Integrating forest inventory and LiDAR observations to uncover the role of plant traits on cooling and humidifying effects in urban areas
Data files
Jun 13, 2025 version files 325.48 KB
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35Plots_LAIEco-traits_Structure_Cooling2.csv
244.88 KB
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Data_Analysis_Linear_mixed-effects_models.R
28.13 KB
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Figure3_BoxPlots.R
8.31 KB
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Figure4_ForestPlots.R
9.20 KB
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Figure5_RelativeContribution.R
4.02 KB
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Graphic_Abstract.R
18.90 KB
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Imp_LMM_Trait_Structure.top.AH5.csv
703 B
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Imp_LMM_Trait_Structure.top.Tem6.csv
934 B
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README.md
8.06 KB
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Relationship-ForestPlot6_AH.csv
1 KB
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Relationship-ForestPlot6_AT.csv
1.34 KB
Abstract
1. 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.
2. To address this issue, we combined traditional forest inventory methods with advanced LiDAR observations to assess 3,883 individual trees and 77 plant species in the urban forests of Shanghai during the summer of 2021. For all trees, we analyzed 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.
3. 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.
4. 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.
Dataset DOI: 10.5061/dryad.2z34tmpz7
Description of the data and file structure
Study Area & Design
This dataset was collected in Shanghai, China (subtropical monsoon climate) during summer 2021 to investigate the cooling and humidifying effects of urban forests. Six parks (44.5 ± 23.7 ha, >80% vegetation cover) and adjacent reference areas (impervious surfaces) were selected. Air temperature (AT) and relative humidity (RH) were measured via mobile transects at four time periods (morning to nighttime), with 1,334 data points across 34 plots (30×30 m).
LiDAR & Field Measurements
- 3D Forest Structure: Integrated aerial (UAV) and terrestrial (hand-held) LiDAR data (6,000–7,000 pts/m²) were used to reconstruct canopy architecture (e.g., leaf area index, crown area) and segment 3,883 individual trees.
- Leaf Traits: For 77 woody species, morphological (leaf area, SLA, LDMC) and stoichiometric traits (N, P, K content) were measured from 9,570 leaves. Community-weighted means (CWM) were calculated using LiDAR-derived leaf areas and biomass.
Key Variables
- Microclimate Effects: Cooling (ΔAT) and humidification (ΔAH) metrics, relative to reference areas.
- Canopy Structure: Coverage, mean foliage height (MFH), and foliage height diversity (FHD).
- Leaf Traits: Community-weighted means (CWM) values for LA (leaf areas), SLA (specific leaf area), LDMC (leaf dry matter content), N (leaf nitrogen content), P (leaf phosphorus content), and K ( leaf potassium content).
Statistical Analysis
Linear mixed-effects models (LMMs) assessed the influence of leaf traits and canopy structure on ΔAT/ΔAH, with park/ride as random effects. Variance decomposition quantified trait contributions.
Files and variables
File: Data_Analysis_Linear_mixed-effects_models.R
Description: Codes of linear mixed-effects model (LMMs). LMMs were used to examine the effects of leaf traits on cooling and humidifying effects.
File: Figure3_BoxPlots.R
Description: Codes for box plots in Figure 3. We used one-way repeated-measures ANOVA and Tukey's post hoc test to examine the significance of cooling and humidifying differences at four time periods.
File: Figure4_ForestPlots.R
Description: Codes for forest plots in Figure 3. The model outputs of Lmms. The forest plots will display parameter estimation of community-level leaf traits and canopy structural features for air temperature reduction and absolute humidity increase during the hours 07:00–10:00, 11:00–14:00, 16:00–19:00, and 21:00–24:00.
File: Figure5_RelativeContribution.R
Description: Codes for relative contribution plots in Figure 3. The relative contribution of community-level leaf traits and canopy structural features for explaining reduction in air temperature and increase in absolute humidity by LMM models.
File: Graphic_Abstract.R
Description: Codes for graphic abstract. It will combine the forest plots and relative contribution plots with good visulization.
File: Imp_LMM_Trait_Structure.top.AH5.csv
Description: Data of LMMs outputs. The relative contribution of community-level leaf traits and canopy structural features for explaining increase in relative humidity during the hours 07:00–10:00, 11:00–14:00, 16:00–19:00, and 21:00–24:00 by LMM models .
