Evaluation of thermal effects on urban road spatial structure: A case study of Xuzhou, China.

Urban heat islands (UHI) are important environmental issue in cities where urban spatial structure has been proven to play an important role in alleviating UHI effects. The relationship between land surface temperature and urban spatial structures has been explored, providing strong support for their cooling effects. Urban roads are the skeleton of urban spatial structures, with obvious spatial structure characteristics; however, research on the relationship between roads and the thermal environment has been mostly focused at the micro and meso level, lacking exploration at the macro spatial structure scale. Xuzhou-a typical average-sized city in China-was selected as the research object and the road system as the carrier. The thermal environmental effects of road elements such as their structural attributes, geometric attributes and unique construction attributes were quantitatively analyzed using geographically weighted regression analysis. The results revealed that 1) the contribution of roads in the study area to the UHI effect is relatively stable; therefore, this area should become an important cooling space to decompose UHI patch connectivity and thus decrease the UHI effect. 2) the self-organizing structural characteristics of urban roads affect their thermal environments where in the straightness of the road structure and road thermal environment showed a clear overall negative correlation And 3) the length and width of the road segments had negative and positive effects on the thermal environment, respectively. The green coverage of the roads has a global negative effect on the thermal environment, but shows obvious spatial non-stationarity. Therefore, green measures must be implemented in different regions. The results here provide a quantitative basis for urban road system planning and urban form management and control that incorporates thermal environment improvements, as well as a reference for the study of urban thermal environments under different spatial forms and planning control systems in other countries and regions.

Delineating groundwater potential zones using integrated remote sensing and GIS in Lahore, Pakistan.

Groundwater depletion and water scarcity are pressing issues in water-limited regions worldwide, including Pakistan, where it ranks as the third-largest user of groundwater. Lahore, Pakistan, grapples with severe groundwater depletion due to factors like population growth and increased agricultural land use. This study aims to address the lack of comprehensive groundwater availability data in Lahore's semi-arid region by employing GIS techniques and remote sensing data. Various parameters, including Land Use and Land Cover (LULC), Rainfall, Drainage Density (DD), Water Depth, Soil Type, Slope, Population Density, Road Density, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Moisture Stress Index (MSI), Water Vegetation Water Index (WVWI), and Land Surface Temperature (LST), are considered. Thematic layers of these parameters are assigned different weights based on previous literature, reclassified, and superimposed in weighted overlay tool to develop a groundwater potential zones index map for Lahore. The groundwater recharge potential zones are categorized into five classes: Extremely Bad, Bad, Mediocre, Good, and Extremely Good. The groundwater potential zone index (GWPZI) map of Lahore reveals that the majority falls within the Bad to Mediocre recharge potential zones, covering 33% and 28% of the total land area in Lahore, respectively. Additionally, 14% of the total area falls under the category of Extremely Bad recharge potential zones, while Good to Extremely Good areas cover 19% and 6%, respectively. By providing policymakers and water supply authorities with valuable insights, this study underscores the significance of GIS techniques in groundwater management. Implementing the findings can aid in addressing Lahore's groundwater challenges and formulating sustainable water management strategies for the city's future.

Quantitative assessment of interplay between urbanization dynamics and land surface temperature variations using generalized additive model coupled PDP for sustainable urban planning and management.

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.

SchoolHEAT: Racial and Ethnic Inequity in School Temperature.

Exposure to high environmental temperature is detrimental to health through multiple pathways. This paper describes disparities in school-based high-temperature exposure at metropolitan schools in the United States. Using school location and sociodemographic data from the National Center for Education Statistics, neighborhood data from the US Census Bureau, and land surface temperature (LST) data from the Aqua Earth-observing satellite mission, we find that for every 10% more Black or Hispanic residents in the neighborhood, schools have LST 0.25 °C and 0.38 °C hotter, respectively. When the Black or Hispanic student population is greater than the neighborhood population, LST is an additional 0.20 °C and 0.40 °C for each 10% increase in students over neighborhood population, respectively. Black and Hispanic students are overrepresented in the hottest schools, making up 58.7% of students in the hottest 20% of schools, compared to only 30.0% of students in the coolest 20% of schools.

