Erik Næsset

Erik Næsset is a Professor at the Norwegian University of Life Sciences (NMBU), Faculty of Environmental Science and Nature Management (MINA), affiliated with the Section for Renewable Energy and Forest Science. He leads research in forest inventory and monitoring through advanced remote sensing technologies, particularly airborne laser scanning (LiDAR). His work is central to the SkogRover research group, which develops innovative methods for assessing forest resources, biodiversity, and carbon storage.

His research focuses on integrating three-dimensional LiDAR data with spectral information from aerial imagery and satellite sensors to improve forest characterization. Key areas include tree species classification, biomass estimation, change detection, and subalpine zone delineation. He emphasizes model-assisted and probability-based sampling approaches for robust forest assessments across scales.

The available publications, including his supervision of Hans Ole Ørka’s 2011 PhD thesis, reveal a consistent trend toward enhancing forest inventory accuracy through multi-source remote sensing. His work spans applications from local forest management in Norway to international projects such as sample mapping in Tanzania, demonstrating both technical rigor and practical relevance.

• Scientific Awards: None listed in the provided text.

Erik Næsset actively supervises PhD students and contributes to academic training through Nordic NOVA network courses. He has participated in numerous research projects and seminars related to forest planning, valuation, and decision support tools. His work often involves collaboration with national and international institutions, and he plays a key role in advancing methodological frameworks for national forest assessments.

The SkogRover research group, which he is part of, operates under a dedicated website and engages in projects like BioDivAbove (biodiversity mapping from above) and REDD monitoring. The team utilizes UAVs, terrestrial laser scanning, and multispectral cameras to collect high-resolution data for ecological modeling and sustainable forest management.