Land Surface Temperature Difference Research Articles

Estimation of Urban-Rural Land Surface Temperature Difference at Different Elevations in the Qinling-Daba Mountains Using MODIS and the Random Forest Model.

Land surface temperature (LST) variations are very complex in mountainous areas owing to highly heterogeneous terrain and varied environment, which complicates the surface urban heat island (SUHI) in mountain cities. Previous studies on the urban heat island (UHI) effect mostly focus on the flat terrain areas; there are few studies on the UHI effect in mountainous areas, especially on the influence of elevation on the SUHI effect. To determine the SUHI in the Qinling–Daba mountains (China), MODIS LST data were first preprocessed and converted to the same elevations (1500 m, 2000 m, 2500 m, 3000 m, and 3500 m) using a digital elevation model and the random forest method. Then, the average LSTs in urban land, rural land, and cultivated land were calculated separately based on the ranges of the invariable urban, rural, and cultivated areas during 2010–2018, and the urban, rural, and cultivated land LST difference were estimated for the same elevations. Results showed that the accuracy of LST estimated using the random forest method is very high (R2 ≥ 0.9) at elevations of 1500 m, 2000 m, 2500 m, 3000 m and 3500 m. The difference in urban, rural, and cultivated lands’ LST has a trend of decrease with increasing elevation, meaning that the SUHI weakens at higher elevations. The average LST of urban areas is 0.52–0.59 °C (0.42–0.57 °C) higher than that of rural and cultivated areas at an elevation of 1500 m (2000 m). The average LST of urban areas is 0.10–1.25 °C lower than that of rural and cultivated areas at elevations of 2500 m, 3000 m, and 3500 m, indicating absence of the SUHI at those elevations.

Exploring seasonal diurnal surface temperature variation in cities based on ECOSTRESS data: A local climate zone perspective.

High urban temperatures affect city livability and may be harmful for inhabitants. Analyzing spatial and temporal differences in surface temperature and the thermal impact of urban morphological heterogeneity can promote strategies to improve the insulation of the urban thermal environment. Therefore, we analyzed the diurnal variation of land surface temperature (LST) and seasonal differences in the Fifth Ring Road area of Beijing from the perspective of the Local Climate Zone (LCZ) using latest ECOSTRESS data. We used ECOSTRESS LST data with a resolution of 70 m to accurately interpret the effects of urban morphology on the local climate. The study area was dominated by the LCZ9 type (sparse low-rise buildings) and natural LCZ types, such as LCZA/B (woodland), LCZD (grassland), and LCZG (water body), mainly including park landscapes. There were significant differences in LST observed in different seasons as well as day and night. During daytime, LST was ranked as follows: summer > spring > autumn > winter. During night-time, it was ranked as follows: summer > autumn > spring > winter. All data indicated that the highest and lowest LST was observed in summer and winter, respectively. LST was consistent with LCZ in terms of spatial distribution. Overall, the LST of each LCZ during daytime was higher than that of night-time during different seasons (except winter), and the average LST of each LCZ during the diurnal period in summer was higher than that of other seasons. The LST of each LCZ during daytime in winter was lower than that of the corresponding night-time, which indicates that it is colder in the daytime during winter. The results presented herein can facilitate improved analysis of spatial and temporal differences in surface temperature in urban areas, leading to the development of strategies aimed at improving livability and public health in cities.

Thermal Contribution of the Local Climate Zone and Its Spatial Distribution Effect on Land Surface Temperature in Different Macroclimate Cities