Persistent heat islands monitoring of Tehran metropolis using temporal analysis of Landsat-8 satellite images.

The management and design of urban areas in metropolises pose significant challenges. Balancing diverse land uses within a metropolitan structure and addressing spatial and environmental constraints are just some of these challenges. Urban heat islands, which stem from factors such as inappropriate construction materials, inadequate building insulation, improper land use locations, and unsuitable built-up density, reflect environmental imbalances within urban infrastructure. Effectively addressing these temperature discrepancies can lead to energy preservation, reduced environmental hazards, and enhanced comfort for urban residents. This study employed Landsat-8 satellite images to identify and monitor positive and convex temperature disparities across various districts of Tehran over 3 years (2018 to 2020) using three different strategies. These disparities are estimated through the differences in land surface temperature from the thermal trend of each district and their persistence has been assessed in seasonal, semi-annual, and annual strategies. The study found that industrial areas, including warehouses, were the significant contributors to the persistent presence of urban heat islands in summer and winter. Open areas with impervious surfaces or bare soil, particularly those lacking sufficient green cover, also significantly contributed to the heat island effect. Certain large shopping centers, often due to their air conditioning systems, were also consistently identified as persistent heat islands. Evaluations demonstrated that over 78.8% of the identified persistent heat islands were meaningful, with most located in the northern and western parts of Tehran.

Gridded dataset on land surface temperature and selected environmental and socioeconomic features in Southeast Asian metropolises.

As Southeast Asia grapples with extreme heat occurrences in recent years, mapping which areas are clustered with elevated temperatures is crucial for monitoring the at-risk population. Identifying the contributing factors to the warming trends in these areas is also vital in formulating adaptation and mitigation strategies. This dataset comprises land surface temperature (LST) in three metropolises in the region - Metropolitan Manila, Bangkok Metropolitan Area, and Greater Jakarta - downloaded and processed from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. We used MODIS' inherent grid system to map LST values at the satellite image's most granular level. We combined them with selected environmental and socioeconomic variables, including building and built-up areas, areas of greeneries, industrial zones, and water bodies, nighttime light (to approximate areas of economic activities), gridded population, distance from water bodies, and indicators on which urban infrastructures, i.e. roads and airports, are present in each grid. Available in shapefile and comma-separate variable file format, this dataset is useful for urban studies in these three cities. The dataset can be easily updated as additional data on LST and other variables becomes available.

Where is the heat threat in a city? Different perspectives on people-oriented and remote sensing methods: The case of Prague.

Extreme heat in urban areas has a severe impact on urban populations worldwide. In light of the threats posed by climate change, it is clear that more holistic and people-oriented approaches to reducing heat stress in urban areas are needed. From this perspective we aim to identify and compare thermal hotspots and places with favourable thermal conditions, based on three different methods - thermal walk, participatory-based cognitive mapping, and remote sensing in a Central European city. Although major hotspots in large low-rise development zones were identified by all three methods, the overall agreement between on-site thermal sensation votes, cognitive maps and surface temperatures is low. In the urban canyon of compact mid-rise and open mid-rise development, the thermal walk method proved to be useful in the identification of the specific (parts of) streets and public spaces where citizens can expect thermal discomfort and experience heat stress, e.g. crossroads, arterial streets with a lack of greenery, north facing unshaded parts of streets, and streets with inappropriate tree spacing. Cognitive maps on an urban neighbourhood scale are not specific enough on a street level; however, as a supplementary method they can help identify discrepancies between on-site sensations and thermal conditions. For further research on effective and cost-efficient urban heat mitigation, we suggest combining thermal walks with numerical model simulations.

Modeling the spatially varying effects of biophysical factors on land surface temperature.

