LiDAR for Forest Inventory: A Complete Guide

LiDAR has fundamentally changed how we measure forests. Traditional inventory methods require field crews to walk transects, manually measuring sample trees with tape and clinometer. It's slow, expensive, and limited to accessible areas. A drone or aircraft with a LiDAR sensor can survey hundreds of hectares in a single day, measuring every tree rather than just samples — and reaching terrain too steep, remote, or dangerous for ground crews.

What LiDAR Measures

LiDAR pulses penetrate forest canopy, recording multiple returns as they pass through leaves and branches on their way to the ground. This vertical profile captures the three-dimensional structure of the forest. From these returns, you can extract tree heights by comparing canopy top to ground level. You can map crown diameters by analyzing the horizontal extent of return clusters. You can calculate canopy cover as the percentage of ground obscured by vegetation.

Combine these metrics with allometric equations — statistical relationships between tree dimensions and wood volume — and you can estimate timber volume and biomass across entire forest stands. This matters enormously for harvest planning, carbon accounting, and sustainable forest management.

Key Deliverables

The Canopy Height Model (CHM) shows vegetation height across your survey area, revealing which stands are tallest and where young growth is emerging. Individual tree detection algorithms find local maxima in the CHM, identifying each stem's location and height. Crown segmentation goes further, drawing boundaries around each tree's canopy to estimate crown area and competition between neighbors.

These products replace sparse sample data with wall-to-wall measurements. You know the height distribution of every stand, not just the plots you happened to sample. You can track changes over time by comparing surveys from different years. You can identify areas of stress, disease, or wind damage that would be invisible from the ground.

Accuracy Considerations

LiDAR-derived forest metrics are highly accurate, but not perfect. Dense canopy can prevent pulses from reaching the ground, affecting terrain model quality. Very small trees may not generate enough returns for reliable detection. Point density matters — sparse data misses fine details that dense data captures. Understanding these limitations helps you design surveys and interpret results appropriately.

Forest Analysis with LidarVisor

LidarVisor automates the forest inventory workflow. Upload your LiDAR data, and our algorithms classify ground points, generate terrain and canopy height models, detect individual trees, and calculate height statistics. Export results as GIS-ready vectors and rasters for integration with your forest management systems.