Forest height estimation based on uav lidar simulated waveform

Chen, Bo-Wei; Li, Zengyuan; Pang, Yong; Liu, Qingwang; Gao, Xianlian; Gao, Jinping; Fu, Anmin

doi:10.1109/igarss.2017.8127595

Cited by 2 publications

(1 citation statement)

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“…Tree height extraction based on airborne LiDAR had been widely used and validated [55,56], but most studies were predicated on accurate single tree segmentation results. Although the PCS algorithm achieved good segmentation accuracy in low-density coniferous forests, it usually failed to achieve satisfactory segmentation accuracy in forests with higher stand densities.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

confidence: 99%