LiDAR and Forest Analytics Bundle

Overview

The LiDAR and Forest Analytics bundle provides specialized point cloud and remote sensing tools for forest inventory, biomass quantification, disturbance detection, and landscape change monitoring. Leverages high-resolution LiDAR elevation and intensity data alongside optical imagery.

Key Concepts

Point Cloud Processing

3D data filtering, classification, and metric derivation from airborne LiDAR (ALS) or terrestrial LiDAR (TLS) acquisitions.

Terrain Modeling

Digital Elevation Models (DEMs), Digital Surface Models (DSMs), and Canopy Height Models (CHMs) derived from point cloud data.

Forest Structure Quantification

Inventory metrics including crown height, crown area, aboveground biomass, basal area, and stem density derived from multispectral and LiDAR data.

Change Detection and Disturbance

Multi-temporal analysis quantifying forest disturbance (harvesting, mortality, insect damage, fire) and recovery trajectories.

Accessibility and Safety

Analysis of terrain and vegetation for pedestrian accessibility, UAV navigability, and hazard identification.

Typical Workflows

Forest Inventory Workflow

  1. Acquire LiDAR and hyperspectral imagery
  2. Quality control and evaluate point cloud metrics (LiDAR QA and Confidence)
  3. Generate terrain and canopy products (LiDAR Terrain Product Suite)
  4. Extract structure metrics and estimate biomass (Forestry Structure and Biomass Analysis)
  5. Validate with field plots
  6. Report inventory maps and statistics

Disturbance Monitoring

  1. Establish baseline forest conditions from LiDAR and imagery
  2. Conduct annual/bi-annual re-acquisition
  3. Detect structural change (LiDAR Change and Disturbance Analysis)
  4. Classify disturbance type (harvesting, insects, disease, fire)
  5. Map recovery progression
  6. Generate disturbance trajectory maps and statistics

Urban Vegetation and Accessibility

  1. Acquire high-resolution LiDAR in urban area
  2. Classify point cloud (ground, vegetation, building, urban features)
  3. Assess vegetation clearance for sidewalk accessibility (Sidewalk Vegetation Accessibility Monitoring)
  4. Identify maintenance priorities
  5. Track year-to-year maintenance compliance

The workflow order below is a practical starting sequence that gets most teams to usable, validated outputs quickly:

  1. LiDAR QA and Confidence
  2. LiDAR Terrain Product Suite
  3. LiDAR Change and Disturbance Analysis
  4. Forestry Structure and Biomass Analysis
  5. Sidewalk Vegetation Accessibility Monitoring

Performance and Data Requirements

  • Point Density: Minimum 4–8 pts/m² for forest inventory; ≥15 pts/m² for detailed structure; ≥50 pts/m² for urban vegetation
  • Vertical Accuracy: ±0.15 m (forest), ±0.10 m (urban) necessary for reliable metrics
  • Multi-Spectral Alignment: Optical data must be coregistered to LiDAR with <0.5 m horizontal accuracy
  • Temporal Consistency: Repeat acquisitions should use same acquisition parameters (sensor, time-of-year, weather)

References

  • ASPRS LiDAR Standards and Best Practices
  • NEON Protocol: Airborne Observation Platform (AOP)
  • Wulder, M. A., et al. (2019). "Lidar and SfM Data Fusion for Measuring Forest Change." Remote Sens. Ecol. Conserv. 5(2), 140–156.