Mobile Laser Scanning in Forest Inventories: Testing the Impact of Point Cloud Density on Tree Parameter Estimation

Area of Science:

• Forestry
• Remote Sensing
• Ecology

Background:

• Forest inventories are crucial for ecosystem management.
• LiDAR technology offers advanced 3D mapping for tree attribute extraction.
• Point cloud density significantly impacts LiDAR-based forest survey accuracy.

Purpose of the Study:

• To investigate and quantify the effect of LiDAR point cloud density on forest parameter measurement accuracy.
• To determine optimal point cloud densities for accurate Diameter at Breast Height (DBH) and Tree Height (TH) estimation.

Main Methods:

• Progressively downsampling high-density LiDAR datasets.
• Extracting tree features (DBH, TH) at varying point cloud densities.
• Comparing extracted features against high-density benchmarks to quantify errors (RMSE).

Main Results:

• Diameter at Breast Height (DBH) estimation requires 600-700 points/m³ for <1 cm error (5% RMSE).
• Accurate Tree Height (TH) estimation (RMSE < 1 m, 5% error) is achievable with densities >300 points/m³.
• Lower densities significantly increase errors in DBH and TH measurements.

Conclusions:

• LiDAR point cloud density is a critical factor for automated forest inventory accuracy.
• Specific density thresholds exist for reliable DBH and TH measurements.
• Findings guide balancing operational efficiency and measurement precision in laser scanning surveys.