A Benchmark of Lidar-Based Single Tree Detection Methods Using Heterogeneous Forest Data from the Alpine Space

Eysn, Lothar; Hollaus, Markus; Lindberg, Eva; Berger, Frédéric; Monnet, Jean‐Matthieu; Dalponte, Michele; Kobal, Milan; Pellegrini, Marco; Lingua, Emanuele; Mongus, Domen; Pfeifer, Norbert

doi:10.3390/f6051721

Cited by 209 publications

(228 citation statements)

References 31 publications

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“…Pelizzano consists of multi-storied mixed forest that has a large amount of trees with different height layers. In contrast, the best detection result occurred in a single-storied coniferous forest (Eysn et al 2015). Warner et al (1998) and Park et al (2014) also mentioned that various types of forest conditions could result in distinctly different results due to different crown morphology characteristics.…”

Section: Introductionmentioning

confidence: 95%

“…Most recent studies on tree detection can be categorized to five main methods: local maxima (LM) filtering (Popescu et al 2002), region-growing , valley-following (Gougeon 1995), watershed segmentation (Kwak et al 2007), and the integrated methods relying on LM (Eysn et al 2015). LM filtering has been widely used to detect trees in remotely sensed images (Pouliot et al 2002;Park et al 2011).…”

Section: Introductionmentioning

confidence: 99%

“…Theoretically, a higher LiDAR point density could be more efficient for tree position and height detection (Eysn et al 2015). Eysn et al (2015) selected the different forest types using different algorithms for comparison accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Eysn et al (2015) selected the different forest types using different algorithms for comparison accuracy. They found that the pilot area Pellizzano, with a maximum LiDAR point density of 121 pts m -2 , showed the worst detection results among all of the 8 pilot areas even with such extremely high point density.…”

Section: Introductionmentioning

confidence: 99%

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Detecting Individual Tree Position and Height Using Airborne LiDAR Data in Chollipo Arboretum, South Korea

Kim¹,

Lee²,

Yoon³

et al. 2016

Terr. Atmos. Ocean. Sci.

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Forest carbon is accurately quantified by observing individual tree positions and heights. This paper proposes a novel algorithm for individual tree detection using Light Detection and Ranging (LiDAR) data in the Chollipo arboretum, South Korea. The proposed algorithm does not need to specify a proper window size for operation, taking advantage over the mostly used local maxima (LM) filtering for forest analysis. Four hundred twenty-nine treetops were detected and the average height and standard errors were 12.74 ± 0.24 m. Reference data were collected from two sources for verifying accuracy: field survey and visual interpretation. Overall, the result was overestimated but showed relatively high accuracy. The field survey detected 87% of the trees with a coefficient of determination (R 2 ) and root mean square error (RMSE) of 0.77 and 1.57 m, respectively. The accuracy index (AI), which examines the correspondence between LiDAR detected and visually interpreted trees, was 91%. The average tree height error between on-site and LiDAR derived data was -1.42 ± 0.64 m and between visually interpreted and LiDAR derived data was -0.84 ± 0.10 m. This study emphasized the choice of algorithm and its parameters depending on forest conditions may influence the individual tree detection result. By comparing our work against previous studies, we found the tree location and height identification accuracy could be improved if different algorithms were used for different types of forests, as well as the LiDAR point density with each algorithm. This study suggests that more accurate individual tree detection could be obtained with different applications based on forest conditions.

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Section: Introductionmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detecting Individual Tree Position and Height Using Airborne LiDAR Data in Chollipo Arboretum, South Korea

Kim¹,

Lee²,

Yoon³

et al. 2016

Terr. Atmos. Ocean. Sci.

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show abstract

“…In addition, algorithms for automatic identification of the individual trees (tree detection) have been continuously improving, using 2D or 3D segmentation of LiDAR data or a combination of LiDAR data and aerial or satellite imagery. Eysn et al (2015) found that forest structure has a significant influence on the tree identification results, with the highest matching rate (86% RMS) for single-layered coniferous and the lowest matching rate (47% RMS) for single-layered mixed forests. Various studies have demonstrated the utility of LiDAR technology for the estimation of height, stand basal area, total tree biomass or foliage biomass mainly in coniferous stands (Hall et al, 2005;Lefsky et al, 2005;García et al, 2010;Edson and Wing, 2011).…”

