Adaptive Mean Shift-Based Identification of Individual Trees Using Airborne LiDAR Data

Abstract

Abstract:Identifying individual trees and delineating their canopy structures from the forest point cloud data acquired by an airborne LiDAR (Light Detection And Ranging) has significant implications in forestry inventory. Once accurately identified, tree structural attributes such as tree height, crown diameter, canopy based height and diameter at breast height can be derived. This paper focuses on a novel computationally efficient method to adaptively calibrate the kernel bandwidth of a computational scheme based on mean shift-a non-parametric probability density-based clustering technique-to segment the 3D (three-dimensional) forest point clouds and identify individual tree crowns. The basic concept of this method is to partition the 3D space over each test plot into small vertical units (irregular columns containing 3D spatial features from one or more trees) first, by using a fixed bandwidth mean shift procedure and a small square grouping technique, and then rough estimation of crown sizes for distinct trees within a unit, based on an original 2D (two-dimensional) incremental grid projection technique, is applied to provide a basis for dynamical calibration of the kernel bandwidth for an adaptive mean shift procedure performed in each partition. The adaptive mean shift-based scheme, which incorporates our proposed bandwidth calibration method, is validated on 10 test plots of a dense, multi-layered evergreen broad-leaved forest located in South China. Experimental results reveal that this approach can work effectively and when compared to the conventional point-based approaches (e.g., region growing, k-means clustering, fixed bandwidth or multi-scale mean shift), its accuracies are relatively high: it detects 86 percent of the trees ("recall") and 92 percent of the identified trees are correct ("precision"), showing good potential for use in the area of forest inventory.

Monitoring Selective Logging in a Pine-Dominated Forest in Central Germany with Repeated Drone Flights Utilizing A Low Cost RTK Quadcopter

“…Six dissimilar test sites were selected, which were located in the Slovenian Alps, and the LiDAR point density ranged from 26 to 97 pts/m 2 for the different sites. Hu et al [45] developed a tree clustering algorithm based on the mean shift theory. The algorithm was applied to LiDAR data, with an average point density of 15 pts/m 2 acquired over a multi-layered, evergreen broad-leaved forest in South China.…”

“…Lu et al [43] LiDAR 10 pls/m 2 USA Pennsylvania/deciduous species (leaf-off) tp = 84% Mongus and Zalik [44] LiDAR 26-97 pls/m 2 Slovenia, Alps/mixed conifer forest av. precision = 0.75 Hu et al [45] LiDAR 15 pt/m 2 Southern China/multi-layered evergreen broad-leaved forest av. precision = 0.92…”

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

“…The advantage of adaptive mean shift for individual tree identification was further proved by Hu et al [43]. Instead of using an allometric function, the points were roughly segmented by a fixed bandwidth mean shift first, and the crown sizes were estimated by an iterative region growing at multiple layers of different heights.…”

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

“…A self-adaptive method calibrating the kernel bandwidth as a function of a local tree allometric model that can adapt to the local forest structure was developed by Ferraz et al in [25]. Our previous study proposed an adaptive mean shift-based clustering approach to segment the 3D forest point clouds and identify individual tree crowns, where much effort was put into the adaptive bandwidth settings: the kernel sizes are automatically changed according to the estimated crown diameters of distinct trees [26]. This approach has been tested over a multi-layered coniferous and broad-leaved mixed forest located in South China: the overall tree detection rate reaches 86% (92% of the detected trees are correct), and a specific detection rate of 48% was observed for the suppressed trees (the "precision" is 67%) [26].…”

“…Our previous study proposed an adaptive mean shift-based clustering approach to segment the 3D forest point clouds and identify individual tree crowns, where much effort was put into the adaptive bandwidth settings: the kernel sizes are automatically changed according to the estimated crown diameters of distinct trees [26]. This approach has been tested over a multi-layered coniferous and broad-leaved mixed forest located in South China: the overall tree detection rate reaches 86% (92% of the detected trees are correct), and a specific detection rate of 48% was observed for the suppressed trees (the "precision" is 67%) [26]. It should be emphasized that nearly 30% more suppressed trees can be identified by this adaptive approach than the one using a fixed kernel bandwidth [26].…”