| 小光斑激光雷达数据估测森林树高研究进展 |
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| 引用本文： | 邢艳秋,尤号田,霍达,孙小添,王蕊.小光斑激光雷达数据估测森林树高研究进展[J].世界林业研究,2014,27(2):29-34. |
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| 作者姓名： | 邢艳秋 尤号田 霍达 孙小添 王蕊 |
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| 作者单位： | 东北林业大学森林作业与环境研究中心, 哈尔滨150040;东北林业大学森林作业与环境研究中心, 哈尔滨150040;东北林业大学森林作业与环境研究中心, 哈尔滨150040;东北林业大学森林作业与环境研究中心, 哈尔滨150040;东北林业大学森林作业与环境研究中心, 哈尔滨150040 |
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| 基金项目： | 中央高校基本科研业务费专项资金支撑项目（DL12EB07）；国家自然科学基金支撑项目（41171274） |
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| 摘 要： | 小光斑激光雷达可以同时获得森林的垂直及水平结构参数,因光斑直径较小,可以做到森林单木结构参数的准确估计,进而推广到样方甚至更大区域森林结构参数的估计,近年来在林业中得到广泛应用。文中主要从树高估计方面对小光斑激光雷达在林业中的应用进行研究,通过对先前类似文献进行归纳总结发现,在小光斑激光雷达估测森林树高方面仍存在着一些问题,从而限制了森林树高估测精度的提高,如点云分类算法、点云密度、森林郁闭度、单木的准确分割等,还对小光斑激光雷达估计森林树高中所存在的问题进行了概括,并提出了改进建议。
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| 关 键 词： | 小光斑激光雷达 树高估测 点云密度 单木分割 |
| 收稿时间： | 2013/9/13 0:00:00 |
| 修稿时间： | 2013/11/18 0:00:00 |
Research Progress in Estimating Forest Tree Height Using Small Footprint Lidar Data |
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| Xing Yanqiu,You Haotian,Huo D,Sun Xiaotian and Wang Rui.Research Progress in Estimating Forest Tree Height Using Small Footprint Lidar Data[J].World Forestry Research,2014,27(2):29-34. |
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| Authors: | Xing Yanqiu You Haotian Huo D Sun Xiaotian and Wang Rui |
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| Institution: | Center for Forest Operations and Environment, Northeast Forestry University, Harbin 150040, China;Center for Forest Operations and Environment, Northeast Forestry University, Harbin 150040, China;Center for Forest Operations and Environment, Northeast Forestry University, Harbin 150040, China;Center for Forest Operations and Environment, Northeast Forestry University, Harbin 150040, China;Center for Forest Operations and Environment, Northeast Forestry University, Harbin 150040, China |
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| Abstract: | Small footprint LiDAR can simultaneously get both vertical and horizontal forest structure parameters and help accurately estimate the structural parameters of single tree owing to its smaller footprint size, and therefore it can be used in plots and even larger regions. Because of this, it has been widely used in forestry in recent years. This paper studied the tree height estimate using small footprint LiDAR. Based on literature reviews, the problems limiting the accuracy of forest tree height estimate were listed, including point cloud classification algorithm, point cloud density, forest canopy density, individual tree segmentation and so on. Finally, some solutions to the problems were put forward. |
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| Keywords: | small footprint LiDAR tree height point cloud density individual tree segmentation |
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