Heesung Woo

Woo, Heesung

Position Type:
Job Title:
Assistant Professor of Advanced Forestry
Forest Engineering, Resources & Management
Office Location:
352 Peavy Forest Science Center (PFSC)
Graduate Major Advisor
Research Areas
Forest Operations Planning and Management
Research Interests
  • Robotics application in forestry
  • Sensor integration in forestry
  • Precision forestry and advanced forestry
  • Machine learning and deep learning in forestry
  • Forest operations and harvesting systems
Research interests include: 1) Autonomous Forest Machinery Development: My primary research interest lies in the development of autonomous forest machinery systems. I aim to contribute to the advancement of robotics and automation technologies for efficient and sustainable forest management, with a focus on designing, building, and optimizing autonomous machines capable of tasks such as tree harvesting, thinning, and transportation; 2) Sensor Integration in Forestry: I am passionate about integrating cutting-edge sensor technologies into forestry practices. My goal is to enhance data quality in the field through the application of various sensors and ICT (Information and Communication Technology) solutions. By leveraging these technologies, I aim to collect precise and real-time data on forest ecosystems, enabling data-driven decision-making for forest management; 3) Precision Forestry: My research interest includes the pursuit of precision forestry techniques. I am dedicated to exploring and implementing advanced technologies, such as remote sensing, LiDAR, and GIS, to improve the accuracy and efficiency of forest management. The objective is to maximize resource utilization, reduce environmental impact, and optimize the overall health and productivity of forested areas; 4) Advanced Forestry Practices: I am committed to investigating and promoting advanced forestry practices that go beyond conventional methods. This includes exploring innovative techniques for tree planting, species selection, forest regeneration, and silvicultural strategies that align with sustainable and environmentally responsible forestry management principles; and 5) Machine Learning and Deep Learning in Forestry: My research also involves the application of machine learning and deep learning techniques, including open-source computer vision libraries like OpenCV, YOLO (You Only Look Once), and point segmentation algorithms. I aim to harness the power of artificial intelligence for tasks such as object detection, species identification, and forest health assessment, ultimately contributing to more efficient and accurate forest monitoring and management.
Selected Publications:
  1. Kim, I., Kim, J. J., Woo, H., and Choi, B. (2022). Feasibility of terrestrial laser scanning system for detecting and monitoring surface displacement of artificial slopes on forest roads. Sensors and Materials, 34(2022).
  2. Lee, Y.K., Woo, H., and Lee, J. (2022). Forest inventory assessment using integrated LiDAR systems: merged points cloud of airborne and mobile laser scanning systems. Sensors and Materials, 34(12), 4583-4597.
  3. Woo, H.; Lee, E.; Acuna, M.; Cho, H.; Han, S.-K. The impact of integrated harvesting systems on productivity, costs, and amount of logging residue in the clear-cutting of a Larix kaempferi (Lamb.) Carr. Stand. Forests (2022), 13, 1941.
  4. Woo, H., Kim, I., and Choi, B. Computer vision techniques in forest inventory assessment: Im-proving the accuracy of tree diameter measurement using a smartphone camera and photogrammetric. Sensors and Materials, (2021), 33.11: 3835-3845.
  5. Woo, H., Acuna M., Choi, B., and Han, S. (2021). FIELD: A software tool that integrates harvester data and allometric equations for a dynamic estimation of forest harvesting residues. Forests (2021), 12(7); doi: 10.3390/f12070834