基于多源遥感数据的城市热环境响应与归因分析——以深圳市为例

We analyzed the annual, monthly, and seasonal variations of urban heat island (UHI) intensity in Istanbul by using meteorological data measured for the period of 1960–2012 at six stations. The UHI on minimum temperature is found to be positive for all seasons, and the average UHI intensity clearly indicates seasonal changes, strongest in summer and weakest in winter. The results demonstrated increase of night time UHI intensity with 0.41–0.50 °C/decade and decrease of daytime UHI intensity with 0.13–0.18 °C/decade at the urban sites. The UHI strengthened with the expansion of the city due to increased population. The influences of meteorological variables on seasonality of the UHI intensity are examined for the days categorized depending on wind, cloud cover, and precipitation values. It is found that the UHI intensity decreases with increasing wind speed and cloud cover. The integrated response of the city atmosphere to wind speed changes differ such that daytime UHI in urban atmosphere intensifies rapidly from calm conditions to the wind speeds of 2–3 m/s, then slightly increases until 4–5-m/s wind speeds and starts to decline afterwards. On the other hand, the nighttime UHI intensities in urban sites continuously decline with the same rate until the wind speeds reach to 5–6 m/s. The difference of daytime UHI between rainy summer days and dry days is around 1 °C which is almost independent of the precipitation amount. Both nighttime and daytime UHI intensities depend on the season and site range approximately between 0.24 and 1.74 °C and − 0.62 and 2.61 °C, respectively. However, the UHI based on minimum temperature for the selected dry days with low wind and clear sky conditions increases to 1.70–3.08 °C. Land surface data from Moderate-Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra show areal extension of the UHI through the north along the Bosphorus between 2000 and 2012, especially in the night observations. The continuous increase of built-up areas, paved roads, and decrease of green areas caused the growth of UHI intensity. The estimated UHI based on land surface temperature (LST) at the most urbanized locations of Istanbul reach to 8 °C for daytime and 6 °C for nighttime.

Based on the land surface temperature (LST), the land cover classification map, vegetation coverage, and surface evapotranspiration derived from EOS-MODIS satellite data, and by the use of GIS spatial analytic technique and multivariate statistical analysis method, the urban heat island (UHI) spatial distribution of the diurnal and seasonal variabilities and its driving forces are studied in Beijing city and surrounding areas in 2001. The relationships among UHI distribution and landcover categories, topographic factor, vegetation greenness, and surface evapotranspiration are analyzed. The results indicate that: (i) The significant UHI occur in Beijing city areas in the four seasons due to high heat capacity and multi-reflection of compression building, as well as with special topographic features of its three sides surrounded by mountains, especially in the summer. The UHI spatial distribution is corresponding with the urban geometry structure profile. The LST difference is approximately 4-6℃between Beijing city and suburb areas, comparatively is 8- 10℃between Beijing city area and outer suburb area in northwestern regions. (ii) The UHI distribution and intensity in daytime are different from nighttime in Beijing city area, the nighttime UHI is obvious. However, in the daytime, the significant UHI mainly appears in the summer, the autumn takes second place, and the UHI in the winter and the spring seem not obvious. The surface evapotranspiration in suburb areas is larger than that in urban areas in the summer, and high latent heat exchange is evident, which leads to LST difference between city area and suburb area. (iii) The reflection of surface landcover categories is sensitive to the UHI, the correlation between vegetation greenness and UHI shows obviously negative. The scatterplot shows that there is the negative correlation between NDVI and LST (R2 = 0.6481). The results demonstrate that the vegetation greenness is an important factor for reducing the UHI, and large-scale construction of greenbelts can considerably reduce the UHI effect.

