Maximum Land Surface Temperature Research Articles

Mammal diversity responses to anthropic, environmental, and seasonal changes within Caatinga seasonal dry forest landscapes

AbstractCaatinga's conservation and biodiversity are threatened due to the intensification of anthropic activities and climate change. The mammals have different responses to seasonal and anthropic changes, however particularly in Caatinga, these effects are still poorly understood. We assessed the influence of anthropic (distance from urban areas and wind farms), environmental (distance from water), and seasonal (Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST)) variables on the number of records and richness of medium and large‐sized mammals in Brazilian Caatinga. We used camera traps in 2016–2017 and 2018–2019, estimated the variation (cv) of NDVI and LST, and generated Euclidean distance maps to anthropic and environmental variables at 250, 500, and 1000 m spatial scales. We performed Generalized Linear Models, used the Akaike information criterion, and calculated model averaging to assess the strength and direction of effect and the uncertainties of the winner models, respectively. The distance from wind farms and maximum LST had a noticeable effect on the number of records and total richness. The distance from wind farms had a negative effect on the records of Dicotyles tajacu and a positive effect on the records of Leopardus pardalis and richness. The maximum LST had a negative effect on the records of L. pardalis and a positive effect on the records of Puma concolor and Cerdocyon thous. Our results emphasize that an unsustainable expansion of wind farms is likely to compromise mammal diversity. We found an opposite pattern for some species regarding LST. However, it is important to highlight that the conservation of vegetation areas on the top of mountains and springs, and the installation of artificial water sources are important strategies to mitigate the impacts of high temperatures on mammals' biodiversity in Caatinga.Abstract in Portuguese is available with online material.

Monitoring the Expansion of Unplanned Urbanization and its Impact on Climate Change based on Google Earth Engine Service, a Case Study of Baghdad / Iraq

The earth's surface comprises different kinds of land cover, water resources, and soil, which create environmental factors for varied animals, plants, and humans. Knowing the significant effects of land cover is crucial for long-term development, climate change modeling, and preserving ecosystems. In this research, the Google Earth Engine platform and freely available Landsat imagery were used to investigate the impact of the expansion and degradation in urbanized areas, watersheds, and vegetative cover on the land surface temperature in Baghdad from 2004 to 2021. Land cover indices such as the Normalized Difference Vegetation Index, Normalized Difference Water Index, and Normalized Difference Built-up Index (NDVI, NDWI, and NDBI) were determined to examine the effects of land cover changes. In addition, the land surface temperature was calculated to assess urbanization expansion's impact on Baghdad's climate warming. The results showed a drastic decrease in vegetative cover and green land, on the other hand, a significant expansion in urbanized areas. Hence, from 2004 to 2021, the urbanized areas and open land rose by 37% and 3%, respectively, while the vegetative cover decreased by 41%. The maximum land surface temperature has risen 4° C, and the minimum land surface temperature has risen 2.5°C.

Assessing the relative importance of dry-season incoming solar radiation and water storage dynamics during the 2005, 2010 and 2015 southern Amazon droughts: not all droughts are created equal

Three severe droughts impacted the Amazon in 2005, 2010, and 2015, leading to widespread above-average land surface temperature (LST) (i.e. positive thermal anomalies) over the southern Amazon in the dry season (Aug–Sep) of these years. Below-average dry-season incoming solar radiation (SW↓) and terrestrial water storage anomaly (TWSA) were simultaneously observed in 2005 and 2010, whereas the opposite was observed in 2015. We found that anomalies in precipitation (P), SW↓, and TWSA combined can well explain dry-season thermal anomalies during these droughts (average R2–0.51). We investigated the causes for opposing anomalies in dry-season SW↓ and TWSA, and found different hydro-climatological conditions preceding the drought-year dry seasons. In 2005 and 2010, P was considerably below average during the wet-to-dry transition season (May–July), causing below-average TWSA in the dry season that was favorable for fires. Increased atmospheric aerosols resulting from fires reduced solar radiation reaching the ground. In 2015, although below-average dry-season P was observed, it was above the average during the wet-to-dry transition season, leading to reduced fires and aerosols, and increased dry-season SW↓. To further examine the impact of opposite hydro-climatological processes on the drought severity, we compared dry-season LST during droughts with the maximum LST during non-drought years (i.e. LSTmax) for all grid cells, and a similar analysis was conducted for TWSA with the minimum TWSA (i.e. TWSAmin). Accordingly, the regions that suffered from concurrent thermal and water stress (i.e. LST > LSTmax and TWSA < TWSAmin) were identified. These regions are mainly observed over the southeast in 2005 and southern Amazon in 2010. In 2015, large-scale dry-season thermal stress was found over central and southeast Amazon with little water stress. This study underlines the complex interactions of different hydro-climatological components and the importance of understanding the evolution of droughts to better predict their possible impacts on the Amazon rainforest.