A growing number of studies have investigated how land surface temperature (LST) is influenced by a variety of driving factors; however, little effort has been made to identify the dominant ones. The suggested method used the Upper Awash Basin (UAB), Ethiopia, as an example to explore the spatial heterogeneity and factors affecting LST, which is critical for selecting effective mitigation strategies to manage the thermal environment. The study employed two models: ordinary least squares (OLS) and geographically weighted regression (GWR). The OLS model was first used to capture the overall relationship between LST and some biophysical factors. The GWR was then utilized to investigate the spatial non-stationary relationships between LST and its influencing biophysical factors. Although the method was tested in UAB, Ethiopia, it can be applied in similar agroecosystems, to identify the dominant factors that influence LST and develop site-specific LST mitigation strategies.•The OLS and GWR models investigated the spatial heterogeneities of the influencing factors and LST.•Biophysical parameters such as enhanced vegetation index (EVI), modified normalized difference water index (MNDWI), normalized difference built-up index (NDBI), normalized difference bareness index (NDBaI), albedo and elevation were used as potential driving environmental factors of LST•The models performance was computed using the adjusted coefficient of determination (adj. R2), Akaike Information Criterion (AICc), and residual sum of squares (RSS).

Remote sensing analysis of spatiotemporal impacts of anthropogenic influence on mountain landscape ecology in Pir Chinasi national park.

Mountain landscapes can be fragmented due to various human activities such as tourism, road construction, urbanization, and agriculture. It can also be due to natural factors such as flash floods, glacial lake outbursts, land sliding, and climate change such as rising temperatures, heavy rains, or drought.The study's objective was to analyze the mountain landscape ecology of Pir Chinasi National Park under anthropogenic influence and investigate the impact of anthropogenic activities on the vegetation. This study observed spatiotemporal changes in vegetation due to human activities and associated climate change for the past 25 years (1995-2020) around Pir Chinasi National Park, Muzaffrabad, Pakistan. A structured questionnaire was distributed to 200 residents to evaluate their perceptions of land use and its effects on local vegetation. The findings reveal that 60% of respondents perceived spatiotemporal pressure on the park. On the other hand, the Landsat-oriented Normalized Difference Vegetation Index (NDVI) was utilized for the less than 10% cloud-covered images of Landsat 5, 7, and 8 to investigate the vegetation degradation trends of the study area. During the entire study period, the mean maximum NDVI was approximately 0.28 in 1995, whereas the mean minimum NDVI was - 2.8 in 2010. QGIS 3.8.2 was used for the data presentation. The impact of temperature on vegetation was also investigated for the study period and increasing temperature trends were observed. The study found that 10.81% (1469.08 km2) of the area experienced substantial deterioration, while 23.57% (3202.39 km2) experienced minor degradation. The total area of degraded lands was 34.38% (or 4671.47 km2). A marginal improvement in plant cover was observed in 24.88% of the regions, while 9.69% of the regions experienced a major improvement. According to the NDVI-Rainfall relationships, the area was found to be significantly impacted by human pressures and activities (r ≤ 0.50) driving vegetation changes covering 24.67% of the total area (3352.03 km2). The area under the influence of climatic variability and change (r ≥ 0.50 ≥ 0.90) accounted for 55.84% (7587.26 km2), and the area under both climatic and human stressors (r ≥ 0.50 < 0.70) was 64%. Sustainable land management practices of conservation tillage, integrated pest management, and agroforestry help preserve soil health, water quality, and biodiversity while reducing erosion, pollution, and the degradation of natural resources. landscape restoration projects of reforestation, wetland restoration, soil erosion control, and the removal of invasive species are essential to achieve land degradation neutrality at the watershed scale.

Spatiotemporal changes of urban heat island effect relative to land surface temperature: a case study of Jinan City, China.

