A Method Coupling NDT and VGICP for Registering UAV-LiDAR and LiDAR-SLAM Point Clouds in Plantation Forest Plots DOI Open Access
Fan Wang, Jiawei Wang, Yun Wu

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(12), P. 2186 - 2186

Published: Dec. 12, 2024

The combination of UAV-LiDAR and LiDAR-SLAM (Simultaneous Localization Mapping) technology can overcome the scanning limitations different platforms obtain comprehensive 3D structural information forest stands. To address challenges traditional registration algorithms, such as high initial value requirements susceptibility to local optima, in this paper, we propose a high-precision, robust, NDT-VGICP method that integrates voxel features register point clouds at stand scale. First, are voxelized, their normal vectors distribution models computed, then transformation matrix is quickly estimated based on pair characteristics achieve preliminary alignment. Second, high-dimensional feature weighting introduced, iterative closest (ICP) algorithm used optimize distance between matching pairs, adjusting reduce errors iteratively. Finally, converges when conditions met, yielding an optimal achieving precise cloud registration. results show performs well Chinese fir stands age groups (average RMSE—horizontal: 4.27 cm; vertical: 3.86 cm) achieves accuracy single-tree crown vertex detection tree height estimation F-score: 0.90; R2 for estimation: 0.88). This study demonstrates effectively fuse collaboratively apply multi-platform LiDAR data, providing methodological reference accurately quantifying individual parameters efficiently monitoring structures.

Language: Английский

Niche and Interspecific Association of Dominant Arbor Species in Quercus Communities in the Qinling Mountains, China DOI Creative Commons
Ruizhi Huang, Qi Wang, Jingyi Sun

et al.

Global Ecology and Conservation, Journal Year: 2025, Volume and Issue: unknown, P. e03404 - e03404

Published: Jan. 1, 2025

Language: Английский

Citations

0
Soil Heavy Metal Accumulation and Ecological Risk in Mount Wuyi: Impacts of Vegetation Types and Pollution Sources DOI Creative Commons

Feng Wu,

Zhu Dong-hai,

Tao Yang

et al.

Land, Journal Year: 2025, Volume and Issue: 14(4), P. 712 - 712

Published: March 26, 2025

Soil heavy metal (HM) contamination has become a critical global environmental issue, predominantly caused by industrial and agricultural operations. This study focuses on Mount Wuyi, UNESCO biodiversity hotspot major tea production base, to examine vegetation-mediated soil HM accumulation under anthropogenic impacts. We analyzed nine HMs (Mn, Cu, Zn, Cd, Hg, As, Pb, Cr, Ni) across diverse vegetation types using geochemical indices Positive Matrix Factorization (PMF) modeling. The findings revealed Mn Zn were dominant elements, Cr Pb concentrations exceeded regional background values 3.47 1.26 times, respectively. demonstrated significant pollution levels, while Cd Hg posed the highest ecological risks. Vegetation type significantly influenced distribution patterns, with cultivated areas shrublands (including gardens) accumulating higher of from transportation sources. Notably, bamboo forests exhibited natural resistance contamination. PMF analysis identified four primary sources: urbanization (27.94%), transport–agriculture activities (21.40%), practices (12.98%), atmospheric deposition (12.96%). These results underscore need for implementing clean energy solutions, phytoremediation strategies, tea-specific detoxification measures maintain security sustainability in this ecologically region.

Language: Английский

Citations

0
A Method Coupling NDT and VGICP for Registering UAV-LiDAR and LiDAR-SLAM Point Clouds in Plantation Forest Plots DOI Open Access
Fan Wang, Jiawei Wang, Yun Wu

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(12), P. 2186 - 2186

Published: Dec. 12, 2024

The combination of UAV-LiDAR and LiDAR-SLAM (Simultaneous Localization Mapping) technology can overcome the scanning limitations different platforms obtain comprehensive 3D structural information forest stands. To address challenges traditional registration algorithms, such as high initial value requirements susceptibility to local optima, in this paper, we propose a high-precision, robust, NDT-VGICP method that integrates voxel features register point clouds at stand scale. First, are voxelized, their normal vectors distribution models computed, then transformation matrix is quickly estimated based on pair characteristics achieve preliminary alignment. Second, high-dimensional feature weighting introduced, iterative closest (ICP) algorithm used optimize distance between matching pairs, adjusting reduce errors iteratively. Finally, converges when conditions met, yielding an optimal achieving precise cloud registration. results show performs well Chinese fir stands age groups (average RMSE—horizontal: 4.27 cm; vertical: 3.86 cm) achieves accuracy single-tree crown vertex detection tree height estimation F-score: 0.90; R2 for estimation: 0.88). This study demonstrates effectively fuse collaboratively apply multi-platform LiDAR data, providing methodological reference accurately quantifying individual parameters efficiently monitoring structures.

Language: Английский

Citations

0