文献类型：期刊文献

中文题名：利用机载激光雷达数据提取单株木树高和树冠

英文题名：Extracting height and crown of individual tree using airborne LIDAR data

作者：刘清旺[1] 李增元[1] 陈尔学[1] 庞勇[1] 武红敢[1]

第一作者：刘清旺

通信作者：Liu, Q.-W.

机构：[1]中国林业科学研究院资源信息研究所

年份：2008

卷号：30

期号：6

起止页码：83-89

中文期刊名：北京林业大学学报

外文期刊名：Journal of Beijing Forestry University

收录：CSTPCD;;EI(收录号：20085111791962);Scopus(收录号：2-s2.0-57449111463);北大核心:【北大核心2004】；CSCD:【CSCD2011_2012】；

基金："973"国家重点基础研究发展计划项目(2007CB714404);"863"国家高技术发展计划项目(2006AA12Z101);"948"国家林业局引进项目(2004-4-64)

语种：中文

中文关键词：激光雷达;激光应用;单株木;树高;树冠;双正切角树冠识别算法

外文关键词：LIDAR; laser applications ; individual tree; tree height ; tree crown; double-tangent tree crownrecognition algorithm

分类号：S758.1

摘要：机载激光雷达是一种主动遥感技术。在林业应用方面,高采样密度激光雷达能够获取单株木三维结构特征,采用不同的数据处理方法,可以得到不同精度的单株木参数。该文利用高采样密度的机载激光雷达数据(离散回波,平均激光点间隔0.52 m、平均光斑直径0.3 m),研究了单株木的树高提取技术和树冠边界识别算法,针对单株木的树冠特征,提出了一种双正切角树冠识别算法;最后,使用重庆铁山坪林场的9个外业样地数据,对单株木树高和冠幅,以及样地平均树高和平均冠幅进行了验证。结果表明,单株木树高和冠幅的R2分别为0.34和0.03,样地平均树高和平均冠幅的R2分别为0.97和0.71,样地尺度的相关性明显高于单株木尺度的相关性。
Airborne LIDAR is an active remote sensing characteristics of individual tree can be observed with technique. The 3D （ three-dimensional ） structural high sampling density LIDAR in forests, and produce changeable accuracy of individual tree parameters based on the different approaches of data processing using airborne LIDAR. This study analyzes the extraction of algorithms from individual tree height and crown diameters and develops a double-tangent crown recognition algorithm according to individual tree features using high sampling density airborne LIDAR data （discrete-return, average laser pulse sampling space is 0.52 m, average Farm in footprint diameter is 0.3 m）. Accompanying the nine field plots of data from the Tieshanping Forest Chongqing City, southwestern China, this study validates the extraction results of tree height and crown diameters at individual tree and plot levels. The results showed that R2 of height and crown diameters for individual tree was 0.34 and 0.03, respectively. R2 of average height and crown diameter for sample plots was 0.97 and 0.71, respectively. This correlation at the plot level is obviously higher than that of any given individual tree level.