High Land Surface Temperature Research Articles

Spatiotemporal detection of land use/land cover changes and land surface temperature using Landsat and MODIS data across the coastal Kanyakumari district, India

This study assesses the changes in land use/land cover (LULC) and land surface temperature (LST) to identify their impacts from 2000 to 2020 along the coast of Kanyakumari district, India using remote sensing techniques. Landsat images are used to estimate the LULC changes and the MODIS data for LST. The Maximum Likelihood Classification (MLC) method is used, and the LULC is classified into six categories: Agriculture Land, Barren Land, Salt Pan, Sandy Beach, Settlement, and Waterbody. Within the two decades of the present change detection study, upheave in the Settlement area of 49.89% is noticed, and the Agriculture Land is exploited by 20.09%. Salt Pan emits a high LST of 31.57 °C, and the Waterbodies are noticed with a low LST of 28.9 °C. However, the overall rate of LST decreased by 0.56 °C during this period. This study will help policymakers make appropriate planning and management to overcome the impact of LULC and LST in the forthcoming years.

Environmental injustice among Hispanics in Santa Clara, California: a human–environment heat vulnerability assessment

In the United States, there is a growing interest in understanding heat stress in lower-income and racially isolated neighborhoods. This study spatially identifies heat-vulnerable neighborhoods, evaluates the relationship between race/ethnicity and temperature exposure, and emphasizes differences among Hispanics by origin to capture environmental injustices in Santa Clara County (SCC), CA. The current methodology uses Landsat 8 via Google Earth Engine to measure the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) to assess the physical environment. The human environment is evaluated using the Modified Darden-Kamel Composite Socioeconomic Index to determine the spatial variability of socioeconomic status (SES) and the Index of Dissimilarity to determine the level of segregation between Hispanics and non-Hispanic Whites and among Hispanics/Latinos. The combination of these assessments comprises a comprehensive human–environment approach for health exposure evaluation by which to define environmental injustice. Results reveal socioeconomic inequalities and an uneven residential distribution between Hispanics and non-Hispanic Whites. Low NDVI and high LST values were found in Mexican neighborhoods, implying possible environmental racism. Almost half the Mexican population lives in highly segregated neighborhoods with low and very low SES, mainly located in East San Jose, where, historically, they have been ghettoized. Mexicans, in general, could be at a higher risk of heat stress and heat mortality during heat waves. Future work should examine additional variables (e.g., housing characteristics, crime, social cohesion, and collective behaviors) to comprehensively evaluate the at-risk Mexican population.

Continental-scale evaluation of three ECOSTRESS land surface temperature products over Europe and Africa: Temperature-based validation and cross-satellite comparison

High spatial resolution land surface temperature (LST, <100 m) is crucial for agricultural water management, crop water stress monitoring, fire mapping, urban heat island study and volcano eruption detection. LST retrievals from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) launched in June 2018, together with the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER, launched in 1999) and the Landsat series (since 1972), comprise the state-of-the-art high spatial resolution LST datasets publicly accessible. Recently, we generated the ECOSTRESS LST product over Europe and Africa using both the temperature and emissivity separation (TES) and split-window (SW) algorithms under the European ECOSTRESS Hub (EEH). Here, we validated the official Jet Propulsion Laboratory (JPL) TES (Collection 1), EEH TES and EEH SW ECOSTRESS LST products over Europe and Africa between August 1, 2018 and December 31, 2021 by comparing against the in-situ measurements at 9 sites over a wide variety of land cover types. Meanwhile, the validation results were compared with those obtained for ASTER and Landsat LST at the same sites for a thorough understanding of the consistency among these high spatial resolution LST products. The results reveal that the three ECOSTRESS LST products have consistent performances, with an overall RMSE around 2 K. A cold bias around 1 K exists for all three ECOSTRESS LST, which is presumably originated from the radiometric calibration of the sensor in Collection 1 data. The Landsat LST shows a similar accuracy, with an RMSE of 2.20 K and bias of 0.54 K. The EEHSW LST show the highest consistency with Landsat LST, possibly due to the identical emissivity correction process. The performance of ASTER LST is also similar, with an RMSE of 1.98 K and bias of 0.9 K. The precisions of all the LST products are around 1.5 K. Future recalibration of the ECOSTRESS Level 1 radiance data in Collection 2 is expected to further improve the accuracy of ECOSTRESS LST. Overall, this study supports the adaptation of LST retrieval algorithms for the future thermal missions.

