Sensing the dependable surficial signatures of temporal groundwater variations in arid coastal regions through geospatial techniques with respect to microclimate changes

Reports on Groundwater level variations and quality changes have been a critical issue, especially in arid regions. An attempt has been made in this study to determine the surface manifestations of groundwater variations through processing imageries for determining the changes in land use, Normalized Differential Building Index (NDBI), Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), along with Groundwater level (GWL) and Electrical conductivity (EC). Decadal variation between these parameters for 2013 and 2023 shows that the average water level had increased by 1.03amsl, while the EC values of groundwater decreased by 418 μS/cm. The decrease in EC values indicates freshwater recharge, promoting natural vegetation, thus reducing the LST values by 3.28 °C. In addition, urban landscaping and relatively lesser emissivity from built-up surfaces than the sandy desert have further reduced the LST. The interrelationship of the parameters indicates that an increase in LST correlates with an increase in NDBI and with less significant changes in NDVI. The lowering of the LST along the coastal regions was inferred to be due to the influence of Sea breeze, adjacent moisture from the ocean, shallow water level, and the shadow effect of the buildings. Further, the increase in water level was mainly attributed to the recent increase in rainfall and the extreme event in 2018. The higher EC in the lesser NDBI regions is attributed to the anthropogenic contamination from agriculture and landfill leachates. Though there was an increase in NDBI, the LST of the region was inferred to be reduced mainly due to the increase in water level and reduction of emission from desert sand by recent urban developments.

Understanding the impact of heatwave on urban heat in greater Sydney: Temporal surface energy budget change with land types

The impact of heatwaves (HWs) on urban heat island (UHI) is a contentious topic with contradictory research findings. A comprehensive understanding of the response of urban and rural areas to HWs, considering the underlying cause of surface energy budget changes, remains elusive. This study attempts to address this gap by investigating a 2020 HW event in the Greater Sydney Area using the Advanced Weather Research and Forecasting (WRF) model with 250-m high resolution. Findings indicate that the HW intensifies the nighttime surface UHI by approximately 4 °C. An analysis of surface energy budgets reveals that urban areas store more heat during the HW due to receiving more solar radiation and less evapotranspiration compared to rural areas. The maximum heat storage flux in urban during the HW can be around 200 W/m2 higher than that during post-HW. The stored heat is released at nightime, raising the air temperature in the urban areas. Forests and savannas have relatively lower storage heat fluxes due to high transpiration and albedo, and the maximum heat storage flux is only around 50 W/m2 higher than that during post-HW. In contrast, a negative synergistic effect is detected between the 2-m UHI and HW. This may be because other meteorological conditions including wind have substantial impacts on the air temperature pattern. The strong hot and dry winds coming from the west resulted in a higher air temperature in the western urban district, and intra-city disparities are higher. Meanwhile, the western forest area also experiences higher temperatures due to the westward winds. In addition, changes in wind direction alter the temperature distribution in the northern rural region. The findings of the present study may provide some insights into urban heat mitigation during HW.

Determining spatial disparities and similarities regarding heat exposure, green provision, and social structure of urban areas - A study on the city district level in the Ruhr area, Germany

Heat islands and ongoing urbanization make cities places where the negative impacts of global climate change on society are becoming increasingly evident. Especially the interplay and potential multiplication of heat, low green provision, and the presence of socially deprived urban dwellers constitutes complex challenges. Emerging climate injustices and potential health issues require a powerful counter-reaction in form of adaptation action. For our study, we consider eight cities located in the densely populated and historically highly segregated Ruhr area in Western Germany, which is one of the largest metropolitan areas in Europe with a heterogeneous distribution of socio-spatial problems, economic potential, heat stress, and green infrastructures. We use land surface temperature (LST), data on green provision (normalized difference vegetation index (NDVI)), and social indicators to reveal the relationships between these indicators on the city district level (n = 275). Therefore, we first analyze the data regarding spatial autocorrelation (Moran's I) and clustering (Gi*) before calculating study area wide and city specific correlations between the three factors regarded. Finally, we conduct a cluster analysis (k-means) to disclose similar areas with or without multiple burdens. Our results show distinct disparities in heat exposure, green availability, and social status between city districts of the study area. We find strong negative correlations between LST and NDVI as well as between NDVI and social status. The relationship between LST and our social indicator remains ambiguous, affirming the necessity of further detailed studies. The cluster analysis furthermore allows for the visualization and classification of districts featuring similar characteristics regarding the researched components. We can discern in parts pronounced climate injustice in the studied cities, with a majority of people living in unfavorable environmental and socio-economic conditions. Our analysis supports governments and those responsible for urban planning in addressing climate injustice in the future.

