Impact of Rapid Urbanization on the City of Bhubaneswar, India

The impact of rapid urbanization in cities on their microclimate is at present a great cause of global concern. One of the major consequences is the unexpected rise in temperatures in the cities compared to their surrounding areas, termed as the Urban Heat Island (UHI) effect. Over the past many years, several Indian cities are under severe stress owing to such extreme anomalous changes in their micro-meteorological conditions making them unfriendly for habitation. Presented here is a case study on Bhubaneswar - one such city on the east coast of India undergoing rapid urbanization in recent times. In this study, Land Surface Temperatures (LST) from MODIS Terra and Aqua instruments at 1 km2 spatial resolution along with the Land Use and Land Cover (LULC) change data from Landsat was used over a 25 km radius about the city for a 15 years' period from 2000 to 2014. Preliminary analyses indicate spatio-temporal changes in LULC to be one of the primary and significant factors responsible for changes in the UHI effect over the city. Investigations on the spatio-temporal variations in LST across the city and its relationship with vegetation cover indicate that overexploitation of various resources demanded by a fast growing population has led to significant changes in LULC patterns in the last few years. Analysis of the changes in the urban energy balance and resulting UHI effect across the city under various urban growth scenarios and different proportions of green urban area are in progress.

Rapid urbanisation in the global south has often introduced substantial and rapid uncontrolled Land Use and Land Cover (LULC) changes, considerably affecting the Land Surface Temperature (LST) patterns. Understanding the relationship between LULC changes and LST is essential to mitigate such effects, considering the urban heat island (UHI). This study aims to elucidate the spatiotemporal variations and alterations of LST in urban areas compared to LULC changes. The study focused on a peripheral urban area of Phnom Penh (Cambodia) undergoing rapid urban development. Using Landsat images from 2000 to 2021, the analysis employed an exploratory time-series analysis of LST. The study revealed a noticeable variability in LST (20 to 69 °C), which was predominantly influenced by seasonal variability and LULC changes. The study also provided insights into how LST varies within different LULC at the exact spatial locations. These changes in LST did not manifest uniformly but displayed site-specific responses to LULC changes. This study accounts for changing land surfaces’ complex physical energy interaction over time. The methodology offers a replicable model for other similarly structured, rapidly urbanised regions utilising novel semi-automatic processing of LST from Landsat images, potentially inspiring future research in various urban planning and monitoring contexts.

Urbanization is a critical topic for metropolitan architects and researchers globally, as rapid urbanization has significant sociological, economic, and environmental effects. As cities expand rapidly, global land use patterns change continuously, which is closely associated with urbanization and increases land surface temperature (LST). This study specifically focuses on Agra, India, a UNESCO heritage city with a semi-arid climate, where urbanization patterns differ significantly from larger metropolitan areas, and aims to investigate the interrelationship between land use and land cover (LULC), LST, and the effect of urban heat island (UHI) on the local climate. Geospatial analyses were carried out for the years 2014, 2019, and 2024 using Landsat spectral imagery to examine the changes in LULC, LST, and UHI in the past decade of this rapidly urbanizing city. LULC was performed using a supervised classification technique, Maximum Likelihood Classification (MLC), while LST was derived using a mono window algorithm. The findings show that the estimated mean Land Surface Temperature (LST) has drastically increased from 28.2 °C in 2014 to 41.7 °C in 2024, representing a rise of 13.4 °C over the past decade. The land use land cover (LULC) map was classified with a supervised classification method based on the maximum likelihood classifier (MLC) method, with the kappa coefficients of 0.932 (2014), 0.915 (2019), and 0.945 (2024), which reflected acceptable results for classifications and mapping of LULC. Compared with field-level surveys, the study achieved a classified accuracy of around 95.1%, 94.2% and 97.8%, respectively. This indicates a significant upward trend in temperature. Furthermore, the percentage change in the built-up area within this timeframe is 37.80 km2, reflecting considerable land use and land cover changes. UHI hotspots have also seen a significant rise in the past decade. The findings exhibit that densely built areas experience the highest temperatures compared to open areas, vegetation, and water bodies. This research contributes valuable insights into the effects of urbanization and land use changes on the urban thermal environment. The results can be instrumental in policymakers developing effective strategies to mitigate urbanizations environmental impacts, ultimately enhancing residents quality of life.

