文献类型：期刊文献

中文题名：基于机载LiDAR单束激光穿透指数的白桦林LAI估测

英文题名：Estimation of birch forest LAI based on single laser penetration index of airborne LiDAR data

作者：邢艳秋[1] 霍达[1] 尤号田[1] 田昕[2] 焦义涛[1] 谢杰[1] 姚松涛[1]

第一作者：邢艳秋

机构：[1]东北林业大学森林作业与环境研究中心;[2]中国林业科学研究院资源信息研究所

年份：2016

卷号：27

期号：11

起止页码：3469-3478

中文期刊名：应用生态学报

外文期刊名：Chinese Journal of Applied Ecology

收录：CSTPCD;;Scopus;北大核心:【北大核心2014】；CSCD:【CSCD2015_2016】；PubMed;

基金：林业公益性行业科研专项(201504319);国家重点基础研究发展计划项目(2013CB733404)资助~~

语种：中文

中文关键词：叶面积指数;机载激光雷达;强度校正;激光分束;单束激光穿透指数

外文关键词：leaf area index （LAI） ; airborne LiDAR ; intensity normalization ; laser beam splitting;single laser penetration index.

分类号：S787.2

摘要：森林叶面积指数(LAI)是描述森林冠层结构和树木生长状况的一个重要指标.本研究以内蒙古依根地区为研究区,在充分考虑机载激光雷达不同回波脉冲之间差异的基础上,对LiDAR点云数据进行分束处理,同时利用传感器与目标之间的距离对LiDAR点云强度进行校正,在由点云校正强度计算传统激光穿透指数(LPI)的同时提出了一个新的激光穿透指数,即单束激光穿透指数(LPI_s),并分别采用LPI和LPI_s两种激光穿透指数在4种不同LiDAR数据采样尺度(直径分别为5、10、15和20 m)下分别应用理论模型和经验模型两种不同的建模方法对森林LAI进行估测,以期通过激光分束来提高森林LAI的估测精度.结果表明:分束激光穿透指数均值(LPI_(mean))估测LAI的效果明显好于未分束LPI的估测效果,且当LiDAR数据采样尺度为15 m时,LPI_(mean)的经验模型(R2=0.80,平均绝对偏差MAD为0.11)和理论模型(R^2=0.77,MAD=0.16)的估测结果均达到最佳.最后综合应用最佳经验模型与理论模型各自的优点绘制了研究区的白桦林LAI分布图.
Forest leaf area index （LAI） is an important indicator to describe the forest canopy structure and growth status of trees. In this paper, the Yigen area of Inner Mongolia was selected as the study area. Taken full account of the differences among different echo types, the LiDAR point cloud data were split into different single lasers. Then, intensity normalization was implemented for LiDAR point cloud data with the range between sensor and target. Based on the normalized intensity data, a new laser penetration index, called single laser beam penetration index （ LPIs） , was calcu- lated along with the calculation of traditional LPI. These two laser penetration indexes were used to estimate the forest LAI based on the theoretical model and empirical model on four different sam- pling scales （5, 10, 15, and 20 m）, respectively, which aimed to improve the retrieval accuracy of forest LAI through laser beam splitting. The results showed that the forest LAI estimated from mean LPIs（ LPImean ） was obviously better than that from traditional LPI. In addition, both of the em- pirical [ RE = 0.80, mean absolute deviation （MAD） = 0.11 ] and theoretical models （ R2 = 0.77, MAD=0.16） achieved the best performances with sampling scale of 15 m. The mapping of birch forest LAI for the study area was derived by theoretical models. integrating both the advantages of best empirical and