Urbanization is closely associated with land use land cover (LULC) changes that correspond to land surface temperature (LST) variation and urban heat island (UHI) intensity. Major districts of Bangladesh have a large population base and commonly lack the resources to manage fast urbanization effects, so any rise in urban temperature influences the population both directly and indirectly. However, little is known about the impact of rapid urbanization on UHI intensity variations during the winter dry period in the major districts of Bangladesh. To this end, we aim to quantify spatiotemporal associations of UHI intensity during the winter period between 2000 and 2019 using remote-sensing and geo-spatial tools. Landsat-8 and Landsat-5 imageries of these major districts during the dry winter period from 2000 to 2020 were used for this purpose, with overall precision varying from 81% to 93%. The results of LULC classification and LST estimation showed the existence of multiple UHIs in all major districts, which showed upward trends, except for the Rajshahi and Rangpur districts. A substantial increase in urban expansion was observed in Barisal > 32%, Mymensingh > 18%, Dhaka > 17%, Chattogram > 14%, and Rangpur > 13%, while a significant decrease in built-up areas was noticed in Sylhet < −1.45% and Rajshahi < −3.72%. We found that large districts have greater UHIs than small districts. High UHI intensities were observed in Mymensingh > 10 °C, Chattogram > 9 °C, and Barisal > 8 °C compared to other districts due to dense population and unplanned urbanization. We identified higher LST (hotspots) zones in all districts to be increased with the urban expansion and bare land. The suburbanized strategy should prioritize the restraint of the high intensity of UHIs. A heterogeneous increase in UHI intensity over all seven districts was found, which might have potential implications for regional climate change. Our study findings will enable policymakers to reduce UHI and the climate change effect in the concerned districts.

Impacts of increased urbanization on surface temperature, vegetation, and aerosols over Bengaluru, India

The influence of urban heat island (UHI) on the environment, human health, and urban livability has been and will continue to be a source of concern for humanity. We examined the spatial distribution and intensity of UHI in a metropolitan city Shanghai utilizing long time series of satellite images and land use data. Then we properly and objectively monitored the UHI phenomenon and evaluated its driving variables. Using Landsat 5, 7, and 8 satellite data with China's Land-Use/Cover Datasets (CLCD), we retrieved land surface temperature (LST) and computed the Urban Heat Island Intensity Index (UHII) to measure UHI intensity. When investigating the correlation between impervious surface area and UHI area, we also made use of the Pearson correlation coefficient. The study found that the LST in Shanghai fluctuated upwards from 1990 to 2020, with the downtown, suburbs, and outer suburbs increasing by 5.394 degrees, 9.187 degrees, and 5.211 degrees, respectively, with the high-temperature zone expanding to the city's periphery year by year. Meanwhile, the UHII varies dramatically, the average increase of UHI in suburbs is 4.522 degrees, while the average growth of UHI in downtown and outer suburbs is only 0.728 degrees and 0.546 degrees, respectively. The Pearson correlation coefficient between UHI area and impervious surface area is 0.8698331. Shanghai's UHI has been rising year after year, owing to an increase in impervious surface area as a result of urbanization. The causes of the large changes in surface temperature and UHI have yet to be discovered and studied in depth. Overall, these findings show that urbanization has an impact on the establishment and intensity of UHI diffusion. To balance urban development with the urban thermal environment, more measures should be explored.

In frame of global warming, the field of urbanization and urban thermal environment are important issues among scientists all over the world. This paper investigated the influences of urbanization on urban thermal environment as well as the relationships of thermal characteristics to other biophysical variables in Bucharest metropolitan area of Romania based on satellite remote sensing imagery Landsat TM/ETM+, time series MODIS Terra/Aqua data and IKONOS acquired during 1990 - 2012 period. Vegetation abundances and percent impervious surfaces were derived by means of linear spectral mixture model, and a method for effectively enhancing impervious surface has been developed to accurately examine the urban growth. The land surface temperature (Ts), a key parameter for urban thermal characteristics analysis, was also retrieved from thermal infrared band of Landsat TM/ETM+, from MODIS Terra/Aqua datasets. Based on these parameters, the urban growth, urban heat island effect (UHI) and the relationships of Ts to other biophysical parameters have been analyzed. Results indicated that the metropolitan area ratio of impervious surface in Bucharest increased significantly during two decades investigated period, the intensity of urban heat island and heat wave events being most significant. The correlation analyses revealed that, at the pixel-scale, Ts possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at the regional scale, respectively. This analysis provided an integrated research scheme and the findings can be very useful for urban ecosystem modeling.

