An algorithm for the automatic parametrization of wood volume equations from Terrestrial Laser Scanning point clouds: application in <i>Pinus pinaster</i>

Prendes, Covadonga; Cabo, Carlos; Ordóñez, Celestino; Majada, Juan; Canga, Elena

doi:10.1080/15481603.2021.1972712

Cited by 7 publications

(4 citation statements)

References 49 publications

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“…For example, in order to estimate the carbon content of a tree, conventional methods assume the carbon content is a fraction of the dry volume of the tree, with the factor being species dependent [28,29]. With LIDAR point cloud data, it is possible to account for trunk irregularities and branches with segmentation and conic volume estimation methods [7,20].…”

Section: Methodsmentioning

confidence: 99%

From Bark to Byte: Automating Forest Inventory Data Collection Through Camera and Mobile LIDAR

Young

2023

Preprint

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Accurate forest inventory data are essential for tracking carbon sequestration, estimating carbon emissions from deforestation, assessing plant and animal habitats for biodiversity, and predicting environmental risks such as wildfires. Traditional methods of data collection have faced challenges in either scale or precision. The advent of terrestrial laser scanners addressed some of these issues but faced limitations in cost and mobility. This paper proposes a new approach using mobile LIDAR for forest inventory data collection. By integrating advancements in computer vision, the methodology aims to provide comprehensive individual tree data, including parameters like diameter at breast height, volume estimations, species identification, and temporal tracking of individual trees. This proposed research direction addresses current gaps in the use of LIDAR and camera inference for forestry data where existing work does not generate domain context-aware data by narrowly focusing collection on isolated tree attributes.

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Section: Methodsmentioning

confidence: 99%

From Bark to Byte: Automating Forest Inventory Data Collection Through Camera and Mobile LIDAR

Young

2023

Preprint

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“…The point cloud (Figure 1(4)) was analyzed with the methodology for the automatic estimation of dbh and h described in [32,33]. As a result, for each tree, measurements of the diameters of the sections along the stem (spaced 20 cm apart, from 0.5 over the ground to 4.9 m) as well as their centers (defined by X, Y, Z coordinates) were automatically obtained (Figure 2(1)).…”

Section: Data Collection and Inventorymentioning

confidence: 99%

“…Some of the trees did not have the complete range of sections mainly due to (i) the number of points in a section being insufficient to perform the circle fitting or (ii) sections presented anomalies that the methodology detected automatically. In this regard, the methodology includes several filters to detect anomalies [32,33] so the potentially 'wrong' sections are flagged for revision by a human operator who checks whether the fitting is correct and/or if it is possible to fit the circle manually.…”

Section: Data Collection and Inventorymentioning

confidence: 99%

“…Moreover, as described in [33] to have a reference for the data calculated by the algorithm, diameter fitting was manually conducted in the point cloud twice for the same section, each time by a different operator (Op 1 and Op 2 ). As a result, the centers of the sections for all the individual trees within the plot were available and served two purposes: (i) as reference data to assess the performance of the algorithm and (ii) to correct those sections which were labeled as anomalous by the algorithm (Figure 2(1)) to ensure data used to estimate stem shape variables were free of outliers (Figure 2(2)).…”

Section: Data Collection and Inventorymentioning

confidence: 99%

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Automatic Assessment of Individual Stem Shape Parameters in Forest Stands from TLS Point Clouds: Application in Pinus pinaster

Prendes¹,

Canga²,

Ordóñez

et al. 2022

Forests

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Tree morphological characteristics, particularly straightness and lean, significantly influence the value of the commercial products that can be obtained. Despite this, they are not usually evaluated in timber field inventories because traditional techniques are labor-intensive and largely subjective, hence the use of these parameters is limited to research and genetic breeding programs. Here, a non-destructive, fully automated methodology is presented that estimates the parameters for describing straightness and lean using terrestrial laser scanning (TLS) data. It is based on splitting stems into evenly spaced sections and estimating their centers, which are then used to automatically calculate the maximum sagitta, sinuosity, and lean of each tree. The methodology was applied in a breeding trial plot of Pinus pinaster, and the results obtained were compared with field measurements of straightness and lean based on visual classification. The methodology is robust to errors in the estimation of section centers, the basis for calculating shape parameters. Besides, its accuracy compares favorably with traditional field techniques, which often involve problems of misclassification. The new methodology is easy to use, less expensive, and overcomes the drawbacks of traditional field techniques for obtaining straightness and lean measurements. It can be modified to apply to any species and stand typology.

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A user-driven, reduced whole-stand growth model for major hybrid poplar clones in Spain

Castedo‐Dorado

Rodríguez‐Gonzálvez

González-Ferreiro

et al. 2023

Scandinavian Journal of Forest Research

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An algorithm for the automatic parametrization of wood volume equations from Terrestrial Laser Scanning point clouds: application in Pinus pinaster

Cited by 7 publications

References 49 publications

From Bark to Byte: Automating Forest Inventory Data Collection Through Camera and Mobile LIDAR

From Bark to Byte: Automating Forest Inventory Data Collection Through Camera and Mobile LIDAR

Automatic Assessment of Individual Stem Shape Parameters in Forest Stands from TLS Point Clouds: Application in Pinus pinaster

A user-driven, reduced whole-stand growth model for major hybrid poplar clones in Spain

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