The urban heat island (UHI) effect has become increasingly prevalent and significant with the accelerated pace of urbanization, posing challenges for urban planners and policymakers. To reveal the spatiotemporal variations of the urban heat island effect in Jinan City, this study utilized Landsat satellite images from 2009, 2014, and 2019, employing the classic Mono-Window algorithm to extract land surface temperature (LST). Additionally, Geodetector was introduced to conduct a detailed analysis of the relationship between LST in Jinan City and land cover types (vegetation, water bodies, and buildings). The results indicate a significant increase in the severity of the urban heat island effect in Jinan from 2009 to 2019, with the central urban area consistently exhibiting a high-intensity core heat island. Suburban areas of Jinan show a clear trend of merging their heat island effects with the central urban area. The combined area of strong cool island effect zones and cool island effect zones within water bodies reaches 89.7%, while the combined proportion of heat island and strong heat island effect zones in building areas is 62.2%. Vegetation cover (FVC) exerts the greatest influence among all factors on the intensity level of the urban heat island effect. These findings provide a reliable basis for decision-making related to urban planning and construction in Jinan City.

Unraveling the Influence of Satellite-Observed Land Surface Temperature on High-Resolution Mapping of Ground-Level Ozone Using Interpretable Machine Learning.

Accurately mapping ground-level ozone concentrations at high spatiotemporal resolution (daily, 1 km) is essential for evaluating human exposure and conducting public health assessments. This requires identifying and understanding a proxy that is well-correlated with ground-level ozone variation and available with spatiotemporal high-resolution data. This study introduces a high-resolution ozone modeling method utilizing the XGBoost algorithm with satellite-derived land surface temperature (LST) as the primary predictor. Focusing on China in 2019, our model achieved a cross-validation R2 of 0.91 and a root-mean-square error (RMSE) of 13.51 µg/m3. We provide detailed maps highlighting ground-level ozone concentrations in urban areas, uncovering spatial variations previously unresolved, along with time series aligning with established understandings of ozone dynamics. Our local interpretation of the machine learning model underscores the significant contribution of LST to spatiotemporal ozone variations, surpassing other meteorological, pollutant, and geographical predictors in its influence. Validation results indicate that model performance decreases as spatial resolution becomes coarser, with R2 decreasing from 0.91 for the 1 km model to 0.85 for the 25 km model. The methodology and data sets generated by this study offer new insights into ground-level ozone variability and mapping and can significantly aid in exposure assessment and epidemiological research related to this critical environmental challenge.

Monitoring and predicting development of built-up area in sub-urban areas: A case study of Sleman, Yogyakarta, Indonesia.

Monitoring built-up areas in the previous year and possible predictions for the following year are important in planning regional development and controlling the expansion of built-up areas. This study detects changes in the built-up area (2018-2022). It predicts the future (2026) using Landsat satellite imagery in the Sleman Regency, Yogyakarta Special Region, Indonesia study area. Mapping built-up areas is identified using the Normalized Difference Built-Up Index (NDBI). Vegetation conditions were analyzed using the Normalized Difference Vegetation Index (NDVI). Changes in the built-up area are predicted using the CA-Markov chain model for 2026. The prediction is calibrated by comparing the simulated map with the results of the classification of built-up areas in 2022. The research findings show that the built-up area has increased by 12.84 % from 2018 to 2022 and is predicted to increase by 15.48 % in 2026. The existence of built-up areas has an influence on land surface temperatures where the analysis results show a moderate correlation between NDBI and LST, namely 2018 (R2 = 0.401), 2019 (R2 = 0.323), 2020 (R2 = 0.401), 2021 (R2 = 0.415), and 2022 (R2 = 0.384). The higher the NDBI value, the higher the LST value, and vice versa. Therefore, regional development planning, mainly built-up areas, is an important recommendation for decision-makers in the study area.

Summer Heat, Historic Redlining, and Neighborhood Walking among Older Adults: 2017 National Household Travel Survey.

