TY - JOUR
T1 - Deep learning enables image-based tree counting, crown segmentation and height prediction at national scale
AU - Li, Sizhuo
AU - Brandt, Martin
AU - Fensholt, Rasmus
AU - Kariryaa, Ankit
AU - Igel, Christian
AU - Gieseke, Fabian
AU - Nord-Larsen, Thomas
AU - Oehmcke, Stefan
AU - Carlsen, Ask Holm
AU - Junttila, Samuli
AU - Tong, Xiaoye
AU - d’Aspremont, Alexandre
AU - Ciais, Philippe
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
U2 - 10.1093/pnasnexus/pgad076
DO - 10.1093/pnasnexus/pgad076
M3 - Journal article
C2 - 37065619
VL - 2
JO - PNAS Nexus
JF - PNAS Nexus
SN - 2752-6542
IS - 4
M1 - pgad076
ER -