基于点云数据的森林密度估计

Lianjun Chen
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引用次数: 0

摘要

在分析点云数据的特点及其与其他三维数据的差异的基础上,对点云数据的精度问题进行了探讨和研究。从点云密度、滤波方法和插值方法等方面分析了不同地形类别下点云数据的高程精度。采用的研究方法是在不同地形类别、不同点云密度和不同插值方法的情况下,使用不同数量的地面检查点,分析点云数据的精度。然后运用统计理论,对重庆的人口密度进行估算。最终结果表明,该方法能有效估计森林密度,具有较高的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation of the Density of Forest Based on Point Cloud Data
Based on the Analysis of the characteristics of the point cloud data and the difference compared to other three-dimensional data, explore and study the problem of point cloud data accuracy. From these aspects of the point cloud density, filtering methods and the interpolation method, analysis the elevation accuracy of point cloud data under different categories of terrain. The study method used is in case of different terrain category, different points cloud density and different interpolation methods, using the ground checkpoints of different number, analysis the accuracy of point cloud data. Then use the statistical theory, to estimate the density of Chungking. The final results show that this method can effectively estimate the density of the forest, and has high accuracy.
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来源期刊
Journal of Residuals Science & Technology
Journal of Residuals Science & Technology 环境科学-工程:环境
自引率
0.00%
发文量
0
审稿时长
>36 weeks
期刊介绍: The international Journal of Residuals Science & Technology (JRST) is a blind-refereed quarterly devoted to conscientious analysis and commentary regarding significant environmental sciences-oriented research and technical management of residuals in the environment. The journal provides a forum for scientific investigations addressing contamination within environmental media of air, water, soil, and biota and also offers studies exploring source, fate, transport, and ecological effects of environmental contamination.
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