Variables
- Importance: The relative contribution of community-level leaf traits and canopy structural features for explaining (a) reduction in air temperature and (b) increase in absolute humidity by LMM models. (%)
- Trait: Community-level leaf traits and canopy structure. For leaf traits, the Leaf_N_eco, Leaf_P_eco, Leaf_K_eco, LA_eco, SLA_eco, and LDMC_eco represent the leaf nitrogen content, leaf phosphorus content, leaf potassium content, leaf areas, specific leaf area and leaf dry matter content at the community level. For canopy structural features, the Zmean, Evenness, and Cover indicate mean foliage height, foliage height diversity, and canopy coverage of the whole plot, respectively.
- Time: Four time periods during a day. Morning, Noon, Afternoon, and Night are during hours 07:00 – 10:00, 11:00 – 14:00, 16:00 – 19:00 and 21:00 – 24:00, respectively.
File: 35Plots_LAIEco-traits_Structure_Cooling2.csv
Description: Data used for LMMs modeling. It included cooling effects, humidifying effects, canopy structure, and leaf traits.
Variables
- Point_ID: Points ID based on the mobile monitoring system
- Plot_ID: Field plot ID
- Park_ID: Park ID
- Route_ID: Route ID based on the mobile monitoring system
- Time_Period: Four time periods during a day. Morning, Noon, Afternoon, and Night are during hours 07:00 – 10:00, 11:00 – 14:00, 16:00 – 19:00 and 21:00 – 24:00, respectively
- Time: Exact time of the recording
- Longitude: Longitude of the recording point
- Latitude: Latitude of the recording point
- Temp_Ano: Air temperature reduction (℃)
- Hum_Ano: Relative humidity increase (%)
- Leaf_K_eco: Leaf potassium content (%)
- Leaf_N_eco: Leaf nitrogen content (%)
- Leaf_P_eco: Leaf phosphorus content (%)
- LDMC_eco: Leaf dry matter content (g/g)
- LA_eco: Leaf areas (cm2)
- SLA_eco: Specific leaf area (cm2/g)
- Cover: Canopy coverage (%)
- Zmean: Mean foliage height (m)
- Evenness: Foliage height diversity
File: Imp_LMM_Trait_Structure.top.Tem6.csv
Description: Data of LMMs outputs. The relative contribution of community-level leaf traits and canopy structural features for explaining reduction in air temperature during the hours 07:00–10:00, 11:00–14:00, 16:00–19:00, and 21:00–24:00 by LMM models .
Variables
- Importance: The relative contribution of community-level leaf traits and canopy structural features for explaining (a) reduction in air temperature and (b) increase in absolute humidity by LMM models. (%)
- Trait: Community-level leaf traits and canopy structure. For leaf traits, the Leaf_N_eco, Leaf_P_eco, Leaf_K_eco, LA_eco, SLA_eco, and LDM_eco represent the leaf nitrogen content, leaf phosphorus content, leaf potassium content, leaf areas, specific leaf area and leaf dry matter content at the community level. For canopy structural features, the Zmean, Evenness, and Cover indicate mean foliage height, foliage height diversity, and canopy coverage of the whole plot, respectively.
- Time: Four time periods during a day. Morning, Noon, Afternoon, and Night are during hours 07:00 – 10:00, 11:00 – 14:00, 16:00 – 19:00 and 21:00 – 24:00, respectively.
File: Relationship-ForestPlot6_AT.csv
Description: Data of LMMs outputs for air temperature reduction. It was used for forest plots.
Variables
- Time: Four time periods during a day. Morning, Noon, Afternoon, and Night are during hours 07:00 – 10:00, 11:00 – 14:00, 16:00 – 19:00 and 21:00 – 24:00, respectively.
- Factors: Community-level leaf traits and canopy structure. For leaf traits, the Leaf N, Leaf P, Leaf K, LA, SLA, and LDMC represent the leaf nitrogen content, leaf phosphorus content, leaf potassium content, leaf areas, specific leaf area and leaf dry matter content at the community level. For canopy structural features, the Zmean, Evenness, and Cover indicate mean foliage height, foliage height diversity, and canopy coverage of the whole plot, respectively.
- Estimate: Estimates from the model
- SE: Sdandard errors of the estimate
- xmin: min value of the estimate
- xmax: max value of the estimate
- t: t value from the model
- p: p value from the model
File: Relationship-ForestPlot6_AH.csv
Description: Data of LMMs outputs for air humidity reduction. It was used for forest plots.
Variables
- Same as for Relationship-ForestPlot6_AT.csv
Code/software
R version 4.1.2 was used for all data analyses.
