniques

Abstract

Airborne LiDAR (Light Detection And Ranging) remote sensing for individual tree-level forest inventory necessitates proper extraction of individual trees and accurate measurement of tree structural parameters. Due to the inadequate tree finding capability offered by LiDAR technology and the complex patterns of forest canopies, significant omission and commission errors occur frequently in the segmentation results. Aimed at error reduction and accuracy refinement, this paper presents a novel adaptive mean shift-based clustering scheme aided by a tree trunk detection technique to segment individual trees and estimate tree structural parameters based solely on the airborne LiDAR data. Tree trunks are detected by analyzing points' vertical histogram to detach all potential crown points and then clustering the separated trunk points according to their horizontal mutual distances. The detected trunk information is used to adaptively calibrate the kernel bandwidth of the mean shift procedure in the fine segmentation stage by applying an original 2D (two-dimensional) estimation of individual crown diameters. Trunk detection results and LiDAR point clusters generated by the adaptive mean shift procedures serve as mutual references for final detection of individual trees. Experimental results show that a combination of adaptive mean shift clustering and detected tree trunk can provide a significant performance improvement in individual tree-level forest measurement. Compared with conventional clustering techniques, the trunk detection-aided mean shift clustering approach can detect 91.1% of the trees ("recall") with a higher tree positioning accuracy (the mean positioning error is reduced by 33%) in a multi-layered coniferous and broad-leaved mixed forest in South China, and 93.5% of the identified trees are correct ("precision"). The tree detection brings the estimation of structural parameters for individual trees up to an accuracy level: −2.2% mean relative error and 5.8% relative RMSE (Root Mean Square Error) for tree height and 0.6% mean relative error and 21.9% relative RMSE for crown diameter, respectively.

Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data

“…A spherical kernel was chosen instead of a cylinder-shaped kernel, as in previous studies. Both the segmentation and localization results were improved by detecting the tree trunks first, in order to complement the adaptive mean shift segmentation [44].…”

Accuracy of Ground Surface Interpolation from Airborne Laser Scanning (ALS) Data in Dense Forest Cover

“…Meanwhile, for most algorithms, major vertical errors (of over 1 m) are associated with less than 0.05 percent of validation points. Digital Terrain Model (DTM) resolution, ground slope and point cloud density influence the quality of the ground surface model, while for canopy density we find a less significant link with the quality of the interpolated DTMs.data can be used for tree inventories [17][18][19][20], monitoring vegetation health [21][22][23][24] or generating Canopy Height Models (CHMs) [16,25].The main advantage of LiDAR, with regard to forestry, is the fact that laser pulses can penetrate the canopy cover, with some of the pulses reaching the ground level. Therefore, geomorphological data is collected even if the ground is covered by forest vegetation, allowing the generation of a detailed and accurate ground surface model.In general, a data structure that models the elevation over an area is called a Digital Elevation Model (DEM).…”

“…This filtered point cloud, containing only points which are at (or close to) the ground level, can afterwards serve for the interpolation of a continuous surface that models the variation of ground elevation.A ground model obtained from ALS data can be used in forestry for various purposes, such as: (1) the development of forest road networks [27], (2) the identification of existing forest roads [28,29], (3) the optimisation of forest skid trails [30] and (4) finding optimal harvesting solutions [31-33]. Furthermore, a DTM is generally an intermediate product when estimating various forest parameters from LiDAR data [18,[34][35][36][37]; the accuracy of the DTM will therefore influence the quality of subsequent estimations [38].In the case of dense forest cover, the penetration rate of laser pulses is hindered, resulting in a lower density of ground-level observations [39]. Furthermore, ground points will not have a uniform spatial distribution, with most of them concentrating inside gaps in the canopy cover.…”

“…data can be used for tree inventories [17][18][19][20], monitoring vegetation health [21][22][23][24] or generating Canopy Height Models (CHMs) [16,25].…”

“…A ground model obtained from ALS data can be used in forestry for various purposes, such as: (1) the development of forest road networks [27], (2) the identification of existing forest roads [28,29], (3) the optimisation of forest skid trails [30] and (4) finding optimal harvesting solutions [31-33]. Furthermore, a DTM is generally an intermediate product when estimating various forest parameters from LiDAR data [18,[34][35][36][37]; the accuracy of the DTM will therefore influence the quality of subsequent estimations [38].…”