Terrestrial or Airborne LiDAR Point Clouds
Georeferenced 3D point cloud covering tree locations
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Research Method
PAD Calculation from Different LiDAR Sources
Method for deriving plant area density (PAD) or similar structural tree parameters from various LiDAR-based sources
Overview
This method provides a standardized approach for calculating plant area density (PAD) from different LiDAR scanning sources, including terrestrial laser scanning (TLS), airborne LiDAR, and mobile laser scanning (MLS). PAD is a key structural parameter for representing trees in microclimate and airflow simulations.
The method includes workflows for point cloud pre-processing, voxel-based analysis, and PAD profile extraction. It addresses source-specific challenges such as occlusion effects, scan resolution differences, and canopy penetration variability.
Purpose
Enable researchers and modelers to derive consistent tree structural parameters from diverse LiDAR data sources, supporting tree representation in urban microclimate simulations and digital twin applications.
Input-Output Workflow
Required Input Data
Tree Location Coordinates
Trunk positions for individual tree extraction
Scan Metadata (Optional)
Scanner specifications, acquisition parameters, and quality indicators
Expected Output
- Vertical PAD profiles (height-resolved plant area density)
- Crown structural parameters (height, width, volume, LAI estimates)
- Quality indicators and uncertainty metadata
Processing Steps
Point Cloud Pre-processing
Noise filtering, ground point removal, and coordinate system alignment
Individual Tree Extraction
Segment point cloud into individual trees using location coordinates and geometric criteria
Voxel Grid Creation
Discretize tree point cloud into 3D voxel grid (typically 0.5m resolution)
PAD Calculation
Compute plant area density for each voxel based on point density and voxel occupancy
Profile Extraction
Generate vertical PAD profiles and crown structural parameters
Quality Assessment
Evaluate completeness, check for occlusion artifacts, and document limitations
Assumptions and Limitations
Important Considerations
- Occlusion effects: TLS may underestimate PAD in upper canopy; airborne LiDAR may underestimate lower canopy
- Leafless conditions: Method works best with full-canopy scans; winter scans underestimate leaf area
- Species variability: PAD-to-LAI conversion factors are species-dependent
Downloadable Protocol
PAD Calculation Protocol
Step-by-step protocol with code examples, validation guidelines, and quality assessment procedures