Assessing the quantitative relationships between the impervious surface area and surface heat island effect during urban expansion

Exploring urban land surface temperature using spatial modelling techniques: a case study of Addis Ababa city, Ethiopia

Urban areas worldwide are experiencing escalating temperatures due to the combined effects of climate change and urbanization, leading to a phenomenon known as urban overheating. Understanding the spatial distribution of land surface temperature (LST) and its driving factors is crucial for mitigation and adaptation of urban overheating. So far, there has been an absence of investigations into spatiotemporal patterns and explanatory factors of LST in the city of Addis Ababa. The study aims to determine the spatial patterns of land surface temperature, analyze how the relationships between LST and its factors vary across space, and compare the effectiveness of using ordinary least squares and geographically weighted regression to model these connections. The findings showed that the spatial patterns of LST show statistically significant hot spot zones in the north-central parts of the study area (Moran's I = 0.172). The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic.The result revealed non-stationarity (p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic. The result revealed non-stationarity ((p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. Consequently, Localized understanding of the spatial patterns and the driving factors of LST has been formulated.

Research on the spatiotemporal coupling relationships between land use/land cover compositions or patterns and the surface urban heat island effect

Urbanization leads to changes in landscape configuration and land use/land cover (LULC) patterns, and these changes are important factors affecting the surface urban heat island (SUHI) effect. However, from the perspective of spatiotemporal changes, quantitative analytical results regarding the impacts of the LULC composition, configuration, and pattern in inland plateau lakeside cities on the SUHI effect, and the responsive relationships among these factors remain unclear. By combining satellite remote sensing data with analytical methods, such as urban-rural gradients, spatial statistics, and landscape pattern indices, the impacts of LULC changes on the SUHI effect in Kunming, China, are revealed. The results show the following. (1) The explosive growth in impervious surfaces (ISs) caused by urbanization, leading to changes in the LULC composition, configuration and pattern, is the main reason for the deterioration of the SUHI effect. Over the past 30 years, Kunming's ISs have increased by 304.58 km², SUHI has expanded by 764.26 km², and the regional average land surface temperature (LST) has increased by 1 °C. (2) This study also found that a large area of bare ground is another important reason for the sharp rise in LST, explaining why bare land (BL) has the highest average LST (28.72 °C). (3) The pattern of LULC can well explain the spatial distribution characteristics of SUHIs. The normalized difference built-up index (NDBI), normalized difference bareness index (NDBaI), and LST have the same change curve along the urban-rural gradient, while the normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), and LST have opposite trends. (4) ISs and water body (WB) are the main types of warming and cooling, respectively, but the warming effect of ISs is greater than the cooling effect of WB. From the average value of the correlation coefficient with LST, NDBI (0.84) > MNDWI (-0.63). (5) Kunming's remote sensing index values do not have simple linear relationships with the LST. NDBaI, NDBI, and LST show significant exponential relationships, and NDVI, MNDWI, and LST show significant quadratic polynomial relationships. (6) The dominant landscape type determines the correlation between the landscape shape index (LSI) and the LST of green spaces (GSs). (7) Adopting a simple and regular landscape layout can effectively reduce the SUHI effect. These research results could provide a scientific decision-making basis for the spatial urban planning and ecological construction of Kunming and could have practical significance for guiding the green, healthy, and sustainable development of the city.

Simulating the Relationship between Land Use/Cover Change and Urban Thermal Environment Using Machine Learning Algorithms in Wuhan City, China

The changes of land use/land cover (LULC) are important factor affecting the intensity of the urban heat island (UHI) effect. Based on Landsat image data of Wuhan, this paper uses cellular automata (CA) and artificial neural network (ANN) to predict future changes in LULC and LST. The results show that the built-up area of Wuhan has expanded, reaching 511.51 and 545.28 km2, while the area of vegetation, water bodies and bare land will decrease to varying degrees in 2030 and 2040. If the built-up area continues to expand rapidly, the proportion of 30~35 °C will rise to 52.925% and 55.219%, and the affected area with the temperature >35 °C will expand to 15.264 and 33.612 km2, respectively. The direction of the expansion range of the LST temperature range is obviously similar to the expansion of the built-up area. In order to control and alleviate UHI, the rapid expansion of impervious layers (built-up areas) should be avoided to the greatest extent, and the city’s “green development” strategy should be implemented.