Long-Term Spatiotemporal Characteristics and Impact Factors of Land Surface Temperature of Inhabited Islands with Different Urbanization Levels

Surface thermal environment (STE) is closely related to the comfort and health of residents, affecting regional livability, and its spatial and temporal changes are deeply affected by the urbanization process. Considering there is a lack of effective comparative analysis on STE in different urbanized inhabited islands, the special geographical unit and vital human settlement environment, long-term spatiotemporal characteristics and impact factor quantitative analyses were performed in two inhabited islands via the RS and GIS methods. The results suggest that the surface heat amplitude of the highly urbanized Xiamen Island decreases, with the surface heat intensity continuing to increase from 2000 to 2020, while that of the lowly urbanized Kinmen Island is reversed. Although the land surface temperature (LST) of the two inhabited islands shows similar spatial distribution characteristics with evident cold/hot spots, the geographical distribution characteristics of high LST zones are significantly different, and the thermal landscape of Xiamen Island is more fragmented, discrete, and simple in shape, as revealed by the landscape metrics. We demonstrate that the area proportion between cooling land (water body and greenland) and warming land (bare land and impervious surface) is the most influential factor of LST in the two islands while the marine environment is a unique contributor to STE of inhabited islands compared with inland cities, where the seawater around the island can reduce LST over a range of distances, and the influence of elevation on LST is mostly indirect. These results provide a scientific basis and case support for understanding the STE situation of inhabited islands with different urbanization levels.

Environmental Assessment of Land Surface Temperature Using Remote Sensing Technology

The rapid growth of urbanization exposes the environment to severe issues that threaten the quality of life. High land surface temperature (LST) is one of the most prominent issues in large cities where anthropogenic activities are accumulated, energy consumption and gas emissions increase, forming urban heat islands (UHI). In this research, the LST and the UHI formations were used as indicators to inspect and evaluate the environmental status of Kuwait’s urban area, which recently recorded a notable rise in air temperature. The LST spatial and temporal changes were examined and analyzed by utilizing satellite images of Landsat-8 for the period 2013–2020. The relationship between LST and air temperature was determined using the linear regression method. UHIs formation sites were investigated on different surfaces using the land use map. Results revealed that the LST average increased by 3°C in 2020 within seven years, and a strong positive relationship was observed between the LST and the air temperature. The UHI was mainly concentrated in industrial areas, oil fields, and airports. Moreover, the LST in the rural area reached 41.47°C, which is 4°C higher than it was in 2013. UHI intensity was calculated to assess the extent of variability in the LST between urban and rural areas; this intensity was lower in 2020 than in 2013 due to vegetation reduction in 2020. The continuous rise of the LST in Kuwait could lead to an inevitable environmental issue. Vegetation and water bodies play a significant role in moderating the LST, and these types of land coverage are rare in Kuwait. This research may contribute to controlling the temperature rise in UHI accumulation areas by following appropriate environmental solutions, sustainable urban planning, and encouraging the greening process.

Effect of Landscape Structure on Land Surface Temperature in Different Essential Urban Land Use Categories: A Case Study in Jiaozuo, China

Understanding the relationship between different essential urban land use categories and the urban thermal environment is essential for urban planning, resource allocation and decision support. However, most of the spatiotemporal correlations between land use types and LST are focused on industrial land use and urban green space, and there are fewer discussions on the totality of urban land use types. Here, using multi-source remote sensing images, correlation analysis and the stepwise regression method, we elaborate the relationship between landscape structure and land surface temperature (LST) across the different seasons of 850 planning management units in Jiaozuo, China. Our results show that the degree of explanation of surface temperature by landscape structure increased with a fine division of land use. The imprint of urban–rural gradients on LST was largely masked by the land use categories at the regional scales. Moreover, the tridimensional structure of buildings significantly affected the LST of residential regions, and the large number of low-rise buildings in urban planning practice contributes to high LSTs. This study provides a comprehensive analysis of the effects of each land use type and landscape structure on surface temperature in urban space and also provides strategies and methods for urban planning in rapidly developing regions of the country.