Local climate zones (LCZs) provide a comprehensive framework to examine surface urban heat islands (SUHIs), but information is lacking on their thermal contributions and spatial effects in different macroclimate cities. A standard framework for distinguishing between the cooling effect and heating effect and spatial effect analysis based on the LCZ scheme was conducted in five distinct macroclimate cities, i.e., Yuanjiang (arid climate), Jinghong (tropical climate), Kunming (subtropical climate), Zhaotong (temperate climate), and Shangri-La (alpine climate). The results indicated that (1) built-up zones presented heating effects in Jinghong and Shangri-La, but opposite results were observed in Yuanjiang and Zhaotong. (2) The thermal contributions of natural zones with dense trees (LCZAs) and waterbodies (LCZGs) showed cooling effects in the five cities regardless of season. (3) The spatial effect of heating LCZs on land surface temperature (LST) was more significant than that of cooling LCZs in Jinghong and Shangri-La, but the opposite results occurred in Yuanjiang and Kunming. Moreover, the spatial effect was lower in Zhaotong than in other cities. (4) Lower LST differences between natural zones and built-up zones in winter than in summer decreased the spatial effects. In summary, the thermal contributions of LCZs and their spatial heating/cooling effects were different among five distinct climate backgrounds, which implies that targeted measures must be used in different macroclimates.

The Surface Urban Heat Island and Key Mitigation Factors in Arid Climate Cities, Case of Marrakesh, Morocco

The use of vegetation is one of the effective methods to combat the increasing Urban Heat Island (UHI). However, vegetation is steadily decreasing due to urban pressure and increased water stress. This study used air temperature measurements, humidity and an innovative advanced earth system analysis to investigate, at daytime, the relationship between green surfaces, built-up areas and the surface urban heat island (SUHI) in Marrakesh, Morocco, which is one of the busiest cities in Africa and serves as a major economic centre and tourist destination. While it is accepted that UHI variation is generally mitigated by the spatial distribution of green spaces and built-up areas, this study shows that bare areas also play a key role in this relationship. The results show a maximum mean land surface temperature difference of 3.98 °C across the different city neighbourhoods, and bare ground had the highest correlation with temperature (r = 0.86). The correlation between the vegetation index and SUHI is decreasing over time, mainly because of the significant changes in the region’s urban planning policy and urban growth. The study represents a relevant overview of the factors impacting SUHI, and it brings a new perspective to what is known so far in the literature, especially in arid climate areas, which have the specificity of large bare areas playing a major role in SUHI mitigation. This research highlights this complex relationship for future sustainable development, especially with the challenges of global warming becoming increasingly critical.

Seasonal Differences in Land Surface Temperature under Different Land Use/Land Cover Types from the Perspective of Different Climate Zones

The process of urbanization is accelerating, and land surface temperature (LST) is increasing, seriously threatening human health. Therefore, it is crucial to explore the differences in LST of different land use/land cover (LULC) types. Using MOD11A2 and MCD12Q1 data, this study explored the seasonal differences in LST of each LULC type from the perspective of different climate zones. The results showed that the maximum and minimum LSTs during the day were higher than those at night. During the day, the LSTs of urban and built-up and barren lands were higher than those of forests, grasslands, and water bodies; at night, the LSTs of urban and built-up lands decreased but remained high, while barren lands showed a significant decrease to LSTs even lower than those of water bodies. In addition, the difference in daytime LST of the LU16 type (barren lands) in different climatic zones was the most obvious and was much higher than that of other LULC types in the middle temperate and south temperate zones, but much lower than those in the middle subtropical and north subtropical zones. This comparison of the LST differences of each LULC type under different climate backgrounds provides an important reference for rational urban planning.

Land Surface Temperature Regulation Ecosystem Service: A Case Study of Jaipur, India, and the Urban Island of Jhalana Reserve Forest

Although Land Surface Temperatures (LSTs) are on the rise globally, the distribution of LSTs varies depending on the land cover type. Urban Heat Island and Urban Cool Island effects act differently, especially in semi-arid regions. Therefore, we identify demi-decadal, seasonal, and zonal differences in LSTs in a semi-arid region in the city of Jaipur, where zones include rural and urban areas that encircle the Jhalana Reserve Forest (JRF). After deriving LSTs from remotely sensed thermal bands of Landsat satellites’ Multi-spectral datasets, we found that there is a significant difference in LST (p < 0.01) among the zones. In addition, LSTs were found to be significantly lower in JRF compared to Urban and Rural areas in all seasons and all study years, which indicates the urban cooling effect due to the presence of the forest. Nevertheless, summer LSTs have warmed with a mean difference of 4.8 °C between 2000 and 2020. Therefore, our study supports the promotion of Urban Forests, especially in semi-arid zones, for inculcating LST regulation ecosystem services to enrich and enhance the standard of living of the human population.