Land use and land cover (LULC) changes resulting from rapid urbanization are the foremost causes of increases in land surface temperature (LST) in urban areas. Exploring the impact of LULC changes on the spatiotemporal patterns of LST under future climate change scenarios is critical for sustainable urban development. This study aimed to project the LST of Nanjing for 2025 and 2030 under different climate change scenarios using simulated LULC and land coverage indicators. Thermal infrared data from Landsat images were used to derive spatiotemporal patterns of LST in Nanjing from 1990 to 2020. The patch-generating land use simulation (PLUS) model was applied to simulate the LULC of Nanjing for 2025 and 2030 using historical LULC data and spatial driving factors. We simulated the corresponding land coverage indicators using simulated LULC data. We then generated LSTs for 2025 and 2030 under different climate change scenarios by applying regression relationships between LST and land coverage indicators. The results show that the LST of Nanjing has been increasing since 1990, with the mean LST increased from 23.44 °C in 1990 to 25.40 °C in 2020, and the mean LST estimated to reach 26.73 °C in 2030 (SSP585 scenario, integrated scenario of SSP5 and RCP5.8). There were significant differences in the LST under different climate scenarios, with increases in LST gradually decreasing under the SSP126 scenario (integrated scenario of SSP1 and RCP2.6). LST growth was similar to the historical trend under the SSP245 scenario (integrated scenario of SSP2 and RCP4.5), and an extreme increase in LST was observed under the SSP585 scenario. Our results suggest that the increase in impervious surface area is the main reason for the LST increase and urban heat island (UHI) effect. Overall, we proposed a method to project future LST considering land use change effects and provide reasonable LST scenarios for Nanjing, which may be useful for mitigating the UHI effect.

Modelling future land use land cover changes and their impacts on urban heat island intensity in Guangzhou, China

The mountainous region of Asir is experiencing rapid and unsystematic urbanization leading to an increase in land surface temperatures (LST), which poses a challenge to human well-being and ecological balance. Therefore, it is necessary to study the interaction between land use and land cover (LULC)-induced urbanization and LST using advanced geostatistical techniques. In addition, understanding the urbanization process and urban density is essential for effective urban planning and management. The aim of this study was to investigate the interaction between the urbanization process, urban density and the associated LST. Using the Random Forest Algorithm, LULC mapping was conducted for the years 1990, 2000 and 2020. Metrics such as land cover change rate (LCCR), land cover index (LCI), landscape expansion index (LEI), mean landscape expansion index (MLEI) and area-weighted landscape expansion index (AWLEI) were used to understand urbanization processes and LULC changes. Convolutional kernels were used to model urban density, and the mono-window algorithm was applied to analyse LST in the selected years. In addition, the study assessed the Surface Urban Heat Island (SUHI) contribution index to LULC and used Generalized Additive Models (GAMs) in conjunction with Partial Dependence Plots (PDPs) to understand the relationship between urbanization processes, urban density and LST. In a detailed 30-year study, the application of the RF algorithm showed significant shifts in LULC with an overall validation accuracy of over 85%. Urban areas grew dramatically from 69.40 km2 in 1990 to 338.74 km2 in 2020, while water areas decreased from 1.51 to 0.54 km2. Dense vegetation increased from 43.36 to 52.22 km2, indicating positive ecological trends. The LST analysis showed a general warming, with the mean LST increasing from 40.51°C in 1990 to 46.73°C in 2020 and the highest temperature category (50-60°C) increasing from 0.78 to 33.35 km2. The built-up area of cities tripled between 1990 and 2020, with the Landscape Expansion Index reflecting significant growth in suburban areas. The modeling of urban density shows increasing urbanization in the centre, which will expand significantly to the east by 2020. The contribution of LULC to LST and the Urban Heat Island (SUHI) effect was evident, with built-up areas showing a constant temperature increase. GAMs confirmed a statistically significant relationship between urban density and LST, with different effects for different types of urban expansion. This comprehensive study quantitatively sheds light on the complicated dynamics of urbanization, land cover change and temperature variation and provides important insights for sustainable urban development.

