Land Surface Temperature Difference Research Articles

Urban land use, land cover change and urban microclimate dynamics in Addis Ababa, Ethiopia

Land surface temperature (LST) increases and urban heat island (UHI) variability are the major urban climatology problems arising in urban development. This study attempts to assess the effects of urban land use and land cover change on microclimate dynamics in Addis Ababa city. Three different sets of remotely sensed data from Landsat 5 TM (1990), Landsat 7 ETM+ (2005) and Landsat 8 OLI/TIRS (2021) were used for the study. LSTs were retrieved from Landsat5 TM and Landsat7 ETM+ using a mono window,and the thermal infrared band (TR-10) of Landsat–8 was used to retrieve LST. Regression and correlation analyses of the LST, normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) were performed in SPSS V23. The study also examined the different residential urban morphology types (UMTs) of the LST and NDVI. The selected built-up blocks of UMTs included apartments, villas and mud houses. These UMTs are extracted by digitizing them from the Google Earth explorer. The results from this study showed that the proportion of urban green space (UGS) to other LULC types decreased from 120.4 km2 in 1990 to 76.26 km2 in 2021. However, the built-up area increased at a rate of 216.5 km2 (39.03%) from 1990 to 2021. The rapid expansion of built-up land in the study area was the main factor influencing the increase in LST. The residential UMTs exhibited significant differences in mean LSTs and NDVIs. The results indicate that UMT inhibited by Villia had the highest mean NDVI value and that the highest mean LST was observed in Apartment. The results of multiple linear regression analysis clearly indicate that built-up and green vegetation contributed 92.2% of the LST variations with R2 = 0.92 and VIF ≤ 10 in Addis Ababa city. The results of the study indicate that strengthening public participation in urban greening and optimizing the NDVI and NDBI are important strategies for mitigating the effects of microclimate change and that sustaining urban development and providing better quality of life for the urban population are important.

Coupling effects of building-vegetation-land on seasonal land surface temperature on street-level: A study from a campus in Beijing

The land surface temperature (LST) of urban streets is significantly influenced by the surrounding urban environment. In the cold areas of northern China, this influence exhibits a seasonal pattern. Currently, in areas with noticeable seasonal variations, the coupling effects of urban elements on the annual stability of street LST remain unclear. This study developed a new metric, Normalized Winter-Summer Land Surface Temperature Difference, to measure the year-round LST stability of campus streets. Three key urban elements affecting street-level LST in winter and summer were identified: building, vegetation, and land. The influencing characteristics for winter and summer LST were quantified and ranked respectively. The study reveals that the green space ratio dominates summer LST, while fraction vegetation coverage, leaf area index and average building height dominate winter LST. We found that morphological characteristics of buildings and vegetation cannot fully explain the impact of the urban physical environment on street-level LST. For winter street LST studies, greater emphasis should be placed on non-morphological characteristics and land. Based on the impact mechanisms and critical thresholds of key features in winter and summer, this study establishes an interpretable spatial classification method. By assigning seasonal weights to influencing features, it provides comprehensive design strategies that consider both winter and summer features to enhance annual LST stability and climate resilience in urban spaces facing extreme meteorological conditions.

Correcting an Off-Nadir to a Nadir Land Surface Temperature Using a Multitemporal Thermal Infrared Kernel-Driven Model during Daytime

Land surface temperature (LST) is a fundamental parameter in global climate, environmental, and geophysical studies. Remote sensing is an essential approach for obtaining large-scale and frequently updated LST data. However, due to the wide field of view of remote sensing sensors, the observed LST with diverse view geometries suffers from inconsistency caused by the thermal radiation directionality (TRD) effect, which results in LST products being incomparable, especially during daytime. To address this issue and correct current off-nadir LSTs to nadir LSTs, a semi-physical time-evolved kernel-driven model (TEKDM) is proposed, which depicts multitemporal TRD patterns during the daytime. In addition, we employ a Bayesian optimization method to calibrate seven unknown parameters in the TEKDM. Validation results using the U.S. Climate Reference Network (USCRN) sites show that the RMSE (MBE) for GOES-16 and MODIS off-nadir LST products is reduced from 3.29 K (−2.0 K) to 2.34 K (−0.02 K), with an RMSE reduction of 0.95 K (29%) and a significant reduction in systematic bias. Moreover, the proposed method successfully eliminates the angular and temporal dependence of the LST difference between the satellite off-nadir LST and in situ nadir LST. In summary, this study presents a feasible approach for estimating the high-accuracy nadir LST, which can enhance the applicability of LST products in various domains.

