The influence of urban tree characteristics on environmental noise in Montreal, Canada
Bibliographic record
Abstract
BACKGROUND AND AIM: Vegetation can reduce environmental noise, but limited information exists regarding how specific characteristics of trees of the urban forest influence environmental noise. We investigated the physical characteristics of urban trees that influence environmental noise measurements in Montreal, Canada. METHODS: We used Light Detection and Ranging (LiDAR) point cloud data from 2015 to extract the characteristics of all trees across the island of Montreal. Needle and broadleaf trees were distinguished with a Random Forest algorithm. Based on individual tree characteristics, we computed the total area of the tree crown footprint, the mean tree crown centroid height, the mean volume of tree crowns, and the percentage of broadleaf trees within various buffers (250 to 1000m) around 87 noise measurement sites across the city. Noise measurements were taken over a two-week period in the spring of 2010. Random Forest regression models were used to estimate the variation in noise around measurement sites related to tree characteristics, the Normalized Difference Vegetation index (NDVI), and distances of the measurement sites to major noise sources (highways, railways, and roads). RESULTS:The 24-hour equivalent noise levels averaged across the 87 monitoring sites were 57.5 + 5.1 dBA. The mean crown centroid tree height (5.2 + 0.4m) and the total area of crown footprint (130.7k + 63.4m2) within 500m of each site location were the strongest predictors of measured noise levels. The percentage increased mean squared errors indicated that in 500m buffers, the total area of the crown footprint (29.2%) and the mean crown centroid height (12.6%) were associated with a stronger noise decrease than NDVI (3.2%); similar patterns were observed with other buffers. CONCLUSIONS:Our findings suggest that tree crown footprint and centroid height, and not just the overall amount of vegetation, may play a vital role in reducing urban noise levels. KEYWORDS: Built environment, Green space, Noise
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".