Impact of urban land use and land cover change on urban heat island and urban thermal comfort level: a case study of Addis Ababa City, Ethiopia.

The increase in the urban heat island is caused by the replacement of vegetation cover by impervious surfaces. As the population of Addis Ababa City has increased dramatically, the vegetation cover and other land cover classes have been converted into built-up areas. This study attempted to examine the relationship between urban heat islands and urban thermal comfort (UTCL) and land use and land cover (LULC) change using geospatial technologies in Addis Ababa City, Ethiopia. Landsat TM 1991, Landsat ETM + 2005, and Landsat OLI/TIRS 2021 data were used in this study. During the study period, LULC change, land surface temperature (LST), and urban heat island were calculated using the multispectral and thermal infrared bands (1991-2021). Results revealed that the built-up area in 1991 was 96.6 km2 (18.3%), and increased to 165.4 km2 (31.4%) and 277.2 km2 (52.6%) by 2005 and 2021, respectively. In contrast, agriculture and vegetation land cover classes were declined by 66.8 km2 and 25.7 km2, respectively between 1991 and 2021. Rapid conversion of LULC change increases the mean LST of Addis Ababa City by 8.3°C over the last three decades. According to the results, a high LST was recorded over built-up regions and areas with little vegetative cover. Furthermore, the central areas of the study area suffered a greater UHI effect than the surrounding areas. The results of the urban thermal field variance index (UTFVI) revealed that the UHI varies greatly across the city. Strong, stronger, and strongest urban heat islands dominated the central, southwestern, and southeastern suburbans of the study area, respectively. The excellent comfort level has declined from 16.3 km2 (3.1%) in 1991 to 12.1 km2 (2.3%) in 2021. The study proposed that local community awareness needs to be raised for environmental conservation through the establishment of urban green spaces that reduce UHI and increase comfort in Addis Ababa City.

Spatio-temporal variations of land surface temperature using Landsat and MODIS: case study of the City of Tshwane, South Africa

Urbanisation is accelerating urban land use dynamics and this has a significant impact on land surface temperature (LST). Impervious surfaces and increase in air pollution has led to the increase in land surface temperature. This study reports on the use of geospatial technologies to monitor and quantify changes in LST using remotely sensed data in the City of Tshwane. Land surface temperature was retrieved using the winter and summer Landsat datasets for 1997 and 2015 and the MODIS data from 2000 to 2015. Land surface temperature was extracted using emissivity and satellite temperature as input parameters. The spatial and temporal variations in the LST were retrieved to show the effects of land cover change on LST. Change in LST was also analysed on different land cover types using transects across the study area. The study revealed an increase in land surface temperature between the years. It also showed that impervious surfaces had a higher LST compared to the non-impervious surfaces. The results revealed variations in LST between non-cropped and cropped agricultural areas, where the former had higher LST than the latter. Temporal trends revealed a notable increase in LST in the urban areas and there were some seasonal variations in LST with high LST values in summer and low values in winter. Cross-section analysis along transects revealed spatio-temporal thermal variations across different land cover types.

Integrated Influencing Mechanism of Potential Drivers on Seasonal Variability of LST in Kolkata Municipal Corporation, India

Increasing land surface temperature (LST) is one of the major anthropogenic issues and is significantly threatening the urban areas of the world. Therefore, this study was designed to examine the spatial variations and patterns of LST during the different seasons in relation to influencing factors in Kolkata Municipality Corporation (KMC), a city of India. The spatial distribution of LST was analyzed regarding the different surface types and used 25 influencing factors from 6 categories of variables to explain the variability of LST during the different seasons. All-subset regression and hierarchical partitioning analyses were used to estimate the explanatory potential and independent effects of influencing factors. The results show that high and low LST corresponded to the artificial lands and bodies of water for all seasons. In the individual category regression model, surface properties gave the highest explanatory rate for all seasons. The explanatory rates and the combination of influencing factors with their independent effects on the LST were changed for the different seasons. The explanatory rates of integration of all influencing factors were 89.4%, 81.4%, and 88.7% in the summer, transition, and winter season, respectively. With the decreasing of LST (summer to transition, then to winter) more influencing factors were required to explain the LST. In the integrated regression model, surface properties were the most important factor in summer and winter, and landscape configuration was the most important factor in the transition season. LST is not the result of single categories of influencing factors. Along with the effects of surface properties, socio-economic parameters, landscape compositions and configurations, topographic parameters and pollutant parameters mostly explained the variability of LST in the transition (11.22%) and summer season (15.22%), respectively. These findings can help to take management strategies to reduce urban LST based on local planning.

