LiDAR-Based Forest Inventory: Methods and Implementation

Airborne LiDAR (Light Detection and Ranging) technology has transformed forest inventory methodology over the past two decades. By providing detailed three-dimensional structural information on forest canopies, LiDAR enables more accurate and cost-effective biomass estimation compared to traditional ground sampling alone, particularly for large-area assessments.

LiDAR Technology Fundamentals

Data Acquisition

Airborne laser scanning systems:

• Laser pulse emission: Near-infrared wavelengths (typically 1064 nm or 1550 nm)
• Return detection: Multiple returns from canopy layers and ground surface
• Position determination: Integration with GPS/IMU for precise georeferencing
• Point density: 1-20 points per square meter depending on flying height and scan angle
• Coverage: 500-2000 meters swath width with 20-50% overlap between flight lines

Point Cloud Characteristics

Raw LiDAR data provides:

• X, Y, Z coordinates: Three-dimensional position for each laser return
• Intensity: Return signal strength indicating surface reflectance
• Return number: First, intermediate, or last return from pulse
• Classification: Automated or manual assignment to ground, vegetation, buildings
• Timestamp: Enables trajectory reconstruction and quality control

Data Processing Workflow

Standard processing sequence:

1. Trajectory resolution: GPS/IMU integration for sensor position/orientation
2. Point cloud generation: Conversion of raw waveforms to 3D coordinates
3. Quality control: Overlap analysis, vertical accuracy assessment
4. Ground classification: Automated algorithms (e.g., progressive TIN densification)
5. Normalization: Height above ground calculation for vegetation returns
6. Derivative products: Canopy height models, intensity images, metrics

Forest Measurement Approaches

Area-Based Methods

Statistical correlation between LiDAR metrics and field plots:

Methodology:

• Establish field sample plots with conventional measurements (diameter, height, species)
• Extract LiDAR-derived metrics for plot areas (height percentiles, density, intensity)
• Develop regression models predicting forest attributes from LiDAR metrics
• Apply models wall-to-wall across study area using LiDAR coverage

Advantages:

• Statistically robust with proper field sampling design
• Handles complex forest structures (mixed species, multi-layered canopies)
• Provides uncertainty quantification through prediction intervals
• Compatible with existing forest inventory frameworks

Metrics commonly used:

• Height percentiles (95th, 90th, 75th, 50th, 25th)
• Canopy density metrics (percentage first returns above thresholds)
• Canopy cover (percentage ground returns)
• Statistical moments (mean, standard deviation, skewness, kurtosis)
• Intensity statistics (mean, percentiles of return intensity)

Individual Tree Detection

Explicit identification and measurement of individual trees:

Algorithms:

• Local maxima identification: Height-based crown peak detection
• Watershed segmentation: Delineation of crown boundaries
• RANSAC-based methods: Robust detection under occlusion and overlap
• Machine learning: Neural networks for complex crown shapes

Measurements extracted:

• Tree location (X, Y coordinates)
• Tree height (crown peak to ground)
• Crown diameter (horizontal extent of delineated crown)
• Crown volume (3D characterization from point cloud)

Species classification:

• Intensity patterns (reflectance differences among species)
• Geometric features (crown shape, branch architecture)
• Multispectral LiDAR (species-specific spectral signatures)
• Integration with optical imagery (color, texture, phenology)

Advantages:

• Direct tree measurements compatible with growth models
• Enables tree-level management prescriptions
• Provides spatial pattern information (clustering, gaps)
• Supports biodiversity assessment through structural diversity

Limitations:

• Accuracy decreases in dense forests with crown overlap
• Understory trees often missed due to canopy occlusion
• Computationally intensive for large areas
• Requires validation with high-quality field data

Biomass Estimation Methods

Direct Allometry

Tree-level biomass from detected individuals:

Approach:

• Individual tree detection provides height and crown diameter
• Apply allometric equations relating dimensions to biomass
• Sum tree-level estimates within management units
• Aggregate to stand or landscape scales

Allometric models:

• Species-specific equations preferred (diameter-height-biomass)
• Regional equations when species unavailable
• Crown volume relationships for LiDAR-specific applications
• Component models (stem, branch, foliage) for detailed accounting

Uncertainty sources:

• Detection errors (omission and commission)
• Measurement errors (height, crown dimensions)
• Allometric model uncertainty
• Species misclassification

Area-Based Regression

Statistical modeling approach:

Model development:

• Field plots provide reference biomass from conventional measurement
• Extract LiDAR metrics for plot areas
• Develop regression models (linear, nonlinear, machine learning)
• Validate with independent test data
• Apply across entire LiDAR coverage

Model forms:

• Multiple linear regression with metric selection
• Nonlinear models (power functions, exponential)
• Random forest regression (ensemble methods)
• Neural networks for complex relationships

Validation metrics:

• R² (coefficient of determination)
• RMSE (root mean square error)
• Bias (systematic over/under-estimation)
• Relative RMSE (percentage error)

Implementation Considerations

Flight Planning

Acquisition specifications:

• Pulse density: Minimum 4-8 points/m² for forest inventory
• Flying height: Balance between productivity and point density
• Scan angle: Limit to ±15° from nadir for vertical accuracy
• Timing: Leaf-on for deciduous, consistent phenology across area
• Weather: Avoid rain, fog, high winds affecting aircraft stability

Ground Reference Data

Field sampling design:

• Plot size: 0.04-0.1 hectares for area-based methods
• Plot shape: Circular plots for easier LiDAR metric extraction
• Sample intensity: 0.5-2% of area depending on heterogeneity
• Stratification: Sample across forest types, ages, densities
• Timing: Synchronize with LiDAR acquisition (within 1 year)

Measurements:

• All trees above threshold (typically 10 cm diameter)
• Diameter at breast height (DBH)
• Total height (subsample for height-diameter models)
• Species identification
• Live/dead status
• Crown class (dominant, codominant, intermediate, suppressed)

Quality Assurance

Verification procedures:

• Vertical accuracy: Check against RTK-GPS ground surveys
• Alignment: Verify overlap areas for consistency
• Completeness: Ensure no data gaps or low-density areas
• Classification: Validate ground/vegetation separation
• Outliers: Identify and investigate anomalous values

Cost-Benefit Analysis

Economic considerations:

• LiDAR acquisition: $20-100 per acre depending on area, density, terrain
• Field sampling: $500-2000 per plot for complete measurement
• Data processing: $10-30 per acre for standard products
• Model development: Fixed cost, amortized across large areas

Break-even analysis:

• Small areas (<1000 acres): Conventional inventory often more cost-effective
• Medium areas (1000-10,000 acres): LiDAR competitive with reduced field sampling
• Large areas (>10,000 acres): LiDAR significantly reduces cost per acre
• Repeat measurements: LiDAR advantage increases with monitoring frequency

Application Examples

California Compliance Carbon Offsets

Implementation on 180,000-acre timberland property:

• Area-based approach with 200 field plots
• Models developed for mixed conifer and hardwood forest types
• Achieved <10% RMSE meeting protocol requirements
• First LiDAR inventory to pass ARB field verification
• Reduced inventory cost 40% compared to conventional sampling

Temperate Rainforest Biomass Mapping

Pacific Northwest old-growth inventory:

• Individual tree detection in structurally complex forests
• Integration with multispectral imagery for species classification
• Biomass estimates for carbon offset baseline
• Monitoring protocol for permanence verification

Tropical Forest REDD+ Project

Amazon basin avoided deforestation initiative:

• Stratified sampling across degradation gradients
• LiDAR transects calibrated with field plots
• Systematic monitoring for leakage detection
• Integration with radar for cloud-free change detection

Emerging Technologies

Spaceborne LiDAR

Satellite platforms providing global coverage:

• GEDI (Global Ecosystem Dynamics Investigation): Waveform sampling for biomass
• ICESat-2 (Ice, Cloud, and land Elevation Satellite): Photon-counting altimetry
• Applications: Large-area baseline mapping, change detection, model calibration
• Limitations: Sparse sampling (25-60m footprints), not wall-to-wall

UAV LiDAR

Drone-based systems:

• Advantages: Low cost, high flexibility, ultra-high point density
• Applications: Small-area intensive inventory, validation, change detection
• Limitations: Limited coverage, regulatory restrictions, processing complexity

Multispectral LiDAR

Sensors with multiple wavelengths:

• Capability: Species discrimination, moisture content, leaf chemistry
• Status: Operational systems emerging (green + near-infrared)
• Potential: Improved biomass models, health monitoring, fire risk

Integration with Optical/Radar

Multi-sensor approaches:

• LiDAR + Hyperspectral: Structure and spectral properties
• LiDAR + SAR: All-weather monitoring, penetration differences
• LiDAR + Multispectral: Cost-effective species mapping
• Fusion methods: Machine learning combining complementary data

Best Practices

Data Acquisition

Specifications for forest inventory:

• Minimum 8 points/m² pulse density
• ±15° maximum scan angle
• Calibrated intensity values
• IMU/GPS quality indicators documented
• Multiple returns recorded

Processing

Standard procedures:

• Consistent ground classification algorithms
• Height normalization verified against survey data
• Systematic outlier detection and removal
• Metric extraction using established libraries (FUSION, LAStools, lidR)
• Documentation of all processing steps

Modeling

Statistical rigor:

• Independent validation dataset (30% of plots)
• Cross-validation for small sample sizes
• Residual analysis for model assumptions
• Sensitivity analysis for parameter uncertainty
• Conservative estimates for carbon accounting

Reporting

Transparency requirements:

• Methodology documentation
• Accuracy assessment with statistical metrics
• Uncertainty quantification
• Quality control procedures
• Data availability and archiving

LiDAR-based forest inventory has matured into a reliable, cost-effective approach for large-area biomass assessment. Proper implementation following established methodologies ensures accurate results meeting regulatory standards for carbon offset verification and sustainable forest management.

Need support with LiDAR-based forest inventory? Contact Arbos to discuss project-specific methodology development and implementation.