Epsilon Archive for Student Projects

Abstract

As a result of technological advancements, there is growing interest in exploring the potential of autonomous machines to contribute to sustainable, efficient, and economic forest management. The development of new autonomous machine systems simultaneously places new demands on information and decision-making processes to make the right choices and achieve optimal outcomes. During site preparation and planting with autonomous machines, access to information about the structure and characteristics of the soil is crucial for selecting suitable planting spots.
Ground-penetrating radar is a technology that uses electromagnetic pulses to investigate subsurface materials, making it intriguing for forestry applications to detect rocks, obstacles, and other characteristics of the soil. Currently, knowledge is lacking regarding the potential of ground-penetrating radar in Nordic conditions and how different conditions affect its performance.
This report presents a method for creating reference data that can be used to evaluate ground-penetrating radar technology to increase the knowledge of radar within forestry. The reference data was generated through a combination of image processing, georeferencing, and computer-aided design. The report resulted in three-dimensional models of various ground profiles that can serve as a basis for investigating the ability to detect rocks and obstacles in the soil by radar. These models can also be used to assess other types of radar technologies, such as airborne ground-penetrating radar, and can contribute to future research where knowledge of soil characteristics is essential.