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Evaluating UAV and Handheld LiDAR Point Clouds for Radiative Transfer Modeling Using a Voxel-Based Point Density

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|January 28, 2026
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Summary

This study assessed Unmanned Aerial Vehicle-based Light Detection and Ranging (UAV-LiDAR) and handheld LiDAR scanners (HLSs) for forest radiative transfer models. Integrating both platforms is crucial for comprehensive 3D forest structure mapping.

Keywords:
SLAMcanopy structurecorrelation analysisnear-infraredocclusionpoint cloud

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Area of Science:

  • Forestry
  • Remote Sensing
  • Geospatial Analysis

Background:

  • Accurate 3D forest structural information is vital for improving radiative transfer models (RTMs).
  • Different LiDAR platforms offer complementary data, but their integration requires careful evaluation.

Purpose of the Study:

  • To evaluate the potential of Unmanned Aerial Vehicle-based Light Detection and Ranging (UAV-LiDAR) and handheld LiDAR scanners (HLSs) for forest RTMs.
  • To assess the effectiveness of data fusion for achieving volumetric completeness in forest structural characterization.

Main Methods:

  • Utilized a Voxel-Based Point Density Proxy (VPDP) to analyze forest structure in a Larix kaempferi forest.
  • Computed coverage gap ratio (CGR) to compare UAV-LiDAR and HLS structural capture.
  • Performed radiative transfer simulations and correlated results with Sentinel-2 NIR reflectance.

Main Results:

  • UAV-LiDAR effectively captured upper canopy structure (10-45% CGR), while HLS excelled in understory detection but showed high upper-canopy CGR (>40%).
  • Data fusion reduced CGR below 10%, indicating strong complementarity.
  • UAV-LiDAR (r = 0.73-0.75) showed better correlation with Sentinel-2 NIR reflectance than HLS (r = 0.64-0.69).

Conclusions:

  • Upper-canopy modeling is critical for nadir-viewing sensors.
  • While HLS integration did not improve reflectance correlation, fusion is essential for volumetric completeness.
  • A voxel size of 50-100 cm balances structural detail and radiative stability, guiding LiDAR platform selection for forest monitoring.