Simulating influences of land use/land cover composition and configuration on urban heat island using machine learning

Urban heat island (UHI) has become a worldwide concern under global warming and urbanization waves, which pose significant threats to human health and urban sustainability. Despite Land Use/Land Cover (LULC) being regarded as a crucial factor influencing UHI, few studies simulated the nonlinear influences from LULC compositions and configurations, especially considering the difference between monocentric and polycentric cities. Therefore, we measured the patterns of LULC and UHI from 2006 to 2022 using cases of a monocentric city (Chengdu) and a polycentric city (Chongqing). Further, we simulated future and land surface temperature (LST) using Machine Learning (ML) models, and further calculated the simulated UHI in 2030 by urban-rural LST differences. The results showed that Chengdu had prominent UHI intensity in cores and the surrounding satellite towns. Chongqing's main center and subcenters had high UHI intensity under clustered patterns. ML results indicated that built-up percentage was the most crucial factor driving LST. Configuration factors dominated in impacting Chengdu's LST, while composition factors were more critical in Chongqing. Moreover, future UHI is estimated to decrease in Chongqing's core, compared with the remaining high UHI in Chengdu's core. These results might provide insights for understanding and mitigating future UHI effects.

Evaluating the thermal environmental alterations due to photovoltaic installations in the kushida river basin, Japan

The construction of Photovoltaic (PV) provides renewable energy, but it is also impacting the basins environment. The study simulates the annual construction status of PV installations within the research area over a time series from 2013 to 2023, and to understanding of the trends in Land Surface Temperature (LST) changes due to the PVs. The results revealed that the number of PV installations in the study area increased most significantly between 2018 and 2019, with the newly installed area during this period being approximately 5.2 times larger than 2013. Although large-scale construction slowed after 2020, small and medium-sized PV installations continued. The study found that the LST around PV which built from 2013 to 2023 increased by an average of 2.85 °C. Also, LST-difference (LST-D) changes with the seasons. The analysis of the relationship between geographical conditions and LST difference revealed that in hilly and mountainous areas with higher elevations, the impact of PV construction on LST was lower. And found that the distance of the PV from the watercourse and the surrounding land cover also had an impact to LST changes, with spatial variability between the different impact variables as analysed by the Geographically Weighted Regression (GWR) model.

Spatial coupling relationship between architectural landscape characteristics and urban heat island in different urban functional zones

The process of urbanization exacerbates the urban heat island phenomenon. In order to reasonably identify urban functional zones, and reveal the influence of buildings on land surface temperature (LST). Guiyang, a multi-mountainous city in China, was taken as the research area, Open Street Map (OSM) and land use type data were used to identify urban functional zones. Subsequently, the correlation between urban building morphology metrics and LST was analyzed using Pearson correlation and boosted regression tree (BRT) model. The results showed that: (1) There were significant differences in LST among different urban functional zones. (2) The relative contribution value of building density (BD) to land surface temperature was high, and it significantly exacerbated the urban heat island effect across all urban functional zones, with the maximum impact on LST reaching 2.5 °C in Utility zone. (3) Mean building height (MBH), Southerly wind speed (S_Wind speed) and Landscape shape index of building (LSI_Building) had the effect of alleviating thermal environment, and the mitigation degree of land surface temperature was the highest in Residential zone and Ecological zone, reaching 0.6 °C, 0.8 °C and 2.0 °C, respectively. This study enhances our understanding of the relationship between urban functions and LST, providing valuable insights for mitigating the urban thermal environment and promoting sustainable development.