Modeling the relationship between urban tree canopy, landscape heterogeneity, and land surface temperature: A machine learning approach

Cities across the United States and around the globe are embracing urban greening as a strategy for mitigating the effects of rising temperatures on human health and quality-of-life. Better understanding how the spatial configuration of tree canopy influences land surface temperature should help to increase the positive impacts of urban greening. This study applies a machine learning approach for modeling the relationship between urban tree canopy, landscape heterogeneity, and land surface temperature (LST) using data from nine cities located in nine different climate zones of the United States. We collected summer LST data from the U.S. Geological Survey (USGS) Analysis Ready Data series and processed them to derive mean, minimum, and maximum LST in degrees Fahrenheit for each Census block group within the cities considered. We also calculated the percentage of each block group comprised by the land cover designations in the 2016 or 2019 National Land Cover Database (NLCD) maintained by the USGS, depending on the vintage of the available LST data. High resolution tree canopy data were purchased for all the study cities and the spatial configuration of tree canopy was measured at the block group level using established landscape metrics. Landscape metrics of the waterbodies were also calculated to incorporate the cooling effects of waterbodies. We used a Generalized Boosted Regression Model (GBM) algorithm to predict LST from the collected data. Our results show that tree canopy exerts a consistent and significant influence on predicted land surface temperatures across all study cities, but that the configuration of tree canopy and water patches matters more in some locations than in others. The findings underscore the importance of considering the local climate and existing landscape features when planning for urban greening.

Quantifying Spatio-temporal dynamics of land surface temperature for the rapidly growing city of Bahir Dar, Northwest Ethiopia using multidated Landsat data

Urban heat island (UHI) refers to the occurrence of very high temperature in urban areas as compared to the surrounding rural areas. Land surface temperature (LST) is a key parameter in urban environmental analysis and varies with different land use/covers. The present study aims to explore the spatiotemporal dynamics of land surface temperature of Bahir Dar City and its surroundings using multidated Landsat 5 TM and Landsat 8 OLI/TIRS data of 1984, 1994, 2009, and 2019. The thermal infrared data has been corrected and converted to top of the atmospheric radiance and later to temperature in °C from Kelvin scale Using ArcMap software. The time series data pertaining to 1984, 1994, 2009, and 2019, showed a gradual increase in maximum LST and its expanse. Out of the total area of 213.55 km2, low (‹17 °C) to medium (‹23 °C) LSTprevailed in 65.88% (140.7 km2) of the area, whereas high (›29 °C) LST existed in 34.12% (72.85 km2). Though the low, medium, and high LST classes increased through 1984, 1994, 2009, and 2019, and medium and high-temperature classes became dominant in the expanse. The temporal variation of the Normalized Difference Vegetation Index (NDVI), as an indicator of urban heat intensity, showed the highest NDVI of 0.68 in 1994 and the lowest NDVI value of 0.54 in 2019. LST has a negative statistical correlation with NDVI with a coefficient of determination R2 = 0.99, 0.99, 0,94 & 0.98 for 1984, 1994, 2009, and 2019, respectively. NDVI and NDBI have very strong correlation with LST (R2>94%), which is quite different from previous studies in other study areas.

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.