Assessing the impacts of vegetation cover loss on surface temperature, urban heat island and carbon emission in Penang city, Malaysia

Growth in urban areas contributes to environmental degradation through increased land surface temperature (LST), exacerbating the urban heat island (UHI) effect. This study examined how land use and land cover (LULC) characteristics of Shillong City are linked to the UHI phenomenon. The LULC was classified into five broad categories: agricultural land, barren land, settlement, vegetation, and water bodies. The results show that the study area experienced notable changes in the LULC pattern from 1993 to 2023, with settlement areas increasing by 10.96%, transforming previously barren lands. The emergence and growth of settlements (and/or built-up areas) and impervious surfaces have led to a steady increase in LST. The settlement land use class had an average LST of 17.45 °C in 1993, 21.56 °C in 2003, 21.37 °C in 2013, and 21.75 °C in 2023. From 1993 to 2023, surface temperatures in settlement areas rose by a maximum of 4.3 °C, while barren land and vegetated areas also saw an increase of 4.9 °C and 4.0 °C, respectively. The relationship between LULC and the LST has been evaluated to identify hotspot areas. The highest temperatures are found in crowded and dense built-up areas, while the lowest temperatures are found in vegetated areas and water bodies. The findings also reveal a clear warming trend over the 30-year period, marked by a substantial decrease in areas with LST below 12 °C and between 12–17 °C, highlighting a shift towards warmer temperatures. Projected LULC changes indicate that urban areas will experience significant growth, increasing from 17.36% of the total area in 2023 to 21.39% in 2030, and further to 28.56% by 2050. The results suggest that the settlement land use class will increase by 11.2%, accompanied by a decrease in agricultural lands, vegetation, and water bodies.

The rapid urbanization of Srinagar city, located in the Himalayan region, has led to significant changes in land use and land cover (LULC), resulting in an increase in the Urban Heat Island (UHI) phenomenon. This study investigates the relationship between Land Surface Temperature (LST) and LULC changes over a decade, from 2010 to 2022, focusing on four key categories: built-up areas, agricultural land, water bodies, and natural vegetation. Using Landsat satellite data, we analysed seasonal variations in LST across these categories and calculated the Urban Thermal Field Variance Index (UTFVI) to assess the ecological impact. The findings reveal a substantial increase in LST, particularly in built-up areas, where maximum temperature increased from 34.6°C in 2010 to 37.19°C in 2022. Additionally, the UTFVI analysis showed a decline in areas with "Excellent" ecological conditions, dropping from 57.91% in December 2010 to 47.24% in December 2022, while areas categorized as "Worst" ecological conditions increased, indicating a worsening UHI effect. These results highlight the growing environmental challenges posed by urbanization in Srinagar city, necessitating urgent sustainable urban planning interventions. Keywords: Land surface temperature (LST), Urban heat island (UHI), Urban thermal field variance index (UTFVI), Land use land cover (LULC), Urbanisation

Time series analysis of land use and land cover changes related to urban heat island intensity: Case of Bangkok Metropolitan Area in Thailand