A transferable approach to assessing green infrastructure types (GITs) and their effects on surface urban heat islands with multi-source geospatial data

Urban green infrastructure (GI) is essential for mitigating surface urban heat islands (SUHIs) and strengthening urban resilience to climate change, thereby contributing to the achievement of sustainable development goals in urban areas. A ‘green infrastructure types’ (GITs) scheme was recently developed to examine the role of amount, composition, and configuration of GI in providing effective thermal cooling in Australia. However, the GIT scheme has not been applied to an urban environment in other countries, so its general suitability for SUHI assessment needs to be further investigated. Taking the urban core area of Beijing as a case study, a multi-level classification method was developed in this study for GIT mapping using bi-seasonal high-resolution Gaofen-1 satellite imagery, time-series Sentinel-2 A/B and SDGSAT-1 satellite data, and open vector datasets. The time series of 30 m land surface temperature (LST) data was created by blending high temporal resolution MODIS and Landsat data. Statistical analysis was performed to identify factors that may influence the cooling effect of GI. The results demonstrate significant LST differences among the GITs in summer. The aquatic GITs with a water fraction >50% (5.6–7.6 °C) and pervious GITs with a high tree fraction (5.4–5.6 °C) provided the largest cooling effect. The cooling capacity of mixed and pervious GITs was found closely related to the proportion of pervious surface and woody vegetation. Increasing the area of irrigated grass from low (28%) to medium (61%) in mixed surfaces produced only a marginal effect on surrounding LSTs. The GIT mapping approach combined with SUHI analysis provides a transferable and promising framework for examining the cooling effect of GI at the neighborhood level in a consistent manner.

Utility of thermal remote sensing for evaluation of a high‐resolution weather model in a city

AbstractProgress in high‐resolution numerical weather prediction (NWP) for urban areas will require new modelling approaches and extensive evaluation. Here, we exploit land surface temperature (LST) data from Landsat‐8 to assess 100 m resolution NWP for London (UK) on four cloud‐free days. The LST observations are directional radiometric temperatures with non‐negligible uncertainties. We consider the challenges of informative comparison between the Landsat LST and the NWP scheme's internal characterisation of the complete surface temperature. The LST spatial coverage allows large‐scale observation–model differences to be explored. In one case, obvious spatial artifacts in the NWP surface temperature are observed relative to the Landsat LST. These are found to be related to the NWP's initial method of downscaling of soil moisture using soil properties. Updated model runs have higher spatial correlation between model and Landsat LST. In cases where meteorological conditions favour the formation of horizontal convective rolls, warmer air temperatures associated with updraughts in the mixed layer extend inappropriately to the urban surface. This manifests as warm stripes in the model surface temperature that are not present in the Landsat LST. NWP–Landsat LST differences are larger in more built‐up areas on days nearer summer solstice. This is largely attributed to urban thermal anisotropy, as Landsat preferentially views warmer urban surfaces, whereas the model LST represents all surfaces. We evaluate two approaches to quantify this sampling effect, but further work is needed to fully constrain it and facilitate more informative model evaluation.

FIRE WEATHER INDEX AND FOREST FIRE DANGER MAPPING: INSIGHTS FROM A CASE STUDY IN ANTALYA - MANAVGAT FOREST, TURKIYE

Abstract. Forest fires in Türkiye, like in other regions, have detrimental effects on wildlife habitats, water quality, air pollution, climate change, and the economy. These fires become particularly concerning during the dry summer months. In 2021, forest fires affected over 150 thousand hectares of land across the country, with the Manavgat district in Antalya province alone witnessing the burning of approximately 60 thousand hectares of forest area. This study aims to assess the applicability and suitability of Fire Weather Index (FWI) data derived from meteorological station data in the Antalya region, as well as EFFIS FWI data generated using satellite-based meteorological information, for fire danger mapping during the Manavgat forest fire that occurred between 28 July and 6 August 2021. Additionally, correlation analyses were performed between the two FWI datasets and other relevant variables, including the difference in Normalized Burn Ratio (dNBR), the difference in Land Surface Temperature (dLST), and Fire Radiative Power (FRP) data detected from MODIS and VIIRS satellites. The results of the correlation analysis indicated that the FWI values obtained using in-situ meteorology station data showed much higher correlations than FWI values obtained from EFFIS, with the highest correlation (73%) observed with dLST data. Consequently, the fire danger map was created using the in-situ meteorological data, given its stronger correlation. The results prominently revealed a widespread high-risk level across the entire Antalya province, with the Manavgat district classified into the "Extreme" and "Very Extreme" FWI classes, emphasizing the critical importance of utilizing in-situ meteorological data for precise fire danger assessments and proactive fire management strategies.

