Risk assessment of urban trees is an incipient practice in Latin America, generally performed with foreign methods, due to the lack of qualified personnel and locally validated or adapted methodology. This article evaluates the application of three methods on street trees in Montevideo city, Uruguay: Tree Hazard Risk Evaluation and Treatment System (THREATS), Quantified Tree Risk Assessment (QTRA) and Best Management Practices - Tree Risk Assessment (ISA BMP). Three assessors with similar experience applied three methods in 36 trees of three widely used genera, totaling 324 assessments and 1,296 data. The methods were decomposed into the components: Likelihood of Failure, Likelihood of Impact, Consequence and Risk Rating. The data were statistically analyzed through a generalized linear mixed model (p<0.05), for the factors: assessor, method, genus, and their interactions. Results showed no significant differences among assessors, but there were differences among methods, specifically for the Likelihood of Impact and Risk Rating components. The ISA BMP method presented higher means in these last two components. Still, this method is suggested for street trees in Montevideo until a more appropriate method is adapted or developed for local conditions. Highlights No significant differences were found among assessors, which encourages standard training for all assessors. The ISA BMP method presented the highest results for the likelihood of impact and risk rating. Target occupation rate influenced differences among genera found for the likelihood of impact component. The ISA BMP method is suggested for street trees risk assessment in Montevideo, until a method is adapted or developed. The absence of descriptors and categorizations, as well as the application time, need to be improved.

Tree mechanical stability is essential for forest management and urban safety. Although static pulling tests are currently the standard for non-destructive advanced risk assessments, these tests have significant methodological limitations. Large trees require high applied forces to produce measurable signals, which poses safety risks and causes equipment wear. Conversely, structurally compromised ancient, veteran, or dead trees (snags) may yield poor signal-to-noise ratios at low loads, leading to unstable model fits and unreliable safety factor extrapolations. Additionally, standard inclinometers often experience interference from motion-induced accelerations. This study introduces a high-resolution, low-noise measurement approach that resolves small basal inclinations and stem bending responses. This method uses laser-based tracking to monitor stem bending, torsion, and inclination under mechanical load. Experimental data were collected by combining traditional pulling tests with this novel system, as well as by conducting a pilot study that monitored tree movement during low-strength wind gusts. The proposed method enables more precise characterization of the initial load-response curve. Improving the signal-to-noise ratio at lower force levels allows for more robust safety extrapolations. When combined with a 3D LiDAR scan, the method can reveal deviations from the theoretical bending line in order to locate internal defects and variations in wood properties. These findings bridge a critical gap in tree risk assessment by improving the applicability of static testing to massive trees, as well as ecologically valuable yet structurally vulnerable snags and ancient and veteran trees.