Spatial characterization of global heat waves using satellite-based land surface temperature

The warming world greatly suffers the increase of frequency, severity and duration of heat wave events, which would cause the significant societal and environmental damages and implications from local to global scales. In order to eliminate the limitations of site observations in accurate spatial extent identification and large scale monitoring, this study tried to investigate global heat waves in 2018 using spatial datasets of land surface temperature (LST) derived from AQUA TIR sensors. A 15-year daily maximum LST dataset was used to identify the hot days and heat wave events using the relative threshold method based on the probability density function analysis of LST sequences, then they were adopted in analyzing spatial patterns of duration, frequency and intensity of heat wave events using heat wave numbers (HWN), the frequency of the appeared heat wave days (HWF) and the amplitude of heat wave impacts (HWA) indices, respectively. Our results indicated that more heat wave events and longer heat wave duration happened in low and middle latitudes under the warmer land surface in 2018 than last decade, especially many heat wave events occurred in the environmentally fragile regions and the dense population area. Urban regions achieved the largest HWA by 45.5 ± 6.9℃. Both annual HWF and HWN highlighted seven hot spot regions influenced by heat waves, and most of them distributed around the latitude 30°N and the latitude 30°S. Extreme heat wave of hot spot regions in the Southern Hemisphere mainly happened in winter and spring in contrast to that in North Hemisphere appeared in summer and autumn. Dry climate could contribute to the occurrence of heat waves, and heat wave indices variations also implied the compound consequences between heat waves and drought. Our findings demonstrate that remote sensing datasets are capable of providing the continuous whole-Earth coverage of heat wave changes, and they would play more important roles in preventing or mitigating the impacts of extreme heat events on people and natural environment.

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

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

Towards understanding climate change: Impact of land use indices and drainage on land surface temperature for valley drainage and non-drainage areas

The continuous increase of urbanization and industrialization brought various climatic changes, leading to global warming. The unavailability of meteorological data makes remotely sensed data important for understanding climate change. Therefore, the land surface temperature (LST) is critical in understanding global climate changes and related hydrological processes. The main objective of this work is to explore the dominant drivers of land use and hydrologic indices for LST in drainage and non-drainage areas. Specifically, the relationship between LST changes, land use, and hydrologic indices in Northeast Qena, Egypt, was investigated. The Landsat 5 and 8 imagery, Geographic Information System (GIS), and R-package were applied to identify the change detection during 2000–2021. The normalized difference between vegetation index (NDVI), bare soil index (BSI), normalized difference built-up, built-up index (BUI), modified normalized difference water index (MNDWI), and soil-adjusted vegetation index (SAVI) were employed. The non-drainage or mountain areas were found to be more susceptible to high LST values. The comprehensive analysis and assessment of the spatiotemporal changes of LST indicated that land use and hydrologic indices were driving factors for LST changes. Considerably, LST retrieved from the Landsat imaginary showed significant variation between the maximum LST during 2000 (44.82°C) and 2021 (50.74°C). However, NDBI has got less spread during the past (2000) with 10–13%. A high negative correlation was observed between the LST and NDVI, while the SAVI and LST positively correlated. The results of this study provide relevant information for environmental planning to local management authorities.

Land use/land change detection and determination of land surface temperature variation in green belt (Nasirabad) district of Balochistan, Pakistan

The current study determined the changes in Land Use/Land Change (LU/LC) and variation in land surface temperature (LST) in the Green Belt (Nasirabad district) area of Balochistan, Pakistan. To achieve this, we used GIS software (ArcMap 10.7.1) to analyze remote sensing data acquired from Landsat imagery taken in 1993, 2003, 2013, and 2023. A supervised classification technique using the maximum likelihood algorithm (MLC) was applied to generate a ground-truth LU/LC classification. Based on our findings, almost 415.28 km2 (− 12.89%) of formerly undeveloped land has been transformed into urban neighborhoods and green spaces during the last three decades. Between 1993 and 2023, the study area gained 288.29 km2 (8.94%) of vegetation and 136.10 km2 (4.22%) of settled land. Minimum, maximum, and average LST changes were recorded as 7.50, − 5.06, and 1.22 °C for the whole thirty years. Overall, the analysis data showed that an increase in human settlements in the area investigated led to a rise in mean LST (1.22 °C). Finally, GIS and RS may be used together to track land usage over time, a crucial piece of data for eco-friendly planning. While the LU/LC and LST provide valuable insights into the rational and optimal use of land resources, the implications of policy remain constrained.

