Tree parameter extraction method based on new remote sensing technology and terrestrial laser scanning technology

IF 3.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Aiguo Wang , Jun Wang , Haiming Li , Jian Hu , Haiyuan Zhou , Xinyu Zhang , Xuan Liu , Wanying Wang , Wenjin Zhang , Siting Wu , Ningyang Jiao , Yihao Wang
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引用次数: 0

Abstract

Ground LiDAR is a terrestrial LiDAR system that is often used for terrain and geomorphic mapping. Ground-based LiDAR can be used to collect more local and short-range data, making it ideal for mapping smaller areas with high precision. In order to solve the rapid extraction of tree parameters in the national public welfare forest survey, the ground-based LIDAR was used to obtain the point cloud of trees, and the point cloud data was registered, denoised, normalized, sliced, parameter extracted, etc., and the parameters of individual trees in the forest were obtained. The Bland-Altman consistency test is used to test whether the method of extracting tree parameters from point clouds is consistent with the traditional measurement method. The experimental results show that the point cloud data obtained by the ground-based LIDAR can quickly, conveniently and accurately extract the tree parameters, which is consistent with the traditional tree parameter extraction method, and has the advantages than the traditional tree parameter measurement, such as point cloud, image and traceability. It has a unique advantage in establishing a tree database. It is suggested that LIDAR should be used for forest survey in the future.

基于新型遥感技术和地面激光扫描技术的树木参数提取方法
地面激光雷达是一种地面激光雷达系统,通常用于地形和地貌测绘。地基激光雷达可用于采集更多局部和短程数据,因此非常适合高精度绘制较小区域的地图。为解决全国公益林调查中树木参数的快速提取问题,利用地基激光雷达获取树木点云,并对点云数据进行注册、去噪、归一化、切片、参数提取等处理,得到森林中单株树木的参数。采用 Bland-Altman 一致性检验法检验从点云提取树木参数的方法与传统测量方法是否一致。实验结果表明,地基激光雷达获取的点云数据可以快速、方便、准确地提取树木参数,与传统的树木参数提取方法一致,与传统的树木参数测量方法相比,具有点云化、影像化、可追溯等优点。在建立树木数据库方面具有独特的优势。建议今后在森林调查中使用激光雷达。
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来源期刊
Big Data Research
Big Data Research Computer Science-Computer Science Applications
CiteScore
8.40
自引率
3.00%
发文量
0
期刊介绍: The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic. The journal will accept papers on foundational aspects in dealing with big data, as well as papers on specific Platforms and Technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered. Occasionally the journal may publish whitepapers on policies, standards and best practices.
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