Evaluating the spatiotemporal variations of daytime surface and canopy urban heat islands: an arid climate case study

Spatiotemporal variations of surface urban heat island (SUHI) and canopy urban heat island (CUHI) are compared in this study for the arid city of Isfahan, Iran, using two datasets for land surface temperature (LST) and air temperature (AT), and three different indices: urban-rural LST difference, urban thermal field variance index (UTFVI) and urban-rural AT difference. The inverted SUHI intensity was shown to vary between −13.9 °C in summer and −1.5 °C in winter, while the hourly average CUHI intensity ranged between −4.4 °C in summer and 6.3 °C in autumn. The spatial variation of CUHI showed significant differences in UHI intensity between urban sites (p < 0.05). Based on UTFVI variation, the non-SUHI and strongest SUHI were dominant phenomena in urban sites. In contrast, SUHI intensities were negative, representing only the non-SUHI phenomenon. The correlation between SUHI and CUHI values was not significant, indicating that intensities of CUHI and corresponding SUHI are not comparable in arid climates. These results demonstrate that for arid climates, determining UHI based on UTFVI, and the urban-rural AT difference, can provide detailed information about spatiotemporal variations of UHI.

Quantitative valuation of green roofs’ cooling effects under different urban spatial forms in high-density urban areas

In the context of global climate change, the increasing urban heat island (UHI) effect is a serious challenge to sustainable urban development. The construction of green roofs is an important means to optimize the urban thermal environment, especially in high-density urban areas. However, the cooling effect of green roofs in different urban spatial forms varies. Thus, the quantitative evaluation of the cooling effect of green roofs is significant for the scientific and effective mitigation of the UHI effect. This study took the central district of Chengdu City as a typical area, clearly classified urban spatial form types based on the LCZ theory, and quantified the relative land surface temperature (RLST) difference between before (2014) and after (2018) the implementation of green roofs based on Landsat 8 remote sensing image retrieval. Numerical analysis methods were used to statistically evaluate the cooling effect of green roofs under different urban spatial form types by GIS. The results showed that (1) the cooling effect of green roofs varied significantly among different urban spatial form types; (2) the best performance of the cooling effect of green roofs was in Compact Highrise (LCZ1), which could reduce RLST by 0.94 °C, while the worst was in Lightweight Lowrise (LCZ7). Based on the above performance of the cooling effect of green roofs, the construction of green roofs can be prioritized and zoned, and the design strategies of green roofs under different urban spatial form types can be proposed. Our study will provide a basis for developing design guidance for green roofs and scientific support for mitigating the urban thermal environment.

Monitoring Urbanization Induced Surface Urban Cool Island Formation in a South Asian Megacity: A Case Study of Bengaluru, India (1989–2019)