Relationships between Land Surface Temperatures and Neighboring Environment in Highly Urbanized Areas: Seasonal and Scale Effects Analyses of Beijing, China

Urban environments have a strong influence on the land surface temperature (LST) in urban areas. Understanding the relationship between LST and urban environmental factors can help develop effective strategies to reduce high LSTs in urban areas, which is critical for mitigating the urban heat island effect. Previous studies have focused on the correlation between LST and the environmental factors that drive its formation, without considering the influences of the neighboring environment and the vertical expansion of highly urbanized areas. Notably, the correlation between LST and its neighboring environment in different seasons remains unclear. In this study, we selected central Beijing in China as our study area and employed the moving window method to characterize the environmental factors of the neighboring environment of the central LST cell. We explored eight environmental factors from three layers: normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), modified normalized difference water index (MNDWI), building density (BD), building height (BH), building volume (BV), sky view factor (SVF), and road density (RD). The Pearson correlation and extreme gradient boosting (XGB) regression methods were applied to measure the correlation between LST and the different factors in moving windows of different sizes. The results indicated that the correlation between NDVI, MNDWI, and LST was considerably different in the winter and other seasons. However, NDBI was positively correlated with LST in all seasons, although the correlation was strongest/weakest in summer/winter. Among building-related factors, BD and BH were more strongly correlated with LST, and the positive/negative correlation between BD/BH and LST was stronger in summer/winter. The correlation between LST and its neighboring environment varied with increasing window size, and this variation differs significantly between winter and other seasons. In spring, summer, and autumn, the strength of the correlation between LST and its neighboring environment showed an “inverted V” pattern with increasing window size. The optimal spatial scales to explore the influence of neighboring environments on the LST of 30-m cells were 210 m and 270 m. This study revealed the seasonal correlation between LST and its neighboring environment while explaining the variation at a spatial scale. Notably, this study can provide a new perspective for understanding the driving mechanism of the urban thermal environment, while contributing to its scientific optimization and management.

Assessment of the TsHARP method for spatial downscaling of land surface temperature over urban regions

Nowadays, acquiring high spatial and temporal land surface temperature (LST) is one of the significant factors in urban climate studies. This study aims to assess the sharpening thermal imagery (TsHARP) method, which is developed for determining the LST in urban regions. Whereas the original TsHARP method correlates the Normalized Difference Vegetation Index (NDVI) with the LST, the current study seeks to evaluate the performance of the TsHARP method by correlating between the LST and NDVI over urban areas. Additionally, it is intended to investigate the relationship between the impervious surfaces (IS) and LST. At this point, the Advanced Spaceborne Thermal Emission and Reflection Radiometer ASTER image of the year 2003 was used to retrieve the IS as well as the LST of the Kuala Lumpur city, Malaysia. To retrieve the main LST, IS and NDVI, ASTER Level1B data were used. On the other hand, ASTER land surface temperature products (AST08) was utilized to evaluate the downscaled LST (DLST) image. With selection of the ~1/4 of the pixels with lowest coefficient of variation (CV), the LST has more correlation with the IS in comparison with the NDVI, while the correlation between the IS and LST was increased in this study. The IS index illustrates the most optimal correlation for the urban LST (R2 = 0.59). Furthermore, the TsHARPIS-based method depicted a good correlation between the DLST and the ASTER LST product (RMSE = 1.5 °C, R2 = 0.83) compared to the NDVI (RMSE = 4 °C, R2 = 0.73). Consequently, it is found that the application of TsHARPIS-based method to ASTER remote sensing data is a robust/low-cost approach for spatial downscaling of the LST over urban regions.

