Comparison of Mature Douglas-Firs’ Crown Structures Developed with Two Quantitative Structural Models Using TLS Point Clouds for Neighboring Trees in a Natural Regime Stand

Fang, Ruochen; Strîmbu, Bogdan M.

doi:10.3390/rs11141661

Cited by 15 publications

(15 citation statements)

References 57 publications

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“…The quantitative structure model (QSM) is a geometric model that describes a complete above-ground tree in a hierarchical order [26]. QSM can reconstruct a 3D model based on the point clouds of the individual tree, and then extract tree attributes [27,28]. QSM can directly calculate the tree volume from the TLS data, and further derive AGB by combining the wood density information [29,30].…”

Section: Introductionmentioning

confidence: 99%

“…The remote sensing estimation of AGB is almost entirely dependent on the allometric form based on tree height or DBH. QSM does not require any prior information about allometric growth when estimating AGB [28]. Accurate tree information provided by LiDAR data is necessary to improve the calibration and verification of AGB remote sensing estimates [31][32][33][34].…”

Section: Introductionmentioning

confidence: 99%

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AdQSM: A New Method for Estimating Above-Ground Biomass from TLS Point Clouds

et al. 2020

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Forest above-ground biomass (AGB) can be estimated based on light detection and ranging (LiDAR) point clouds. This paper introduces an accurate and detailed quantitative structure model (AdQSM), which can estimate the AGB of large tropical trees. AdQSM is based on the reconstruction of 3D tree models from terrestrial laser scanning (TLS) point clouds. It represents a tree as a set of closed and complete convex polyhedra. We use AdQSM to model 29 trees of various species (total 18 species) scanned by TLS from three study sites (the dense tropical forests of Peru, Indonesia, and Guyana). The destructively sampled tree geometry measurement data is used as reference values to evaluate the accuracy of diameter at breast height (DBH), tree height, tree volume, branch volume, and AGB estimated from AdQSM. After AdQSM reconstructs the structure and volume of each tree, AGB is derived by combining the wood density of the specific tree species from destructive sampling. The AGB estimation from AdQSM and the post-harvest reference measurement data show a satisfying agreement. The coefficient of variation of root mean square error (CV-RMSE) and the concordance correlation coefficient (CCC) are 20.37% and 0.97, respectively. AdQSM provides accurate tree volume estimation, regardless of the characteristics of the tree structure, without major systematic deviations. We compared the accuracy of AdQSM and TreeQSM in modeling the volume of 29 trees. The tree volume from AdQSM is compared with the reference value, and the determination coefficient (R2), relative bias (rBias), and CV-RMSE of tree volume are 0.96, 6.98%, and 22.62%, respectively. The tree volume from TreeQSM is compared with the reference value, and the R2, relative Bias (rBias), and CV-RMSE of tree volume are 0.94, −9.69%, and 23.20%, respectively. The CCCs between the volume estimates based on AdQSM, TreeQSM, and the reference values are 0.97 and 0.96. AdQSM also models the branches in detail. The volume of branches from AdQSM is compared with the destructive measurement reference data. The R2, rBias, and CV-RMSE of the branches volume are 0.97, 12.38%, and 36.86%, respectively. The DBH and height of the harvested trees were used as reference values to test the accuracy of AdQSM’s estimation of DBH and tree height. The R2, rBias, and CV-RMSE of DBH are 0.94, −5.01%, and 9.06%, respectively. The R2, rBias, and CV-RMSE of the tree height were 0.95, 1.88%, and 5.79%, respectively. This paper provides not only a new QSM method for estimating AGB based on TLS point clouds but also the potential for further development and testing of allometric equations.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

AdQSM: A New Method for Estimating Above-Ground Biomass from TLS Point Clouds

et al. 2020

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“…Three-dimensional reconstruction of trees based on terrestrial laser scanning (TLS) point clouds can directly obtain the structure or size distribution of main-branch of trees, the base and height of tree canopy [24][25][26][27]. The proposed method produces a geometric model that describes the components of a complete overground tree in hierarchical order [3,28,29].…”