Many world cities have been going through thermal state intensification induced by the uncertain growth of impervious land. To address this challenge, one of the megacities of South Asia, Bengaluru (India), facing intense urbanization transformation, has been taken up for detailed investigations. Three decadal (1989–2019) patterns and magnitude of natural coverage and its influence on the thermal state are studied in this research for assisting urban planners in adopting mitigation measures to achieve sustainable development in the megacity. The main aim of this research is to monitor the surface urban cool island (SUCI) in Bengaluru city, one of the booming megacities in India, using Landsat data from 1989 to 2019. This study further focused on the analysis of land surface temperature (LST), bare surface (BS), impervious surface (IS), and vegetation surface (VS). The SUCI intensity (SUCII) is examined through the LST difference based on the classified categories of land use/land cover (LU/LC) using urban-rural grid zones. In addition, we have proposed a modified approach in the form of ISBS fraction ratio (ISBS–FR) to cater to the state of urbanization. Furthermore, the relationship between LST and ISBS–FR and the magnitude of the ISBS–FR is also analyzed. The rural zone is assumed based on &amp;lt;10% of the recorded fraction of IS (FIS) along the zones in the urban-rural gradient (URG). It is observed that SUCII hiked by 1.92°C in 1989, 4.61°C in 2004, and 2.66°C in 2019 between demarcated urban and rural zones along URG. Furthermore, the results indicate a high expansion of impervious space in the city from 1989 to 2019. The alteration in the city landscape mostly occurs due to impervious development, causing the intensification of SUCI. The mean LST (MLST) has a negative relationship with the fraction of VS (FVS) and a positive relationship with the fraction of BS (FBS). In addition, the ISBS–FR shows intense enlargement. The findings of the present study will add to the existing knowledge base and will serve as a road map for urban and landscape planning for environmental enrichment and sustainability of the megacity of Bengaluru.

Comparing Coarse-Resolution Land Surface Temperature Products over Western Australia

Satellite-derived land surface temperature (LST) has commonly been used to monitor global temperature changes. The MODIS MYD11A2 product is the most common coarse-resolution product used for this purpose. An updated MODIS product (MYD21A2) and new VIIRS (VNP21A2) product have also recently become available. This study analyses eight-day, quality-controlled, LST imagery over Western Australia (WA) for the three products for an urban and a non-urban area for the years 2013, 2016, and 2019. An analysis of the data indicates that (i) the averaged daytime LST difference between the three products for Perth city over the three years was 1.32 °C, while at night it was 0.89 °C; (ii) the averaged daytime difference over the Kimberley region was 7.02 °C with a night average difference of 2.39 °C; and (iii) both the MYD21A2 and VNP21A2 products still appear to record anomalous monthly LST values, particularly in the humid Kimberley monsoonal months. The overall objective of the National Aeronautics and Space Administration (NASA) is to ensure that the LST values of the two satellite system products are comparable, so evidence of LST value differences will require further investigation, especially if the older product is to be replaced by the newer systems.

The role of blue green infrastructure in the urban thermal environment across seasons and local climate zones in East Africa

Rapid urbanisation and climate change are two major trends in Africa in need of further investigation. In this paper, the urban thermal environment and vegetation abundance in four East African cities (Khartoum, Addis Ababa, Kampala and Dar es Salaam) were characterised, providing new insights into the role and potentials of blue green infrastructure in differing climate regions. The Local Climate Zone (LCZ) framework was employed to detect the seasonal Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) derived from Landsat-8 data. Significant LST differences between LCZs in dry and rainy seasons were confirmed using a Welch's T test. The LCZs were found to offer potentially new approaches to investigating issues pertaining to urban heating in data-scarce regions. Greater surface urban heat island (SUHI) intensity during the rainy season was apparent in Khartoum and Addis Ababa, emphasising the importance of seasonality in urban thermal studies. Spatial correlations between EVI and LST within each LCZ were analysed through Moran's I and further illustrated the complex relationships of vegetation and thermal behaviour in urban areas. Despite these complexities, urban blue green infrastructure was found to moderate the SUHI, with different types of intervention required across different LCZs.

Exploring Adaptive UHI Mitigation Solutions by Spatial Heterogeneity of Land Surface Temperature and Its Relationship to Urban Morphology in Historical Downtown Blocks, Beijing

