Land Surface Temperature Values Research Articles

Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during the summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation (R2 = 0.879) compared to Landsat (R2 = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09 °C and 2.16 °C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30 °C and 2.24 °C, with the bare land class showing the highest fluctuation 2.46 °C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual datasets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as the warmest classes at 39.51 °C and 38.20 °C, respectively, and classifies water at 35.96 °C, dense vegetation at 35.52 °C, and sparse vegetation 37.71 °C as the coldest, which is a trend that is consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects.

Analytical study of land surface temperature for evaluation of UHI and UHS in the city of Chandigarh India

The Land Surface Temperature (LST) plays a crucial role in the ecological well-being of cities worldwide, intricately tied to the presence of vegetation. This investigation aims to assess the relationship between seasonal LST fluctuations and the Normalized Difference Vegetation Index (NDVI), as well as the correlation between LST and the Normalized Difference Built-up Index (NDBI), utilizing Pearson's correlation coefficient technique with extensive Landsat-8 data spanning 2016 to 2020. Additionally, the study evaluates the ecological and thermal qualities of the city using the Urban Thermal Field Variance Index (UTFVI). The ecological evaluation based on UTFVI for 2020 indicates that 52.03% of the area falls under the excellent category and 31.82% under the worst category. Notably, there is a significant increase in summer mean LST from 32.42 °C in 2016 to 38.27 °C in 2020. Identified Urban Heat Islands (UHIs), indicative of the warmest areas within city limits due to human activities, show an increasing trend in the summer UHI temperature from 36.92 °C to 38.06 °C between 2016 and 2020, respectively. The study reveals a robust inverse correlation between LST and NDVI, as well as a direct relationship between LST and NDBI across the entire city. The differentiation between UHI and non-UHI areas is achieved using the LST range. Furthermore, LST maps pinpoint UHI hotspots in Chandigarh, highlighting areas unsuitable for human habitation due to extreme heat. Validation of LST values employs Moderate Resolution Imaging Spectroradiometer (MODIS) data as a baseline, where MOD11A1 data's thermal infrared band offers a spatial resolution of 1 km, contrasting with Landsat 8 (Band-10) 100 m resolution. Remarkably, a highly positive correlation is observed between approximated LST values derived from Landsat 8 and MOD11A1 data, without necessitating upscaling or downscaling.

Diurnal variation of air pollutants and their relationship with land surface temperature in Bengaluru and Hyderabad cities of India

Air pollution and Land Surface Temperature (LST) are crucial components of urban environments. As urbanization and industrialization increase, the two components affect the natural environment of the urban areas. The present study investigated the daytime and nighttime spatiotemporal distribution of air pollutants and examined their relationship with LST for Bengaluru and Hyderabad cities of India. Daytime/nighttime/seasonal/spatial variations of air pollutants and LST, sensitivity of air pollutant in affecting LST, relationship between air pollutants and LST, and hotspots/coldspots analysis for LST and air pollutants was carried out. The study utilized, air pollutants data of 5-years from air quality monitoring stations and LST data of overlapping period from MODIS satellite. PM2.5 exceeded the national and international air quality standards while all other parameters were within specified limits/guideline values, at both the cities. The study found that LST had maximum sensitivity towards PM2.5 and NO2 for both the cities indicating that both these parameters are more effective in causing changes in the LST values. High Urban Thermal Field Variance Index (UTFVI) values for urban areas demonstrated strong surface urban heat island (SUHI) during nighttime in both the cities. Pearson's correlation analysis showed that LST, at Hyderabad, was positively correlated with PM2.5 (r = 0.714) during daytime. Relationship of LST with all air pollutants and meteorological parameters was investigated simultaneously and very strong relationship of LST with all other parameters was observed for both daytime and nighttime for both the cities. The R2 values at Bengaluru were 0.98 and 0.92 for nighttime and daytime, respectively, while the corresponding values were 0.87 and 0.93, respectively for Hyderabad. Hotspot analysis showed that total area of hotspots/coldspots undergoes temporal variations and the corresponding area during daytime was higher/lower compared to nighttime for different parameters. The trend of increase/decrease of daytime area of hotspots/coldspots, over nighttime area, was also different from different parameters as well as it was observed different for both the cities. Overall, the present study provides holistic insights into the interaction between air pollutants and LST in urban contexts. By understating these interactions and variations it helps urban planning and policymaking mitigate the challenge of the rise in LST and air pollutants.