Section: Introductionmentioning

confidence: 99%

A New Quantitative Approach to Tree Attributes Estimation Based on LiDAR Point Clouds

et al. 2020

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Tree-level information can be estimated based on light detection and ranging (LiDAR) point clouds. We propose to develop a quantitative structural model based on terrestrial laser scanning (TLS) point clouds to automatically and accurately estimate tree attributes and to detect real trees for the first time. This model is suitable for forest research where branches are involved in the calculation. First, the Adtree method was used to approximate the geometry of the tree stem and branches by fitting a series of cylinders. Trees were represented as a broad set of cylinders. Then, the end of the stem or all branches were closed. The tree model changed from a cylinder to a closed convex hull polyhedron, which was to reconstruct a 3D model of the tree. Finally, to extract effective tree attributes from the reconstructed 3D model, a convex hull polyhedron calculation method based on the tree model was defined. This calculation method can be used to extract wood (including tree stem and branches) volume, diameter at breast height (DBH) and tree height. To verify the accuracy of tree attributes extracted from the model, the tree models of 153 Chinese scholartrees from TLS data were reconstructed and the tree volume, DBH and tree height were extracted from the model. The experimental results show that the DBH and tree height extracted based on this model are in better consistency with the reference value based on field survey data. The bias, RMSE and R2 of DBH were 0.38 cm, 1.28 cm and 0.92, respectively. The bias, RMSE and R2 of tree height were −0.76 m, 1.21 m and 0.93, respectively. The tree volume extracted from the model is in better consistency with the reference value. The bias, root mean square error (RMSE) and determination coefficient (R2) of tree volume were −0.01236 m3, 0.03498 m3 and 0.96, respectively. This study provides a new model for nondestructive estimation of tree volume, above-ground biomass (AGB) or carbon stock based on LiDAR data.

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“…The application of LiDAR in forestry, especially backpack LiDAR and terrestrial laser scanning (TLS), which are high-precision three-dimensional laser point cloud active remote sensing platforms, provides an effective technical means for obtaining tree branch information. In recent years, there have been many studies on the acquisition of tree parameters based on three-dimensional laser point clouds, mainly focusing on single tree structures, single tree/population canopy parameters, and leaf distribution [1][2][3][4][5], but there has been little quantitative research on the extraction of tree branch information.…”

Section: Introductionmentioning

confidence: 99%

“…QSM has also been used in tree species identification [23] and in forest radiation transmission simulation [5]. In addition, QSM also performed well in the extraction of structural parameters such as tree diameter at breast height [3] and crown width [4]. Among the three quantitative structural models (QSMs) mentioned above, PypeTree has high hardware requirements that are hard to meet in the field; SimpleTree is time-consuming and cannot obtain effective model results in a relatively short time; TreeQSM is mainly aimed at tall forest trees, but it is no application on fruit trees with thin, dense, and overlapping branches.…”

Section: Introductionmentioning

confidence: 99%

Apple Tree Branch Information Extraction from Terrestrial Laser Scanning and Backpack-LiDAR

et al. 2020

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The branches of fruit trees provide support for the growth of leaves, buds, flowers, fruits, and other organs. The number and length of branches guarantee the normal growth, flowering, and fruiting of fruit trees and are thus important indicators of tree growth and yield. However, due to their low height and the high number of branches, the precise management of fruit trees lacks a theoretical basis and data support. In this paper, we introduce a method for extracting topological and structural information on fruit tree branches based on LiDAR (Light Detection and Ranging) point clouds and proved its feasibility for the study of fruit tree branches. The results show that based on Terrestrial Laser Scanning (TLS), the relative errors of branch length and number are 7.43% and 12% for first-order branches, and 16.75% and 9.67% for second-order branches. The accuracy of total branch information can reach 15.34% and 2.89%. We also evaluated the potential of backpack-LiDAR by comparing field measurements and quantitative structural models (QSMs) evaluations of 10 sample trees. This comparison shows that in addition to the first-order branch information, the information about other orders of branches is underestimated to varying degrees. The root means square error (RMSE) of the length and number of the first-order branches were 3.91 and 1.30 m, and the relative root means square error (NRMSE) was 14.62% and 11.96%, respectively. Our work represents the first automated classification of fruit tree branches, which can be used in support of precise fruit tree pruning, quantitative forecast of yield, evaluation of fruit tree growth, and the modern management of orchards.

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Comparison of Mature Douglas-Firs’ Crown Structures Developed with Two Quantitative Structural Models Using TLS Point Clouds for Neighboring Trees in a Natural Regime Stand

Cited by 15 publications

References 57 publications

AdQSM: A New Method for Estimating Above-Ground Biomass from TLS Point Clouds

AdQSM: A New Method for Estimating Above-Ground Biomass from TLS Point Clouds

A New Quantitative Approach to Tree Attributes Estimation Based on LiDAR Point Clouds

Apple Tree Branch Information Extraction from Terrestrial Laser Scanning and Backpack-LiDAR

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