No known studies have examined the relationships between urban heat islands, historic redlining, and neighborhood walking in older adults. We assessed whether (1) individual and neighborhood characteristics (including redlining score) differ by neighborhood summer land surface temperature (LST); (2) higher LST is associated with less neighborhood walking, and whether associations differ by historic redlining score; and (3) neighborhoods with discriminatory redlining scores have greater LSTs. We used data on 3982 ≥ 65 years old from the 2017 National Household Travel Survey. Multivariable negative binomial and linear regressions tested associations between LST z-score (comparing participant's neighborhood LST to surrounding region's LST) and self-reported neighborhood walking and the association between living in neighborhoods redlined as "definitely declining" or "hazardous" (versus "still desirable"/"best") and LST z-score. LSTs were higher for those in neighborhoods with higher area deprivation scores and more African American/Black residents. Older adults living in neighborhoods with higher summer LST z-scores had fewer minutes of neighborhood walking/day. This association seemed limited to individuals with neighborhood redlining scores of "still desirable"/"best." Neighborhood redlining scores of "definitely declining" or "hazardous" (versus "still desirable" and "best") were associated with greater neighborhood summer LSTs. Overall, these findings suggest that historically redlined neighborhoods may experience urban heat island effects more often. While older adults living in hotter neighborhoods with "still desirable" or "best" redlining scores may less often engage in neighborhood walking, those in neighborhoods with redlining scores of "definitely declining" and "hazardous" do not seem to decrease neighborhood walking with higher LSTs. Future work is needed to elucidate the impact of extreme heat on health-promoting behaviors such as walking and the types of interventions that can successfully counteract negative impacts on historically disadvantaged communities.

Urban sprawl impact assessment on the land surface temperature over the green capital of Gujarat using a geospatial approach.

In developing countries, examining land use land cover (LULC) change pattern is crucial to understanding the land surface temperature (LST) effect as urban development lacks coherent policy planning. The variability in LST is often determined by continuously changing LULC patterns. In this study, LULC change effect analysis on LST has been carried out using geometric and radiometric corrected thermal bands of multi-spectral Landsat 7 ETM + and 8 TIRS/OLI satellite imagery over Gandhinagar, Gujarat, in the years 2001 and 2022, respectively. Maximum likelihood classification (MLC) was applied to assess LULC change while an NDVI-based single-channel algorithm was used to retrieve LST using Google Earth Engine (GEE). Results showed a substantial change in built-up (+ 347.08%), barren land (- 50.74%), and vegetation (- 31.66%). With the change in LULC and impervious surfaces, the mean LST has increased by 5.47 ℃. The impact of sparse built-up was seen on vegetation and agriculture as a maximum temperature of > 47 ℃ was noticed in all LULC classes except agriculture, where the temperature reached as high as > 49 ℃ in 2022. Since Gandhinagar is developing a twin-city plan with Ahmedabad, this study could be used as a scientific basis for sustainable urban planning to overcome dynamic LULC change and LST impacts.

Ground Urban Heat Island: Strengthening the Connection Between Spaceborne Thermal Observations and Urban Heat Risk Management.

As urbanization progresses under a changing climate, urban populations face increasing threats from chronically higher heat exposures and more frequent extreme heat events. Understanding the complex urban thermal exposure patterns becomes crucial for effective heat risk management. The spatial advantage of satellite thermal observations positions surface urban heat islands (SUHI) as a primary measure for such applications at the city scale. However, satellite-inherent biases pose considerable uncertainties. To improve the representation of human-relevant heat exposure, this study proposes a simple but effective satellite-based measure- ground urban heat island (GUHI), focusing solely on radiant temperatures from urban ground elements. Leveraging ECOSTRESS land surface temperature product and radiation-based statistical downscaling, diurnally representative GUHIs were evaluated over NYC. The findings reveal that overall GUHI is consistently warmer than SUHI diurnally. However, GUHI exhibits complex spatial contrasts with SUHI, primarily influenced by vegetation coverage. Various indicators associated with urban structures and materials were examined, showing important but dissimilar roles in shaping the spatial dynamics of GUHI and SUHI. This study highlights the value of satellite thermal observations compared to air temperature while addressing uncertainties in widely adopted practices of using them. By improving the depiction of human-related urban heat patterns from Earth observations, this research offers valuable insight and more reliable measures to address the urgent requirements for urban heat risk management globally.

