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Related Experiment Video

Updated: Apr 13, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
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Tree structural modeling from leaf-on point clouds for biomass carbon stock estimation.

Yuto Nakamura1,2, Taiga Yamazaki2, Hiroaki Shirakawa2

  • 1Institutes of Innovation for Future Society, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Aichi, Japan.

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Summary
This summary is machine-generated.

Accurately measuring urban tree biomass is vital. A new Skeleton Generative Method (SkeletonGM) uses mobile laser scanning to create 3D tree models, improving biomass estimation accuracy.

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Area of Science:

  • Forestry
  • Biomass Estimation
  • Remote Sensing

Background:

  • Urban trees are valuable biomass sources.
  • Traditional methods struggle with urban tree structural variability.
  • Existing LiDAR methods face challenges with dense foliage.

Purpose of the Study:

  • To develop a method for accurate urban tree biomass assessment.
  • To overcome limitations of current 3D modeling techniques for dense foliage.
  • To improve biomass estimation by accounting for tree structure.

Main Methods:

  • Developed the Skeleton Generative Method (SkeletonGM) for tree reconstruction.
  • Utilized mobile laser scanning data for skeletal point cloud generation.
  • Converted skeletal data to 3D models (AdTree) and calculated biomass using wood density.

Main Results:

  • SkeletonGM successfully reconstructed tree structures from dense foliage.
  • Biomass estimates showed strong agreement with established allometric equations (R² = 0.95 and 0.94).
  • Mean absolute percentage errors were 14.1% and 14.6% for hinoki cypress and dawn redwood.

Conclusions:

  • The Skeleton Generative Method (SkeletonGM) offers a reliable approach for urban tree biomass assessment.
  • This method enhances accuracy by considering detailed tree architecture.
  • SkeletonGM has significant potential for improving urban forest inventory and management.