A high spatiotemporal resolution land surface temperature research over Qinghai-Tibet Plateau for 2000–2020

As ‘the third pole of the world’, the land surface temperature (LST) of the Qinghai-Tibet Plateau (QTP) has a profound impact on the climate of central Asia and even the whole earth. Studying the impact of the LST over QTP depends on long time and high spatiotemporal resolution LST dataset. However, the unavailability of such dataset has hindered LST-related researches: one of the most important reasons is that traditional spatiotemporal fusion methods such as Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) have heavy computation demands to process big data. To fill this gap, this paper outlines one new cloud spatiotemporal fusion method by combining Google Earth Engine and STARFM to develop for the first time a 3-hourly 30 m LST dataset over the QTP from 2000 to 2020 through fusing Landsat and GLDAS-2.1 derived LST data. The outlined method first fuses the LSTs obtained from Landsat and GLDAS-2.1 data within one year to synthesize the LST of the entire QTP on one base time, and then the base time LST is spatiotemporally fused with GLDAS-2.1 LSTs on the base and prediction times to derive the 3-hourly 30 m QTP's LSTs on prediction times. The outlined method provides a promising technical scheme for batch processing big data in combining traditional spatiotemporal fusion methods with cloud computing platforms. Derived LST dataset is validated by station observations at multiple time and spatial scales to have high accuracy, which provides a guarantee for analyzing water and heat exchange and climate change over the QTP.

Temporal and Spatial Variation of Land Surface Temperature in Recent 20 Years and Analysis of the Effect of Land Use in Jiangxi Province, China

Under the background of global warming, it is of great significance to study the temporal and spatial evolution of land surface temperature (LST) on long-time scale and the impacts of land use in the fields of urban thermal environment and regional climate change. Based on MODIS LST long time series remote sensing data, the temporal and spatial evolution characteristics of pixel-wise LST in Jiangxi Province, the middle inland province of China from 2000 to 2020 were analyzed by using Theil-Sen + Mann-Kendall, coefficient of variation and Hurst index, and the response of LST to land use was identified by combining the contribution and diversity index. The results showed as follows: (1) LST was generally distributed as "high in Middle-East-West-South and low in North-northwest-southeast direction". LST showed an overall downward trend, indicating a weakening of the warming trend. The dynamic trend of LST was characterized by more descending than ascending tendency. The dynamic stability showed a coexistence of high and low fluctuation tendency, with a higher proportion of medium and low fluctuation areas having obvious spatial differences. The overall dynamic sustainability was characterized by uncertainty of future change trend. (2) The LST were strongly affected by land use in the past 20 years. Firstly, the areas of high LST were mostly located in construction land and unused land, while the areas of low LST were mostly in water area and forest land. However, forest land and water area of high temperature were gradually turned to construction land later on. Secondly, the land use structure and pattern had an strong effects on LST. With the increase of the area proportion of different land use, the LST showed significant differences. The more complex the spatial pattern of land use, the more obvious its impact on LST. The research results will provide some reference for the regions with the same characteristics as Jiangxi Province to deal with LST under the background of global climate change.

Thermal infrared remote sensing and soil gas radon for detecting blind geothermal area

Geothermal resources in the Wugongshan area are abundant and most closely associated with faults. Due to the dense vegetation covering the research area, locating the geothermal resources using conventional ground technologies is costly and challenging. This paper proposes and implements a novel method in the area under examination. More specifically, remote sensing information (Landsat 8) and known geological information are used to delineate the geothermal target area. On January 19, 2021, the land surface temperature in the study area ranged between 7.7 and 21.2 °C. Combining the geothermal geological features of the regional area, two areas, namely Tangjiashan and Sanjiang, are delineated as geothermal target areas. The probable location of faults is determined by measuring the soil gas radon concentration. The relative coefficient KQ of radon activity attained in profiles varies between 1.7 and 13.2. The average values of KQ between different areas are ranked as Wenjia > Tangjiashan > Hongjiang > Sanjiang. Generally, areas with higher KQ of radon activity have higher land surface temperature (LST). Thus, the KQ and LST can be used to evaluate the shallow geothermal potential. Finally, by integrating the interpretation of remote sensing and soil radon measurements, the fault location is identified, and the result is successfully used to guide the drilling of geothermal wells in the Tangjiashan area.

