Multisource approach for mapping canopy and habitat composition in boreal forests

Application period: 11 June – 10 August, 2026

Research project description

There is a growing need for accurate forest habitat and biodiversity data to support sustainable land use and forest management and to ensure that biodiversity, including rare and threatened habitats, is effectively integrated into decision-making. Remote sensing offers robust tools for habitat mapping and assessment; however, achieving policy-relevant monitoring requires parallel development of high-quality field data and drone-based methods for data collection and validation.

The main aim of this PhD research is to develop a multisource-data approach for canopy composition characterization and habitat mapping. The study will utilize drone-based data, airborne and Satellite data. The doctoral researcher will conduct research in close cooperation with Digital Geosciences Research Group as well as collaborators School of Forest Sciences and Finnish environment institute (SYKE). Geographically study will focus on boreal forests and mountain birch forests close to the rapidly changing northern treeline. Work will focus on biodiversity indicators eg. deadwood (standing-downed), certain tree species like European aspen (Populus tremula) and goat willow (Salix Caprea). Another aim is comparison of canopy dynamics of mountain birch forests and Northern boreal forests using LAI and ALS data.

Mapping and characterizing deadwood and delineating ecologically significant habitat types in boreal forests using optical drone imagery and dense LiDAR point clouds will be in focus. Study will use dense LiDAR data (National Land Survey), optical airborne and satellite data. Validation data will be provided by SYKE, validation data: sampling includes ecologically significant habitats 10-20 sites 2026 and selected drone data from sites.

Academic background and skills of the applicant

An ideal candidate holds a master’s degree in geography, forestry, ecology, environmental science, geoinformatics, computer science, or a closely related field. The candidate is expected to have a strong interest in forests, biodiversity, and remote sensing, as well as a commitment to high-quality research. An interdisciplinary or multidisciplinary research mindset is highly valued. Familiarity with multiscale and multisource Earth observation data and products, such as field, UAV, airborne, and spaceborne sensor data, is expected. Solid programming skills in R or Python are required.

The successful candidate demonstrates a proactive and collaborative attitude, enjoys working as part of a team, and is motivated to engage with land management actors and other relevant stakeholders. Experience in research dissemination and/or application-oriented activities is considered an advantage. The ability and willingness to undertake research periods with national and international partners are also regarded as advantageous. The selected candidate must also fulfil the language skills requirements of the Doctoral Programme of the Faculty of Social Sciences and Business Studies at UEF (see the https://www.uef.fi/en/degree-programme/doctoral-programme-of-the-faculty-of-social-sciences-and-business-studies (Eligibility and admission criteria)).

Doctoral programme and research group

Doctoral education in the University of Eastern Finland is arranged in seven discipline specific or thematic doctoral programmes. This research project will be located in the Doctoral Programme of the Faculty of Social Sciences and Business Studies, and the submitting department is the Department of Geographical and Historical Studies.

The candidate will join the Digital Geosciences Research Group (DGRG), which consists of 12 researchers and focuses on the use of geospatial and Earth observation (EO) data and methods in environmental and societal research. The department also hosts the state-of-the-art UEF Drone Lab, which provides extensive drone infrastructure, including drones and sensors for RGB, multispectral, thermal, LiDAR, and hyperspectral data acquisition.

Partners / Secondments

Finnish Environment Institute (SYKE), hosted by Senior Coordinator Topi Tanhuanpää

Estonian University of Life sciences, hosted by Associate Professor Mait Lang

Other interdisciplinary, international, intersectoral collaboration

Intersectoral and interdisciplinary collaboration is strengthened through three jointly organised summer schools involving academic supervisors, non-academic partners, and other stakeholders, enabling hands-on interaction and knowledge exchange.

Supervisors and related research

Timo Kumpula

Timo Kumpula is Professor of Geography at the University of Eastern Finland’s Department of Geographical and Historical Studies. He has published 100 peer-reviewed journal articles. His research focuses on land cover and land use change, forest biodiversity, ecosystem services, and remote sensing applications. Kumpula leads the department’s Digital Geosciences Research Group (DGRG), which comprises 12 researchers, as well as the UEF Drone Lab. He has supervised eight completed doctoral theses and is currently supervising five doctoral students.

Lauri Korhonen

Lauri Korhonen is Associate Professor of Remote Sensing of Forest Resources at the University of Eastern Finland’s School of Forest Sciences. He has published 93 peer-reviewed journal articles focusing on the estimation of forest variables such as leaf area index, canopy cover, biomass, volume, and species composition. He has supervised five completed doctoral theses and is currently supervising seven doctoral students.

Topi Tanhuanpää

Topi Tanhuanpää is a Senior Coordinator at the Finnish Environment Institute (SYKE), where he is responsible for developing the national habitat type monitoring programme. He also serves as Secretary of the Forests Subgroup of the Finnish assessment of the IUCN Red List of Ecosystems. Tanhuanpää has a strong background in object-based remote sensing of ecologically significant forest components. He has supervised one completed doctoral thesis and is currently supervising one doctoral thesis.

Kuzmin, A., Korhonen, L., Kukkonen, M., Tanhuanpää, T., Maltamo, M., & T. Kumpula (2026). Which UAV sensor is optimal for the classification of tree species and standing dead trees in boreal forests. submitted to Remote Sensing in ecology and conservation.

Tanhuanpää, T., Mikkonen, N., Kujala, H. Heinaro, E., Mäyrä, J. & T. Kumpula (2023). Input data resolution affects the conservation prioritization outcome of spatially sparse biodiversity features. Ambio. https://doi.org/10.1007/s13280-023-01885-6

Zhang, S., Korhonen, L., Lang, M., Pisek, J., Díaz, G.M., Korpela, I., Xia, Z., Haapala, H., Maltamo, M. 2024. Comparison of Semi-physical and Empirical Models in the Estimation of Boreal Forest Leaf Area Index and Clumping with Airborne Laser Scanning Data. IEEE Transactions on Geoscience and Remote Sensing 62, 5701212.

Mäkinen, K., Korhonen, L., Maltamo, M. 2024. Improving airborne laser scanning-based species-specific forest volume estimation using Sentinel-2 time series. European Journal of Remote Sensing, 2422315.

Heinaro, E., Tanhuanpää, T., Yrttimaa, T., Holopainen, M., & Vastaranta, M. (2021). Airborne laser scanning reveals large tree trunks on forest floor. Forest Ecology and Management, 491, 119225.

Mäyrä, J., Tanhuanpää, T., Kuzmin, A., Heinaro, E., Kumpula, T., & Vihervaara, P. (2025). Using UAV images and deep learning to enhance the mapping of deadwood in boreal forests. Remote Sensing of Environment, 329, 114906.