A comparative study for obtaining effective Leaf Area Index from single Terrestrial Laser Scans by removal of wood material

Arslan, Adil Enis; Erten, Esra; Inan, Mühittin

doi:10.1016/j.measurement.2021.109262

Cited by 11 publications

(6 citation statements)

References 23 publications

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“…However, tree heights are highly correlated with TLS results (with a correlation coefficient of 0.73). To obtain more information from ALS data, the tree trunks can be modeled using other, more complex, methods from the under canopy data such as those implemented and explained in another article (Arslan et al, 2021) and DBH can be estimated from it.…”

Section: Resultsmentioning

confidence: 99%

“…These 2D imaging technologies provide incomparable information about biomass levels. However, the need for 3D structures requiring high levels of detail and accuracy, especially for dendrometry of forestry, makes 3D Point Cloud data (PCD) indispensable in forestry applications (Cabo et al, 2018;Mielcarek et al, 2018;Arslan et al, 2021;Shimizu et al, 2022). Over the last 20 years, Light Detection and Ranging (LiDAR) data systems have become one of the keyways to learn about the structure, change and distribution of forest resources, thanks to their rapid, accurate and non-destructive results (Popescu, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, the ground-based LiDAR systems, namely TLS, provide information that cannot be extracted using airborne LiDAR data, and with dense PCD. Thus, it is easy to obtain plot-level information in a short time (Koren et al, 2017;Cabo et al, 2018;Arslan et al, 2021;Tan et al, 2021;Lizuka et al, 2022) The popularity of TLS is due to its capacity to display 3D forest models with millimeter-level precision enables to estimate forest stand parameters like DBH. Recently, image-based PCD from Unmanned Aerial Vehicles (UAV) has been widely recognized in forestry because it is low-cost and provides canopy-related information in larger areas (Koc-San et al, 2018;Demir, 2018;Liang et al, 2019;Guerra-Hernandez et al, 2021;Shimizu et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimations of Forest Stand Parameters in Open Forest Stand Using Point Cloud Data from Terrestrial Laser Scanning, Unmanned Aerial Vehicle and Aerial LiDAR Data

Arslan

Inan

Çelik

et al. 2022

European Journal of Forest Engineering

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Two of the very basic forestry parameters, the Breast Height Diameter (DBH) and Tree Height (TH) are very effective when characterizing forest stands and individual trees. The traditional measurement process of these parameters takes a lot of time and consumes human power. However, because of the development of PC power and digital storage in recent years, 3D Point Cloud (PC) gains quickly provide a very detailed view of forestry parameters. PC data sources include Airborne LiDAR Systems (ALS), Terrestrial Laser Scanning (TLS) and finally, the Unmanned Air Vehicle (UAV) for forestry applications. In this study, the PC datasets from these sources were used to study the feasibility of the DBH and TH values of a D-stage oak stand. The DBH and TH estimates are compared with the onsite measurements, which are considered to be fundamental truths, to their performance due to overall error statistics, as well as the cost of calculation and the difficulties in data collection. The results show that the computer data obtained by TLS has the best average square error (0.22 cm for DBH and 0,051 m for TH) compared to other computer data. The size of Pearson correlation between TLS-based and on-site-based measurements has reached 0.97 and 0.99 for DBH, respectively.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimations of Forest Stand Parameters in Open Forest Stand Using Point Cloud Data from Terrestrial Laser Scanning, Unmanned Aerial Vehicle and Aerial LiDAR Data

Arslan

Inan

Çelik

et al. 2022

European Journal of Forest Engineering

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“…(1) Plane fitting based on RANSAC RANSAC was used to perform plane fitting on each cluster class clustered by defects of rain spots, and the number of iterations k and error tolerance range error dis were set. The number of iterations is responsible for ensuring that the RANSAC algorithm can perform enough epochs to find the best model estimation, and the error tolerance range indicates that the points are allowed to be distributed within the range of distances from the prediction model [37][38][39][40].…”

Section: () Onmentioning

confidence: 99%

Research on 3D Phenotypic Reconstruction and Micro-Defect Detection of Green Plum Based on Multi-View Images