Rapid changes in land use and land cover (LULC) have ecological and environmental effects in metropolitan areas. Since the 1990s, Saudi Arabia's cities have undergone tremendous urban growth, causing urban heat islands, groundwater depletion, air pollution, loss of ecosystem services, etc. This study evaluates the variance and heterogeneity in land surface temperature (LST) because of LULC changes in Abha-Khamis Mushyet, Saudi Arabia, from 1990 to 2020. The research aims to determine the impact of urban biophysical parameters on the High-High (H-H) LST cluster using geospatial, statistical, and machine learning techniques. The support vector machine (SVM) was used to map LULC. The land surface temperature (LST) has been derived using the mono-window algorithm (MWA). The local indicator of spatial associations (LISA) model was implemented on the spatiotemporal LST maps to identify LST clusters. Also, the parallel coordinate plot (PCP) approach was employed to examine the relationship between LST clusters and urban biophysical variables as a proxy of LULC. LULC maps show that urban areas rose by > 330% between 1990 and 2020. Built-up areas had an 83.6% transitional probability between 1990 and 2020. In addition, vegetation and agricultural land have been transformed into built-up areas by 17.9% and 21.8% respectively between 1990 and 2020. Uneven LULC changes in terms of built-up areas lead to increased LST hotspots. High normalized difference built-up index (NDBI) was linked to LST hotspots but not normalized difference water index (NDWI) or normalized difference vegetation index (NDVI). This research could help policymakers develop mitigation strategies for urban heat islands.

A time series analysis of urbanization induced land use and land cover change and its impact on land surface temperature with Landsat imagery

Rapid urbanization causes significant changes in land use and land cover (LULC), contributing to Urban Heat Islands (UHIs) and environmental challenges. This study analyzes two decades of Landsat satellite imagery to examine LULC changes and their impact on Land Surface Temperature (LST) in Aligarh City, India. Supervised classification using maximum likelihood techniques produced LULC maps with 85% accuracy. The results show a 21.10% increase in built-up areas, from 2338.83 hectares in 2001 to 2832.66 hectares in 2021, alongside a 24.55% decline in vegetation, from 620.28 hectares to 467.91 hectares. LST increased by 11.35°C, with a total maximum rise of 10.70°C between 2001 and 2021. Regression analysis revealed strong correlations between LST and spectral indices, with NDVI showing a negative correlation (R² = 0.89, 0.83, 0.78) and NDBI a positive one (R² = 0.87, 0.89, 0.79). The Aligarh main city area consistently recorded higher LST values than the AMU campus, with 88.60% of the city area in the 40–45°C range by 2021, compared to 52.87% for the campus. This study underscores the need for sustainable urban strategies to mitigate UHI effects and promote climate resilience.

With the rapid process of urbanization, the urban heat island (UHI), the phenomenon where urban regions become hotter than their surroundings, is increasingly aggravated. The UHI is affected by multiple factors overall. However, it is difficult to dissociate the effect of one aspect by widely used approaches such as the remote-sensing-based method. To qualify the response of surface UHI to the land use and land cover (LULC) changes, this study took the numerical land model named u-HRLDAS (urbanized high-resolution land data assimilation system) as the modeling tool to investigate the effect of LULC changes on the UHI from 1980 to 2013 in Wuhan city, China. Firstly, the simulation accuracy of the model was improved, and the summer urban heat environment was simulated for the summer of 2013. Secondly, taking the simulation in 2013 as the control case (CNTL), the LULC in 1980, 1990, and 2000 were replaced by the LULC while the other conditions kept the same as the CNTL to explore the effect of LULC on UHI. The results indicate that the proper configuration of the modeling setup and accurate surface input data are considered important for the simulated results of the u-HRLDAS. The response intensity of UHI to LULC changes after 2000 was stronger than that of before 2000. From the spatial perspective, the part that had the strongest response intensity of land surface temperature to LULC changes was the region between the third ring road and the inner ring road of Wuhan. This study can provide a reference for cognizing the urban heat environment and guide policy making for urban development.

Time-series studies of land surface temperature in Damascus, Syria through MODIS by Google Earth Engine