The climate adaptive characteristics of urban inside/outside water bodies based on their cooling effect in Poyang and Dongting lake regions, China

Most publications have focused on the cooling effect of urban inside water bodies. However, the climate adaptive characteristics of urban inside/outside water bodies is seldom studied. In this paper, three types of water bodies, i.e., urban inside water bodies, urban outside discrete water bodies and large water bodies are identified according to their relative spatial relationships with built-up areas. The climate adaptive landscape characteristics of water bodies are analyzed based on water bodies' cooling effect (WCE) inside and outside cities in the Poyang Lake and Dongting Lake regions. Seventy-three Landsat TM/OLI/TIRS images acquired from 1989 to 2019 are employed. Landscape scale characteristics of urban inside/outside water bodies are described by area, water depth, perimeter to area ratio (PARA) and distance-weighted area index (DWAI). Three temperature-related parameters are calculated to estimate the WCE in different conditions. Climate adaptive characteristics of water bodies inside/outside cities are determined by correlation and regression analysis. Results show that: 1) The long river shape, depth, orientation and fluidity of urban inside water bodies are benefit to enhance their cooling effect; 2) the distance of urban outside water bodies from built-up areas are positive correlated with their cooling effect; 3) the optimal acreage of large water bodies are >2500 km² and 1111-1287.5 km² for climate adaption of Poyang Lake and Dongting Lake, respectively. Simultaneously, the WCE of urban outside large water bodies is related with human activities and climate conditions. The results of our study provide a significant contribution to blue-space planning in cities, and provide insights into actionable climate adaption planning in inland large lake areas.

Did the Covid-19 restrictions influence land surface temperatures in Southeast Asia? A study from Ho Chi Minh City, Vietnam

There have been a prolonged lockdown period and reduction in human activities in most of the major cities in the world during the Covid-19 pandemic period between the early 2020 and the late 2021. Such a reduction in human activities was believed to have influenced pollution levels and land surface temperatures (LST) in urban areas. This paper describes the variations in LSTs before, during and after the Covid-19 lockdown in Ho Chi Minh City in southern Vietnam, which is the economic hub of the country. For this purpose, Landsat-8 OLI and TIRS images acquired between 2015 and 2022 were used. It is observed that there was a significant reduction of 1 to 1.8 °C in LST in open areas, excepting impervious surfaces and built-up areas, during the strict lockdown period in Ho Chi Minh City, and an increase in LST since then. The observed reduction in LST during the lockdown period in Ho Chi Minh City is in agreement with the reduction in greenhouses gases during the same period in recent studies. Human mobility and industrial activities have been restored in November 2021 in the study area which would explain the regain in LST in the post-lockdown period.

Megacities' environmental assessment for Iraq region using satellite image and geo-spatial tools

