Deep learning enables image-based tree counting, crown segmentation, and height prediction at national scale
Sizhuo Li, Martin Brandt, Rasmus Fensholt, Ankit Kariryaa, Christian Igel, Fabian Gieseke, Thomas Nord-Larsen, Stefan Oehmcke, Ask Holm Carlsen, Samuli Junttila, Xiaoye Tong, Alexandre d’Aspremont, Philippe Ciais
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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.