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Mónica Herrero-Huerta1,2,3, Roderik Lindenbergh1, Pablo Rodríguez-Gonzálvez2,4

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|April 25, 2018
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This study presents an automated method using Mobile LiDAR System data to estimate urban tree structural parameters like Diameter at Breast Height (DBH) and Crown Base Height. The approach offers a scalable solution for city-level urban tree inventory and management.

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Area of Science:

  • Urban forestry
  • Geospatial analysis
  • Remote sensing

Background:

  • Accurate urban tree data is crucial for city planning, hazard assessment, and environmental monitoring.
  • Existing methods for urban tree parameter estimation can be labor-intensive and time-consuming.

Purpose of the Study:

  • To develop an innovative, automated methodology for estimating individual urban tree structural parameters using Mobile LiDAR System data.
  • To validate the accuracy and scalability of the proposed methodology for city-level urban tree analysis.

Main Methods:

  • Diameter at Breast Height (DBH) estimation using RANSAC circle fitting and extreme point distance for non-circular trees.
  • Tree size extraction via connectivity analysis, Crown Base Height calculation using voxelization.
  • Canopy Volume estimation with mesh generation and α-shape methods, and tree location determination via Principal Component Analysis.

Main Results:

  • High correlation (R²=0.92 for DBH in 20 cm height bin) and coefficients (>0.91 for other parameters) achieved against field measurements.
  • Validation performed on 29 trees and testing on a larger dataset of 58 trees demonstrated methodology feasibility.
  • Investigated the influence of point density and height bins on DBH estimation accuracy.

Conclusions:

  • The proposed automated methodology is feasible and scalable for comprehensive urban tree analysis.
  • This approach provides accurate structural parameter estimation, supporting efficient urban forest management.
  • Mobile LiDAR System data combined with advanced algorithms offers a powerful tool for urban tree inventory.