A high-resolution monitoring approach of canopy urban heat island using a random forest model and multi-platform observations

Abstract

Abstract. Due to rapid urbanization and intense human activities, the urban heat island (UHI) effect has become a more concerning climatic and environmental issue. A high-spatial-resolution canopy UHI monitoring method would help better understand the urban thermal environment. Taking the city of Nanjing in China as an example, we propose a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a random forest (RF) model. Firstly, the observed environmental parameters, e.g., surface albedo, land use/land cover, impervious surface, and anthropogenic heat flux (AHF), around densely distributed meteorological stations were extracted from satellite images. These parameters were used as independent variables to construct an RF model for predicting air temperature. The correlation coefficient between the predicted and observed air temperature in the test set was 0.73, and the average root-mean-square error was 0.72 ∘C. Then, the spatial distribution of CUHII was evaluated at 30 m resolution based on the output of the RF model. We found that wind speed was negatively correlated with CUHII, and wind direction was strongly correlated with the CUHII offset direction. The CUHII reduced with the distance to the city center, due to the decreasing proportion of built-up areas and reduced AHF in the same direction. The RF model framework developed for real-time monitoring and assessment of high spatial and temporal resolution (30 m and 1 h) CUHII provides scientific support for studying the changes and causes of CUHII, as well as the spatial pattern of urban thermal environments.

Using open data to reveal factors of urban susceptibility to natural hazards and man-made hazards: case of Milan and Sofia

“…The above-described procedure is quite standard (e.g., Choubin et al 2020;Dang et al 2020;Ebrahimy et al 2020;Ahmad et al 2021;Chen et al 2022). However, the definition of a list of conditioning factors is not so straightforward as a variety of factors directly or indirectly play a role in the urban susceptibility to the hazard occurrence.…”

Potential Urban Barrier Effect to Alter Patterns of Cloud‐To‐Ground Lightning in Beijing Metropolis

“…Figure 5b shows that the upper-bifurcation airflow continued to move towards the city center. The proportion of compact-rise in LCCs exceeded 90%, and the spatially average velocity drops significantly with an increase in building density (Chen et al, 2022;Mei et al, 2017). When the bifurcation airflow approached LCCs, the velocity of bifurcation airflow weakened to about 3.6 m/s.…”

Typhoon-associated air quality over the Guangdong–Hong Kong–Macao Greater Bay Area, China: machine learning-based prediction and assessment

“…The higher the importance value, the more influential the feature. Because of these advantages, RF models have been applied to analyze causal relationships between variables and provide a powerful tool for determining the dominant factors among variables Yang et al 2020;Zeng et al 2020;Li et al 2021;Venter et al 2021;Chen et al 2022).…”