Quantitative analysis of the building-level relationship between building form and land surface temperature using airborne LiDAR and thermal infrared data

Buildings are an important component of urban form and have complex relationships with land surface temperature (LST). However, the limited availability of high-spatial-resolution data has constrained studies of these relationships at fine scales. In this study, we used airborne LiDAR data, airborne thermal infrared images, and building vector data to quantify the relationships between seven building form indicators and summer daytime LST at the building level for the central area of Tokyo. Results showed that: (i) The correlation with summer daytime LST was most negative for maximum building height, followed by building external surface area and building volume. The “tall-slim” form of buildings had the potential to help mitigate summer daytime LST. (ii) High mean LSTs during the daytime in the summer were associated mainly with low-rise buildings, residential buildings, and wooden buildings; whereas high-rise buildings, office buildings, and reinforced concrete buildings were associated with low mean LSTs. (iii) The correlations between building form and summer daytime LST depended on the building type. Summer daytime LST correlated more strongly with maximum building height in mid-rise buildings than in high-rise buildings. This study provides insights that could enable the improvement of urban building form with respect to the surface thermal environment.

Estimation of land surface temperature for Coimbatore District using Landsat imagery

The impact of climate change is visually witnessed in the present environment by various natural disasters. This phenomenon of land surface temperature is one of the significant aspects to be estimated for the study of climate change. The increase in Land Surface Temperature (LST) may be due to ongoing developments in the field of urbanization and globalization. The objective of the study was to estimate the increase in the LST in relation to the Normalized Difference Vegetation Index (NDVI) and assess the spatial variation in the LST due to land use/land cover change. The study utilized Landsat 8 data to assess the land-use changes and their relation with LST in one of the main urbanized cities, i.e. Coimbatore district of Tamil Nadu, using Landsat imagery due to the availability of various land cover types by using the mathematical expressions in ARCMAP software. This study compares the LST between 2015 and 2020 to observe the change in the NDVI and LST over a period of 5 years in the Coimbatore district. There was an increase of 1°C in 5 years and the area of high LST had been increased comparatively. The maximum LST was found to be 73°C in 2015, which increased to 74°C in the year 2020 ;and the minimum LST was found to be 15°C in 2015, which increased to 19°C in the year 2020 depicting the ongoing change in the land use of the district. The study findings will help promulgate sustainable urban land-use policies and can be used for mitigating climate change.

Fractal and multifractal spatiotemporal patterns of land surface temperatures in a coastal city

ABSTRACT In order to discover the fractal and multifractal nature of land surface temperature (LST) spatiotemporal patterns, this study computed the LST in thecoastal city, Xiamen, southeastern China using Landsat TM/OLI/TIRS images from 1994 to 2015, and introduced two-dimensional box-counting and two-dimensional multifractal box-counting methods to quantitatively characterize the LST pattern. Results suggest that increasing the binarization threshold decreased the box-counting dimensions with values from 1.86 to 1.65 in 1994, 1.67 in 2000, 1.56 in 2004, 1.57 in 2010, and 1.56 in 2015, respectively. The two-dimensional multifractal approach employs a set of intertwined nonfractal subsets over different spatial scales to describe the LST patterns. LST pattern possesses a increased multifractality and the similar multifractal spectra of left-hook shapes throughout the study period, suggesting the increasing trends of the spatial heterogeneity in LST distribution. The probability of a given pixel having a high LST value is consistently high in the study area, as indicated by the positive ratio between the regions in which the probability measure appears most concentrated and those in which it is most sparse. Rapid urbanization and the large-scale urban land surface changes in the coastal city determine the variation in the multifractal parameters.

Understanding Growth-Induced Trends in Local Climate Zones, Land Surface Temperature, and Extreme Temperature Events in a Rapidly Growing City: A Case of Bulawayo Metropolitan City in Zimbabwe

