I am a physical geographer specializing in remote sensing for biodiversity and ecosystem monitoring. My lab and I combine ecological field observations with UAV, airborne and satellite data to assess plant diversity, species and functional composition, vegetation structure, biomass, plant traits, habitat types, phenology and ecosystem recovery. We have extensive experience in hyperspectral imaging and uncrewed aerial laser scanning (ULS), as well as satellite time-series analysis, GIS and machine-learning methods for deriving ecological indicators across forest, grassland and other open-vegetation ecosystems. My current research focuses on upscaling field-based biodiversity measurements to satellite observations, monitoring grassland biodiversity and phenology, and assessing forest regeneration following drought, storms and bark-beetle disturbances to support biodiversity conservation and ecosystem management under climate change.

Biogeography
Forestry and forest sciences
Geosciences
Physical Geography
Public Health and Environmental Health
Remote Sensing
Urban Ecology

I am interested in joining projects that connect biodiversity research with innovative monitoring approaches and practical conservation applications. My lab and I can contribute expertise in ecological remote sensing, including hyperspectral imaging and LiDAR, satellite time-series analysis, GIS and machine learning. We are particularly interested in projects that integrate field observations with UAV, airborne or satellite data to assess biodiversity, vegetation structure, ecosystem condition, ecosystem functioning and environmental change across spatial scales. Our main interests include biodiversity and ecosystem functioning in forests, open ecosystems and urban environments. Participation would provide valuable opportunities for methodological exchange, data integration and collaboration across disciplines, regions and monitoring networks.

biodiversity monitoring, ecological remote sensing, hyperspectral imaging, lidar, uav remote sensing, satellite time series, vegetation structure, ecosystem functioning, machine learning, urban ecology

Nordic Oikos 2026