Kasper Johansen

King Abdullah University of Science and Technology

Papers

Total Citations

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About

Kasper Johansen is a leading researcher at the intersection of robotics, remote sensing, and forestry science. His work focuses on developing autonomous ground-based systems to revolutionize the way we measure and monitor tree structural properties. Johansen’s key contributions center on integrating low-resolution LiDAR sensors with agile robotic platforms to efficiently and accurately estimate critical tree metrics—such as diameter at breast height (DBH), tree height, and crown volume. This approach directly addresses the limitations of traditional, time-intensive manual surveys, offering a scalable solution for non-destructive biomass and carbon stock estimation. His most-cited paper (2023, 4 citations) demonstrates the feasibility of using LiDAR on a ground-based robot for mapping tree attributes, while his more recent work (2025) refines this methodology for even greater precision. Though early in citation impact, Johansen’s research is pivotal for advancing precision forestry and environmental monitoring, promising to automate data collection that underpins climate change mitigation and sustainable forest management.