Assessment of the responses of the urban thermal environment to climate is important, especially because of their possible influence on low- and high-temperature extreme events. This study assessed the combination of remotely sensed land surface temperature (LST) and local climate zones (LCZs) with in situ air temperature-retrieved extreme temperature indices. It aimed to assess the effect of urban growth on the three-dimensional thermal environment in the Bulawayo metropolitan area, Zimbabwe. LST and LCZ were derived from the Landsat data for 1990, 2005, and 2020, while extreme temperature indices and trends were derived from daily minimum and maximum temperature data from a local weather station. Results showed that the built LCZ expanded at the expense of vegetation-based LCZ. Average LST for each LCZ increased from 1990 to 2020, which was attributed to background warming, while the expansion of high LST areas was associated with LCZ transitions. Although average minimum temperature decreased, cool nights increased, warmest nights remained unchanged, and the lowest minimum increased, the highest minimum temperatures decreased, but the trends were not statistically significant (p &amp;gt; 0.05). Indices of daytime warming showed significant changes, which includes an increase in average maximum temperature (p = 0.002), increase in lowest maximum temperature (p = 0), increase in the number of very warm days (p = 0.004), and decrease in the number of cool days (p = 0). The significant increase in daytime extremes was attributed to an increase in highly absorbing LCZ and daytime pollution due to industrial activities. The study also concluded that development in water areas or siltation of water bodies has a greater warming effect than other LCZ changes. The findings show that development needs to consider potential effects on the thermal environment and temperature extremes.

Assessment of land use/land cover changes and its effect on land surface temperature using remote sensing techniques in Southern Punjab, Pakistan.

Land surface temperature (LST) is defined as a phenomenon which shows that microclimate of an urban system gets heated much faster than its surrounding rural climates. The expansion of buildings has a noteworthy influence on land use/land cover (LULC) due to conversion of vegetation land into commercial and residential areas and their associated infrastructure by which LST is accelerated. The objective of the research was to study the impact of changes in LULC on LST of Southern Punjab (Pakistan) through remote sensing (RS) data. Landsat images of 30-year duration (1987, 1997, 2007 and 2017) were employed for identifying vegetation indices and LST in the study region. These images also helped to work out normalized difference water index (NDWI) and normalized difference built-up index (NDBI) maps. There was an increase from 29620 (3.63 %) to 88038 ha (10.8 %) in built-up area over the 30 years. LST values were found in the range 12-42 °C, 11-44 °C, 11-45 °C and 11-47 °C in the years 1987, 1997, 2007 and 2017, respectively. Regression coefficients (R2) 0.81, 0.78, 0.84 and 0.76 were observed between NDVI and LST in the corresponding years respectively. Our study showed that NDVI and NDWI were negatively correlated with less LST; however, NDBI showed positive correlation with high LST. Our study gives critical information of LULC and LST and will be a helpful tool for policy makers for developing effective policies in managing land resources.

Decarbonization of the Cargo Shipping Fleet

Abstract In an effort to combat climate change, the International Maritime Organization (IMO) has set ambitious goals for the reduction of greenhouse gas emissions from ships, with a target of at least a 50% reduction of total annual greenhouse gas (GHG) emissions, including carbon, from 2008 levels by 2050, with a further goal of zero GHG emissions within this century. Numerous technologies are under development to address these new goals, but the implementation of these new technologies is quite uncertain. Cargo ship owners face the challenge of determining how to best employ and possibly upgrade the current fleet to meet interim goals while awaiting the maturation of future technologies. This article describes a methodology and computer code that provide a rapid assessment of the impact of various fuel-saving technologies on an existing cargo ship’s fuel consumption, thus providing the ship owner fundamental data indicating which upgrades and practices warrant further, more detailed investigation. Background According to the National Oceanic and Atmospheric Administration, the amount of carbon dioxide in the atmosphere has increased by 1.78 ppm per year on average since 1980, and the increase is accelerating. Through the 1980s and 1990s, the increase was around 1.5–1.6 ppm per year, but the growth rate has averaged 2.4 ppm per year since 2010 (Tans et al. 2020). Rising levels of carbon dioxide in the atmosphere adversely impact the environment in many ways. For example, increased levels of carbon dioxide dissolving in sea water increase the acidity of the oceans; as pH levels drop, organisms like oysters and corals have trouble maintaining their hard shells and skeletons made from calcium carbonate. If pH levels get too low, the calcium carbonate structures begin dissolving (NOAA 2020). Another example can be found in the NOAA Arctic Report Card in which each year shows an Arctic that is becoming warmer, less frozen, and more fragile; the 2020 report includes data on high land-surface air temperatures, low snow extent, low minimum sea-ice extent, and extreme wildfires (Thoman et al. 2020).