Urban areas are quickly established, and the overwhelming population pressure is triggering heat stress in the metropolitan cities. Climate change impact is the key aspect for maintaining the urban areas and building proper urban planning because spreading of the urban area destroyed the vegetated land and increased heat variation. Remote sensing-based on Landsat images are used for investigating the vegetation circumstances, thermal variation, urban expansion, and surface urban heat island or SUHI in the three megacities of Iraq like Baghdad, Erbil, and Basrah. Four satellite imageries are used aimed at land use and land cover (LULC) study from 1990 to 2020, which indicate the land transformation of those three major cities in Iraq. The average annually temperature is increased during  30 years like Baghdad (0.16 °C), Basrah (0.44 °C), and Erbil (0.32 °C). The built-up area is increased 147.1 km² (Erbil), 217.86 km² (Baghdad), and 294.43 km² (Erbil), which indicated the SUHI affects the entire area of the three cities. The bare land is increased in Baghdad city, which indicated the local climatic condition and affected the livelihood. Basrah City is affected by anthropogenic activities and most areas of Basrah were converted into built-up land in the last 30 years. In Erbil, agricultural land (295.81 km²) is increased. The SUHI study results indicated the climate change effect in those three cities in Iraq. This study's results are more useful for planning, management, and sustainable development of urban areas.

Delineation of urban expansion influences urban heat islands and natural environment using remote sensing and GIS-based in industrial area

Land transformation monitoring is essential for controlling the anthropogenic activities that could cause the degradation of natural environment. This study investigated the urban heat island (UHI) effect at the Asansol and Kulti blocks of Paschim Bardhaman district, India. The increasing land surface temperature (LST) can cause the UHI effect and affect the environmental conditions in the urban area. The vulnerability of the UHI effect was measured quantitatively and qualitatively by using the urban thermal field variation index (UTFVI). The land use and land cover (LULC) dynamics are identified by utilizing the remote sensing and maximum likelihood supervised classification techniques for the years 1990, 2000, 2010, and 2020, respectively. The results indicated a decrease around 19.05 km², 15.47 km², and 9.86 km² for vegetation, agricultural land, and grassland, respectively. Meanwhile, there is an increase of 35.69 km² of the built-up area from the year 1990 to 2020. The highest LST has increased by 11.55 °C, while the lowest LST increased by 8.35 °C from 1990 to 2020. The correlation analyses showed negative relationship between LST and vegetation index, while positive correlation was observed for built-up index. Hotspot maps have identified the spatio-temporal thermal variations in Mohanpur, Lohat, Ramnagar, Madhabpur, and Hansdiha where these cities are mostly affected by the urban expansion and industrialization developments. This study will be helpful to urban planners, stakeholders, and administrators for monitoring the anthropological activities and thus ensuring a sustainable urban development.

Air Temperature Reductions at the Base of Tree Canopies

Trees in urban settings have a significant role in regulating urban hydrologic cycles. Urban trees, either as standalone plantings or as part of a tree pit, are an increasingly popular stormwater management tool. Beyond their aesthetic contribution to urban environments, trees are widely accepted as reducing the ambient air temperature. However, there is limited long-term quantitative information regarding the temperature mitigation performed by urban trees through the use of temperature sensors over a large urban area. This study monitored air temperature at locations throughout the city of Camden, New Jersey. Sensors were installed under canopies of trees of different sizes throughout the city using a statistical experimental design. The tree size (small or large) and canopy (intersecting or nonintersecting), along with the street orientation (predominantly north-south or east-west) and time of day (daylight, nighttime, or full-day), were experimental design factors. Sensors attached to poles along the streets or in parking lots served as controls. This study recorded temperatures at 10-min intervals from early August through late November 2017 using logging thermistors mounted in radiation shields about 4 m above the ground surface. Using the maximum daily air temperature at control sites, all temperature data were categorized into three groups of hot, average, and cool days. The groups were analyzed separately using the analysis of variance to test the significance of the categorical variables. During hot days (a maximum temperature larger than 30°C), there was a meaningful statistical difference between recorded mean air temperatures under trees with intersecting canopies and the control sites. A categorical analysis of street orientation for hot and average days showed that during the daytime, east-west streets were hotter than north-south streets, while this trend reversed at night when north-south streets were hotter than east-west streets. For cool days, there were no differences for the studied categorical factors.