Correcting land surface temperature from thermal imager by considering heterogeneous emissivity

It is fundamental to obtain accurate land surface temperature (LST) to study surface energy process. Infrared thermal imagers are commonly used for deriving LST on the basis of radiance measurements. However, when deriving LST from brightness temperature of a blackbody in thermal imagers, thermal imagers only allow setting a fixed land surface emissivity (LSE). This causes uncertainty in retrieving thermal infrared (TIR) temperature from heterogeneous surfaces with varied LSE, such as those covered in vegetation. Corrections can be made using the Normalized Vegetation Index (NDVI). However, commercial thermal imagers provide only red (R), green (G), and blue (B) bands without a near infrared band so NDVI cannot be calculated on the same instrument and is commonly not available. We propose an alternative method to estimate LSE using RGB-based vegetation index. Thereafter the estimated LSE was used to correct the TIR temperature derived from the fixed LSE. An experiment was conducted to validate the proposed correcting method. The results show that 1) the corrected LST values were closer to the ground truth, with a mean absolute error (MAE) of 0.41 ± 0.34 °C, whereas the MAE was 0.75 ± 0.56 °C for the uncorrected LST; 2) the more heterogeneous the surface, the greater the difference between the corrected and uncorrected LST values, indicating the necessity of LST correction when applying thermal imagers over heterogeneous surface.

The impact of clear-sky biases of land surface temperature on monthly evapotranspiration estimation

Remotely sensed land surface temperature (LST) is critical for retrieving evapotranspiration (ET). However, due to cloud contamination, LST is often limited to clear-sky conditions and the differences between clear-sky and all-sky LST will lead to clear-sky biases of LST. Consequently, the accuracy of ET varies drastically under different weather conditions. To evaluate the impact of clear-sky biases on ET estimation, the monthly ET under clear-sky and all-sky conditions was estimated using a nonparametric method at several sites with different humid conditions and vegetation coverage in 2014. 35.7 % of the clear-sky LST values were acquired in the arid region (Heihe River Basin) with different vegetation coverage. As the climate became more humid, only 14.2 % of the clear-sky LST data were available (Poyang Lake Basin). The clear-sky biases of LST variation at the sites resulted in a significant reduction in the accuracy of monthly ET, with an increase in the relative error (RE) of approximately 16.6 %. The impact of clouds can be reduced by at most half by the introduction of all-sky LST products, with a significant decline (6.3 % ∼ 34.2 % of ET) in the error contribution of LST to monthly ET. The variation in vegetation coverage of land cover and the change in humidity of the climate significantly affected the influence of the clear-sky biases of LST on the ET estimation and the error contribution to ET. Replacing all-sky LST with clear-sky LST in areas with less vegetation coverage exacerbates the underestimation of ET estimation and strengthens the error contribution of LST with the decline in RE (error contribution of LST) from −7.7 % (−6.3 %) in densely vegetated areas to −29.7 % (−34.2 %) in non-vegetated areas, especially in the summer. Similarly, clear-sky biases of LST tended to have a relatively significant impact on the error contributions of LST to monthly ET estimation when the climate became humid, with a relatively significant enlarging from −6.3 % in the arid area to −10.0 % in the humid area, especially in the rainy season in the humid area (−61.3 % of ET). This study contributes to the understanding of the relationship between clear-sky biases and vegetation coverage/humidity. Quantitative analysis of the impact of clear-sky biases of LST on monthly ET estimation will be helpful for improving the accuracy of ET retrieval in the future.

