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

Li,Sizhuo; Brandt, Martin; Fensholt, Rasmus; Kariryaa, Ankit; Igel, Christian; Gieseke, Fabian; Nord-Larsen, Thomas; Oehmcke, Stefan; Carlsen, Ask Holm; Junttila, Samuli; Tong, Xiaoye; d’Aspremont, Alexandre; Ciais, Philippe

Abstract

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.

Keywords
deep learning; remote sensing; biomass; machine learning; artificial intelligence; data science