Urban heat island (UHI) intensity and magnitude estimations: A systematic literature review

The severity of urban heat islands (UHIs) is increasing due to global and urban climate change. The damage caused by UHIs is also increasing. To establish a plan to improve the deteriorating thermal environment in cities due to UHIs and to minimize the damage, further research is needed to accurately estimate and analyze the intensity and magnitude of UHIs. This systematic literature review (SLR) is an in-depth review of 51 studies obtained through a five-step filtering process focusing on their analysis of the spatial extent of UHIs, the UHI concept that was used for UHI estimation, and the UHI estimation and analysis methods. This SLR confirmed the need for accurate UHI intensity and magnitude estimation and analysis to reset the existing UHI classification based on the variety of vertical and horizontal ranges where UHIs occur. The results also indicated that the existing UHI energy concepts for estimating UHIs need to be modified and developed to reflect the three-dimensional physical form of the city. Finally, this SLR clarifies the need to develop an optimized analysis method for UHI research. The review results of this SLR will inform future studies and be the cornerstone for establishing policies and plans that can accurately predict and respond to the damage caused by UHIs.

Impacts of urban form and urban heat island on the outdoor thermal comfort: a pilot study on Mashhad

There is an increasing demand for cooling cities because of its importance on human health and the quality of life in outdoor urban spaces. However, the development of methods in improving outdoor thermal comfort and zoning cities based on outdoor thermal comfort is still challenging. In this work, we propose a new approach to cities zoning from the lens of outdoor thermal comfort in the arid climate of the city of Mashhad, Iran, and investigate the impacts of urban form characteristics on pedestrian thermal comfort. The effects of complex urban form parameters including height to width (H/W) ratio, canyon orientation, tree canopy cover, and building surface materials on the thermal comfort of pedestrians were studied in the arid climate of Mashhad. Microclimate simulation and analysis is conducted in ENVI-met software, and ArcMap is used to calculate Mashhad urban heat islands. Path analysis in SPSS presents an urban form formulation, which predicts approximate outdoor thermal comfort condition in current and future urban context of Mashhad and other cities with the same climate. We finally demonstrate the use of our research method as an alternative method for all cities: urban heat island (UHI) zoning can be used as a substitute for urban form zonings based on outdoor thermal comfort, especially in large cities where data collection on urban form can be difficult due to limited time and resources.

Intraurban social risk and mortality patterns during extreme heat events: A case study of Moscow, 2010-2017

There is currently an increase in the number of heat waves occurring worldwide. Moscow experienced the effects of an extreme heat wave in 2010, which resulted in more than 10,000 extra deaths and significant economic damage. This study conducted a comprehensive assessment of the social risks existing during the occurrence of heat waves and allowed us to identify the spatial heterogeneity of the city in terms of thermal risk and the consequences for public health. Using a detailed simulation of the meteorological regime based on the COSMO-CLM regional climate model and the physiologically equivalent temperature (PET), a spatial assessment of thermal stress in the summer of 2010 was carried out. Based on statistical data, the components of social risk (vulnerabilities and adaptive capacity of the population) were calculated and mapped. We also performed an analysis of their changes in 2010-2017. A significant differentiation of the territory of Moscow has been revealed in terms of the thermal stress and vulnerability of the population to heat waves. The spatial pattern of thermal stress agrees quite well with the excess deaths observed during the period from July to August 2010. The identified negative trend of increasing vulnerability of the population has grown in most districts of Moscow. The adaptive capacity has been reduced in most of Moscow. The growth of adaptive capacity mainly affects the most prosperous areas of the city.

Turning down the heat: An enhanced understanding of the relationship between urban vegetation and surface temperature at the city scale