Zhang

Huo

Liu

et al. 2023

Forests

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Rain spots on green plum are superficial micro-defects. Defect detection based on a two-dimensional image is easily influenced by factors such as placement position and light and is prone to misjudgment and omission, which are the main problems affecting the accuracy of defect screening of green plum. In this paper, using computer vision technology, an improved structure from motion (SFM) and patch-based multi-view stereo (PMVS) algorithm based on similar graph clustering and graph matching is proposed to perform three-dimensional sparse and dense reconstruction of green plums. The results show that, compared with the traditional algorithm, the running time of this algorithm is lower, at only 26.55 s, and the mean values of camera optical center error and pose error are 0.019 and 0.631, respectively. This method obtains a higher reconstruction accuracy to meet the subsequent plum micro-defect detection requirements. Aiming at the dense point cloud model of green plums, through point cloud preprocessing, the improved adaptive segmentation algorithm based on the Lab color space realizes the effective segmentation of the point cloud of green plum micro-defects. The experimental results show that the average running time of the improved adaptive segmentation algorithm is 2.56 s, showing a faster segmentation speed and better effect than the traditional K-means and K-means++ algorithms. After clustering the micro-defect point cloud, the micro-defect information of green plums was extracted on the basis of random sample consensus (RANSAC) plane fitting, which provides a theoretical model for further improving the accuracy of sorting the appearance quality of green plums.

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“…In comparison to ALS, terrestrial laser scanning (TLS) can collect a much higher point cloud density (1000 pts/m 2 ), greatly improving the measurement accuracy of vegetation structure. Although the scanning range is limited, it is suitable for collecting 3D structures at a fine scale and minimizing canopy occlusion in contrast with ALS [15], enabling the possibility of quantifying volume or biomass [21,29,30] and calculating leaf indices, including the leaf area index (LAI) and leaf area density (LAD) [31][32][33]. The voxel-based approach has proven to be effective in quantifying structural parameters and estimating the biomass of trees and surface fuelbeds using TLS data [34][35][36].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Three Approaches for Estimating Understory Biomass in Yanshan Mountains

Li,

Hu,

Xing

et al. 2024

Remote Sensing

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Aboveground biomass (AGB) of shrubs and low-statured trees constitutes a substantial portion of the total carbon pool in temperate forest ecosystems, contributing much to local biodiversity, altering tree-regeneration growth rates, and determining above- and belowground food webs. Accurate quantification of AGB at the shrub layer is crucial for ecological modeling and still remains a challenge. Several methods for estimating understory biomass, including inventory and remote sensing-based methods, need to be evaluated against measured datasets. In this study, we acquired 158 individual terrestrial laser scans (TLS) across 45 sites in the Yanshan Mountains and generated metrics including leaf area and stem volume from TLS data using voxel- and non-voxel-based approaches in both leaf-on and leaf-off scenarios. Allometric equations were applied using field-measured parameters as an inventory approach. The results indicated that allometric equations using crown area and height yielded results with higher accuracy than other inventory approach parameters (R2 and RMSE ranging from 0.47 to 0.91 and 12.38 to 38.11 g, respectively). The voxel-based approach using TLS data provided results with R2 and RMSE ranging from 0.86 to 0.96 and 6.43 to 21.03 g. Additionally, the non-voxel-based approach provided similar or slightly better results compared to the voxel-based approach (R2 and RMSE ranging from 0.93 to 0.96 and 4.23 to 11.27 g, respectively) while avoiding the complexity of selecting the optimal voxel size that arises during voxelization.

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A comparative study for obtaining effective Leaf Area Index from single Terrestrial Laser Scans by removal of wood material

Cited by 11 publications

References 23 publications

Estimations of Forest Stand Parameters in Open Forest Stand Using Point Cloud Data from Terrestrial Laser Scanning, Unmanned Aerial Vehicle and Aerial LiDAR Data

Estimations of Forest Stand Parameters in Open Forest Stand Using Point Cloud Data from Terrestrial Laser Scanning, Unmanned Aerial Vehicle and Aerial LiDAR Data

Research on 3D Phenotypic Reconstruction and Micro-Defect Detection of Green Plum Based on Multi-View Images

Comparison of Three Approaches for Estimating Understory Biomass in Yanshan Mountains

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