The Trend Inconsistency between Land Surface Temperature and Near Surface Air Temperature in Assessing Urban Heat Island Effects

How can the floor area types of a university campus mitigate the increase of urban air temperature?

The urban heat island (UHI) under the current climate change scenario could have a major impact on the lives of urban residents. The presence of green areas undoubtedly mitigates the UHI, and modifies some selected anthropized surfaces with particular characteristics (e.g., albedo). Here, we use a university campus as a good template of the urban context to analyze the mitigation effect of different surface types on the air temperature warming. This study provides some of the best practices for the future management of land surface types in urban areas. Through the development of a simple air temperature mitigation index (ATMI) that uses the temperature, water content (WC), and albedo of the investigated surface types, we find the green and anthropized surfaces according to their areal distribution and mitigation effects. The findings address the importance of poorly managed green areas (few annual mowings) and anthropized materials that permit a good balance between water retention capacity and high albedo. In the case of impervious surfaces, priority should be given to light-colored materials with reduced pavement units (blocks or slabs) to reduce the UHI.

Study on air temperature estimation and its influencing factors in a complex mountainous area

Near-surface air temperature (Ta) is an important parameter in agricultural production and climate change. Satellite remote sensing data provide an effective way to estimate regional-scale air temperature. Therefore, taking Gansu section of the upper Weihe River Basin as the study area, using the filtered reconstructed high-quality long-time series normalized difference vegetation index (NDVI), interpolated reconstructed land surface temperature (LST), surface albedo, and digital elevation model (DEM) as the input data, the back-propagation artificial neural network algorithm (BP-ANN) was combined with a multiple linear regression method to estimate regional air temperature, and the influencing factors of air temperature estimation were analyzed. This method effectively compensates for the fact that air temperature data provided by a single station cannot represent regional air temperature information. The result shows that the temperature estimation accuracy is high. In terms of interannual variation, the air temperature in the study area showed a slightly increasing trend, with an average annual increase of 0.047°C. The calculation results of the interannual variation rate of temperature showed that the area with increased air temperature accounted for 75.8% of the total area. In terms of seasonal variation, compared with that in summer and winter, the air temperature rising trend in autumn was obvious, and the air temperature in the middle of the study area decreased in spring, which is prone to frost disasters. LST and NDVI in the study area were positively correlated with air temperature, and their positive correlation distribution areas accounted for 93.62% and 94.34% of the total study area, respectively. NDVI, LST and DEM influence the temperature change in the study area. The results show that there is a significant positive correlation between NDVI and air temperature, and the change of NDVI has a positive effect on the spatiotemporal variation of air temperature. The correlation coefficient between LST and air temperature in the southeast of the study area is negative, and there is a difference. In addition, the correlation coefficient between LST and air temperature in other areas of the study area is positive. The air temperature decreased with elevation, air temperature decreases by 0.27°C every hundred meters.

Combining Spatial and Temporal Data to Create a Fine-Resolution Daily Urban Air Temperature Product from Remote Sensing Land Surface Temperature (LST) Data

Remotely sensed land surface temperature (LST) is often used as a proxy for air temperature in urban heat island studies, particularly to illustrate relative temperature differences between locations. Two sensors are used predominantly in the literature, Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS). However, each has shortcomings that currently limit its utility for many urban applications. Landsat has high spatial resolution but low temporal resolution, and may miss hot days, while MODIS has high temporal resolution but low spatial resolution, which is inadequate to represent the fine grain heterogeneity in cities. In this paper, we overcome this inadequacy by combining high spatial frequency Environmental Services (ES), Landsat-driven Normalized Difference Vegetation Index (NDVI), and MODIS low spatial frequency background LST at different spatial frequency bands (spatial spectral composition). The method is able to provide fine scale LST four times daily on any day of the year. Using data from Paris in 2019 we show that (1) daytime cooling by vegetation reaches a maximum of 30 °C, above which there is no further increase in cooling. In addition, (2) the cooling is relatively local and does not extend further than 200 m beyond the boundary of the NBS. This model can be used to quantify the benefits of NBS in providing cooling in cities.

Approaches for identifying heat-vulnerable populations and locations: A systematic review

Heat related morbidity and mortality, especially during extreme heat events, are increasing due to climate change. More Americans die from heat than from all other natural disasters combined. Identifying the populations and locations that are under high risk of heat vulnerability is important for urban planning and design policy making as well as health interventions. An increasing number of heat vulnerability/risk models and indices (HV/R) have been developed based on indicators related to population heat susceptibility such as sociodemographic and environmental factors. The objectives of this study are to summarize and analyze current HV/R's construction, calculation, and validation, evaluate the limitation of these methods, and provide directions for future HV/R and related studies. This systematic review used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and used 5 datasets for the literature search. Journal articles that developed indices or models to assess population level heat-related vulnerability or risks in the past 50 years were included. A total of 52 papers were included for analysis on model construction, data sources, weighting schemes and model validation. By synthesizing the findings, we suggested: (1) include relevant and accurately measured indicators; (2) select rational weighting methods and; (3) conduct model validation. We also concluded that it is important for future heat vulnerability models and indices studies to: (1) be conducted in more tropical areas; (2) include a comprehensive understanding of energy exchanges between landscape elements and humans; and (3) be applied in urban planning and policy making practice.

Effect of Land Surface Temperature on Urban Heat Island in Varanasi City, India

Crucial changes in urban climate can be witnessed due to rapid urbanisation of cities across the world. It is important to find a balance between urban expansion and thermal environment quality to guarantee sustainable urban development. Thus, it is a major research priority to study the urban heat island (UHI) in various fields, i.e., climate change urban ecology, urban climatology, urban planning, mitigation and management, urban geography, etc. The present study highlighted the interrelationship between land surface temperature (LST) and the abundance of impervious cover and green cover in the Varanasi city of Uttar Pradesh, India. For this purpose, we used various GIS and remote-sensing techniques. Landsat 8 images, land-use–land-cover pattern including urban/rural gradients, and grid- and metric-based multi-resolution techniques were used for the analysis. From the study, it was noticed that LST, density of impervious cover, and density of green cover were correlated significantly, and an urban gradient existed over the entire city, depicting a typical UHI profile. It was also concluded that the orientation, randomness, and aggregation of impervious cover and green cover have a strong correlation with LST. From this study, it is recommended that, when planning urban extension, spatial variation of impervious cover and green cover are designed properly to ensure the comfort of all living beings as per the ecological point of view.