Capturing urban heat island formation in a subtropical city of China based on Landsat images: implications for sustainable urban development

Predicting the impacts of urban development on urban thermal environment using machine learning algorithms in Nanjing, China

The urban thermal environment undergoes significant influences from changes in land use/land cover (LULC). This article uses CA-ANN and ANN algorithms to forecast LULC and changes in the urban thermal environment in Nanjing for the years 2030 and 2040. It investigates the interplay between LULC changes, land surface temperature (LST), and the urban thermal field variance index (UTFVI). The findings reveal that urban land exhibited a significant expansion trend from 2000 to 2019, reaching 1083.43 km2 in 2019. The forecast indicates that urban land may increase by 8.79% and 10.92% by 2030 and 2040, respectively. Conversely, vegetation and bare land may decrease. The LST is likely to continue to rise, accompanied by a significant expansion of the high temperature range and a contraction of the low temperature range. By 2030 and 2040, the area with LST<20 °C is likely to decrease by 2.17% and 3.19%, while the area with LST>30 °C is likely to expand by 5.68% and 8.08%, respectively. The UTFVI area of urban land may decrease at none and middle levels but may notably expand at stronger and strongest levels. The areas with UTFVI at none, weak, and middle levels show a declining trend, while the increase in UTFVI at the strong level may exceed 46.29% and the strongest level of UTFVI may continue to expand. This study offers new insights into urban sustainable development and thermal environment governance.

Evaluation of seasonal ecological vulnerability using LULC and thermal state dynamics using Landsat and MODIS data: a case study of Prayagraj City, India (1987-2018)

Prayagraj city (India) has been selected as a smart city by the Ministry of Housing and Urban Affairs, Government of India in 2015. However, long-term spatiotemporal seasonal Land Use/Land Cover (LULC) dynamics and Land Surface Temperature (LST) interactions with ecological vulnerability for different seasons are lacking. Accordingly, this research has been carried out to study the seasonal (summer and winter) LULC and its change pattern, thermal dynamics, and their role in exploring the ecological state over Prayagraj city and its surroundings using multi-temporal Landsat (1987-2018) and MODIS Terra data (2007-2018) at both diurnal and nocturnal scenarios. The LULC classification was carried out using Maximum Likelihood Classifier (MLC) by adopting the Anderson classification scheme with more than 85% of overall accuracy. The Landsat data-based LST has been estimated using Mono-Window Algorithm (MWA) for diurnal scenario whereas MODIS-based LST was calculated for nocturnal scenario. Ecological vulnerability state has been evaluated both in day-time and night-time using Urban Thermal Field Variance Index (UTFVI) in summer and winter during 1987-2018 and 2007-2018, respectively. Overall, built-up land increased the most by 18.25% which was responsible for massive urbanization during 1987-2018. In contrast, forest land decreased by 2.22% during 1987-2018. The most vulnerable class was agriculture land followed by forest land irrespective of seasons. Thermal state was intensified by mean LST by 1.25 ℃ in summer and 0.58 ℃ in winter in day-time. However, in night-time, the mean LST intensified by 6.64 ℃ in summer and 1.86 ℃ in winter. The excellent ecological class having no SUHI effects declined in summer during 1988-2018 by 1.59% but surged in winter by 12.33% during 1987-2018 in north-west regions at day-time, whereas in night-time the excellent ecological class having no SUHI effects severely declined in summer as well as in winter during 2007-2018 by 11.1% and 1.32%, respectively. However, the worst ecological class having strongest SUHI effects severely spread in night-times compared to day-time which mainly concentrated in central core part of the city during 2007-2018 by 5.33%. The present study has generated a comprehensive long-term geospatial database which can be used for urban planning to achieve sustainable development to make Prayagraj city a truly smart city in future.

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.

The Impact of Central Heating on the Urban Thermal Environment Based on Multi-Temporal Remote Sensing Images

Research on the impact of anthropogenic heat discharge in a thermal environment is significant in climate change research. Central heating is more common in the winter in Northeast China as an anthropogenic heat. This study investigates the impact of central heating on the thermal environment in Shenyang, Changchun, and Harbin based on multi-temporal land surface temperature retrieval from remote sensing. An equivalent heat island index method was proposed to overcome the problem of the method based on a single-phase image, which cannot evaluate all the central heating season changes. The method improves the comprehensiveness of a thermal environment evaluation by considering the long-term heat accumulation. The results indicated a significant increase in equivalent heat island areas at night with 22.1%, 17.3%, and 19.5% over Shenyang, Changchun, and Harbin. The increase was significantly positively correlated with the central heating supply (with an R-value of 0.89 for Shenyang, 0.93 for Changchun, and 0.86 for Harbin; p < 0.05). The impact of central heating has a more significant effect than the air temperature. The results provide a reference for future studies of urban thermal environment changes.

Simulating the Relationship between Land Use/Cover Change and Urban Thermal Environment Using Machine Learning Algorithms in Wuhan City, China

The changes of land use/land cover (LULC) are important factor affecting the intensity of the urban heat island (UHI) effect. Based on Landsat image data of Wuhan, this paper uses cellular automata (CA) and artificial neural network (ANN) to predict future changes in LULC and LST. The results show that the built-up area of Wuhan has expanded, reaching 511.51 and 545.28 km2, while the area of vegetation, water bodies and bare land will decrease to varying degrees in 2030 and 2040. If the built-up area continues to expand rapidly, the proportion of 30~35 °C will rise to 52.925% and 55.219%, and the affected area with the temperature >35 °C will expand to 15.264 and 33.612 km2, respectively. The direction of the expansion range of the LST temperature range is obviously similar to the expansion of the built-up area. In order to control and alleviate UHI, the rapid expansion of impervious layers (built-up areas) should be avoided to the greatest extent, and the city’s “green development” strategy should be implemented.

Spatial Analysis of Urbanization Patterns in Four Rapidly Growing South Asian Cities Using Sentinel-2 Data

The rapid and dominant urbanization in Asian cities has fueled concerns regarding the local and global efforts toward urban sustainability. Specifically, South Asian cities have been a topical issue concerning ecological and environmental threats due to their unplanned and haphazard urban development. However, comparative urbanization studies in South Asian cities remain uncommon. Therefore, in this study, we sought to comparatively examine the land use and land cover (LULC) dynamics and to detect the urbanization patterns of four rapidly developing South Asian lowland cities: Mumbai (India), Colombo (Sri Lanka), Karachi (Pakistan), and Dhaka (Bangladesh). Sentinel-2 (10 m) data and various geospatial approaches, including urban–rural gradient and grid-based methods, statistics, and urban landscape metric techniques, were used to facilitate the analysis. The study revealed that Mumbai, Karachi, and Dhaka had larger built-up landscapes compared to Colombo. Mumbai had the highest percentage of green spaces, followed by Colombo. Dhaka and Karachi had relatively small percentages of green spaces. Colombo and Dhaka had more croplands, which consistently increased along the urban–rural gradient compared to Mumbai and Karachi. Karachi showed that the only major land use was built-up, while most of the areas were left as open lands. On the other hand, Colombo’s urban setup was more fragmented than the other three cities. Mumbai and Karachi had larger patches of urban footprints compared to Colombo and Dhaka. Thus, this study provides vital information on the past land utilization priorities in the four cities, and comparatively proffers guidance on certain critical areas of focus for local, regional, and global future sustainable urban planning.