Woodam Chung

1. Introduction In forestry, decision-making often hinges on measurements obtained from individual trees. Given that such decisions can substantially influence both environmental and economic outcomes at broad scales, minimizing measurement errors and biases, and securing ample data to sufficiently decrease estimation errors are essential.

Abstract : Forests are traditionally characterized by stand-level descriptors, such as basal area, mean diameter, and stem density. In recent years, there has been a growing interest in enhancing the resolution of forest inventory to examine the spatial structure and patterns of trees across landscapes. The spatial arrangement of individual trees is closely linked to various non-monetary forest aspects, including water quality, wildlife habitat, and aesthetics.

Abstract : Object detection and tracking are tasks that humans can perform effortlessly in most environments. Humans can readily recognize individual trees in forests and maintain unique identifiers during occlusion. For computers, on the other hand, this is a complex problem that decades of research have been dedicated to solving. This paper presents a computer vision approach to object detection and tracking tasks in forested environments.