The influence of landscape patterns on urban thermal environment has received remarkable attention with the rapid urbanization process worldwide. Although numerous studies have revealed the relationship between landscape patterns and the urban thermal environment, there is limited effort to quantify the contributions of landscape patterns and anthropogenic heat to urban heat islands (UHI) under the seasonal and daily dynamics. This study aims to identify the effects of landscape patterns and anthropogenic heat on UHI intensity in the daytime and nighttime as well as in different seasons. We first used remotely sensed images to extract the landscape patterns and land surface temperature. Anthropogenic heat was quantified through energy consumption data. Multivariate stepwise regression was used to quantify the effects of landscape patterns and anthropogenic heat on UHI intensity. Eight models were constructed for daytime and nighttime UHI intensity in spring, summer, fall, and winter, respectively. Results demonstrated that the presence of buildings is the main factor affecting UHI intensity in urban area for three seasons except summer. Basically, the contributions of landscape configuration to the UHI in the daytime is higher than landscape composition, while the opposite is true at night. The contribution of anthropogenic heat was low in all seasons due to the high correlation with non-monolayer buildings. The nighttime UHI intensity models fit better than the daytime, especially in the winter. This study contributes to the understanding of landscape influence on UHI intensity and provides practical guidelines for landscape planning to mitigate UHI effects.

The dynamics of urban heat island (UHI) in urban thermal environments will be more complex and crucial along with the region rapid growth, coupled with increasingly an unforeseen global temperature condition. This study aims to observe the characteristics of UHI and its effects on land units in coastal cities in Southeast Sulawesi province from 2019 to 2023. Landsat 8/9 imagery is used for Land Surface Temperature (LST) retrieval. Urban Landscape Composition (ULC) is developed based on the spectral index method through VrNIR-BI, NDVI and MNDWI. Land units are developed using overlay techniques from four input data including ULC, terrain slope, soil type and geological formation. The results of the study reveal that LST variations significantly affect UHI formation throughout the study area. Surface temperatures in both regions continued to increase throughout 2021-2023, is getting warmer in 2023 between August and November. Meanwhile, the ULC conditions in both regions experienced major transformations, especially in the areas of impervious surface and green space. Nevertheless, over 70% of the total area in both regions is covered by vegetation. Spatially, the UHI changes in Kendari city tends to moved towards the western region and are more massive to the southern region. Meanwhile, the UHI area in Baubau city is still relatively in the downtown area, but several areas in the northern region of Baubau are beginning to show substantial UHI concentration. Global warming that occurred in 2023 further worsened the UHI in both regions. The UHI area is typically affect more land units categorized as impervious surface with relatively flat terrain slopes and hapludalfs soil types. This study emphasizes the importance of studying the dynamics of microclimate in urban thermal environments in the disaster mitigation efforts, providing valuable insights for regional development planning strategies to create environmentally friendly, resilient and sustainable cities  

The present study focuses on the assessment of urban heat island (UHI) and urban energy fluxes over Indian metropolitan cities. For this purpose, a high-resolution numerical Weather Research and Forecasting (WRF) model and a coupled single-layer urban canopy model (WRF-UCM) has been implemented over Delhi, Kolkata and Hyderabad to evaluate the performance of these models in capturing the impact of urban areas in simulating the UHI over different land use land cover (LULC) classes. The model initial conditions are obtained from NCEP-FNL reanalysis data. The model simulations reveal that the air temperature at 2 m height (T2) and UHI intensity are overestimated by WRF whereas the WRF-UCM simulations are matching well with ERA5-Land reanalysis data considered as observations. For the WRF-UCM simulation, the statistical errors in T2 and UHI intensity are within the desirable limits and the index of agreement (IoA) is more than 0.8 over the built-up areas (BAs). By comparing with the satellite observations, the relative surface UHI is noticed to be better captured over the BA by WRF-UCM simulation. The sensible heat flux simulated by WRF is highly overestimated whereas WRF-UCM is in good agreement with the satellite observation over the BA. Interestingly, the latent heat flux is reasonably well simulated over all the LULC classes. The results derived from the present study have shown the performance of WRF-UCM in simulating the spatial variation of UHI and fluxes of sensible and latent heat-related varied LULC classifications over metropolitan cities of India.

