Deep learning enables image-based tree counting, crown segmentation and height prediction at national scale

Research output: Contribution to journalJournal articleResearchpeer-review


  • Fulltext

    Final published version, 51.5 MB, PDF document

Sustainable tree resource management is the key to mitigating climate warming, fostering a green economy, and protecting valuable habitats. Detailed knowledge about tree resources is a prerequisite for such management but is conventionally based on plot-scale data, which often neglects trees outside forests. Here, we present a deep learning-based framework that provides location, crown area, and height for individual overstory trees from aerial images at country scale. We apply the framework on data covering Denmark and show that large trees (stem diameter >10 cm) can be identified with a low bias (12.5%) and that trees outside forests contribute to 30% of the total tree cover, which is typically unrecognized in national inventories. The bias is high (46.6%) when our results are evaluated against all trees taller than 1.3 m, which involve undetectable small or understory trees. Furthermore, we demonstrate that only marginal effort is needed to transfer our framework to data from Finland, despite markedly dissimilar data sources. Our work lays the foundation for digitalized national databases, where large trees are spatially traceable and manageable.
Original languageEnglish
Article numberpgad076
JournalPNAS Nexus
Issue number4
Number of pages16
Publication statusPublished - 2023

Number of downloads are based on statistics from Google Scholar and

No data available

ID: 339738474