Soil Adjusted Vegetation Index, Normalize Difference Buildup Index, and Land Surface Temperature between 1987 and 2023 in Abuja Municipal Area Council, Nigeria

Abuja Municipal Area Council (AMAC) is experiencing rapid urban expansion, which is expected to impact land surface temperatures (LST). This paper evaluates the trends in soil adjusted vegetation index (SAVI), normalize difference buildup index (NDBI), and land surface temperature (LST) between 1987 and 2023 in AMAC using Landsat 4 Thematic Mapper and Landsat 8 Operational Land Imager/Thermal Infrared Sensor imagery, respectively. Results show that in 1987, SAVI ranged from -0.126 to 0.477, NDBI from -0.186 to 0.678, and LST from 27.18 to 46.4 oC. In 2023, SAVI ranged from -0.253 to 0.71, NDBI from -0.308 to 0.619, and LST from 23.89 to 46.57 oC. Analysis showed an increase in vegetation in 2023 compared to 1987. Built-up and bareland areas became more concentrated in the northeast in 2023 compared to 1987, and temperature reductions were observed in areas with increased vegetation, notably in the south and southwest. Correlation analysis indicated a strong negative relationship (-0.772) between SAVI and LST in 1987, weakening in 2023 (-0.389). NDBI and LST remained moderately positively correlated (0.645 in 1987, 0.621 in 2023). Significant differences (P<0.01) were observed between 1987 and 2023 SAVI, NDBI, and LST values. These findings have important implications for environmental monitoring, and urban planning in rapidly urbanizing areas such as AMAC

QGIS-based modeling and analysis of urban dynamics affecting land surface temperature towards climate hazards in coastal zones of Portugal

AbstractClimate risk creates considerable concern due to the density of natural and socio-economic assets in coastal areas. Monitoring land use/cover changes, detecting population growth, and analyzing their impact on land surface temperature (LST) are necessary for effective urban management. In this study, land use/land cover (LULC), population, and LST changes in coastal regions of Portugal. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery was examined using World Bank population and CORINE data. Changes in land use types and LST values from 1990 to 2018 were analyzed. At the same time, LULC predictions were made using the Modules for Land Use Change Simulation (MOLUSCE) plug-in included in the QGIS software, and population projections were analyzed with LULC predictions in 2046. The results show the significant impact of land use on temperatures. It has been demonstrated that green and water areas can effectively cool cities. In the LULC changes between 1990 and 2018, the Leiria region stands out, with an annual increase of 4.04% in built areas from 121.58 to 259.06 km2. According to the simulations between 2018 and 2046, it was predicted that 18.74% of agricultural areas and 14.43% of forest areas would be transformed into built environments. The study is also essential as it confirms that the MOLUSCE plug-in can be effectively applied to land cover simulation on a large regional scale.

Spatio-temporal Analysis of Land Surface Temperature in West Tripura District, India

Land surface temperature is a crucial factor that affects many ecological and environmental processes, such as the urban heat island effect, the health of the plants, and the effects of climate change. An increase in global temperature and erratic rainfall has been observed as a result of the weather's change during the recent past. Understanding the Spatio-temporal change of the land surface temperature can provide us with insights for sustainable land use management, urban planning, and also aid in planning and formulating plans to combat climate change. The present study area is the most populated district in Tripura and has experienced some of the fastest transformation in urban infrastructure. This study's objective was to investigate at how the land surface temperature (LST) varies across space and time in the West Tripura District. Landsat- 8 OLI and Landsat-5 ETM was the source of data to investigate the purpose of the study. The LST data was derived from thermal bands of the satellite imagery using remote sensing algorithm in ArcGIS and NDVI was computed using NIR and RED bands. Upto the year 2020, steady rise in built up areas was observed with a decline in land cover until 2010. However, the year 2020 witnessed an increase in vegetation cover. Similarly, the land surface temperature revealed higher values till 2010 may be attributed to loss of vegetation cover and subsequent decrease in values of land surface temperature after 2010 may be attributed to afforestation and growing popularity of plantation schemes.

