An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning

By Harri Kaartinen, Juha Hyyppä, Xiaowei Yu, Mikko Vastaranta, Hannu Hyyppä, A. Kukko, Markus Holopainen, Christian Heipke, M. Hirschmugl, Felix Morsdorf and 6 more. Remote Sensing, 2012

499 citations recorded by the source corpus.

The objective of the “Tree Extraction” project organized by EuroSDR (European Spatial data Research) and ISPRS (International Society of Photogrammetry and Remote Sensing) was to evaluate the quality, accuracy, and feasibility of automatic tree extraction methods, mainly based on laser scanner data. In the final report of the project, Kaartinen and Hyyppa (2008) reported a high variation in the quality of the published methods under boreal forest conditions and with varying laser point densities. This paper summarizes the findings beyond the final report after analyzing the results obtained in different tree height classes. Omission/Commission statistics as well as neighborhood relations are taken into account. Additionally, four automatic tree detection and extraction techniques were added to the test. Several methods in this experiment were superior to manual processing in the dominant, co-dominant and suppressed tree storeys. In general, as expected, the taller the tree, the better the location accuracy. The accuracy of tree height, after removing gross errors, was better than 0.5 m in all tree height classes with the best methods investigated in this experiment. For forest inventory, minimum curvature-based tree detection accompanied by point cloud-based cluster detection for suppressed trees is a solution that deserves attention in the future.

Subfields: Ecology, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/0c7427818739930db417c0fa081b4d461346b5c0

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Citations in the corpus

3-D voxel-based solid modeling of a broad-leaved tree for accurate volume estimation using portable scanning lidar (2013, background): “…et al., 2009; Holmgren and Persson, 2004; Hosoi et al., 2010; Hosoi and Omasa, 2006, 2007, 2009, 2012; Hopkinson et al., 2004; Hyyppä et al., 2001; Kaartinen et al., 2012; Lefsky and McHale, 2008; Lefsky et al., 2002; Means et al., 1999; Næsset et al., 2004; Omasa et al., 2000, 2002, 2003, 2007,…”
High-Resolution Airborne UAV Imagery to Assess Olive Tree Crown Parameters Using 3D Photo Reconstruction: Application in Breeding Trials (2015, background): “Among the latter, ultrasonic sensors, stereo images, radar and LIDAR or even X-ray [12,13] have been proposed, with LIDAR (light detection and ranging) laser pulse technology (both on terrestrial or airborne platforms) one of the most sound and accepted approaches for detailed measurement and monito”
Trends in Automatic Individual Tree Crown Detection and Delineation - Evolution of LiDAR Data (2016, methodology): “Since full-waveform LiDAR can produce dense point clouds, it is also expected to improve individual tree detection, especially in the case of trees that are not dominant in the canopy [26].”
Estimating Pinus palustris tree diameter and stem volume from tree height, crown area and stand-level parameters (2014, methodology): “Given a good algorithm for individual tree detection (Lee et al. Journal of Forestry Research (2014) 25(1): 43−52 44 2010; Kaartinen et al. 2012; Li et al. 2012), remote sensing techniques, such as LiDAR, can be used to provide accurate tree height and crown area estimates (Nelson et al. 1988;…”
Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds (2016, unknown): “The automatic identification of individual trees and the extraction of tree attributes from a 3D point cloud have been shown to be dependent on several factors such as the properties of the point cloud, the properties of the forest, and also the algorithm extracting individual trees [30].”
Individual Tree Detection and Classification with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging (2017, background): “Tree density has a strong impact on the detection accuracy of individual trees in forests using point cloud data [73,74].”
A Benchmark of Lidar-Based Single Tree Detection Methods Using Heterogeneous Forest Data from the Alpine Space (2015, methodology): “[24], as well as with the results for the study areas in Germany and Norway in the benchmark published by Vauhkonen et al.”
Review of optical-based remote sensing for plant trait mapping (2013, background): “0 m irrespective to a tree height (Kaartinen et al., 2012).”