Heat stress brought on by the intensification of urban heat island (UHI) has caused many negative effects on human beings, which were found to be more severe in highly urbanized old towns. With the inconsistent findings on how urban spatial morphological characteristics influence land surface temperature (LST) and gaps between design practices being found, we chose Beijing Old Town (BOT) as the study area and took the basic planning implementation module “block” as a study to reveal the spatial heterogeneity of LST and its relationship to multiple urban morphological characteristics with higher spatial resolution calculated via WorldView3. Our results have shown that (1) UHI effect was significant and spatially heterogeneous in BOT, and significant hot areas with high LST value and small LST differences were found, as cold areas were the exact opposite. (2) The proportion of vegetated area, water, impervious surface, and urban spatial structure indicators i.e., building coverage ratio, mean height, highest building index, height fluctuation degree, space crowd degree and sky view factor were identified as significantly affecting the LST of blocks in BOT. (3) The effects of GBI components and configuration on LST varied within different block types; generally, blocks with GBI with larger patches that were more complex in shape, more aggregated, and less fragmented were associated with lower LST. Finally, in the context of integrating our study results with relevant planning and design guidelines, a strategy sample of adaptive GBI planning and vegetation design for blocks with different morphological features was provided for urban planners and managers to make a decision on UHI mitigation in the renewal process of BOT.

Comparison between Air Temperature and Land Surface Temperature for the City of São Paulo, Brazil

This study aims to identify the relationship between changes in temperature regarding urbanization processes and seasonality in the city of São Paulo, located in the Tropic of Capricorn. The land surface temperature (LST) results were compared to official weather stations measurements, identifying in the spring–summer period 65.5% to 86.2% accuracy, while in the autumn–winter period, the results ranged from 58.6% to 93.1% accuracy, when considering the standard deviation and the temperature probe error. The mean MAE and mean RMSE range from 1.2 to 1.9 °C, with 83.0% of the values being ≤2.7 °C, and the coefficient of determination values are R = 0.81 in spring–summer and R = 0.82 in autumn–winter. Great thermal amplitude was estimated in the spring–summer season, with a difference in LST of the built-up space and rural area ranging from 5.8 and 11.5 °C, while in the autumn–winter season, the LST is more distributed through the city, with differences ranging from 4.4 to 8.5 °C. In addition, the current study suggests remote sensing as a reliable, cheap, and practical methodology to assist climate in order to support public policies and decision-making actions regarding environmental and urban planning.

Impacts of afforestation on land surface temperature in different regions of China

To assess the impacts of afforestation on land surface temperature (LST) in China and provide a basis for future afforestation and warming mitigation policies, adjacent forest and open land are compared using ten years of satellite data. Most regions in China exhibit a diurnal asymmetry in the magnitude and sign of the effects of afforestation, with daytime cooling and nighttime warming. Afforestation has annual daily cooling effects in most regions of China because the magnitude of annual nighttime warming is generally weaker than that of the annual daytime cooling. We attributed the warming and cooling effects of afforestation to the changes of evapotranspiration (ET) and albedo. The annual average albedo of forest is smaller than that of open land, and the albedo differences between forest and open land vary spatially, with larger differences in places with snow cover. In the annual average, ET over forest is greater than that over open land, with the largest values in the southern of Yangtze River. The daytime differences in LST between forest and open land are driven by the cooling effects of ET and warming effects of albedo, and the cooling effects of ET dominate. The nighttime warming effects of afforestation is related with the release of the energy stored in the soil during daytime. Evergreen broadleaf forest and deciduous broadleaf forest are recommended as the choice of afforestation in south and in north of the Yangtze River respectively to decrease the LST if the afforestation program in China is to be expanded.

Influence of short-term surface temperature dynamics on tree orchards energy balance fluxes

Land surface temperature (LST) plays an essential role in developing and applying precision agriculture protocols, especially for calculating crop evapotranspiration (ETc) by surface energy balance (SEB) approaches; and for determining crop water status. However, LST is quite dependent on the meteorological conditions, which can rapidly vary. This variability, together with the limited meterological data acquisition frequency in most weather stations, can lead to the miscalculation of the SEB components, especially relevant when used for irrigation purposes.The present study assessed the temporal dynamic of LST in a very short period of time (20-minutes) through the acquisition of multiple thermal imagery. Additionally, a combination of SEB approach with Eddy Covariance technique was performed for quantifying the effect that LST variations have on the sensible (H) and latent (LE) heat fluxes.Even under steady meteorological conditions, temporal variations in LST of 3.5 and 4.0 K were observed for tree canopy and sunny bare soil surfaces, respectively. These LST oscillations reached values of about 7.8 and 17.9 K for tree canopies and bare soil when heterogeneous meteorological conditions were observed (i.e. cloud presence). Such LST differences translated into H and LE differences of about 26 and 19%, respectively; with variations up to 5 (for H) and 2.7 times (for LE) under fast-varying meteorological conditions.The obtained results suggest the necessity of acquiring thermal imagery when steady meteorological conditions exist or, otherwise, ensuring the collection of instantaneous meteorological data for applying post-processing corrections. This is of importance when incorporating the obtained ETc maps into precision irrigation protocols.