Four-decade response of land surface temperature to urban expansion in Beijing

Urban expansion with increased impervious surface area (ISA) can affect the thermal regimes by altering the water and energy balance. Previous studies have indicated that ISA is the primary surrogate of rising land surface temperature (LST). However, urban-induced LST changes are not yet well quantified because of the temporal limitations of the LST datasets and the uncertainties from climate variabilities. In this study, we integrated multiple satellite-based products (vegetation coverage, albedo and radiation forcing) into a physically-based land surface model i.e., Variable Infiltration Capacity (VIC), to detect multi-temporal scale response of LST to the increasing ISA in Beijing. Heat transfer-related parameters in the model were updated along with urbanization processes. The integrated modeling presented high performance for simulating LST, as evaluated with ground-based observations and the MODerate-resolution Imaging Spectroradiometer (MODIS) LST product. We found that the average LST over Beijing increased from 10.3 °C in 1980–1990 to 11.2 °C in 2010–2020, with nearly 33% contributed by the urban growth. The urban-induced thermal effect was particularly strong in summer daytime and winter nighttime. The frequency of heat days (the day with the maximum LST over 40 °C) in a year for Beijing appeared to linearly correlate with the impervious surface fraction (ISF) when ISF > 25%. For the urbanizing area, moreover, the four-decade LST estimates indicated that the annual overheating duration was obviously extended with a rate of about 5 days per decade. We therefore conclude that the urban expansion in Beijing not only amplified heat stress but also altered heat phenology, which have implications for urban planning and the construction of heat mitigation facilities.

Understanding the linkages between spatio-temporal urban land system changes and land surface temperature in Srinagar City, India, using image archives from Google Earth Engine.

Land-use and land-cover (LULC) is an important component for sustainable natural resource management, and there are considerable impacts of the rapid anthropogenic LULC changes on environment, ecosystem services, and land surface processes. One of the significant adverse implications of the rapidly changing urban LULC is the increase in the Land Surface Temperature (LST) resulting in the urban heat island effect. In this study, we used a time series of Landsat satellite images from 1992 to 2020 in the Srinagar city of the Kashmir valley, North-western Himalaya, India to understand the linkages between LULC dynamics and LST, derived from the archived images using the Google Earth Engine (GEE). Furthermore, the relationship between LST, urban heat island (UHI), and biophysical indices, i.e., Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI), was also analysed. LULC change detection analysis from 1992 to 2020 revealed that the built-up area has increased significantly from 12% in 1992 to 40% in 2020, while the extent of water bodies has decreased from 6% in 1992 to 4% in 2020. The area under plantations has decreased from 26% in 1992 to 17% in 2020, and forests have decreased from 4 to 2% during the same period. Urban sprawl of Srinagar city has resulted in the depletion of natural land covers, modification of natural drainage, and loss of green and blue spaces over the past four decades. The study revealed that the maximum LST in the city has increased by 11°C between 1992 and 2020. During the same period of time, the minimum LST in the city has increased by 5°C, indicating the impact of urbanization on the city environment, which is reflected by the observed changes in various environmental indices. UHI impact in the city is quite evident with the maximum LST at the city centre having increased from 13.03°C in 1992 to 22.01°C in 2020. The findings shall serve as a vital source of knowledge for urban planners and decision-makers in developing sustainable urban environmental management strategies for Srinagar city.

Understanding the relationship between Urban Heat Island and Urban Vegetation by reviewing three decades of satellite images for the Chennai Metropolitan Area

