Accuracy of a LiDAR-Based Individual Tree Detection and Attribute Measurement Algorithm Developed to Inform Forest Products Supply Chain and Resource Management

1. Introduction
Sustainable forest inventory and supply chain management relies on accurate and up-to-date information that describes the dynamic changes in composition, structure, and health of forest stands. This data is essential to accurately forecast growth and yield over large areas and where field inventory access is limited due to natural hazards or topography [1,2]. While on-the-ground field inventory has been the standard source of this information, remote sensing technologies such as airborne scanning Light Detection and Ranging (LiDAR), also referred to as Airborne Laser Scanning (ALS), can gather three-dimensional forest structural data over larger areas at a lower cost [3]. The three-dimensional mapping and laser return intensity information that ALS provides has since the mid 2000’s been demonstrated to enable precision forestry, or the ability to accurately identify and model tree-level attributes (e.g., live/dead status, height, stem diameter, age) needed for forest management decision making [1,2,4–8]. However, many challenges remain in tree-level data acquisition, including the difficulty of identifying individual trees and tree species in forests with complex composition and structure [9,10].