Development of a Method for Estimating Height from LiDAR Data
Chizuka Fujishima (a*), Junichi Susaki (b), Yoshie Ishii (c)

a) Student, Graduate School of Engineering, Kyoto University, Japan
*fujishima.chizuka.24t[at]st.kyoto-u.ac.jp
b) Professor, Graduate School of Engineering, Kyoto University, Japan
c) Assistant Professor, Graduate School of Engineering, Kyoto University, Japan

Abstract

Currently, optical satellites are mainly used to observe land cover and topography. They provide full-color image and high spatial resolution data, but some problems exist including limitation of high-precision use of 3D maps and potential errors of several meters in estimated ground heights under forest canopy. To address these issues, altimeter LiDAR satellites with full-waveform LiDAR are now being developed. Full-waveform LiDAR is the technology to continuously acquire reflected intensity of LiDAR and record it as a reflected waveform. In addition, coordinated observation of commercial small optical observation systems and altimeter LiDAR satellite is expected to enable the generation of most advanced 3D terrain information in the world. For the practical use of altimeter LiDAR satellites, this study develops a method to estimate tree height from 3D point cloud data.
Based on the methods adopted in existing LiDAR missions, I propose a method to estimate tree height from reflected waveform created from point cloud data. Also, considering the correlation between waveform and point cloud data, I propose a method to estimate tree height directly from point cloud data. The feature of this study is applying the assumption that there are two types of point clouds -ground points and vegetation points- and they exist according to a separate distribution. The estimated heights are validated by comparing them to the true value. The minimum RMSE was 2.20 m for the waveform-based estimation and 0.31 m for the point cloud-based estimation. Especially in flat areas, most values could be estimated with an error of less than 1.00 m. In addition, the accuracy of point cloud separation had a significant impact on the estimation accuracy. Future tasks are clarifying the relationship between the reflected waveform and point cloud data and developing a method for creating continuous maps with optical images through deep learning.

Keywords: LiDAR- Point Cloud- Height Estimation

Topic: Topic C: Emerging Technologies in Remote Sensing