Attribution of local land surface temperature variations response to irrigation over the North China Plain

Irrigation substantially alters land surface temperature (LST) in different regions of the world. Studies have recently focused on quantifying irrigation-induced LST change based on remote sensing technology due to its high spatiotemporal resolution. However, the biophysical mechanisms of irrigation on LST remains poorly understood. Here we first investigated the impact of irrigation on LST during 2003–2012 over the North China Plain (NCP), which is one of the most intensively irrigated areas around the word. We then attributed the mechanisms underlying LST change between adjacent irrigated and non-irrigated croplands based on two surface energy balance-based methods: the Decomposed Temperature Metric (DTM) method and the intrinsic biophysical mechanism (IBM) method. The results indicate that at annual scale, irrigation produce an overall cooling effect over the NCP, with the mean observed LST change of −0.098 K, calculated LST change of −0.096 K for DTM method and −0.165 K for IBM method, respectively. Furthermore, the agreement between the annual observed and calculated LST difference indicate that DTM is a more robust method than IBM in quantifying irrigation-induced LST change over the NCP. The attribution method DTM reveals that components of albedo and emissivity has an average cooling effect of −0.012 K and −0.005 K, respectively, while incoming radiation lead to a weak warming effect of +0.01 K. The enhanced turbulent fluxes of latent heat flux dominate the cooling effect (−0.174 K on average), further offsets the sensible heat flux warming effect (+0.085 K). Another attribution method IBM demonstrates that the annual cooling effect of irrigation is mostly induced by changes in aerodynamic resistance (−0.175 K), whereas the biophysical contributions of albedo (−0.0005 K) and Bowen ratio (+0.001 K) have a negligible impact on LST. This study provides a useful reference for assessing local climate impact of irrigation when implementing environmental protection projects.

Geographical Detection of Urban Thermal Environment Based on the Local Climate Zones: A Case Study in Wuhan, China

The urban morphology has impacts on the urban thermal environment, which has drawn extensive attention, especially in metropolitan regions with intensive populations and high building densities. This study explored the relationship between the urban morphology and spatial variation of land surface temperature (LST) in Wuhan by using the local climate zone (LCZ) and seven natural and social factors. A deep learning model (light LCZ model) was used to generate LCZ map in Wuhan, and a geographic detector model was utilized to explore the driving mechanism of LST spatial differentiation. The results show that the LST difference between LCZ classes in summer is greater than that in winter, and the LST of the built-up classes (LCZ 1–10) are significantly higher than that of the vegetation classes in summer. Among the six residential building classes (i.e., LCZ 1–6), LCZ 1 is characterized by compact and high buildings and has the largest average LST. Building density and height have a warming effect, and the building density has a stronger effect than the height. Compared with other natural and social factors, LCZ has the largest explanatory power for LST spatial differentiation in the main urban area and surrounding areas of Wuhan, with explanatory power (q) values reaching 0.660 (summer) and 0.316 (winter). The types of interaction for all pairwise cases are mutual and nonlinear. The strongest interaction is MNDWI-NDBI combination (0.780) in summer and LCZ-NDBI combination (0.460) in winter.