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).

Assessing the Relationship between Land Surface Temperature and Composition Elements of Urban Green Spaces during Heat Waves Episodes in Mediterranean Cities

In the context of escalating global temperatures and intensified heat waves, the Mediterranean region emerges as a noteworthy hotspot, experiencing a surge in the frequency and intensity of these extreme heat events. Nature-based solutions, particularly management of urban green infrastructure (UGI) areas, have shown promising outcomes in adapting urban areas to the challenges posed by heat waves. The objective of the current study is twofold: firstly, to identify the compositional patterns of strategically distributed small public green spaces, demonstrating their enhanced capacity to mitigate the impact of heat waves in the Mediterranean region; secondly, to assess the association, direction, and explanatory strength of the relationship between the composition elements of the UGI areas and area typology, specifically focusing on the variation in land surface temperature (LST) values during heat wave episodes spanning from 2017 to 2023. The methodology involved obtaining land surface temperature (LST) values from satellite images and classifying green areas based on composition, orientation, and typology. Ordinal multiple regressions were conducted to analyze the relationship between the considered variables and LST ranges during heat wave episodes that occurred from 2017 to 2023. The findings indicate an increase in LST ranges across many areas, emphasizing heightened thermal stress in a Mediterranean medium-sized compact city, Granada (in the southeast of the Iberian Peninsula). Traditional squares, pocket parks and gardens, and pedestrian areas with trees and impervious surfaces performed better in reducing the probability of exceeding LST values above 41 °C compared to other vegetated patches mainly occupied by herbaceous vegetation and grass. The study concludes by advocating for the strategic incorporation of vegetation, especially trees, along with traditional squares featuring semipermeable pavement with trees and shrubbery, as a potential effective strategy for enhancing resilience against extreme heat events. Overall, this research enhances our understanding of LST dynamics during heat waves and offers guidance for bolstering the resilience of urban green spaces in the Mediterranean region.

Soil moisture mapping for drought monitoring in urban areas

Soil moisture is an important indicator for drought monitoring. Mapping of soil moisture in this study uses remote sensing, namely Landsat 9 OLI imagery, because it can be relied upon as a cheap source of information, and its good temporal resolution or revisit period. Two parameters indirectly related to soil moisture, namely vegetation were analyzed using the Normalized Difference Vegetation Index (NDVI) approach, and land surface temperature (LST). Drought analysis was verified using the Normalized Difference Drought Index (NDDI). The remote sensing imagery used in this study is Landsat 9 OLI imagery by selecting images with 30% cloud cover from 1 January 2022 to 31 December 2022 with the support of the cloud-based Google Earth Engine computing platform. The results of the analysis indicate high LST values in the southern part of the study area whose dominant land use is built-up areas, namely in the sub-districts of Depok, Gamping, Ngaglik, and Mlati. The effect of vegetation on soil moisture is indicated by the NDVI value, which has a relatively strong positive correlation with SMI (R= 0.46). The SMI value is in contrast to LST, where the spatial distribution of high SMI is spread in the northern part, namely Pakem, Cangkringan, and Turi districts. On the other hand, a low SMI is spread across the central and southern parts of the study area, which have a high drought index (extreme moderate). Overall, it is concluded that the SMI has the potential to map drought and is a reliable index for initial analysis of drought risk management.

Assessing the influence of elevation on satellite derived normalized difference vegetation index and land surface temperature in Rajasthan

