The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Photo of Hakim Abdi

Hakim Abdi

Researcher

Photo of Hakim Abdi

Mapping forest tree species and their uncertainty using Earth observation and National Forest Inventory data : towards operational monitoring in Sweden

Author

  • Abdulhakim M. Abdi
  • Fan Wang

Summary, in English

Timely, detailed information on forest composition is essential for effective management, biodiversity protection, and understanding ecosystem dynamics. This study maps the distribution of seven dominant tree species in Swedish forests and produces spatially explicit, pixel-level estimates of classification uncertainty. The mapping framework integrates multitemporal Sentinel-1 radar and Sentinel-2 optical observations with field data from the Swedish National Forest Inventory and auxiliary predictors describing topography and canopy height. We trained a Bayesian-optimized extreme gradient boosting model on spatiotemporal metrics derived from these datasets and quantified classification confidence through entropy computed from the class-probability outputs. We applied a spatial block partitioning approach to limit the effects of spatial autocorrelation between optimization and validation data and ensure a more realistic assessment of the model’s generalization capacity. Model overall accuracy reached 85% (F1 = 0.82) using a 60 m spatial block validation. Under a more conservative 200 m block configuration, performance decreased to F1 = 0.63, reflecting reduced training data availability. The county-level species coverage derived from the classification aligned closely with published figures from the Swedish Forest Agency (Spearman’s ρ = 0.94, 95% CI: 0.89–0.96, p < 0.001). Variable importance analysis showed that Sentinel-2 spectral bands, particularly shortwave-infrared and red-edge captured during spring and summer, contributed most to species discrimination, while Sentinel-1 backscatter provided complementary structural information. The integration of forest inventory data, Earth observation, and machine learning to produce tree species maps and a spatially explicit measure of prediction uncertainty yields a robust and reproducible framework for large-area forest mapping. The results provide detailed, spatially continuous information on species composition along with an accompanying confidence surface. This offers practical value for ecological assessments, regional planning, and emerging legislative and environmental goals. The data are freely available for download and the maps can be interactively visualized using this link: https://ee-treespec.projects.earthengine.app/view/treespec.

Department/s

  • Department of Earth and Environmental Sciences (MGeo)
  • LU Profile Area: Nature-based future solutions
  • Centre for Advanced Middle Eastern Studies (CMES)
  • MECW: The Middle East in the Contemporary World
  • Biodiversity and Conservation Science
  • BECC: Biodiversity and Ecosystem services in a Changing Climate

Publishing year

2026

Language

English

Publication/Series

International Journal of Remote Sensing

Document type

Journal article

Publisher

Taylor & Francis

Topic

  • Earth Observation
  • Physical Geography
  • Forest Science

Keywords

  • forest monitoring
  • National Forest Inventory
  • remote sensing
  • Sentinel-1
  • Sentinel-2
  • tree species mapping

Status

Epub

Research group

  • Biodiversity and Conservation Science

ISBN/ISSN/Other

  • ISSN: 0143-1161