Evaluation of the Urban Microclimate in Catania Using Multispectral Remote Sensing and GIS Technology

This study focuses on the determination and examination of both the Land Surface Temperature (LST) and the atmospheric temperature in the city of Catania Sicily (Italy), through freely available satellite remote sensing images from the Sentinel-2 and MODIS missions. Satellite images were processed as raster data in free and open-source GIS environments. The GIS software allows the retrieval, processing of the satellite images for the estimation of the LST and the atmospheric temperature with a very coarse spatial resolution. In particular, the proposed procedure allows increasing the spatial resolution of satellite images, from 250 m (LRES) to 10 m (HRES) through the principle of “Disaggregation of thermal images”. The analysis provided georeferenced maps which show the LST, as well as the atmospheric temperature within the investigated area with a very fine resolution, 10 m. Such spatial resolution reveals evident correlations between areas with different urban densities and their microclimate. An important result of this study is that significant LST differences can be observed during both day (15–17 °C) and night (2–3 °C) between green and built-up areas. The outcomes of this study highlight the effectiveness of the combined use of satellite remote sensing and GIS for analyzing the thermal response of urbanized areas with different built density.

A multicity analysis of daytime Surface Urban Heat Islands in India and the US.

• In the US, urban land surface temperature (LST) was higher compared to rural LST. • Despite urbanization, urban LST in India was lower compared to rural LST. • Non-photosynthetic vegetation (NPV) and barren land types influence the results. • Built-up indices used here do not differentiate sparse vegetation and built-up surfaces. • New methods are needed for quantifying urbanization's impact on LST in India. Daytime surface UHI (SUHI) and the factors influencing it across highly populated cities in India and the United States (US) were evaluated. Urban-rural mean land surface temperature (LST) differences (ΔT S ) were calculated and correlated with vegetation and built-up surface densities, quantified using the Normalized Difference Vegetation Index (NDVI) and Index Based Built-Up Index (IBI) respectively. The results from the US show a conventional SUHI phenomenon, and LST decrease with increasing vegetation and decreasing built-up surfaces. However, in India, despite existing urban densities, urban areas are cooler than the rural surroundings. The average LST was higher and NDVI was lower compared to the US, and NDVI and LST correlations were weak. Furthermore, in India, the average IBI values calculated for built-up areas, cropland, and other sparse vegetation land covers were approximately equal. The prevalence of non-photosynthetic vegetation (NPV) and barren soil in India are impacting spectral indices and the correlations. Differences in ΔT S and spectral indices trends between India and US highlight the shortcomings of the indices used in appropriately identifying Indian LULC types and establish the need for localized SUHI research methods that can specifically analyze the impact of urbanization on SUHI in India.

Assessing the intraurban differences in vegetation coverage and surface climate in a heterogeneous area

Urban trees play a critical role in alleviating land surface temperatures in cities. In remote sensing studies, vegetation indices are widely used to examine the relationship between Land Surface Temperature (LST) and vegetation cover. The vegetation cover can be measured using the Normalised Difference Vegetation Index (NDVI). In this study, the LST-NDVI relationship was assessed in each of the seven city-regions (A-G) in Johannesburg using Moderate-Resolution Imaging Spectroradiometer (MODIS) datasets to provide a basis for urban ecological planning and environmental protection. This study's specific objective was to determine the intraurban differences in vegetation coverage and LST in the seven city-regions over 19 years. The relationship between LST and NDVI was also examined over the years of study. The results showed a significant intraurban difference in LST and NDVI values in the city-regions with a negative correlation between them, ranging from −0.03 to −0.76. The LST values increased in all the city-regions with the highest value of 20.1°C in city-region G, followed by 19.6°C in city-region E. The vegetation cover decreased over the years, with the lowest NDVI value in city-region G (0.39), followed by city-regions F (0.43) and D (0.48). The city-regions with high LST and low vegetation cover include the city-centre and highly populated suburbs. This indicates that areas with greater vegetation cover have low LSTs and vice versa. These findings provide useful information for municipal authorities and other stakeholders to undertake appropriate decisions to tackle Urban Heat Island (UHI) effects by adopting effective urban planning and management interventions.