Land surface temperature (LST) and its interaction with normalized difference vegetation index (NDVI) is crucial for better understanding of environmental changes in current scenario. However, very few or scanty research on the interrelationship between LST, NDVI and topographic elements has been done in India. Therefore, the purpose of conducting this study was to examine, how LST and NDVI change as a function of elevation in Rajasthan. In present study, MODIS derived NDVI and LST and digital elevation model (DEM) from shuttle radar topography mission (SRTM) have been used. Results revealed that the LST and NDVI both were significantly influenced by elevation. Elevation, NDVI and LST varied from -6 to 1698 m, -0.09 to 0.65 and 24 to 45°C throughout the study region. In contrast to LST, which has a decreasing gradient from western to eastern portions, the spatial variability of NDVI has decreasing gradients from southern and eastern to western regions. The highest mean LST value (39.76 ± 0.2.9 0C) was obtained at an elevation range of -6 to 168 m, whereas NDVI value (0.38 ± 0.06) at elevation ranges of 589 – 1698 m. The analysis of the correlations between LST, NDVI and elevation indicated that the elevation has strong positive correlation with NDVI (r2 = 0.26) and negative correlation with LST (r2 = 0.28). Findings from this kind of research can be utilized as a platform for environmental and land use planning for sustainable ecosystem management.

Time series analysis of landsat data for urban heat island monitoring in Semarang City

The urban heat island (UHI) condition has received significant attention because it has the potential to negatively impact life quality and comfort, especially livability and level of comfort. Semarang City, the capital of Central Java, has been attracting many people to live and causing land use conversion to built-up areas. This study aims to observe the land surface temperature (LST) trend and to analyse the UHI spatiotemporal pattern in Semarang based on satellite time series data within 10 years. Land cover and LST were extracted from Landsat data of 2008, 2013, and 2019. Based on the results, it was observed that the vegetated area has reduced from 20.347 Ha to 7.241 Ha, while the average LST value has increased from 29.24°C to 31.31°C in 2008 and 2019 respectively. We also found that the UHI pattern, which was initially detected only in a few areas in 2008 and was focused in the city centre, has gradually spread to the east and south of Semarang in 2019. The result also shows the spatiotemporal trend of land cover conversion and UHI areas, which can be beneficial for mitigation planning in the future.

Identification of geothermal potential zone associated with land surface temperature derived from Landsat 8 data using split-window algorithm

This study was conducted in the geothermal potential area of Mount Seulawah, Aceh province. Study was conducted using remote sensing methods in order to estimate and map the land surface temperature (LST). Data used in this study is Landsat 8 OLI and TIRS. Methods used in this study were split-window algorithm (SWA) to obtain the LST through NDVI analysis and landcover classification, and image enhancement to obtain the lineament density through visual interpretation referring to the results of band ratio and filter techniques. The results of the NDVI analysis show that the study area has a value in the range of -0.57 – 0.97. The results of the LST analysis show that the surface temperature in the area has a value in the range of 21.32 - 32.88 oC. Areas that have high LST values can be estimated as areas that have anomalies. Based on the results of the lineament density analysis, the higher density value in an area can indicate the better permeability where geothermal manifestations come out. The direction of the lineaments that control the study area is dominated by the northwest-southeast in accordance with the direction of the main stress, namely the Seulimum fault and the Aceh fault which also has a northwest-southeast direction. The overall results show that the Seulawah geothermal area is quite potential to be developed for the sustainable natural resource.

Sensing the dependable surficial signatures of temporal groundwater variations in arid coastal regions through geospatial techniques with respect to microclimate changes

Reports on Groundwater level variations and quality changes have been a critical issue, especially in arid regions. An attempt has been made in this study to determine the surface manifestations of groundwater variations through processing imageries for determining the changes in land use, Normalized Differential Building Index (NDBI), Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), along with Groundwater level (GWL) and Electrical conductivity (EC). Decadal variation between these parameters for 2013 and 2023 shows that the average water level had increased by 1.03amsl, while the EC values of groundwater decreased by 418 μS/cm. The decrease in EC values indicates freshwater recharge, promoting natural vegetation, thus reducing the LST values by 3.28 °C. In addition, urban landscaping and relatively lesser emissivity from built-up surfaces than the sandy desert have further reduced the LST. The interrelationship of the parameters indicates that an increase in LST correlates with an increase in NDBI and with less significant changes in NDVI. The lowering of the LST along the coastal regions was inferred to be due to the influence of Sea breeze, adjacent moisture from the ocean, shallow water level, and the shadow effect of the buildings. Further, the increase in water level was mainly attributed to the recent increase in rainfall and the extreme event in 2018. The higher EC in the lesser NDBI regions is attributed to the anthropogenic contamination from agriculture and landfill leachates. Though there was an increase in NDBI, the LST of the region was inferred to be reduced mainly due to the increase in water level and reduction of emission from desert sand by recent urban developments.

