森林库存中的移动激光扫描:测试点云密度对树木参数估计的影响
Nadeem Ali Khan1, Giovanni Carabin1, Fabrizio Mazzetto1
1Faculty of Agricultural, Environmental and Food Sciences, Free University of Bozen-Bolzano, Piazza Università 1, 39100 Bolzano, Italy.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
使用LiDAR进行准确的森林库存需要特定的点云密度. 乳房高度直径 (DBH) 对于5%的RMSE需要600-700点/m3,而树高度 (TH) 需要超过300点/m3.
科学领域:
- 林业林业 林业 林业 林业
- 遥感 遥感 遥感 遥感
- 生态生态学 生态生态学
背景情况:
- 森林库存对于生态系统管理至关重要.
- 激光雷达技术为树属性提取提供先进的3D映射.
- 点云密度显著影响基于LiDAR的森林调查准确性.
研究的目的:
- 调查和量化LiDAR点云密度对森林参数测量准确度的影响.
- 为了确定最佳的点云密度,以准确地估计胸高直径 (DBH) 和树高 (TH).
主要方法:
- 逐步减少高密度LiDAR数据集的样本.
- 在不同点云密度下提取树特征 (DBH,TH).
- 将提取的特征与高密度基准进行比较,以量化错误 (RMSE).
主要成果:
- 乳房高度直径 (DBH) 估计需要600-700点/m3的误差为<1厘米 (5%RMSE).
- 精确的树高 (TH) 估计 (RMSE <1 m,5%误差) 可以在密度>300点/m3的情况下实现.
- 较低的密度显著增加了DBH和TH测量的误差.
结论:
- 激光雷达点云密度是自动森林库存准确性的关键因素.
- 对于可靠的DBH和TH测量,存在特定的密度值.
- 结果指导在激光扫描调查中平衡运营效率和测量精度.
相关概念视频
01:19Survival Tree
388
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
388
01:29Application of Linearization and Approximation
50
A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...
50