Estimation of Urban Forest Characteristic Parameters Using UAV-Lidar Coupled with Canopy Volume

Zhang, Bo; Li, Xuejian; Du, Huaqiang; Zhou, Guomo; Mao, Fangjie; Huang, Zihao; Zhou, Lv; Xuan, Jie; Gong, Yulin; Chen, Chao

doi:10.3390/rs14246375

Cited by 12 publications

(11 citation statements)

References 47 publications

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“…A higher LAI indicates enhanced utilization of light energy by plants, leading to improved growth conditions and increased aboveground biomass (AGB). According to Zhang et al [29], the incorporation of a canopy structure variable in the ULS-based AGB estimation model for urban forests resulted in enhanced accuracy in various models. Breast diameter estimation was conducted by Xiong et al [95] using UAV-LiDAR to extract tree height and crown diameter, which were then utilized in a binary heteroscedastic growth equation for estimating the most suitable aboveground biomass (AGB) method for individual trees (R 2 of 0.77, RMSE of 15.99 kg).…”

Section: Feature Screeningmentioning

confidence: 99%

“…This has attracted a large number of scholars to use LiDAR to carry out forest AGB research [27], such as Mariano et al [28], who explored the AGB estimation model construction method based on LiDAR height, intensity, or a combination of height and intensity data. Zhang et al [29] also accurately and successfully estimated the AGB of the urban forest based on the UAV-LiDAR and coupled it with the structural characteristics of the canopy.…”

Section: Introductionmentioning

confidence: 99%

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UAV-LiDAR Integration with Sentinel-2 Enhances Precision in AGB Estimation for Bamboo Forests

Zhang,

Zhao,

Chen

et al. 2024

Remote Sensing

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Moso bamboo forests, recognized as a distinctive and significant forest resource in subtropical China, contribute substantially to efficient carbon sequestration. The accurate assessment of the aboveground biomass (AGB) in Moso bamboo forests is crucial for evaluating their impact on the carbon balance within forest ecosystems at a regional scale. In this study, we focused on the Moso bamboo forest located in Shanchuan Township, Zhejiang Province, China. The primary objective was to utilize various data sources, namely UAV-LiDAR (UL), Sentinel-2 (ST), and a combination of UAV-LiDAR with Sentinel-2 (UL + ST). Employing the Boruta algorithm, we carefully selected characterization variables for analysis. Our investigation delved into establishing correlations between UAV-LiDAR characterization parameters, Sentinel-2 feature parameters, and the aboveground biomass (AGB) of the Moso bamboo forest. Ground survey data on Moso bamboo forest biomass served as the basis for our analysis. To enhance the accuracy of AGB estimation in the Moso bamboo forest, we employed three distinct modeling techniques: multivariate linear regression (MLR), support vector regression (SVR), and random forest (RF). Through this approach, we aimed to compare the impact of different data sources and modeling methods on the precision of AGB estimation in the studied bamboo forest. This study revealed that (1) the point cloud intensity of UL, the variables of canopy cover (CC), gap fraction (GF), and leaf area index (LAI) reflect the structure of Moso bamboo forests, and the variables indicating the height of the forest stand (AIH1, AIHiq, and Hiq) had a significant effect on the AGB of Moso bamboo forests, significantly impact Moso bamboo forest AGB. Vegetation indices such as DVI and SAVI in ST also exert a considerable effect on Moso bamboo forest AGB. (2) AGB estimation models constructed based on UL consistently demonstrated higher accuracy compared with ST, achieving R2 values exceeding 0.7. Regardless of the model used, UL consistently delivered superior accuracy in Moso bamboo forest AGB estimation, with RF achieving the highest precision at R2 = 0.88. (3) Integration of ST with UL substantially improved the accuracy of AGB estimation for Moso bamboo forests across all three models. Specifically, using RF, the accuracy of AGB estimation increased by 97.7%, with R2 reaching 0.89 and RMSE reduced by 124.4%. As a result, the incorporation of LiDAR data, which reflects the stand structure, has proven to enhance the accuracy of aboveground biomass (AGB) estimation in Moso bamboo forests when combined with multispectral remote sensing data. This integration serves as an effective solution to address the limitations of single optical remote sensing methods, which often suffer from signal saturation, leading to lower accuracy in estimating Moso bamboo forest biomass. This approach offers a novel perspective and opens up new possibilities for improving the precision of Moso bamboo forest biomass estimation through the utilization of multiple remote sensing sources.

