A Crown Morphology-Based Approach to Individual Tree Detection in Subtropical Mixed Broadleaf Urban Forests Using UAV LiDAR Data

Owing to the complex forest structure and large variation in crown size, individual tree detection in subtropical mixed broadleaf forests in urban scenes is a great challenge. Unmanned aerial vehicle (UAV) light detection and ranging (LiDAR) is a powerful tool for individual tree detection due to its ability to acquire high density point cloud that can reveal three-dimensional crown structure. Tree detection based on a local maximum (LM) filter, which is applied on a canopy height model (CHM) generated from LiDAR data, is a popular method due to its simplicity. However, it is difficult to determine the optimal LM filter window size and prior knowledge is usually needed to estimate the window size. In this paper, a novel tree detection approach based on crown morphology information is proposed. In the approach, LMs are firstly extracted using a LM filter whose window size is determined by the minimum crown size and then the crown morphology is identified based on local Gi* statistics to filter out LMs caused by surface irregularities contained in CHM. The LMs retained in the final results represent treetops. The approach was applied on two test sites characterized by different forest structures using UAV LiDAR data. The sensitivity of the approach to parameter setting was analyzed and rules for parameter setting were proposed. On the first test site characterized by irregular tree distribution and large variation in crown size, the detection rate and F-score derived by using the optimal combination of parameter values were 72.9% and 73.7%, respectively. On the second test site characterized by regular tree distribution and relatively small variation in crown size, the detection rate and F-score were 87.2% and 93.2%, respectively. In comparison with a variable-size window tree detection algorithm, both detection rates and F-score values of the proposed approach were higher.

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Erratum to: Estimation of carbon storage based on individual tree detection in Pinus densiflora stands using a fusion of aerial photography and LiDAR data

Science China Life Sciences ◽

10.1007/s11427-010-4064-7 ◽

2010 ◽

Vol 53 (9) ◽

pp. 1162-1162 ◽

Cited By ~ 1

Author(s):

So-Ra Kim ◽

Doo-Ahn Kwak ◽

Woo-Kyun Lee ◽

Yowhan Son ◽

Sang-Won Bae ◽

...

Keyword(s):

Carbon Storage ◽

Aerial Photography ◽

Pinus Densiflora ◽

Lidar Data ◽

Individual Tree ◽

Tree Detection

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Development of an automated individual tree detection model using point cloud LiDAR data for accurate tree counts in a Pinus radiata plantation

Australian Forestry ◽

10.1080/00049158.2016.1153770 ◽

2016 ◽

Vol 79 (2) ◽

pp. 126-136 ◽

Cited By ~ 10

Author(s):

Amrit Kathuria ◽

Russell Turner ◽

Christine Stone ◽

Joaqin Duque-Lazo ◽

Ron West

Keyword(s):

Pinus Radiata ◽

Point Cloud ◽

Lidar Data ◽

Individual Tree ◽

Detection Model ◽

Tree Detection

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Moving to Automated Tree Inventory: Comparison of UAS-Derived Lidar and Photogrammetric Data with Manual Ground Estimates

Remote Sensing ◽

10.3390/rs13010072 ◽

2020 ◽

Vol 13 (1) ◽

pp. 72

Author(s):

Luiz Felipe Ramalho de Oliveira ◽

H. Andrew Lassiter ◽

Ben Wilkinson ◽

Travis Whitley ◽

Peter Ifju ◽

...

Keyword(s):

Forest Inventory ◽

Three Dimensional ◽

Point Clouds ◽

Detection Algorithm ◽

Unmanned Aircraft ◽

Airborne Lidar ◽

Lidar Data ◽

Individual Tree ◽

Tree Detection

Unmanned aircraft systems (UAS) have advanced rapidly enabling low-cost capture of high-resolution images with cameras, from which three-dimensional photogrammetric point clouds can be derived. More recently UAS equipped with laser scanners, or lidar, have been employed to create similar 3D datasets. While airborne lidar (originally from conventional aircraft) has been used effectively in forest systems for many years, the ability to obtain important tree features such as height, diameter at breast height, and crown dimensions is now becoming feasible for individual trees at reasonable costs thanks to UAS lidar. Getting to individual tree resolution is crucial for detailed phenotyping and genetic analyses. This study evaluates the quality of three three-dimensional datasets from three sensors—two cameras of different quality and one lidar sensor—collected over a managed, closed-canopy pine stand with different planting densities. For reference, a ground-based timber cruise of the same pine stand is also collected. This study then conducted three straightforward experiments to determine the quality of the three sensors’ datasets for use in automated forest inventory: manual mensuration of the point clouds to (1) detect trees and (2) measure tree heights, and (3) automated individual tree detection. The results demonstrate that, while both photogrammetric and lidar data are well-suited for single-tree forest inventory, the photogrammetric data from the higher-quality camera is sufficient for individual tree detection and height determination, but that lidar data is best. The automated tree detection algorithm used in the study performed well with the lidar data, detecting 98% of the 2199 trees in the pine stand, but fell short of manual mensuration within the lidar point cloud, where 100% of the trees were detected. The manually-mensurated heights in the lidar dataset correlated with field measurements at r = 0.95 with a bias of −0.25 m, where the photogrammetric datasets were again less accurate and precise.

