Forest Metrics and Individual Tree Attributes Extraction From Airborne Laser Scanning Point Clouds

Airborne laser scanning (ALS) provides advanced capabilities for acquiring precise three-dimensional information about land cover and vegetation. This paper introduces a comprehensive methodology for extracting forest metrics and individual tree attributes from high-density DALES benchmark data acquired using ALS. The approach involves several key steps: loading and visualizing LAZ point cloud data, segmenting ground and vegetation, normalizing elevation to address terrain effects, and computing critical forest metrics such as canopy cover, gap fraction, and leaf area index. A Canopy Height Model (CHM) is created to facilitate accurate tree detection and segmentation, allowing for the extraction of individual tree attributes including apex location, height, crown diameter, and area. This method provides detailed insights into forest structure and tree characteristics, significantly enhancing forest management and ecological research.