A Region-Based Hierarchical Cross-Section Analysis for Individual Tree Crown Delineation Using ALS Data

Zhao, Yinghui; Hao, Yongmao; Zhen, Zhen; Qian, Ying

doi:10.3390/rs9101084

Cited by 27 publications

(18 citation statements)

References 62 publications

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“…was evaluated by directly comparing the resulting polygons to manually-drawn tree polygons based on visual interpretation of the CHM and high-resolution RGB imagery (7.5 cm resolution). The fraction of overlapping area between the two sets was calculated both from the viewpoint of the reference and segmentation result (see Formulas in Table 2 and Zhao et al, 2017). Based on both fractions, segmentation results were classified as a good match, over-or underestimated and mismatch ( Table 2).…”

Section: = − Tree Index H H First Lastmentioning

confidence: 99%

“…Using a relatively simple classification and segmentation algorithm, we generated segmentation results of acceptable quality that, given some minor manual corrections, could easily be fed into the remainder of our tree health workflow. Recently, many advanced tree segmentation techniques have been proposed (Zhao et al, 2017;Zhen et al, 2016), which could be used to improve our results even further. One specific suggestion would be to locate individual tree stems using the LiDAR data, which could then be used as an additional constraint in the canopy segmentation process (Reitberger et al, 2009).…”

Section: Tree Segmentationmentioning

confidence: 99%

“…Its lower spatial resolution (typically 2-4 m) can be compensated through fusion with airborne LiDAR data, providing highly detailed structural information. The added value of the LiDAR component for urban tree health assessment lies in its potential to estimate LAI (Alonzo et al, 2015;Klingberg et al, 2017;Morsdorf et al, 2006;Oshio et al, 2015) and to delineate individual tree objects in a highly accurate way (Alonzo et al, 2014;Zhao et al, 2017;Zhen et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Urban tree health assessment using airborne hyperspectral and LiDAR imagery

Degerickx

Roberts

McFadden

et al. 2018

International Journal of Applied Earth Observation and Geoinfor

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Urban trees provide valuable ecosystem services but are at the same time under continuous pressure due to unfavorable site conditions. In order to better protect and manage our natural capital, urban green managers require frequent and detailed information on tree health at the city wide scale. In this paper we developed a workflow to monitor tree defoliation and discoloration of broadleaved trees in Brussels, Belgium, through the combined use of airborne hyperspectral and LiDAR data. Individual trees were delineated using an object-based tree detection and segmentation algorithm primarily based on LiDAR data with an average accuracy of 91%. We constructed Partial Least Squares Regression (PLSR) models to derive tree chlorophyll content (RMSE = 2.8 μg/ cm²; R² = 0.77) and Leaf Area Index (LAI; RMSE = 0.5; R² = 0.66) from the average canopy spectrum. Existing spectral indices were found to perform significantly worse (RMSE > 7 μg/cm² and > 1.5 respectively), mainly due to contamination of tree spectra by neighboring background materials. In the absence of local calibration data, the applicability of PLSR to other areas, sensors and tree species might be limited. Therefore, we identified the best performing/least sensitive spectral indices and proposed a simple pixel selection procedure to reduce disturbing background effects. For LAI, laser penetration metrics derived from LiDAR data attained comparable accuracies as PLSR and were suggested instead. Detection of healthy and unhealthy trees based on remotely sensed tree properties matched reasonably well with a more traditional visual tree assessment (93% and 71% respectively). If combined with early tree stress detection methods, the proposed methodology would constitute a solid basis for future urban tree health monitoring programs.

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Section: = − Tree Index H H First Lastmentioning

confidence: 99%

Section: Tree Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Urban tree health assessment using airborne hyperspectral and LiDAR imagery

Degerickx

Roberts

McFadden

et al. 2018

International Journal of Applied Earth Observation and Geoinfor

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“…This system evaluates the applicability of low consumer grade cameras attached to unmanned aerial vehicles (UAVs) and a structure-from-motion (SfM) algorithm for automatic individual tree detection using a local-maxima-based algorithm on UAV-derived CHM. Zhao et al [22] presented a method of region-based hierarchical cross-section analysis for individual tree crown delineation using ALS data. This method uses the features of a mountain-like topographic surface and adopts the vertical structure of crowns to detect treetops and delineate crown boundaries.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Single Trees in Airborne Laser Scanning Data through Gradient Orientation Clustering