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A Local Projection-Based Approach to Individual Tree Detection and 3-D Crown Delineation in Multistoried Coniferous Forests Using High-Density Airborne LiDAR Data

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2018.2865014 ◽

2019 ◽

Vol 57 (2) ◽

pp. 1168-1182 ◽

Cited By ~ 4

Author(s):

Aravind Harikumar ◽

Francesca Bovolo ◽

Lorenzo Bruzzone

Keyword(s):

High Density ◽

Airborne Lidar ◽

Coniferous Forests ◽

Lidar Data ◽

Individual Tree ◽

Local Projection ◽

Tree Detection ◽

Crown Delineation ◽

Airborne Lidar Data

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Measuring Tree Height with Remote Sensing—A Comparison of Photogrammetric and LiDAR Data with Different Field Measurements

Forests ◽

10.3390/f10080694 ◽

2019 ◽

Vol 10 (8) ◽

pp. 694 ◽

Cited By ~ 9

Author(s):

Selina Ganz ◽

Yannek Käber ◽

Petra Adler

Keyword(s):

Remote Sensing ◽

Tree Height ◽

Field Measurements ◽

Test Site ◽

Aerial Images ◽

Lidar Data ◽

Individual Tree ◽

Indirect Measurements ◽

Remote Sensing Techniques ◽

Remote Sensing Methods

We contribute to a better understanding of different remote sensing techniques for tree height estimation by comparing several techniques to both direct and indirect field measurements. From these comparisons, factors influencing the accuracy of reliable tree height measurements were identified. Different remote sensing methods were applied on the same test site, varying the factors sensor type, platform, and flight parameters. We implemented light detection and ranging (LiDAR) and photogrammetric aerial images received from unmanned aerial vehicles (UAV), gyrocopter, and aircraft. Field measurements were carried out indirectly using a Vertex clinometer and directly after felling using a tape measure on tree trunks. Indirect measurements resulted in an RMSE of 1.02 m and tend to underestimate tree height with a systematic error of −0.66 m. For the derivation of tree height, the results varied from an RMSE of 0.36 m for UAV-LiDAR data to 2.89 m for photogrammetric data acquired by an aircraft. Measurements derived from LiDAR data resulted in higher tree heights, while measurements from photogrammetric data tended to be lower than field measurements. When absolute orientation was appropriate, measurements from UAV-Camera were as reliable as those from UAV-LiDAR. With low flight altitudes, small camera lens angles, and an accurate orientation, higher accuracies for the estimation of individual tree heights could be achieved. The study showed that remote sensing measurements of tree height can be more accurate than traditional triangulation techniques if the aforementioned conditions are fulfilled.

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A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data

Remote Sensing ◽

10.3390/rs13020322 ◽

2021 ◽

Vol 13 (2) ◽

pp. 322

Author(s):

Melissa Latella ◽

Fabio Sola ◽

Carlo Camporeale

Keyword(s):

Point Clouds ◽

Individual Tree ◽

Tree Distribution ◽

Tree Detection ◽

Forest Modeling ◽

Point Density ◽

Potential Applications ◽

Field Campaigns ◽

The Individual ◽

Individual Trees

Nowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work, we propose a novel algorithm that aims to overcome these difficulties and yield the coordinates and the height of the individual trees on the basis of the point density features of the input point cloud. The algorithm was tested on twelve deciduous areas, assessing its performance on both regular-patterned plantations and stands with randomly distributed trees. For all cases, the algorithm provides high accuracy tree count (F-score > 0.7) and satisfying stem locations (position error around 1.0 m). In comparison to other common tools, the algorithm is weakly sensitive to the parameter setup and can be applied with little knowledge of the study site, thus reducing the effort and cost of field campaigns. Furthermore, it demonstrates to require just 2 points·m−2 as minimum point density, allowing for the analysis of low-density point clouds. Despite its simplicity, it may set the basis for more complex tools, such as those for crown segmentation or biomass computation, with potential applications in forest modeling and management.

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