检索结果-南通市图书馆

A tree detection method based on trunk point cloud section in dense plantation forest using drone Li DAR data

Forest Ecosystems 2023年第1期10卷 37-45页

作者： Yupan Zhang Yiliu Tan Yuichi Onda Asahi Hashimoto Takashi Gomi Chenwei Chiu shodai inokoshi Center for Research in Isotopes and Environmental Dynamics University of Tsukuba1-1-1TennodaiTsukubaIbaraki305-8572Japan Graduate School of Systems and Information Engineering University of Tsukuba1-1-1TennodaiTsukubaIbaraki305-8572Japan Department of International Environmental and Agricultural Science Tokyo University of Agriculture and Technology3-5-8SaiwaieFuchuTokyo183-8509Japan

Single-tree detection is one of the main research topics in quantifying the structural properties of forests. Drone Li DAR systems and terrestrial laser scanning systems produce high-density point clouds that offer great promise for forest inventories in limited areas. However, most studies have focused on the upper canopy layer and neglected the lower forest structure. This paper describes an innovative tree detection method using drone Li DAR data from a new perspective of the under-canopy structure. This method relies on trunk point clouds, with undercanopy sections split into heights ranging from 1 to 7 m, which were processed and compared, to determine a suitable height threshold to detect trees. The method was tested in a dense cedar plantation forest in the Aichi Prefecture, Japan, which has a stem density of 1140 stems·ha^(-1) and an average tree age of 42 years. Dense point cloud data were generated from the drone Li DAR system and terrestrial laser scanning with an average point density of 5000 and 6500 points·m^(-2), respectively. Tree detection was achieved by drawing point-cloud section projections of tree trunks at different heights and calculating the center coordinates. The results show that this trunk-section-based method significantly reduces the difficulty of tree detection in dense plantation forests with high accuracy(F1-Score=0.9395). This method can be extended to different forest scenarios or conditions by changing section parameters.

关键词： Tree detection Trunk sections Forest Drone LiDAR

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：