Simple method for direct crown base height estimation of individual conifer trees using airborne LiDAR data

Area of Science:

• Forestry Science
• Remote Sensing
• Ecology

Background:

• Crown base height (CBH) is crucial for forest management, fuel treatment, and climate studies.
• Accurate individual tree CBH estimation, especially in mixed forests without field data, remains a challenge.
• Airborne LiDAR offers promising data for CBH estimation, though direct methods are underdeveloped.

Purpose of the Study:

• To develop and validate a novel method for directly estimating individual-tree crown base height (CBH) from airborne LiDAR data.
• To address the limitations of indirect regression-based methods and the need for field measurements.
• To provide an efficient and effective solution for CBH estimation in mixed-species forests.

Main Methods:

• A two-step approach involving noise/understory removal and percentile ranking profile generation with spline curve analysis.
• Direct estimation of CBH at the individual tree level, bypassing the need for field measurements.
• Application and validation in a mixed conifer forest in the Sierra Nevada, California.

Main Results:

• The method achieved high accuracy in direct individual-tree CBH estimation with RMSE of 1.62 m, R² of 0.88, and bias of 3.36%.
• Accuracy varied by tree height, with taller trees showing higher uncertainties.
• Black oak trees exhibited the highest estimation accuracy (RMSE 0.52 m), while conifer species showed strong results (R² 0.82–0.93).

Conclusions:

• The proposed LiDAR-based method accurately estimates individual-tree CBH in mixed forests without field data.
• The method demonstrates significant potential for large-scale forest applications, including ecological and climate change studies.
• Further analysis revealed height-dependent accuracy variations and species-specific performance.