RT Journal Article
T1 Quantifying urban tree canopy cooling capacity using Bayesian hierarchical models and satellite imagery
A1 Ruiz-Valero, Ángel
A1 Pereña-Ortiz, Jaime Francisco
A1 Martín Lozano, Isidro
A1 Cortés-Molino, Álvaro
A1 Cozano-Pérez, Pablo
A1 Galindo-Ruiz, Begoña
A1 Díaz-Galiano, Luis Alberto
A1 Salvo-Tierra, Ángel Enrique
K1 Vegetación y clima
K1 Cubierta arbórea - Efectos del clima
K1 Árboles de ciudad
K1 Zonas verdes
AB The urban heat island phenomenon significantly affects thermal comfort in cities. Urban trees offer ecosystem services that can help cool built-up areas, making cities more livable environments. Regarding the cooling capacity associated with increasing urban trees canopy cover, it is often analyzed at the city level. This study aimed to quantify the local-scale cooling benefits of increasing tree canopy cover to inform urban planning.Using Landsat 8 satellite imagery to measure land surface temperature, we applied a Bayesian hierarchical models (BHMs) with Integrated Nested Laplace Approximation (R-INLA) to estimate cooling effects in 900 m2 urban units, accounting for other factors and spatial autocorrelation through a Gaussian field.The model achieved a root mean squared error of 0.989°C ± 0.255°C and 0.833 ± 0.06 of observations were covered by the Highest Posterior Probability Interval of the Posterior Predictive Distribution (p-PPD-HPDI), both metrics calculated in the test sets of 10-folds spatial cross-validation. Considering other factors responsible of land surface temperature distribution and spatial autocorrelation using a Gaussian field, the model shows that increasing canopy cover in an observational unit by 450 m2 is associated with a reduction of about 0.268°C (Quantile0.025 0.241, Quantile0.975 0.295), ceteris paribus.These findings highlight the significant local cooling potential of urban tree planting at scales relevant to planning decisions. By quantifying these effects, the study underscores the value of integrating tree canopy increase into urban design and policy to enhance comfort, resilience, and overall urban sustainability.
PB Wiley
YR 2025
FD 2025-09-22
LK https://hdl.handle.net/10630/40007
UL https://hdl.handle.net/10630/40007
LA eng
NO Ruiz-Valero, A´ ., Pereña-Ortiz, J. F., Martín-Lozano, I., Cortés-Molino, A´ ., Cozano-Pérez, P., Galindo-Ruiz, B., Díaz-Galiano, L. A., & Salvo-Tierra, A´ . E. (2025). Quantifying urban tree canopy cooling capacity using Bayesian hierarchical models and satellite imagery. Plants, People, Planet, 1–15. https://doi.org/10.1002/ppp3.70085
NO Societal Impact Statement Cities are getting hotter because of climate change and urban development, increasing risks to health and well‐being. We analyzed how increasing urban tree canopy cover in city areas of 900 m² can reduce land surface temperatures, using detailed aerial‐LiDAR and satellite data with Bayesian hierarchical models. It was observed that an increase in tree canopy cover produces a cooling effect irrespective of the conditions of the urbanized environment. The goal is to support policymakers and decision‐makers with insights to strategically incorporate urban trees into planning frameworks, fostering sustainable, resilient, and healthy urban environments, and advancing climate change adaptation efforts. The urban heat island phenomenon significantly affects thermal comfort in cities. Urban trees offer ecosystem services that can help cool built‐up areas, making cities more livable environments. Regarding the cooling capacity associated with increasing urban trees canopy cover, it is often analyzed at the city level. This study aimed to quantify the local‐scale cooling benefits of increasing tree canopy cover to inform urban planning. Using Landsat 8 satellite imagery to measure land surface temperature, we applied a Bayesian hierarchical models (BHMs) with Integrated Nested Laplace Approximation (R‐INLA) to estimate cooling effects in 900 m² urban units, accounting for other factors and spatial autocorrelation through a Gaussian field. The model achieved a root mean squared error of 0.989°C ± 0.255°C and 0.833 ± 0.06 of observations were covered by the Highest Posterior Probability Interval of the Posterior Predictive Distribution (p‐PPD‐HPDI), both metrics calculated in the test sets of 10‐folds spatial cross‐validation. Considering other factors responsible of land surface temperature distribution and spatial autocorrelation using a Gaussian field, the model shows that increasing canopy cover in an observational unit by 450 m² is associated with a reduction of about 0.268°C (Quantile0.025 0.241, Quantile0.975 0.295), ceteris paribus. These findings highlight the significant local cooling potential of urban tree planting at scales relevant to planning decisions. By quantifying these effects, the study underscores the value of integrating tree canopy increase into urban design and policy to enhance comfort, resilience, and overall urban sustainability.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 19 ene 2026