ANALISIS PERUBAHAN URBAN HEAT ISLAND DAN PENGARUHNYA TERHADAP INDEKS KENYAMANAN KOTA BANJARBARU

The Banjarbaru administrative status has changed from an Administrative City to a City, and in 2022 become the Provincial Capital. Population growth and infrastructure/building development due to these changes may affect environmental changes. A significant increase in air temperature against its normal due to environmental changes can trigger climate change. Urban Heat Island (UHI) is a phenomenon recognized by the higher temperature conditions in urban areas compared to surrounding areas. UHI identification is known by calculating the difference in Land Surface Temperature (LST) values using remote sensing (satellite data). An increase in the value of LST in an area can trigger the appearance of UHI and make the environment less comfortable. The LST value is also related to population, vegetation, residential areas, and built-up land. Thermal Humidity Index (THI) is an index used to determine comfort level based on temperature and humidity conditions. This study aims to analyze spatiotemporal changes in UHI phenomena in the Banjarbaru area during three administrative status periods (1998 - 2022) and its effect on the comfortable index. This study uses Landsat satellite data (consist Visible, Near Infrared, and Thermal Bands) and Relative Humidity (RH) from 2 weather stations in Banjarbaru. The result shows that the distribution of UHI in the Banjarbaru City area increases around industrial areas, settlements, government centers, and along arterial roads that are open land without trees. In general, the Banjarbaru area experienced an increase in the comfort index. Initial conditions belonging to the comfortable and less comfortable classes changed to become less comfortable to uncomfortable, especially in the North Banjarbaru sub-district and around Syamsudin Noor Airport ( Landasan Ulin sub-district).

Comparing Characteristics of the Urban Thermal Environment Based on the Local Climate Zone in Three Chinese Metropolises

Urban landscape has important effects on urban climate, and the local climate zone (LCZ) framework has been widely applied in related studies. However, few studies have compared the relative contributions of LCZ on the urban thermal environment across different cities. Therefore, Beijing, Shanghai, and Shenzhen in China were selected to conduct a comparative study to explore the relationship between LCZ and land surface temperature (LST). The results showed that (1) both the composition and spatial configuration of LCZ had obvious differences among the three cities. Beijing had a higher area proportion of compact mid-rise and low-rise LCZ types. The spatial pattern of LCZ in Shenzhen was especially quite different from those of Beijing and Shanghai. (2) Shenzhen had the strongest summer surface urban heat island (UHI) intensity and the largest UHI region area. However, the proportion of urban cooling island areas was still the highest in Shenzhen. (3) Different LCZs showed significant LST differences. The largest LST difference between the LCZs reached 5.57 °C, 4.50 °C, and 12.08 °C in Beijing, Shanghai, and Shenzhen, respectively. Built-up LCZs had higher LSTs than other LCZ types. (4) The dominant driving LCZs on LST were different among these cities. The LST in Beijing was easily influenced by built-up LCZ types, while the cooling effects generated by LCZ G(water) were much stronger than built-up LCZs’ warming effects in Shanghai. These results indicated that the effect of the LCZ on LST had significant differences among LCZ types and across cities, and the dominant LCZs should be given more priority in future urban planning.

Using satellite-derived land surface temperatures to clarify the spatiotemporal warming trends of the Alborz Mountains in northern Iran

The Alborz Mountains are some of the highest in Iran, and they play an important role in controlling the climate of the country’s northern regions. The land surface temperature (LST) is an important variable that affects the ecosystem of this area. This study investigated the spatiotemporal changes and trends of the nighttime LST in the western region of the Central Alborz Mountains at elevations of 1500–4000 m above sea level. MODIS data were extracted for the period of 2000–2021, and the Mann–Kendall nonparametric test was applied to evaluating the changes in the LST. The results indicated a significant increasing trend for the monthly average LST in May–August along the southern aspect. Both the northern and southern aspects showed decreasing trends for the monthly average LST in October, November, and March and an increasing trend in other months. At all elevations, the average decadal change in the monthly average LST was more severe along the southern aspect (0.60°C) than along the northern aspect (0.37°C). The LST difference between the northern and southern aspects decreased in the cold months but increased in the hot months. At the same elevation, the difference in the lapse rate between the northern and southern aspects was greater in the hot months than in the cold months. With increasing elevation, the lapse rate between the northern and southern aspects disappeared. Climate change was concluded to greatly decrease the difference in LST at different elevations for April–July.

