We demonstrate a novel computational efficient method to classify tree species in LiDAR point clouds.

Our results can provide a wider scientific basis for the pulling test method. They help to validate risk assessments and studies based on static load tests and increase their credibility and acceptance, thereby contributing significantly to the efficacy of research experiments as well as to the prospects of conserving valuable trees.

To produce accurate and repeatable tomograms of trees with complex shapes, it is essential to measure the sensor positions precisely. Accuracy of the position of sensors affects estimated stress wave velocity, the shape of the tomogram, the size of the defect, and the estimated loss in load carrying capacity.

The use of AI in tree risk assessment not only supports the user but can also be used to disseminate knowledge and promote the standardization of decision-making in tree assessment.

Tilt of > 0,2° at wind speed of 50-60 km/h leads to critical tilt values at gale winds

Provided that sensor positions are accurately recorded and documented, tomograms can reliably be repeated by different assessors over periods of years. Assessments based on complex calculations like loss of section modulus should be treated with caution, because they accumulate and amplify all sources of variation.

Static pulling tests were performed to compare the mechanical stability of Eurasian aspen (Populus tremula L.) and silver birch (Betula pendula Roth.) in both urban and peri-urban forests. The loading resistance of the species differed, with birch being more stable than aspen. Additionally, the mechanical stability of birch did not differ between trees growing in urban and peri-urban forests, suggesting static pulling tests are a suitable method for comparing trees from completely different growing conditions.

Precise sensor positions in tomography can be measured quickly.

Three studies compared the wind load estimated from dynamic measurements of root plate tilt in storms to the results of a standard pulling test software. For most trees, both estimates were in close agreement.

The presence of Heterobasidion spp. had a significant negative effect on the bending moment at primary and secondary failure of the tested trees irrespectively of soil type.