Potential of Sentinel-2 time series in capturing boreal forest structural diversity
Year: 2026
Type: article
Abstract: Forest structural diversity plays a key role in forest ecosystem functions. Capturing the temporal dynamics of forest structural variables could improve the characterization of forest structural diversity. However, the potential of seasonal time series from optical satellite data to predict the structural diversity in boreal forests remains underexplored. This study addresses two research questions: 1) What is the connection between boreal forest structural diversity and its spectral dynamics across seasons? 2) What is the potential of Sentinel-2 time series data for predicting boreal forest structural diversity compared to using a single image? Forest structural variables were derived from airborne LiDAR data and individual tree maps from two study areas in southern Finland. We analyzed the seasonal dynamics of Sentinel-2 reflectance and vegetation indices in relation to the forest structural diversity and predicted structural variables using random forest models with varying numbers of Sentinel-2 image dates. Results show that the diversity of the forest structure affects the seasonal dynamics of Sentinel-2 reflectance data from boreal forests during the spring-summer period. Stands with sparser canopy cover, a lower mean canopy height, higher stem density or higher Shannon index show greater temporal variability in terms of their spectral properties. The prediction accuracy improved for the mean canopy height, first echo cover index, volume of broadleaved trees, Shannon index, and stem density using a time series of Sentinel-2 images rather than a single image. These findings improve the characterization of boreal forest structural diversity through optical satellite time series and encourage further research into their application for monitoring seasonal and inter-annual dynamics in forest ecosystems.
Source: Ecological Informatics
Institutions: Aalto University, Natural Resources Institute Finland
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDG: Life on land
Open Access status: gold
Estimated APC paid: $2,770
Awards: Artificial Intelligence for Twinning the Diversity, Productivity and Spectral Signature of Forests, Forest-Human-Machine Interplay - Building Resilience, Redefining Value Networks and Enabling Meaningful Experiences, G6323710127, Artificial Intelligence for Twinning the Diversity, Productivity and Spectral Signature of Forests
OpenAlex ID: W7153606089