The cooling and warming effects of potential forest transition on local land surface temperature in Northeast China

Forest cover change directly affects the surface energy balance by altering the radiative and non-radiative properties of the surface. These competing processes may yield considerable biophysical impacts on local and regional climate that depend on background climate and specific forest type. Here we compared the differences in land surface temperature (LST) between temperate forests and adjacent croplands/grasslands to quantify the potential biophysical effects of transitions from croplands/grasslands to forests in Northeast China, based on multiple satellite observation products from 2001 to 2016. Forests were found to show a slight lower daytime LST and strong higher nighttime LST than adjacent croplands or grasslands. As a result, the daily LST of forests was 0.15 ± 0.06 ℃ and 0.32 ± 0.04 ℃ higher than that of croplands and grasslands, respectively. On the seasonal scale, the warming effect of cropland/grassland-to-forest transition during non-growing season outweighed the cooling effect during growing season, and dominated the annual positive LST response. Moreover, the cooling effect of cropland-to-forest transition during growing season was stronger in the early growth stage than late growth stage (−1.65 ± 0.9 ℃ in May to June, −0.39 ± 0.5 ℃ in July to September). The net cooling effect of warm temperate broadleaved forests was found to shift toward net warming effect of cold temperate coniferous forests with increasing latitude. The daytime cooling effect of forests was mainly driven by increased non-radiative evapotranspiration. Overall, the radiative process related to the solar energy dominated the net warming effect of forest transitions over Northeast China. Estimating the climate feedback potential of forests could be useful to provide theoretical references for the adaptation and mitigation strategies for climate change.

Spatiotemporal Analysis of Urban Heat Islands and Vegetation Cover Using Emerging Hotspot Analysis in a Humid Subtropical Climate

Research on the temporal and spatial changes of the urban heat island effect can help us better understand how urbanization, climate change, and the environment are interconnected. This study uses a spatiotemporal analysis method that couples the Emerging Hot Spot Analysis (EHSA) technique with the Mann–Kendall technique. The method is applied to determine the intensity of the heat island effect in humid subtropical climates over time and space. The data used in this research include thermal bands, red band (RED) and near-infrared band (NIR), and Landsat 7 and 8 satellites, which were selected from 2000 to 2022 for the city of Sari, an Iranian city on the Caspian Sea. Pre-processed spectral bands from the ‘Google Earth Engine’ database were used to estimate the land surface temperature. The land surface temperature difference between the urban environment and the outer buffer (1500 m) was modeled and simulated. The results of this paper show the accuracy and novelty of using Emerging Hotspot Analysis to evaluate the effect of vegetation cover on the urban heat island intensity. Based on the Normalized Difference Vegetation Index (NDVI), the city’s land surface temperature increased by approximately 0.30 °C between 2011 and 2022 compared to 2001 to 2010. However, the intensity of the urban heat island decreased during the study period, with r = −0.42, so an average −0.031 °C/decade decrease has been experienced. The methodology can be transferred to other cities to evaluate the role of urban green spaces in reducing heat stress and to estimate the heat budget based on historical observations.

Comparative study and effects of urban green scape on the land surface temperature of a large metropolis and green city

Previous studies have provided valuable insights into the impact of green space (GS) on land surface temperature (LST). However, there is a need for in-depth comparative research on changing landscape patterns in cities and their effects on the urban thermal environment. This study investigates the spatial arrangement of GS and the influence of impervious surfaces on LST in urban areas, examining their cooling and warming effects in the urban landscapes of Beijing and Islamabad. The study aims to assess the impact of the spatial arrangement of GS on LST using a moving window of 1 km2 to analyze the overall effect of landscape patterns on the urban environment. Using Gaofen (GF–2) and Landsat–8 satellite data, we examined the biophysical surface properties of core urban areas. The results indicate a significant difference in the mean LST of 5.44 °C and 3.31 °C between impervious surfaces and GS in Beijing and Islamabad, respectively. The barren land and GS in Islamabad experience a higher LST of 3.39 °C compared to Beijing, which accounts for 1.39 °C. In Beijing, configuration metrics show no significant effect on urban LST, while edge density (ED) exhibits a slightly negative trend. In contrast, in the city of Islamabad, the landscape shape index (LSI), patch density (PD), and number of patches (NP) metrics have a significant influence on LST. The cooling effect of GS patches (0.1–0.5 ha) is more pronounced, while that of GS patches of 15–20 ha shows no significant effect on LST. The temperature difference (TD) of 5.01 °C was observed from the edge of GS in Beijing and 3.3 °C in Islamabad. Considering Islamabad's lush green scape compared to Beijing, this study suggests that Islamabad may experience an increase in LST in the future due to urbanization. This study's findings may assist urban policy-makers in designing sustainable green city layouts that effectively address future planning considerations.