Assessing the cooling potential of climate change adaptation measures in rural areas.

Atmospheric heat has become a major public concern in a rapidly warming world. Evapotranspiration, however, provides effective land surface cooling during the vegetation period. Adversely, modern cultural landscapes - due to both water and potential evapotranspiration pathways lacking - are increasingly incapable of offering this important benefit. We hypothesised that concerted measures for a revived landscape water retention can fuel plant transpiration, especially during dry periods, and thus contribute to climate change adaptation by stabilising the regional climate. Seeking nature-based ways to an improved landscape water retention, we used the land surface temperature (LST) as a proxy for landscape mesoclimate. For our drought-prone rural study area, we identified potential candidate environmental predictors for which we established statistical relationships to LST. We then, from a set of potential climate change adaptation measures, mapped selected items to potential locations of implementation. Building on that, we evaluated a certain measures' probable cooling effect using (i) the fitted model and (ii) the expected expression of predictors before and after a hypothetical measure implementation. In the modelling, we took into account the spatial and temporal autocorrelation of the LST data and thus achieved realistic parameter estimates. Using the candidate predictor set and the model, we were able to establish a ranking of the effectiveness of climate adaptation measures. However, due to the spatial variability of the predictors, the modelled LST is site-specific. This results in a spatial differentiation of a measure's benefit. Furthermore, seasonal variations occur, such as those caused by plant growth. On average, the afforestation of arable land or urban brownfields, and the rewetting of former wet meadows have the largest cooling capacities of up to 3.5 K. We conclude that heat countermeasures based on fostering both evapotranspiration and landscape water retention, even in rural regions, offer promising adaptation ways to atmospheric warming.

Prioritising urban heat island mitigation interventions: Mapping a heat risk index.

The global climate is under threat from increasing extreme heat, evidenced by rising temperatures and a surge in hot days. Heat waves are intensifying worldwide, impacting cities and residents, as demonstrated by the record-breaking heat experienced in the UK in 2022, which resulted in over 4500 deaths. Urban heat islands (UHIs) exacerbate these heat waves, making city residents more vulnerable to heat-related deaths. UHIs occur when temperatures in urban areas exceed those in surrounding rural areas due to the heat-absorbing properties of urban structures. Implementing mitigation strategies, such as green infrastructure, is crucial for enhancing urban resilience and reducing vulnerability to UHIs. Effectively addressing UHIs requires a systematic approach, including developing risk maps to prioritise areas for UHI mitigation strategies. Using remote sensing, GIS, and SPSS correlational analysis, the research aims to develop and assess a Heat Risk Index (HRI). This index integrates UHI spatial intensity, current green cover, and population density at the district level to develop the risk index. This study stands out for its novel approach to developing the HRI, focusing on the localised impact of the UHI in Manchester City, identifying high-risk heat-vulnerable districts, and prioritising implementing effective UHI mitigation strategies. The findings highlight the importance of this approach, revealing that approximately 30 % of Manchester City is affected by UHI effects, with areas near the city centre, characterised by higher population density and reduced green cover, being particularly vulnerable. Furthermore, the study suggests that applying HRIs at a more localised level, such as the neighbourhood level rather than the district level, would provide more relevant and targeted insights for mitigating UHI. A more localised index would offer tailored insights into the unique conditions of each neighbourhood within the districts, enabling more effective mitigation strategies. The HRI developed in this paper serves as a test for a more nuanced and comprehensive index, considering additional variables related to population vulnerability and city urban structure.

Investigating the interplay between urban dynamics and environmental factors in Thimphu, Bhutan: a satellite remote sensing approach.

