This study explores cross-species variations in structural metrics derived from individual tree lidar scans and analyzes the structural characteristics of trees at both the individual and plot levels. Understanding the species composition of forests is vital for assessing ecosystem health and biodiversity. For this reason, much research has been dedicated to distinguishing between species through remotely sensed datasets; however, the goal of remote species identification remains unrealized. Progress has been hindered by two key challenges: 1) the lack of precisely georeferenced ground validation data, and 2) insufficient consideration of 3D environmental factors such as terrain, understory layers, and the structural entanglement of natural forests. In this study, we address these challenges by compiling a ground validation dataset comprised of precisely high-density laser scans at 60 forest inventory plot locations. Within this dataset, each of the 1200 trees is segmented and paired with a manually verified species label. Using this comprehensive dataset, we first isolate individual trees and investigate cross-species variations in lidar feature metrics. Subsequently, we examine how these structural features vary within the context of the micro-environment of their respective plots. Our analysis also explores how factors such as terrain and neighboring species impact structural feature variance. Our findings suggest that detectable structural features hold significant potential in accurately assessing biodiversity and determining the species composition within forests.

This work presents a methodology for creating digital twins of root system architecture (RSA) that can be used for studying the phenotypic variation in RSA. Growing populations demand increased global food production. To sustainably support this increase, crops must be developed to flourish in nutrient-depleted soils. Since the effectiveness of nutrient uptake is determined by plant rooting system dynamics, much focus has been placed on studying RSA across species and varieties. A particularly effective tool for studying RSA in 3D has been X-ray computer tomography (CT). However, this technology is cost prohibitive and cannot model field-grown samples. A far more cost-effective technology is close-range photogrammetric scanning, which uses multiple 2D images to reconstruct 3D point clouds of RSA. This project develops a point cloud processing pipeline that takes high-density point clouds of soybean rooting structures and generates 3D RSA models. These digital twins are then used as the basis for analyzing the phenotypic variation of the geometric and biometric features of the RSA. We believe this digital twin construction and analysis pipeline will increase the impact of RSA research in support of sustainable food production in nutrient-depleted areas of the world.