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Related Experiment Video

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Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

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

Yuchu Qin¹, Tuong Thuy Vu, Yifang Ban

¹Division of Geodesy & Geoinformatics, Royal Institute of Technology (KTH), 10044 Stockholm, Sweden. yuchu@kth.se

|November 29, 2012

Summary

This summary is machine-generated.

This study introduces a new method for determining range in light detection and ranging (LiDAR) waveforms, improving point cloud generation for complex terrain. The approach enhances accuracy in tree height measurements and surface smoothness.

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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.