Synergistic impacts of Landscape, Soil, and environmental factors on the spatial distribution of soil aggregates stability in the Danjiangkou reservoir area

Soil aggregate stability (SAS) plays a crucial role in determining the soil's resistance to erosion. Conducting a spatial variability analysis of SAS in the reservoir area, which is highly susceptible to soil degradation and erosion, can yield valuable insights for agricultural practices, landscape planning, and environmental management in this region. Nevertheless, existing research remains constrained in its capacity to accurately map and forecast soil aggregates at a spatial level. This study aims to investigate the spatial distribution of SAS in the Danjiangkou reservoir area, as well as to explore the intrinsic linkage mechanisms between changes in soil properties, environmental factors and landscape patterns and SAS. Results revealed significant spatial variations in SAS within the vicinity of the Danjiangkou reservoir area. Notably, soil properties (including clay, available potassium, soil organic matter, and available phosphorus), environmental factors (such as digital elevation model, slope, land surface temperature difference between seasons (ΔLST)), and landscape pattern (largest patch index (LPI), shannon’s diversity index (SHDI), edge density (ED)) significantly affected the spatial variability of SAS, explaining 18.91%, 8.22%, and 8.17% of the total variability of soil SAS, respectively. Structural equation modeling results indicated that altering soil properties serves as the primary approach to enhance SAS. Furthermore, interventions targeting landscape factors such as LPI, SHDI, and ED, alongside environmental factors like ΔLST and slope, exert direct influences on SAS, while also indirectly impacting it through their effects on soil properties. These findings contribute to the design of land planning and soil and water conservation measures in the reservoir watershed, promoting its protection of soil structure and its ecological service function.

Optimizing China’s Afforestation Strategy: Biophysical Impacts of Afforestation with Five Locally Adapted Forest Types

Recent research has mapped potential afforestation land to support China’s goal of achieving “carbon neutrality” and has proposed tree species selection to maximize carbon uptake. However, it overlooked biophysical climatic effects, which have a more significant impact on local temperature than CO2 reduction. This study aims to present a comprehensive understanding of how afforestation in China affects local and regional climates through biophysical processes. It focuses on the latitudinal patterns of land surface temperature differences (ΔLST) between five locally adapted forest types and adjacent grasslands using satellite-based observations. Our key findings are as follows: Firstly, broadleaf forests and mixed forests exhibit a stronger cooling effect than coniferous forests due to differences in canopy structure and distribution. Specifically, the net cooling effects of evergreen broadleaf forests (EBFs), deciduous broadleaf forests (DBFs), and mixed forests (MFs) compared to grasslands are −0.50 ± 0.10 °C (mean ± 95% confidence interval), −0.33 ± 0.05 °C, and −0.36 ± 0.06 °C, respectively, while evergreen needleleaf forests (ENFs) compared to grasslands are −0.22 ± 0.11 °C. Deciduous needleleaf forests (DNFs) exhibit warming effects, with a value of 0.69 ± 0.24 °C. In regions suitable for diverse forest types planting, the selection of broadleaf and mixed forests is advisable due to their enhanced local cooling impact. Secondly, temperate forests have a net cooling effect to the south of 43° N, but they have a net warming effect to the north of 48° N compared to grasslands. We recommend caution when planting DNFs, DBFs, and MFs in northeastern China, due to the potential for local warming. Thirdly, in the mountainous areas of southwestern China, especially when planting ENFs and MFs, tree planting may lead to local warming. Overall, our study provides valuable supplementary insights to China’s existing afforestation roadmap, offering policy support for the country’s climate adaptation and mitigation efforts.

