LiDAR不同强度校正法对樟子松叶面积指数估测的影响

尤号田; 邢艳秋; 彭涛; 丁建华

doi:10.11947/j.AGCS.2018.20170515

测绘学报 >

2018 , Vol. 47 >Issue 2: 170 - 179

DOI: https://doi.org/10.11947/j.AGCS.2018.20170515

LiDAR不同强度校正法对樟子松叶面积指数估测的影响

尤号田 ,
邢艳秋 ,
彭涛 ,
丁建华

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1. 桂林理工大学测绘地理信息学院, 广西桂林 541004;
2. 东北林业大学森林作业与环境研究中心, 黑龙江哈尔滨 150040

尤号田(1985-),男,博士,讲师,研究方向为林业参数遥感定量研究。E-mail:wuliu2007_02@hotmail.com

收稿日期: 2017-09-11

修回日期: 2017-12-11

网络出版日期: 2018-03-02

基金资助

林业公益性行业科研专项经费（201504319）；广西自然科学基金（2017GXNSFDA198016）；桂林理工大学科研启动基金

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Effects of Different LiDAR Intensity Normalization Methods on Scotch Pine Forest Leaf Area Index Estimation

YOU Haotian ,
XING Yanqiu ,
PENG Tao ,
DING Jianhua

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1. College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China;
2. Center for Forest Operations and Environment, Northeast Forestry University, Harbin 150040, China

Received date: 2017-09-11

Revised date: 2017-12-11

Online published: 2018-03-02

Supported by

The Special Fund for Forest Scientific Research in the Public Welfare (No. 201504319);The Natural Science Foundation of Guangxi Province of China (No. 2017GXNSFDA198016);The Foundation of Guilin University of Technology

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摘要

机载激光雷达（LiDAR）强度数据在获取过程中受多种因素影响，各因素影响的有效量化及校正对机载LiDAR强度校正及应用具有重要意义。本文以雷达方程为基础，分别采用距离、入射角及距离和入射角对LiDAR点云强度进行校正，从中提取冠层总强度和强度比值两类参数，用于估测森林叶面积指数（LAI），以期量化各影响因素强度校正对不同类型参数估测森林LAI的影响。结果表明：强度经距离校正能够提高森林LAI的估测精度，而强度经数字高程模型衍生入射角校正非但没能提高估测精度，反而降低了估测精度。强度经距离和入射角综合校正虽能提高森林LAI的估测精度，但结果却低于距离单独校正的结果。与此同时，对冠层总强度参数而言，强度校正前后森林LAI估测结果的差异较为明显，而对强度比值参数而言，强度校正前后森林LAI估测结果差异不大。综上可知，不同因素强度校正对森林LAI估测的影响不同，且影响程度与所用参数变量类型密切相关。因此，在未来强度应用研究中，应根据变量参数类型选择合适的校正方式，以避免不恰当校正造成的成本浪费及精度降低。

关键词： 距离; 入射角; 冠层总强度; 强度比值; 叶面积指数

本文引用格式

尤号田 , 邢艳秋 , 彭涛 , 丁建华 . LiDAR不同强度校正法对樟子松叶面积指数估测的影响[J]. 测绘学报, 2018 , 47(2) : 170 -179 . DOI: 10.11947/j.AGCS.2018.20170515

Abstract

The intensity data of airborne light detection and ranging (LiDAR) are affected by many factors during the acquisition process. It is of great significance for the normalization and application of LiDAR intensity data to study the effective quantification and normalization of the effect from each factor. In this paper, the LiDAR data were normalized with range, angel of incidence, range and angle of incidence based on radar equation, respectively. Then two metrics, including canopy intensity sum and ratio of intensity, were extracted and used to estimate forest LAI, which was aimed at quantifying the effects of intensity normalization on forest LAI estimation. It was found that the range intensity normalization could improve the accuracy of forest LAI estimation. While the angle of incidence intensity normalization did not improve the accuracy and made the results worse. Although the range and incidence angle normalized intensity data could improve the accuracy, the improvement was less than the result of range intensity normalization. Meanwhile, the differences between the results of forest LAI estimation from raw intensity data and normalized intensity data were relatively big for canopy intensity sum metrics. However, the differences were relatively small for the ratio of intensity metrics. The results demonstrated that the effects of intensity normalization on forest LAI estimation were depended on the choice of affecting factor, and the influential level is closely related to the characteristics of metrics used. Therefore, the appropriate method of intensity normalization should be chosen according to the characteristics of metrics used in the future research, which could avoid the waste of cost and the reduction of estimation accuracy caused by the introduction of inappropriate affecting factors into intensity normalization.

Key words： range; angle of incidence; canopy intensity sum; ratio of intensity; leaf area index

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