Guiding urban planners on the cooling returns of different configurations of urban vegetation is important to protect urban dwellers from adverse heat impacts. To this end, we estimated statistical models that fused multi-temporal very fine spatial (20 cm) and vertical (1 mm) resolution imagery, that captures the complexity of urban vegetation, with remotely sensed temperature data to assess how urban vegetation configuration influences urban temperatures. Perth, Western Australia, was used as a case-study for this analysis. Panel regression models showed that within a location an increase in tree and shrub cover has a larger cooling effect than grass coverage. On average, holding all else equal, an approximate 1 km² increase in shrub (tree) cover within a location reduces surface temperatures by 12 °C (5 °C). We included a range of robustness checks for the observed relationships between urban vegetation type and temperature. Geographically weighted regression models showed spatial variation in the cooling effect of different vegetation types; this indicates that i) unobserved factors moderate temperature-vegetation relationships across urban landscapes, and ii) that urban vegetation type and temperature relationships are complex. Machine learning models (Random Forests) were used to further explore complex and non-linear relationships between different urban vegetation configurations and temperature. The Random Forests showed that vegetation type explained 31.84% of the out-of-bag variance in summer surface temperatures, that increased cover of large vegetation within a location increases cooling, and that different configurations of urban vegetation structure can lead to cooling gains. The models in this study were trained with vegetation data capturing local detail, multiple time-periods, and entire city coverage. Thus, these models illustrate the potential to develop locally-detailed and spatially explicit tools to guide planning of vegetation configuration to optimise cooling at local- and city-scales.

The footprint of urban heat island effect in 302 Chinese cities: Temporal trends and associated factors

The urban heat island (UHI) effect has been a concern for decades due its adverse influence on energy consumption, air and water quality, and, most importantly, the health of urban dwellers. Researchers have paid much attention to the magnitude of the UHI effect, but ignored its spatial extent (i.e. footprint) which is another important aspect of the UHI effect. In this study, we systematically analyzed the footprint of surface UHI (SUHI) effect in 302 Chinese cities, especially temporal trends of the footprint, by using multi-source remote sensing data. The footprint of SUHI effect (FP) was estimated by the Gaussian surface, and its temporal trend was examined by the Mann-Kendal and the Sen's slope estimator non-parametric tests. We found the FP showed evident diurnal (daytime > nighttime), seasonal (summer > winter) and inter-city (big cities > small and medium-sized cities) differences. During the period 2003-2016, over 80% of the 302 cities exhibited increasing trends of the FP in annual days and summer days, and the increasing trends were statistically significant (p < 0.05) in about half of these cities. In the nights, the FP increased in more than 70% of the cities, and about one-third of the 302 cities experienced significantly increasing trends of the FP. On average, the annual daytime and annual nighttime FPs increased at a rate of 5.0% per year and 3.8% per year, respectively. More importantly, the correlation analysis indicated that the increase of anthropogenic heat emissions and the decrease of vegetation activities and surface albedos should take lead responsibility for the expansion of the FP in the urbanization process. These results reveal that the spatial extent of heat island effect has expanded significantly in numerous Chinese cities, and this increasing trend will be sustained in the coming years if no more effective measures are carried out.

Spatio-temporal behavior of brightness temperature in Tel-Aviv and its application to air temperature monitoring

This study applies remote sensing technology to assess and examine the spatial and temporal Brightness Temperature (BT) profile in the city of Tel-Aviv, Israel over the last 30 years using Landsat imagery. The location of warmest and coldest zones are constant over the studied period. Distinct diurnal and temporal BT behavior divide the city into four different segments. As an example of future application, we applied mixed regression models with daily random slopes to correlate Landsat BT data with monitored air temperature (Tair) measurements using 14 images for 1989-2014. Our preliminary results show a good model performance with R(2) = 0.81. Furthermore, based on the model's results, we analyzed the spatial profile of Tair within the study domain for representative days.

Data concurrency is required for estimating urban heat island intensity

Urban heat island (UHI) can generate profound impacts on socioeconomics, human life, and the environment. Most previous studies have estimated UHI intensity using outdated urban extent maps to define urban and its surrounding areas, and the impacts of urban boundary expansion have never been quantified. Here, we assess the possible biases in UHI intensity estimates induced by outdated urban boundary maps using MODIS Land surface temperature (LST) data from 2009 to 2011 for China's 32 major cities, in combination with the urban boundaries generated from urban extent maps of the years 2000, 2005 and 2010. Our results suggest that it is critical to use concurrent urban extent and LST maps to estimate UHI at the city and national levels. Specific definition of UHI matters for the direction and magnitude of potential biases in estimating UHI intensity using outdated urban extent maps.