Section: Ground Reference Datamentioning

confidence: 99%

Height Extraction and Stand Volume Estimation Based on Fusion Airborne LiDAR Data and Terrestrial Measurements for a Norway Spruce [<i>Picea abies</i> (L.) Karst.] Test Site in Romania

Apostol

Lorenţ

Petrila

et al. 2016

Not Bot Horti Agrobo

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The objective of this study was to analyze the efficiency of individual tree identification and stand volume estimation from LiDAR data. The study was located in Norway spruce [Picea abies (L.) Karst.] stands in southwestern Romania and linked airborne laser scanning (ALS) with terrestrial measurements through empirical modelling. The proposed method uses the Canopy Maxima algorithm for individual tree detection together with biometric field measurements and individual trees positioning. Field data was collected using Field-Map real-time GIS-laser equipment, a high-accuracy GNSS receiver and a Vertex IV ultrasound inclinometer. ALS data were collected using a Riegl LMS-Q560 instrument and processed using LP360 and Fusion software to extract digital terrain, surface and canopy height models. For the estimation of tree heights, number of trees and tree crown widths from the ALS data, the Canopy Maxima algorithm was used together with local regression equations relating field-measured tree heights and crown widths at each plot. When compared to LiDAR detected trees, about 40-61% of the field-measured trees were correctly identified. Such trees represented, in general, predominant, dominant and co-dominant trees from the upper canopy. However, it should be noted that the volume of the correctly identified trees represented 60-78% of the total plot volume. The estimation of stand volume using the LiDAR data was achieved by empirical modelling, taking into account the individual tree heights (as identified from the ALS data) and the corresponding ground reference stem volume. The root mean square error (RMSE) between the individual tree heights measured in the field and the corresponding heights identified in the ALS data was 1.7-2.2 meters. Comparing the ground reference estimated stem volume (at trees level) with the corresponding ALS estimated tree stem volume, an RMSE of 0.5-0.7 m 3 was achieved. The RMSE was slightly lower when comparing the ground reference stem volume at plot level with the ALS-estimated one, taking into account both the identified and unidentified trees in the LiDAR data (0.4-0.6 m 3 ).

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Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering

Xie

Wang

et al. 2023

Ecology and Evolution

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Light detection and ranging (LiDAR) data can provide 3D structural information of objects and are ideal for extracting individual tree parameters, and individual tree segmentation (ITS) is a vital step for this purpose. Various ITS methods have been emerging from airborne LiDAR scanning (ALS) or unmanned aerial vehicle LiDAR scanning (ULS) data. Here, we propose a new individual tree segmentation method, which couples the classical and efficient watershed algorithm (WS) and the newly developed connection center evolution (CCE) clustering algorithm in pattern recognition. The CCE is first used in ITS and comprehensively optimized by considering tree structure and point cloud characteristics. Firstly, the amount of data is greatly reduced by mean shift voxelization. Then, the optimal clustering scale is automatically determined by the shapes in the projection of three different directions. We select five forest plots in Saihanba, China and 14 public plots in Alpine region, Europe with ULS or ALS point cloud densities from 11 to 3295 pts/m 2 . Eleven ITS methods were used for comparison. The accuracy of tree top detection and tree height extraction is estimated by five and two metrics, respectively. The results show that the matching rate (R match ) of tree tops is up to 0.92, the coefficient of determination (R 2 ) of tree height estimation is up to .94, and the minimum root mean square error (RMSE) is 0.6 m. Our method outperforms the other methods especially in the broadleaf forests plot on slopes, where the five evaluation metrics for tree top detection outperformed the other algorithms by at least 11% on average. Our ITS method is both robust and efficient and has the potential to be used especially in coniferous forests to extract the structural parameters of individual trees for forest management, carbon stock estimation, and habitat mapping.

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A Benchmark of Lidar-Based Single Tree Detection Methods Using Heterogeneous Forest Data from the Alpine Space

Cited by 209 publications

References 31 publications

Detecting Individual Tree Position and Height Using Airborne LiDAR Data in Chollipo Arboretum, South Korea

Detecting Individual Tree Position and Height Using Airborne LiDAR Data in Chollipo Arboretum, South Korea

Height Extraction and Stand Volume Estimation Based on Fusion Airborne LiDAR Data and Terrestrial Measurements for a Norway Spruce [<i>Picea abies</i> (L.) Karst.] Test Site in Romania

Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering

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