Dong

Zhou

Sibin

et al. 2018

Forests

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Currently, existing methods for single-tree detection based on airborne laser scanning (ALS) data usually require some thresholds and parameters to be set manually. Manually setting threshold or parameters is laborious and time-consuming, and for dense forests, the high commission and omission rate make most existing single-tree detection techniques inefficient. As a solution to these problems, this paper proposed an automatic single-tree detection method in ALS data through gradient orientation clustering (GOC). In this method, the rasterized Canopy Height Model (CHM) was derived from ALS data using surface interpolation. Then, potential trees were assumed as approximate conical shapes and extracted based on the GOC. Finally, trees were identified from the potential trees based on the compactness of the crown shape. This method used the gradient orientation information of rasterized CHM, thus increasing the generalization of single-tree detection method. In order to verify the validity and practicability of the proposed method, twelve 1256 m 2 circular study plots of different forest types were selected from the benchmark dataset (NEWFOR), and the results from nine different methods were presented and compared for these study plots. Among nine methods, the proposed method had the highest root mean square matching score (RMS_M = 43). Moreover, the proposed method had excellent detection (M > 47) in both single-layer coniferous and single-layered mixed stands.

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“…It can be used to capture three-dimensional (3D) tree crown attribute data and has been successfully applied to estimate forest parameters at the stand level [ 21 , 22 , 23 ] and tree level [ 24 , 25 , 26 ]. A large number of effective individual tree crown delineation (ITCD) algorithms have emerged in recent decades [ 27 , 28 , 29 , 30 , 31 , 32 ]. These ITCD algorithms offer a basis for extracting relatively fine-scale tree metrics and can facilitate the extraction of the crown profile.…”

Section: Introductionmentioning

confidence: 99%

The Feasibility of Modelling the Crown Profile of Larix olgensis Using Unmanned Aerial Vehicle Laser Scanning Data

Qian

Zhen

et al. 2020

Sensors

Self Cite

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Unmanned aerial vehicle (UAV) laser scanning, as an emerging form of near-ground light detection and ranging (LiDAR) remote sensing technology, is widely used for crown structure extraction due to its flexibility, convenience, and high point density. Herein, we evaluated the feasibility of using a low-cost UAV-LiDAR system to extract the fine-scale crown profile of Larix olgensis. Specifically, individual trees were isolated from LiDAR point clouds and then stratified from the point clouds of segmented individual tree crowns at 0.5 m intervals to obtain the width percentiles of each layer as profile points. Four equations (the parabola, Mitscherlich, power, and modified beta equations) were then applied to model the profiles of the entire and upper crown. The results showed that a region-based hierarchical cross-section analysis algorithm can successfully delineate 77.4% of the field-measured trees in high-density (>2400 trees/ha) forest stands. The crown profile generated with the 95th width percentile was adequate when compared with the predicted value of the existing field-based crown profile model (the Pearson correlation coefficient (ρ) was 0.864, root mean square error (RMSE) = 0.3354 m). The modified beta equation yielded slightly better results than the other equations for crown profile fitting and explained 85.9% of the variability in the crown radius for the entire crown and 87.8% of this variability for the upper crown. Compared with the cone and 3D convex hull volumes, the crown volumes predicted by our profile models had significantly smaller errors. The results revealed that the crown profile can be well described by using UAV-LiDAR, providing a novel way to obtain crown profile information without destructive sampling and showing the potential of the use of UAV-LiDAR in future forestry investigations and monitoring.

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A Region-Based Hierarchical Cross-Section Analysis for Individual Tree Crown Delineation Using ALS Data

Cited by 27 publications

References 62 publications

Urban tree health assessment using airborne hyperspectral and LiDAR imagery

Urban tree health assessment using airborne hyperspectral and LiDAR imagery

Automatic Detection of Single Trees in Airborne Laser Scanning Data through Gradient Orientation Clustering

The Feasibility of Modelling the Crown Profile of Larix olgensis Using Unmanned Aerial Vehicle Laser Scanning Data

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