Point Cloud Filtering Algorithm Based on Icesat-2 Satellite Data and Vegetation Height Inversion

Authors

  • Danlan Tian , Kai Zhu , Song L

Abstract

-Lidar, as an active new type of remote sensing detection method, has a strong ability to detect the
spatial structure and terrain of vegetation, and can accurately measure the vertical structure and height of forest
trees. In 2017, a new generation of space lidar satellite ICESat-2 (Ice, Cloud and Ground Elevation Satellite 2)
was launched. The ATLAS (Advanced Terrain Laser Altimeter System) height measurement system uses
micro-pulse multi-beam photon counting lidar technology. The filtering and classification of point cloud data
is an important part of lidar data application and processing, and is a hot issue in current research. In this study,
taking the operation area of Wangqing Forestry Bureau in Jilin Province as an example, based on the ICESat-2
waveform data and sample site measured data, the laser zenith angle was introduced to modify the
GroundExtent, and a vegetation canopy height estimation model was established to test the ICESat-2 laser sky.
The effect of apex angle on inversion of vegetation canopy height. According to the characteristics of point
cloud data in dense vegetation area, an improved point cloud data filtering algorithm based on moving window
and slope algorithm is proposed.

Published

2020-03-31

Issue

Section

Articles