TreeTool: A tool for detecting trees and estimating their DBH using forest point clouds

Montoya, Omar; Icasio-Hernández, Octavio; Salas, Joaquín

doi:10.1016/j.softx.2021.100889

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…But this method overlooked trees with lower height or smaller crown sizes. Montoya et al (2021) developed software to extract trunk information using a Euclidean clustering algorithm based on TLS point cloud normal, while this software was not accurately depicting the tree canopy. Point cloud clustering methods are based on density, spatial distance, and normal of the points.…”

Section: Key Conceptsmentioning

confidence: 99%

Building Green Decarbonization for Urban Digital Twin – Estimating Carbon Sequestration of Urban Trees by Allometric Equations using Blend Types of Point Cloud

YAO,

Fricker

2024

Blucher Design Proceedings

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Section: Key Conceptsmentioning

confidence: 99%

Building Green Decarbonization for Urban Digital Twin – Estimating Carbon Sequestration of Urban Trees by Allometric Equations using Blend Types of Point Cloud

YAO,

Fricker

2024

Blucher Design Proceedings

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“…There were differences in the accuracy of the results for forest species with different morphologies (Liu et al 2018). Montoya developed a tool to detect trees and estimate their DBH using forest point cloud data, treetool, which is a software tool written in python based on its feasibility in terms of completeness, accuracy, and extraction of DBH (Montoya et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Terrestrial Laser Scanning in Forestry: Accuracy and Efficiency in Measuring Individual Tree Structural Parameters

Li,

Qiao,

Han

et al. 2024

Preprint

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Tree position, tree height and diameter at breast height are extremely important structural parameters in forest sample plot surveys. Compared with traditional manual measurement methods, the advantages of terrestrial laser scanning (TLS) in terms of data acquisition efficiency and accuracy have led to its gradual and widespread use, but the research on the accuracy of TLS in extracting forest structural parameters is still insufficient. This paper analyzes the accuracy and effectiveness of TLS in extracting structural parameters (tree height, diameter at breast height, and location) using poplar and Styphnolobium as examples, using TLS, airborne lidar system (ALS), and combining with field measurements. The results show that TLS has significant shortcomings in measuring tree height due to observation viewpoint limitations and forest obstruction, but is more efficient and accurate for breast diameter within the observation range (radius < 50m): the detection rate of single-station TLS reaches 50–60%, while the maximum measurement error (RMSE) of DBH is 1.6cm; Furthermore, even with RTK support, the single tree location bias (M) of two independent TLS measurements is 0.11m. TLS is of great value in obtaining forest sample parameters efficiently, and this study provides a reference for evaluating the accuracy and reliability of TLS technology in forest sample measurements.

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“…Discriminating the leaves from standing trees (e.g., tree stems and branches) improves the characterization of the tree's structure [13,14], the quantification of stem volume, aboveground biomass (AGB), and the carbon stored in the AGB. Indeed, the most accurate tree measurements can be obtained in TLS point clouds labeled as timber through several tools, such as quantitative structure modeling (QSM), SimpleTree [15], CompuTree (https:// computree.onf.fr/; accessed on 9 September 2023), 3D FOREST (https://www.3dforest.eu/; accessed on 9 September 2023), OPALS (orientation and processing of airborne laserscanning data; https://opals.geo.tuwien.ac.at/; accessed on 9 September 2023), the python tool (i.e., TreeTool) [16], and R packages (e.g., lidR, FORTLS, ITSME) [13,17,18].…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Leaves in a Multi-Layered Mediterranean Forest through Machine Learning Algorithms

Alvites,

Maesano,

Molina-Valero

et al. 2023

Remote Sensing

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Terrestrial laser scanning (TLS) technology characterizes standing trees with millimetric precision. An important step to accurately quantify tree volume and above-ground biomass using TLS point clouds is the discrimination between timber and leaf components. This study evaluates the performance of machine learning (ML)-derived models aimed at discriminating timber and leaf TLS point clouds, focusing on eight Mediterranean tree species datasets. The results show the best accuracies for random forests, gradient boosting machine, stacked ensemble model, and deep learning models with an average F1 score equal to 0.92. The top-performing ML-derived models showed well-balanced average precision and recall rates, ranging from 0.86 to 0.91 and 0.92 to 0.96 for precision and recall, respectively. Our findings show that Italian maple, European beech, hazel, and small-leaf lime tree species have more accurate F1 scores, with the best average F1 score of 0.96. The factors influencing the timber–leaf discrimination include phenotypic factors, such as bark surface (i.e., roughness and smoothness), technical issues (i.e., noise points and misclassification of points), and secondary factors (i.e., bark defects, lianas, and microhabitats). The top-performing ML-derived models report a time computation ranging from 8 to 37 s for processing 2 million points. Future studies are encouraged to calibrate, configure, and validate the potential of top-performing ML-derived models on other tree species and at the plot level.

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TreeTool: A tool for detecting trees and estimating their DBH using forest point clouds

Cited by 7 publications

References 40 publications

Building Green Decarbonization for Urban Digital Twin – Estimating Carbon Sequestration of Urban Trees by Allometric Equations using Blend Types of Point Cloud

Building Green Decarbonization for Urban Digital Twin – Estimating Carbon Sequestration of Urban Trees by Allometric Equations using Blend Types of Point Cloud

Terrestrial Laser Scanning in Forestry: Accuracy and Efficiency in Measuring Individual Tree Structural Parameters

Discrimination of Leaves in a Multi-Layered Mediterranean Forest through Machine Learning Algorithms

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