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Section: Feature Screeningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

UAV-LiDAR Integration with Sentinel-2 Enhances Precision in AGB Estimation for Bamboo Forests

Zhang,

Zhao,

Chen

et al. 2024

Remote Sensing

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“…In parallel with the UAV-LiDAR and backpack-LiDAR data acquisition, 113 Metasequoia plants in the study area were measured and located in this study. The diameter at 1.3 m was measured with a diameter-checking steel ruler, and the height of the tree was measured using a Blume-Leiss height gauge, while the position of the single tree was also accurately located using a Huace Smart RTK (better than 0.5 m accuracy) [22,35]. The statistical characteristics of the field-measured data are shown in Table 2.…”

Section: Field Inventory Datamentioning

confidence: 99%

“…The acquisition of forest parameters with spatial location information is a prerequisite for measuring and evaluating the spatial structure of forests. However, related studies around the world have shown that most forest parameters are obtained through manual field surveys, which have disadvantages such as low efficiency, high cost, and small scale [21,22]. It is therefore important for forest resource monitoring and management to minimize the time and labor costs of field surveys while achieving accurate estimates of forest parameters and their structures [23].…”

Section: Introductionmentioning

confidence: 99%

Optimizing the Spatial Structure of Metasequoia Plantation Forest Based on UAV-LiDAR and Backpack-LiDAR

Chen,

Zhou,

et al. 2023

Remote Sensing

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Optimizing the spatial structure of forests is important for improving the quality of forest ecosystems. Light detection and ranging (LiDAR) could accurately extract forest spatial structural parameters, which has significant advantages in spatial optimization and resource monitoring. In this study, we used unmanned aerial vehicle LiDAR (UAV-LiDAR) and backpack-LiDAR to acquire point cloud data of Metasequoia plantation forests from different perspectives. Then the parameters, such as diameter at breast height and tree height, were extracted based on the point cloud data, while the accuracy was verified using ground-truth data. Finally, a single-tree-level thinning tool was developed to optimize the spatial structure of the stand based on multi-objective planning and the Monte Carlo algorithm. The results of the study showed that the accuracy of LiDAR-based extraction was (R2 = 0.96, RMSE = 3.09 cm) for diameter at breast height, and the accuracy of R2 and RMSE for tree height extraction were 0.85 and 0.92 m, respectively. Thinning improved stand objective function value Q by 25.40%, with the most significant improvement in competition index CI and openness K of 17.65% and 22.22%, respectively, compared to the pre-optimization period. The direct effects of each spatial structure parameter on the objective function values were ranked as follows: openness K (1.18) > aggregation index R (0.67) > competition index CI (0.42) > diameter at breast height size ratio U (0.06). Additionally, the indirect effects were ranked as follows: aggregation index R (0.86) > diameter at breast height size ratio U (0.48) > competition index CI (0.33). The study realized the optimization of stand spatial structure based on double LiDAR data, providing a new reference for forest management and structure optimization.

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“…These solutions can be deployed to manage tree stands by monitoring individual trees. Individual trees are identified and modeled with the use of remote sensing methods that rely on point clouds of airborne LiDAR and TLS data [35,36], as well as multispectral photogrammetric data [6,[37][38][39]. Different subsets of the dataset describing individual trees are created through splitting to generate 3D models of dendrological objects [40][41][42][43][44][45] or even to identify tree species [46].…”

Section: Introductionmentioning

confidence: 99%

Modeling the Geometry of Tree Trunks Using LiDAR Data

Tarsha Kurdi,

Gharineiat,

Lewandowicz

et al. 2024

Forests

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The effective development of digital twins of real-world objects requires sophisticated data collection techniques and algorithms for the automated modeling of individual objects. In City Information Modeling (CIM) systems, individual buildings can be modeled automatically at the second Level of Detail or LOD2. Similarly, for Tree Information Modeling (TIM) and building Forest Digital Twins (FDT), automated solutions for the 3D modeling of individual trees at different levels of detail are required. The existing algorithms support the automated modeling of trees by generating models of the canopy and the lower part of the trunk. Our argument for this work is that the structure of tree trunk and branches is as important as canopy shape. As such, the aim of the research is to develop an algorithm for automatically modeling tree trunks based on data from point clouds obtained through laser scanning. Aiming to generate 3D models of tree trunks, the suggested approach starts with extracting the trunk point cloud, which is then segmented into single stems. Subsets of point clouds, representing individual branches, are measured using Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS). Trunks and branches are generated by fitting cylinders to the layered subsets of the point cloud. The individual stems are modeled by a structure of slices. The accuracy of the model is calculated by determining the fitness of cylinders to the point cloud. Despite the huge variation in trunk geometric forms, the proposed modeling approach can gain an accuracy of better than 4 cm in the constructed tree trunk models. As the developed tree models are represented in a matrix format, the solution enables automatic comparisons of tree elements over time, which is necessary for monitoring changes in forest stands. Due to the existence of large variations in tree trunk geometry, the performance of the proposed modeling approach deserves further investigation on its generality to other types of trees in multiple areas.

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Estimation of Urban Forest Characteristic Parameters Using UAV-Lidar Coupled with Canopy Volume

Cited by 12 publications

References 47 publications

UAV-LiDAR Integration with Sentinel-2 Enhances Precision in AGB Estimation for Bamboo Forests

UAV-LiDAR Integration with Sentinel-2 Enhances Precision in AGB Estimation for Bamboo Forests

Optimizing the Spatial Structure of Metasequoia Plantation Forest Based on UAV-LiDAR and Backpack-LiDAR

Modeling the Geometry of Tree Trunks Using LiDAR Data

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