RETRIEVAL OF LEAF AREA INDEX FROM AIRBORNE WAVEFORM LIDAR DATA BASED ON GORT MODEL

被引：0
|
作者：
Zhu, Xiao [1 ,2 ]
Song, Jinling [1 ,2 ]
机构：
[1] Beijing Normal Univ, Fac Geog Sci, Beijing 100875, Peoples R China
[2] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China
来源：
2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019) | 2019年
关键词：
Leaf area index; LiDAR; GORT model; full-waveform;
D O I：
10.1109/igarss.2019.8900264
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
Leaf Area Index (LAI) is an essential variable within many forest models for its impact on mass and energy exchange between canopy and atmosphere. However, LAI of high precision in forest area is difficult to obtain due to the effects of saturation, complex terrain and changeable weather. LiDAR, as an emerging active remote sensing technique, can provide the vertical interactive information between LiDAR system and vegetation. This paper used a physically based method to retrieve forest LAI from full-waveform LiDAR data. At first, the full-waveform LiDAR data were densified, denosied, and decomposed into a series sub-Gaussian components. Then, the decomposed waveform data were applied to retrieve LAI based on the hybrid GORT model. The result showed good agreement with the field measurements.
引用
收藏
页码：7342 / 7345
页数：4
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