Monitoring Land Surface Temperature Relationship to Land Use and Land Cover in Hai Duong Province, Vietnam

This study utilised remote sensing data and ArcGIS 10.8 software to evaluate changes in land use and land cover (LULC) and their effects on land surface temperature (LST) in Hai Duong Province, Vietnam, from 1992 to 2022. Landsat satellite data were pre-processed and classified using supervised methods for the years 1992, 2010, and 2022. In 1992, vegetation cover accounted for 57.89% of land cover, increasing to 84.49% in 2010, but then decreasing again to 66.67% in 2022. In contrast, the built-up area consistently increased, from 2.88% in 1992 to 29.35% in 2022, as most of the barren land present in 1992 became built-up area in 2022. The LST values were calculated from the thermal bands for the years 1992, 2010, and 2022 and ranged from 16.09°C to 34.27°C, 17.04°C to 36.74°C, and 11.03°C to 28.44°C, respectively. In addition, the Normalized Difference Vegetation Index (NDVI) values were calculated using the near-infrared band and the red band, with values ranging from -0.40 to 0.70 over the study period. A linear regression analysis indicated a shift in the correlation between NDVI and LST from positive to negative. This study highlights the significant transformation that occurred in Hai Duong Province due to rapid population density increases, urban growth and infrastructure development, leading to a decline in greenery. These LULC changes can cause severe environmental damage. These research findings will assist policymakers in formulating management strategies and sustainable land-use plans to minimize potential harm and promote sustainable development in the area.

LCZ thermal and exoatmospheric albedo assessment using Landsat 8 land surface temperature and reflectance dataset: Case study of Lisbon

This study investigates microclimate changes induced by urbanization, with a focus on the Urban Heat Island (UHI) phenomenon and the crucial role of Land Surface Temperature (LST). Using the Landsat 8 Land Surface Temperature and Reflectance dataset obtained from the USGS Earth Explorer platform, the research evaluates LST and exoatmospheric albedo characteristics within Lisbon's Metropolitan Area across a defined set of thermal periods from 2015 to 2020. Among the findings, the Bare Soil or Sand LCZ consistently exhibits heightened LST values, while the Large Low Rise urban LCZ consistently records the highest temperatures, exceeding 54 °C and 55 °C in spring and summer and 43.8 °C and 37.8 °C in autumn and winter, respectively. These insights, derived from a meticulous examination of the Landsat 8 dataset and advanced processing methods, bear critical implications for climate change adaptation strategies, providing valuable insights to mitigate the adverse effects of the Urban Heat Island and foster sustainable urban development practices.

Remote Sensing and Field Measurements for the Analysis of the Thermal Environment in the “Bosco Verticale” Area in Milan City

