Integrating forest inventory and <scp>LiDAR</scp> observations to uncover the role of plant traits on cooling and humidifying effects in urban area
Bibliographic record
Abstract
Abstract Outdoor heat stress possesses significant health risks, contributing to thousands of premature deaths each summer. Urban greening has been widely recognized as a potential solution to mitigate this heat threat. However, the optimal way to maximize cooling effects from urban forest—specifically through the influences of plant leaf traits and canopy structure on local climate (temperature and humidity)—remains underexplored. To address this issue, we combined traditional forest inventory methods with advanced LiDAR observations to assess 3883 individual trees and 77 plant species in the urban forests of Shanghai during the summer of 2021. For all trees, we analysed six leaf traits: nitrogen content, phosphorus content, potassium content, leaf area, specific leaf area and leaf dry matter content. Additionally, three canopy structural characteristics—mean foliage height, foliage height diversity, and canopy coverage—were investigated. Near‐surface air temperature and relative humidity within and outside the forests were measured repeatedly using a state‐of‐the‐art mobile monitoring system during four time intervals: 07:00–10:00, 11:00–14:00, 16:00–19:00 and 21:00–24:00. Our findings revealed that leaf stoichiometric traits significantly contribute to the variation in cooling effects, with their relative importance being twice as high as that of canopy structure during morning and afternoon periods. Specifically, nitrogen (N) and phosphorus (P) in leaves positively influenced cooling and humidification, whereas potassium (K) had a negative impact. Synthesis and applications. To enhance the summer cooling potential of urban forests, we recommend incorporating tree species with high leaf N, leaf P and low leaf K in urban park designs, rather than solely expanding canopy coverage. This study highlights the importance of considering plant traits in future urban green design, planning and management for combating heat effectively.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".