The urban heat island (UHI) effect has emerged in the literature as a major challenge to urban well-being, primarily driven by increasing urbanization. To address this challenge, this study investigates the spatiotemporal pattern of the UHI in the fast-growing city of Kisangani and within its urban–rural gradient from 2000 to 2024 using land surface temperature (LST) data from the MODIS 11A2 V6.1 product. Inferential and descriptive statistics were applied to examine the patterns of UHI and the relationships between the LST, building volume density (BVD), and vegetation density expressed by the Normalized Difference Vegetation Index (NDVI). The results showed that the spatial extent of the moderate UHI gradually increased from 16 km2 to 38 km2, while the high UHI increased from 9 km2 to 19 km2. Furthermore, although high UHI values (0.2 < UHI ≤ 0.3) are observed in urban areas and significant differences in UHI variations are detected across urban, peri-urban, and rural zones, the results indicate that the mean UHI in Kisangani’s urban areas remains below 0.2. Therefore, based on average UHI variations, Kisangani’s urban zones exhibit moderate disparities in LST compared to rural areas. Moreover, the LST variations significantly correlate with the building volume and vegetation densities. However, the influence of vegetation density as a predictor of LST gradually decreases while the influence of building volume density increases over time, suggesting the need to implement a synergistic development pathway to manage the interactions between urbanization, landscape change, and ecosystem service provision. This integrated approach may represent a crucial solution for mitigating the UHI effect in regions categorized as high-temperature zones.

The global rise of urbanization has led to the formation of surface urban heat islands and surface urban cool islands. Urban heat islands have been shown to increase thermal discomfort, which increases heat stress and heat-related diseases. In Kuwait, a hyper-arid desert climate, most of the population lives in urban and suburban areas. In this study, we characterized the spatial distribution of land surface temperatures and investigated the presence of urban heat and cool effects in Kuwait. We used historical Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra satellite 8-day composite land surface temperature (LST) from 2001 to 2017. We calculated the average LSTs of the urban/suburban governorates and compared them to the average LSTs of the rural and barren lands. We repeated the analysis for daytime and nighttime LST. During the day, the temperature difference (urban/suburban minus versus governorates) was −1.1 °C (95% CI; −1.2, −1.00, p < 0.001) indicating a daytime urban cool island. At night, the temperature difference (urban/suburban versus rural governorates) became 3.6 °C (95% CI; 3.5, 3.7, p < 0.001) indicating a nighttime urban heat island. In light of rising temperatures in Kuwait, this work can inform climate change adaptation efforts in the country including urban planning policies, but also has the potential to improve temperature exposure assessment for future population health studies.

This study examines the spatial and temporal patterns of the surface urban heat island (SUHI) intensity in the Phoenix metropolitan area and the relationship with land use land cover (LULC) change between 2000 and 2014. The objective is to identify specific regions in Phoenix that have been increasingly heated and cooled to further understand how LULC change influences the SUHI intensity. The data employed include MODerate-resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) 8-day composite June imagery, and classified LULC maps generated using 2000 and 2014 Landsat imagery. Results show that the regions that experienced the most significant LST changes during the study period are primarily on the outskirts of the Phoenix metropolitan area for both daytime and nighttime. The conversion to urban, residential, and impervious surfaces from all other LULC types has been identified as the primary cause of the UHI effect in Phoenix. Vegetation cover has been shown to significantly lower LST for both daytime and nighttime due to its strong cooling effect by producing more latent heat flux and less sensible heat flux. We suggest that urban planners, decision-makers, and city managers formulate new policies and regulations that encourage residential, commercial, and industrial developers to include more vegetation when planning new construction.

