Multi-scale vegetation cooling assessments for urban climate adaptation: a MAUP-informed NDVI–LST analysis in Shanghai
Ting Zhang, Jienan Ye, Ran XuPurpose
As climate warming and urban heat exposure continue to intensify, accurately identifying the cooling effect of vegetation is essential for developing effective urban climate adaptation strategies. However, the relationship between the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) is highly sensitive to spatial scale. Inappropriate scale selection may lead to misjudgments of urban cooling potential due to the Modifiable Areal Unit Problem (MAUP). This study aims to reveal the scale dependency of the NDVI–LST relationship and assess its implications for urban green space planning and climate adaptation decision-making.
Design/methodology/approach
Taking Shanghai as a case study, this research constructs multi-scale grids while maintaining consistent spatial resolution of remote sensing data. Land use change information from 2000 to 2024 is integrated into the analysis. By combining the Random Forest model with the SHAP interpretation method, this study examines the statistical relationship, nonlinear response structure and variations in cooling turning points of NDVI–LST across different scales and land use contexts. This approach enables a systematic assessment of how spatial aggregation methods influence the identification of vegetation cooling effects.
Findings
The results show that as the analysis scale shifts from coarse to fine, local heterogeneity and contextual effects are significantly enhanced. The nonlinear response structure of NDVI–LST and its cooling thresholds undergo systematic changes with scale. At coarse scales, local fluctuations are smoothed out and the overall response becomes more stable. At fine scales, nonlinear characteristics become more pronounced and spatial differences more prominent. From the MAUP perspective, scale selection not only affects the assessment of statistical correlations but may also introduce biases in evaluating the cooling efficiency of green spaces within climate adaptation planning.
Originality/value
This study proposes a reusable multi-scale assessment framework from the MAUP perspective. It reveals the scale sensitivity of the NDVI–LST relationship and its contextual dependency across different land use backgrounds. The findings highlight the critical value of multi-scale information in urban green infrastructure planning and heat risk management. This provides methodological innovation and practical references for developing evidence-based urban climate adaptation strategies.