The ability of the land surface temperature (LST) and normalized difference vegetation index (NDVI) to examine land surface change is regarded as an important climate variable. However, no significant systematic examination of urbanization concerning environmental variables has been undertaken in the narrow valley of Thimphu, Bhutan. Therefore, this study investigated the impact of land use/land cover (LULC) dynamics on LST, NDVI, and elevation, using Moderate Resolution Imaging Spectroradiometer (MODIS) data collected in Thimphu, Bhutan, from 2000 to 2020. The results showed that LSTs varied substantially among different land use types, with the highest occurring in built-up areas and the lowest occurring in forests. There was a strong negative linear correlation between the LST and NDVI in built-up areas, indicating the impact of anthropogenic activities. Moreover, elevation had a noticeable effect on the LST and NDVI, which exhibited very strong opposite patterns at lower elevations. In summary, LULC dynamics significantly influence LST and NDVI, highlighting the importance of understanding spatiotemporal patterns and their effects on ecological processes for effective land management and environmental conservation. Moreover, this study also demonstrated the applicability of relatively low-cost, moderate spatial resolution satellite imagery for examining the impact of urban development on the urban environment in Thimphu city.

Machine learning-based estimation of land surface temperature variability over a large region: a temporally consistent approach using single-day satellite imagery.

Accurate retrieval of LST is crucial for understanding and mitigating the effects of urban heat islands, and ultimately addressing the broader challenge of global warming. This study emphasizes the importance of a single day satellite imageries for large-scale LST retrieval. It explores the impact of Spectral indices of the surface parameters, using machine learning algorithms to enhance accuracy. The research proposes a novel approach of capturing satellite data on a single day to reduce uncertainties in LST estimations. A case study over Chandigarh city using Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine, and Random Forest (RF) reveals RF's superior performance in LST estimations during both summer and winter seasons. All the ML models gave an R-square of above 0.8 and RF with slightly higher R-square during both summer (0.93) and winter (0.85). Building on these findings, the study extends its focus to Ranchi, demonstrating RF's robustness with impressive accuracy in capturing LST variations. The research contributes to bridging existing gaps in large-scale LST estimation methodologies, offering valuable insights for its diverse applications in understanding Earth's dynamic systems.

Comprehensive spatiotemporal evaluation of urban growth, surface urban heat island, and urban thermal conditions on Java island of Indonesia and implications for urban planning.

Urban heat island (UHI) and thermal comfort conditions are among the impacts of urbanization, which have been extensively studied in most cities around the world. However, the comprehensive studies in Indonesia in the context of urbanization is still lacking. This study aimed to classify land use and land cover (LULC) and analyse urban growth and its effects on surface urban heat islands (SUHIs) and urban thermal conditions as well as contributing factors to SUHI intensity (SUHII) using remote sensing in the western part of Java Island and three focused urban areas: the Jakarta metropolitan area (JMA), the Bandung and Cimahi Municipalities (BC), and the Sukabumi Municipality (SKB). Landsat imagery from three years was used: 2000, 2009, and 2019. Three types of daytime SUHII were quantified, namely the SUHII of urban central area and two SUHIIs of urban sprawl area. In the last two decades, urban areas have grown by more than twice in JMA and SKB and nearly 1.5 times in BC. Along with the growth of the three cities, the SUHII in the urban central area has almost reached a magnitude of 6 °C in the last decade. Rates of land surface temperature change of the unchanged urban pixels have magnitudes of 0.25, 0.15, and 0.14 °C/year in JMA, SKB, and BC, respectively. The urban thermal field variance index (UTFVI) and discomfort index (DI) showed that the strongest SUHI effect was most prevalent in urban pixels and the regions were mostly in the very hot and hot categories. Anthropogenic heat flux and urban ratio have positive contributions to SUHII variation, while vegetation and water ratios are negative contributors to SUHII variation. For each city, the contributing factors have a unique magnitude that can be used to evaluate SUHII mitigation options.