Urbanization has led to one of the most important climatic issues i.e., the Urban Heat Island (UHI) phenomenon. In Chennai Metropolitan Area (CMA), the overall distribution of the green cover has gradually succumbed to urbanization whereas the temperatures have soared by 1.3 deg C in the past six decades. Urban vegetation provides shade and protects the buildings from direct solar exposure, thus reduces the UHI. Further, it also sequester large quantities of carbon, reduce storm water runoff and function as noise filters and pollutant traps. In comparison with the international UHI studies, the studies concerning Indian UHI are limited. Hence, the aim of the current research work is to understand the seasonal relationship between Land Surface Temperature (LST) and the Normalised Difference Vegetation Index (NDVI) of CMA using LANDSAT 4, 5, 7 and 8 images captured during the years 1988, 1991, 1996, 2000, 2008, 2013 and 2016. The results are discussed under three parts namely, Part-A, Part-B, and Part-C. Part-A results infer that the CMA is covered predominantly by minimum LST values (9 deg C to 25 deg C) and maximum LST values (25 deg C to 50 deg C) during Monsoon and Summer/Post-Monsoon seasons, respectively. The mean LST of the CMA is between 18 deg C to 31 deg C throughout the year. In Part B analysis results, the study establishes a moderate seasonal correlation between LST and the NDVI values (r-values between -0.2648 and 0.3604). According to Part C results, ‘High Vegetation’ (NDVI values 0.5 to 1) has a significant role in maintaining the average LST during all four seasons. Further, it also has the potential to reduce the LST values during the summer season in CMA. However, due to a moderate correlation, the urban vegetation may reduce the LST value only as an outcome of reducing the rest of the UHI contributors.

Land Surface Temperature in an Arid City: Assessing Spatio-temporal Changes

The sprawl of urban areas and its consequent alteration of the Earth’s surface lead to problems with rising temperatures. This research aims to assess the urban cold/heat island phenomenon and driving factors that currently affect the Land Surface Temperature (LST) pattern of a city in a desert environment (Tulum Valley, Argentina). We selected 50 points for the urban class and 49 points for the rural class. The LST data was obtained for cold and warm periods of the years 1988, 2000, 2010, and 2021. For each study year, the median (annual), minimum (of cold period), and maximum (of warm period) LST values were calculated in 90 × 90 m moving windows (3 × 3 pixels). As driving factors, we assessed vegetation areas, built-up, and bare soil areas, taking into account their spatial abundance (median values), and heterogeneity (standard deviation) in the same moving windows. The Tulum Valley behaved like an UCI in almost every year studied. In the urban class, the LST was positively affected by vegetation during the cold period, whereas in the rural class, it was explained by a negative relationship with vegetation and a positive one with built-up areas. In the urban class during the warm periods, LST had a negative relationship with heterogeneous areas of bare soil and vegetation. Conversely, an increase in built-up areas caused a rise in LST, both in urban and rural classes. Different drivers affected the LST pattern in the Tulum Valley; moreover, these drivers explained more variability in the rural than the urban class.

Impact of land use dynamics on land surface temperature using optical remote sensing data integrated with statistical analysis in Riyadh, Saudi Arabia

Changes in land use and cover (LULC) are now considered as a significant concern because they have a great impact on biodiversity loss and climate changes. Therefore, the present study was applied to assess the effect of land use dynamic on land surface temperature (LST). LULC maps of years; 1991, 2001, 2014, and 2022 were applied using Maximum likelihood classification MLC) while the LST was developed using thermal bands of Landsat images. LULC maps showed a considerable development and improvement in vegetation cover and urbanization. Results showed a negative and positive correlation between LST with NDVI and NDBI respectively. It was revealed that the estimated and predicted LST confirm the reliability of the present model with R2 = 0.95. The study calculated that the maximum LST in 2022 to simulate current state of Riyadh, Saudi Arabia and the results were 32.1, 45.5, 38.7 and 41.3 in winter, summer, autumn and spring respectively. We are now sounding the alarm because the state must act quickly and decisively to stop intensive forestation if it is to avoid very high temperatures by 2030. Our findings point to the need for environmental protection in response to the study area's quick conversion of LULC and increasing trend of LST.

Quantitative assessment of land surface temperature and vegetation indices on a kilometer grid scale.

