Sustainable forest management and precision tree improvement require detailed inventories of tree quantity and quality to support decision-making processes. Accurate forest inventory information can significantly impact the potential for forest resources to meet economic and ecological needs.

Currently, forest inventory data is collected using manual field sampling techniques, often relying on observations by trained experts which introduces substantial sources of error. Recent technological advances offer new methods and techniques that can increase the accuracy of tree quantity and quality measurements. These are often cheaper and faster than conventional approaches.

Researchers are working on methods that take advantage of advances in aerial remote sensing technologies to conduct tree inventory using LiDAR and UAS images. AI-assisted automation allows for individual tree recognition and delineation and remote measurement of biometrics (size, biomass) in planted and natural forests.

OBJECTIVES

1. Develop tools for automated detection and delineation of individual trees and measurement of biometrics for hardwood species using low-density aerial LiDAR. Tools developed from this objective can be applied at stand, landscape, and possibly state level using freely available aerial LiDAR.
2. Develop algorithms for automated detection and delineation of individual trees and measurement of biometrics for hardwood species using UAS orthophotos. Tools developed from this objective can be applied on the stand level and can be employed cheaply and as frequently as the user desires.
3. Disseminate tools to stakeholders and managers. We will coordinate with other iDiF projects to disseminate our developed tools and products to HTIRC stakeholders and other natural resource managers.