Range determination for generating point clouds from airborne small footprint LiDAR waveforms

Area of Science:

• Geospatial Science
• Remote Sensing Technology
• Geomatics Engineering

Background:

• Generating accurate point clouds from light detection and ranging (LiDAR) waveforms is challenging, especially over complex terrain due to waveform deformation.
• Standard commercial software may not fully address issues like peak drift and pulse widening in small footprint LiDAR data.

Purpose of the Study:

• To develop and validate a novel range determination approach for generating high-density point clouds from small footprint LiDAR waveforms.
• To improve the accuracy of 3D coordinate estimation by correcting for waveform deformation and pulse widening.

Main Methods:

• Simulating waveform deformation over complex terrain using convolution.
• Analyzing peak center position drift to identify the first echo.
• Estimating waveform peak start points for range calculation.
• Proposing a range correction method for pulse widening.

Main Results:

• The proposed approach generated more points compared to standard commercial products.
• Field measurements showed more accurate tree height estimations using the developed method.
• The approach achieved smooth surface generation with low standard deviation.
• Comparative analysis with GeocodeWF demonstrated superior performance in specific metrics.

Conclusions:

• The developed range determination approach offers a satisfactory solution for estimating 3D point cloud coordinates.
• It effectively corrects range information in LiDAR waveforms with deformed peaks, particularly over complex terrain.
• The method enhances the quality and accuracy of point cloud data for various geospatial applications.