Influence of selection of rural reference area for quantifying the surface urban heat islands intensity in major South Korean cities

Surface urban heat island intensity (SUHII), which is the difference in land surface temperature (LST) between urban and rural areas, depends on selection of rural reference and studies that have analyzed the impacts of different methods on estimating SUHII are few. This study aims to analyze impact of two different methods for defining rural areas, buffer area around city, and non-urban areas within city, on estimating SUHII in five major cities of South Korea using satellite measurements for period 2018–2022. Using a buffer region around city to define rural reference area results in a higher estimate of SUHII compared to using rural areas within city limits. The largest differences in SUHII were found in Seoul (both day and night) and Busan (night), with Seoul exhibiting a difference of 1.14°C (day) and 1.51°C (night), and the differences are mostly driven by the physical characteristics of the city and geographical location.

Machine learning-based assessment and simulation of land use modification effects on seasonal and annual land surface temperature variations

Rapid urban sprawl adversely impacts the local climate and the ecosystem components. Islamabad, one of South Asia's green and environment-friendly capitals, has experienced major Land Use Land Cover (LULC) changes over the past three decades consequently, elevating the seasonal and annual Land Surface Temperature (LST) in planned and unplanned urban areas. The focus of this study was to quantify the fluctuations in LULC and LST in planned and unplanned urban areas using Landsat data and Machine Learning algorithms involving the Support Vector Machine (SVM) over the 1990–2020 data period. Moreover, hybrid Cellular Automata-Markov (CA-Markov) and Artificial Neural Network (ANN) models were employed to project the future changes in LULC and annual LST, respectively, for the years 2035 and 2050. The findings of the study reveal a distinct difference in seasonal and annual LST in planned and unplanned areas. Results showed an increase of ∼22 % in the built-up area but vegetation and bare soil decreased by ∼10 % and ∼12 %, respectively. Built-up land showed a maximum annual mean LST followed by bare-soil and vegetative surfaces. Seasonal analysis showed that summer months experience the highest LST, followed by spring, autumn and winter. Future projections revealed that the built-up areas (∼27 % in 2020) are likely to increase to ∼37 % and ∼50 %, and the areas under the highest annual mean LST class i.e., ≥28 °C are likely to increase to ∼19 % and ∼21 % in planned, and ∼38 % and ∼42 % in unplanned urban areas for the years 2035 and 2050, respectively. Planned areas have better temperature control with urban green spaces, and controlled infrastructure. The Capital Development Authority of Islamabad may be advised to control the expansion of built-up areas, grow urban forests, and thus mitigate the possible Urban Heat Island (UHI) effect.

Assessment and simulation of thermal environments in Taiyuan urban built-up area, China

The urban heat island (UHI) effect has intensified with increases in impervious surface areas and population densities due to urbanization, which affects the quality of urban life and ecological services. Here, the Moran’s I and hot spot analysis (Getis-Ord Gi*) are used to explore spatial autocorrelation of land surface temperature (LST) in Taiyuan built-up area. Then, the built-up area is divided to 41 sub-areas to accurately explore the urban LST differences caused by different LULC types. Moreover, geographically weighted regression (GWR) is used to analysis the spatial heterogeneity of LST. Finally, we simulate the LST changes using the CA-Markov model in the study area in the year 2030. Our results showed that 1) average LST was 22.76°C in Taiyuan built-up area in 2018. The Highest-temperature areas were distributed in heavy-industry intensive areas in the north, north central, and southeast, whereas the Lowest-temperature areas mainly corresponded to rivers, lakes, urban forests, and green spaces. 2) The Moran’s I gradually decreased from 0.8635 to 0.2097 with an increase in the spatial distance threshold. The optimal recognition effect was obtained at a 400 × 400-m scale. The Getis-Ord Gi* analysis indicated that the cold area was 1248.32 km2 (12.24% of the study area) and the hot area was 43.84 km2 (11.11% of the study area) in 2018. 3) The GWR analysis showed significant spatial non-stationarity in the influence of LULC types on LST. The GWR model was calculated with reference to the observation values of the adjacent areas, so as to better reveal the spatial relationship between artificial surface, woodland, water, grassland, and bare land and LST. 4) The UHI distribution was more concentrated in 2030 than in 2021. The statistics of the proportion and transfer matrix of LST indicated that the proportion of the Highest and Lowest-temperature areas in 2030 decreased and the UHI effect will further intensify. This study could be used to guide sustainable development in cities and provides theoretical support for adjusting the urban spatial structure.