Characterizing annual dynamics of two- and three-dimensional urban structures and their impact on land surface temperature using dense time-series Landsat images

To attain sustainable development goals and understand urban growth patterns, continuous and precise monitoring of built-up area heights is essential. This helps reveal how urban form evolution impacts the thermal environment. Previous research often used isolated images, ignoring the temporal dimension of thermal infrared and reflectance data from Landsat sensors. Additionally, cost-effective and efficient methods for reconstructing time-series built height are lacking. To fill this knowledge gap, we utilized Landsat time-series data to reconstruct the yearly trends in urban form in Beijing, China, spanning from 1990 to 2020. Continuous Change Detection and Classification (CCDC) time series analysis method was used to identify urban growth and renewal years. Employing a reference height for 2020 and logical reasoning method, we reconstructed the annual dynamics of built-up heights, pinpointing years of significant change. Finally, we analyzed the alterations in urban form over the past three decades and their impact on surface temperature changes. Our change detection method achieved an overall accuracy of 86 %, demonstrating its effectiveness in determining the year of change. When compared with data from Lianjia and LiDAR point cloud, our height reconstruction method showed impressive accuracy, with R² values of 0.9773 and 0.9526, respectively. Analysis of summer and winter LST values revealed distinct temperature patterns across different building heights, with mid-rise buildings exhibiting the highest LST in summer and low-rise buildings registering the highest LST in winter. During periods of urban growth, both mean and amplitude values of LST increased, while during urban renewal (demolition), they decreased. The date of annual temperature peaks advanced during urban growth but delayed during urban renewal (demolition). Our time series analysis framework offers a new method for understanding the yearly dynamics of urban form and its influence on surface temperature, with potential applications in carbon emission and urban climate modeling studies.