A new approach to studying the urban heat island in Ljubljana

Ljubljana is the largest urban settlement in Slovenia, resulting in the most pronounced urban heat island (UHI) effect in the country. While numerous in-depth studies of the UHI effect on the surface and in the canopy have already been conducted in the Slovenian capital, our project, funded by the Municipality of Ljubljana, seeks to gain new insights into this phenomenon by applying advanced GIS and GeoAI methods.

As part of the project, a network of 32 urban meteorological stations was set up to record air temperature, relative humidity and air pressure at 10-minute intervals and display them in real time. The station locations were selected based on expert knowledge of the spatial variability of UHI in conjunction with the Local Climate Zone (LCZ) classification.

The air temperature data obtained from these stations were used to map canopy UHI at different times of the day, under optimal meteorological conditions for canopy UHI formation on November 1, 2024, characterised by anticyclonic weather and low wind speeds. A random forest regression model was used for the mapping.

To support the model, several spatial layers representing explanatory variables were derived from various remote sensing datasets, including LiDAR, satellite imagery and orthophotos. These variables included impervious area, mean height of surface features, average building height, mean tree canopy height, normalised difference vegetation index (NDVI), sky view factor, land surface temperature, surface albedo, and land use, among others.

After selecting a subset of explanatory variables, regression modelling was performed at 30 m and 20 m spatial resolution. The results for both resolutions showed a comparable level of accuracy; however, the 20 m resolution performed slightly better (explained variance: 62.04 %; RMSE: 0.34 °C; MAE: 0.30 °C).

Although the main objective of this presentation is to present preliminary methodological results based on a single case study day, the ultimate goal of the project is to develop an algorithm for the automatic generation of canopy UHI maps to be made accessible via a web platform.