The trend of urbanization nowadays has caused serious issues related to climate. One of the most important ones is that of the ‘Urban Heat Island (UHI)’ and it occurs in major cities throughout the world. The most important categories, and therefore the most studied ones, are the canopy urban heat island (CUHI) and surface heat island (SUHI). The aim and the novelty of the current study was to assess different remote sensing approaches to detect the thermal environment of an open area inside a large city. The study was undertaken in an urban area with green spaces, in the Bosco Verticale area in the city of Milan, during the spring and summer period of 2021. The area is characterized by different types of cover materials, which were investigated in terms of surface temperature under shaded and non-shaded conditions. Both field measurements and remote sensing techniques were applied. Remote sensing techniques included downscaling techniques and the usage of different split-window algorithms applied on the Landsat8 satellite sensor data. The land surface temperature (LST) extracted from remote sensing methods was compared with the surface temperature derived from in situ measurements. For the needs of the study, both in situ measurements and the collection of meteorological data from different fixed meteorological stations throughout the city of Milan were carried out. The results revealed the significance of greenery presence inside the urban environment, as a comparison of the meteorological data across the urban area of Milan showed that the areas with a low presence of greenery were found to be warmer than those with a higher presence of green elements. Concerning the field measurements in the study area, the results showed a significant reduction in both surface and air temperature in shaded places. On the other hand, the presence of conventional artificial materials in sunny areas led to relatively high values of both surface and air temperature. The downscaling method showed satisfying results in terms of average LST values; however, some discrepancies appeared in terms of the RMSE index. The application of split-window algorithms has shown that some forms of the ‘Generalized split-window algorithm’ and some forms of the ‘Jimenez-Munoz algorithm’ presented better performance among the studied algorithms. Comparing the LST values derived from the most representative algorithm, the ‘Du, Wan algorithm’, with those derived from downscaling methods, it was found to be quite close. However, under shaded conditions, the results derived from the ‘Split-window algorithm’ were found to be more precise. The application of remote sensing techniques in microscale in urban regions should be further studied in future, as they could be an essential tool for observing microclimatic conditions in urban areas and on building scale.

Impact of anthropological activities on land-use and land-cover changes in the lower Okavango Delta, Botswana

Anthropogenic land alterations in Maun village have transformed natural vegetation into urban infrastructure, including pavements, and residential and commercial areas, leading to elevated Land Surface Temperature (LST). This urban expansion resulted from economic growth driven by population increase and tourism-related development. This study aims to evaluate the relationship between Land Use and Land Cover Changes (LULCCs) and LST. Utilising Landsat 5-TM and Landsat-8 data from 1990, 2000, and 2020, we employed a random forest algorithm for supervised classification, generating Land Use and Land Cover (LULC) maps. The mono-window algorithm was used to extract LST data from Landsat 5 and 8 images, alongside Normalised Difference Vegetation Index (NDVI) maps. s regression analysis assessed the LST-NDVI correlation. Results indicate that urban LULCCs significantly contribute to rising LST. Minimum and maximum LST values for 1990, 2000, and 2020 were 18.6°C, 22.8°C, 22.6°C, and 26.7°C, 34.5°C, and 42.1°C, respectively. NDVI values ranged from −0.2 to 0.56 in 1990, −0.17 to 0.58 in 2000, and 0.07 to 0.46 in 2020. Roads, pavements, barren land, and built-up areas displayed the highest LST (44.6°C), while water bodies and healthy vegetation exhibited the lowest (16.1°C). Additionally, NDVI exhibited a negative correlation with LST. Our findings emphasise the role of human activities in exacerbating LST. They highlight the need for regulated urban growth patterns to ensure sustainable development. Moreover, quantifying spatiotemporal variations in LULC, LST, and NDVI holds importance for conserving land resources and enhancing land use planning policies. Policymakers and city planners can utilise this research to mitigate heat stress effects and promote sustainable urban environments by evaluating distribution maps of LULC, NDVI, and LST.

Mapping land use land cover (LUCL) and its influence on land surface temperature (LST): A case study in Dau Tieng district at Binh Duong province

At present, land surface temperature (LST) is an important index in monitoring the urban heat is-land phenomenon and changes in local climate, as well as global climate. By analyzing the variation in land use land cover (LULC), we can see how changes in LULC will lead to changes in LST and understand the expansion of urbanization related to LST. This study used Landsat 8’s thermal infrared band to extract and calculate LST values in DauTieng district in the period 2015-2022. In addition, this study also classified LULC by using the Support Vector Machine algorithm, then evaluated changes in LST on cropland, forest, water surface, and impervious surface areas. The results showed that in places with dense vegetation cover, LST value was low, whereas when moving to areas with low density of vegetation and residential areas, industrial parks,… LST value gradually increased. In general, changes in LULC are important factors that cause LST to increase or decrease, thereby suggesting solutions to minimize high-temperature areas