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

Fan, Guangpeng; Nan, Liangliang; Chen, Feixiang; Dong, Yanqi; Wang, Zhiming; Li, Hao; Chen, Danyu

doi:10.3390/rs12111779

Cited by 46 publications

(40 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several methods exist for the estimation of stem diameter, and they can be mainly divided into two major categories: algebraic and geometric. The most common geometric methods include Levenberg-Marquardt and Gauss-Newton algorithms; other geometric approaches to approximate the diameter of stem sections include using cylindrical shapes [ 12 , 13 ], skeletonization [ 14 , 15 ], polygons [ 16 ], and Hough transformation [ 17 , 18 ]. Most algebraic methods, such as approaches by Kasa [ 19 ], Pratt [ 20 ], and Taubin [ 21 ], are usually faster and non-iterative compared to geometric methods.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Stem Volume on Plots Using Terrestrial Laser Scanner: A Precision Forestry Application

Panagiotidis

Abdollahnejad

Slavík

2021

Sensors

View full text Add to dashboard Cite

Timber volume is an important asset, not only as an ecological component, but also as a key source of present and future revenues, which requires precise estimates. We used the Trimble TX8 survey-grade terrestrial laser scanner (TLS) to create a detailed 3D point cloud for extracting total tree height and diameter at breast height (1.3 m; DBH). We compared two different methods to accurately estimate total tree heights: the first method was based on a modified version of the local maxima algorithm for treetop detection, “HTTD”, and for the second method we used the centers of stem cross-sections at stump height (30 cm), “HTSP”. DBH was estimated by a computationally robust algebraic circle-fitting algorithm through hierarchical cluster analysis (HCA). This study aimed to assess the accuracy of these descriptors for evaluating total stem volume by comparing the results with the reference tree measurements. The difference between the estimated total stem volume from HTTD and measured stems was 2.732 m3 for European oak and 2.971 m3 for Norway spruce; differences between the estimated volume from HTSP and measured stems was 1.228 m3 and 2.006 m3 for European oak and Norway spruce, respectively. The coefficient of determination indicated a strong relationship between the measured and estimated total stem volumes from both height estimation methods with an R2 = 0.89 for HTTD and R2 = 0.87 for HTSP for European oak, and R2 = 0.98 for both HTTD and HTSP for Norway spruce. Our study has demonstrated the feasibility of finer-resolution remote sensing data for semi-automatic stem volumetric modeling of small-scale studies with high accuracy as a potential advancement in precision forestry.

show abstract

Section: Introductionmentioning

confidence: 99%

Assessment of Stem Volume on Plots Using Terrestrial Laser Scanner: A Precision Forestry Application

Panagiotidis

Abdollahnejad

Slavík

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In 2019, the AdTree method proposed by Shenglan Du et al can accurately reconstruct the three-dimensional geometric and topological structure of trees. In 2020, Guangpeng Fan et al updated and expanded the AdTree method and proposed a new quantitative estimation method for tree parameters [39,42]. Based on Adtree's research, the minimum spanning tree algorithm (MST) was used to extract the initial skeleton of the tree from the input point clouds and trim it.…”

Section: Tree Volume Estimation From 3d Reconstruction-a New Quantitamentioning

confidence: 99%

“…The development of the QSM method for reconstructing the tree volume based on TLS point clouds is very important for estimating the tree AGB. Based on the research of Shenglan Du and Guangpeng Fan [39,42], this paper optimized and updated the original modeling methods and named the latest QSM model AdQSM. For the first time, the accuracy of AdQSM's estimation of DBH, tree height, tree volume, branch volume, and AGB was tested concerning the geometric measurements of trees with destructive sampling.…”

Section: Accuracy Analysis Of Adqsmmentioning

confidence: 99%

“…Specifically, the distances between the input point and the output model are less than 10 cm. In 2020, Guangpeng Fan et al [42] further extended the AdTree method and defined the calculation method for deriving DBH, tree height, and volume from the model. However, the above research did not use destructive tree measurement data to validate the AdTree-based QSM method.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2020

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Shenglan Du et al proposed a method called AdTree [40], which provides a geometric basis for the automatic, detailed, and accurate reconstruction of a 3D tree model. Guangpeng Fan et al updated and extended the AdTree method [41], and proposed a new quantitative estimation method of tree attributes to extract DBH, tree height, and volume [42], and the improved model is called AdQSM [41]. LiDAR point clouds and destructive tree measurement data were used to test the accuracy of AdQSM's estimation of tree attributes such as DBH, tree height, branch length, branch number, volume, and AGB [41][42][43].…”

Section: Introductionmentioning

confidence: 99%

Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Dong

Fan

Zhou

et al. 2021

Forests

Self Cite

View full text Add to dashboard Cite

The quantitative structure model (QSM) contains the branch geometry and attributes of the tree. AdQSM is a new, accurate, and detailed tree QSM. In this paper, an automatic modeling method based on AdQSM is developed, and a low-cost technical scheme of tree structure modeling is provided, so that AdQSM can be freely used by more people. First, we used two digital cameras to collect two-dimensional (2D) photos of trees and generated three-dimensional (3D) point clouds of plot and segmented individual tree from the plot point clouds. Then a new QSM-AdQSM was used to construct tree model from point clouds of 44 trees. Finally, to verify the effectiveness of our method, the diameter at breast height (DBH), tree height, and trunk volume were derived from the reconstructed tree model. These parameters extracted from AdQSM were compared with the reference values from forest inventory. For the DBH, the relative bias (rBias), root mean square error (RMSE), and coefficient of variation of root mean square error (rRMSE) were 4.26%, 1.93 cm, and 6.60%. For the tree height, the rBias, RMSE, and rRMSE were—10.86%, 1.67 m, and 12.34%. The determination coefficient (R2) of DBH and tree height estimated by AdQSM and the reference value were 0.94 and 0.86. We used the trunk volume calculated by the allometric equation as a reference value to test the accuracy of AdQSM. The trunk volume was estimated based on AdQSM, and its bias was 0.07066 m3, rBias was 18.73%, RMSE was 0.12369 m3, rRMSE was 32.78%. To better evaluate the accuracy of QSM’s reconstruction of the trunk volume, we compared AdQSM and TreeQSM in the same dataset. The bias of the trunk volume estimated based on TreeQSM was −0.05071 m3, and the rBias was −13.44%, RMSE was 0.13267 m3, rRMSE was 35.16%. At 95% confidence interval level, the concordance correlation coefficient (CCC = 0.77) of the agreement between the estimated tree trunk volume of AdQSM and the reference value was greater than that of TreeQSM (CCC = 0.60). The significance of this research is as follows: (1) The automatic modeling method based on AdQSM is developed, which expands the application scope of AdQSM; (2) provide low-cost photogrammetric point cloud as the input data of AdQSM; (3) explore the potential of AdQSM to reconstruct forest terrestrial photogrammetric point clouds.

show abstract

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

Cited by 46 publications

References 62 publications

Assessment of Stem Volume on Plots Using Terrestrial Laser Scanner: A Precision Forestry Application

Assessment of Stem Volume on Plots Using Terrestrial Laser Scanner: A Precision Forestry Application

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

Low Cost Automatic Reconstruction of Tree Structure by AdQSM with Terrestrial Close-Range Photogrammetry

Contact Info

Product

Resources

About