Understanding the spatial distribution of land surface temperature (LST) and its impact factors is crucial for mitigating urban heat island effect. However, few studies have quantitatively investigated the spatial non-stationarity and spatial scale effects of the relationships between LST and its impact factors at multi-scales. The main purposes of this study are as follows: (1) to estimate the spatial distributions of urban heat island (UHI) intensity by using hot spots analysis and (2) to explore the spatial non-stationarity and scale effects of the relationships between LST and related impact factors at multiple resolutions (30–1200 m) and to find appropriate scales for illuminating the relationships in a plain city. Based on the LST retrieved from Landsat 8 OLI/TIRS images, the Geographically-Weighted Regression (GWR) model is used to explore the scale effects of the relationships in Zhengzhou City between LST and six driving indicators: The Fractional Vegetation Cover (FVC), the Impervious Surface (IS), the Population Density (PD), the Fossil-fuel CO2 Emission data (FFCOE), the Shannon Diversity Index (SHDI) and the Perimeter-area Fractal Dimension (PAFRAC),which indicate the vegetation abundance, built-up, social-ecological variables and the diversity and shape complexity of land cover types. Our findings showed that the spatial patterns of LST show statistically significant hot spot zones in the center of the study area, partly extending to the western and southern industrial areas, indicating that the intensity of the urban heat island is significantly spatial clustering in Zhengzhou City. In addition, compared with the Ordinary Least Squares (OLS) model, the GWR model has a better ability to characterize spatial non-stationarity and analyze the relationships between the LST and its impact factors by considering the space-varying relationships of different variables, especially at the fine spatial scales (30–480 m). However, the strength of GWR model has become relatively weak with the increase of spatial scales (720–1200 m). This reveals that the GWR model is recommended to be applied in the analysis of UHI problems and related impact factors at scales finer than 480 m in the plain city. If the spatial scale is coarser than 720 m, both OLS and GWR models are suitable for illustrating the correct relationships between UHI effect and its influence factors in the plain city due to their undifferentiated performance. These findings can provide valuable information for urban planners and researchers to select appropriate models and spatial scales seeking to mitigate urban thermal environment effect.

The local climate of cities is changing, and one of the primary reasons for this change is rapid urbanization. The Lahore district is situated in the Punjab province of Pakistan and is mainly comprised of Lahore city. This city is among the fastest expanding cities in Pakistan. Due to this rapid urbanization, the natural land surfaces are being altered, harming the local environment and thus causing the urban heat island (UHI) effect. For the analysis of the UHI effect, the fundamental and essential step is assessing the land surface temperature (LST). Therefore, the current investigation assessed LST to evaluate the UHI effect of the Lahore district. This study used the remote sensing data retrieved from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) and Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor. Different new generation algorithms were initially used, but a convolutional neural network (CNN) model was used based on the accuracy. The model was developed by utilizing the past 19 years’ LST values along with elevation, road density (RD), and enhanced vegetation index (EVI) as input parameters for analyzing and predicting the LST. The LST data of the year 2020 was used for the validation of the outcomes of the CNN model. Among the model predicted LST and observed LST, a high correlation was noticed. The mean absolute percentage error (MAPE), mean absolute error (MAE), and mean squared error (MSE) for the considered two different periods (January and May) were also computed for both the training and validation processes. The prediction error for most parts of the district was within 0.1 K of the observed values. Hence, the formulated CNN model can be utilized as an essential tool for analyzing and predicting LST and thus for the evaluation of the UHI effect at any location.

Industrial and economic development along with the rapid growth in urban population over the global cities is generating a warmer inner core in comparison to the surrounding natural landscape, the common feature of the phenomena of urban heat island (UHI). In the earlier part of the study, the spatial relationship of the changes in the land cover types and land surface temperature (LST) is analysed for metropolitan cities of India during winter season using remote sensing techniques. In the present study, an attempt has been made to estimate the UHI intensities over the metropolitan cities of India during summer season. The LST estimated using the Landsat 7 ETM+ images reveals the presence of a unique form of surface UHIs in the dense built-up areas of different cities. The intensities of UHIs over these cities during summer season are noticed to be in the range of 10.5-14 °C. Delhi possesses the highest UHI intensity in the range of 13.4-14.0 °C, and Kolkata possessed the lowest UHI intensity in the range of 10.5-11.7 °C. Higher-temperature zones (hotspots) are noticed to be increasing within the built-up areas and barren lands. The temperature of hotspots for the northern cities Delhi and Jaipur is observed to be in the range of 45-50 °C and is highest among the cities under consideration. On comparison with the winter results, the LST ranges, UHI intensities and the hotspot ranges for summer season are found be noticeably higher. The LST distribution and the UHI pattern of these cities are found to different during summer season in comparison to the winter season.