Due to expanding populations and thriving economies, studies into the built environment's thermal characteristics have increased. This research tracks and predicts how land use and land cover (LULC) changes may affect ground temperatures, urban heat islands, and city thermal fields (UTFVI). The current study examines land surface temperature (LST), urban thermal field variance index (UTFVI), normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), and land use land cover (LULC) on a kilometer scale. According to the comparative study, the mean LST decreases by 3°C and the NDVI increases considerably. Correlation analysis showed that LST and NDVI are inversely connected, while LST and NDBI are positively correlated. NDVI and NDBI have a strong negative association, while LST and UTFVI have a positive correlation. Urban planners and environmentalists can study the LST's effects on land surface parameters in different environmental contexts during the lockout period. The urban heat island (UHI) phenomenon, in which the land surface qualities of an urban region cause a change in the urban thermal environment, forms and intensifies over an urban area. The minimum and maximum LST in grid number 1 in 2009 was 20.30°C and 29.91°C, respectively, with a mean LST of 25.1°C. There was a decline in the minimum and maximum LST in grid number 1 in 2020 with a minimum and maximum LST of 17.31°C and 25.35°C, respectively, with a mean LST of 21.33°C. There was a 3.8°C drop in the LST of this grid. The minimum and maximum NDVI were also - 0.16 and 0.59, respectively, with an average NDVI value of 0.21. Therefore, it is essential to evaluate and foresee the impact of LULC change on the thermal environment and examines the connection between LULC shifts with subsequent changes in land surface temperature (LST) along with the UHI phenomenon. Maps of the UTFVI reveal positive UHI phenomena, with the highest UTFVI zones occurring over the developed area and none over the adjacent rural territory. During the summer months, the urban area with the strongest UTFVI zone grows noticeably larger than it does during the winter months during the forecasted years. Future policymakers and city planners can mitigate the effects of heat stress and create more sustainable urban environments by evaluating the expected distribution maps of LULC, LST, UHI, and UTFVI.

Evaluating the Spatiotemporal Dynamics of Land Surface Temperature in Relation to the Land Use/Land Cover changes in Nag-Hammadi District, Egypt, using Remote Sensing and GIS

In this study, three multi-temporal remotely sensed data acquired from Landsat-5 Thematic Mapper (TM) and Landsat -8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) in 1990, 2005, and 2020 were used. The maximum likelihood classifier (MLC) was opted to classify land use and land cover (LULC). Land surface temperature (LST) and LULC spectral indices i.e., Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Normalized Difference Latent Heat Index (NDLI) and Bare Soil Index (BSI) have been computed and their relationships were examined. The overall accuracy of LULC was more than 93%. The analyses showed a notable transformation in LULC over the study period. For instance, built-up areas increased 103.7% with a rate of 45.5 ha/year and agriculture land increased by 28.9% with a rate of 186.4 ha/year. Whereas, bare soil was sharply decreased by 36.4% at a rate of 227.7ha/year. The minimum and maximum LST values increased by 2.9°C and 4.9°C, respectively, from 1990 to 2020. Furthermore, LST has a negative relationship with NDVI and NDLI (NDVI: 1990: r2 = 0.62; 2005: r2 = 0.62; 2020: r2 = 0.65. NDLI: 1990: r2 = 0.79; 2005: r2 = 0.78; 2020: r2 = 0.61) and a positive relationship with NDBI and BSI (NDBI: 1990: r2 = 0.68; 2005: r2 = 0.73; 2020: r2 = 0.44. BSI: 1990: r2 = 0.77; 2005: r2 = 0.78; 2020: r2 = 0.53). These results provided useful information about LULC changes and its impact on LST, which are necessary for experts and land-use planners to formulate sustainable LST mitigation policies, create an environmental comfort in Nag-Hammadi district, and other geographical locations with similar conditions.

A practical method for angular normalization on land surface temperature using space between thermal radiance and fraction of vegetation cover

Land surface temperature (LST) retrieved from thermal infrared (TIR) remote sensing data displays strong directional anisotropy. This characteristic makes the LST at different angles incomparable and limits its application. Multi-angle data are usually required to model or retrieve the angular anisotropy, but most of the TIR images are observed from one single angle. This study investigates the relationship between fractional vegetation coverage (FVC) and thermal radiance, and proposes a practical method for angular normalization of LST using an FVC-radiance space LST angular normalization (called the FRAN method) to perform LST angular normalization for two-component (leaf and soil) vegetated canopy using only single-angle, single-temporal TIR data. In this method, the single-angle data are used to construct the space and build a linear relationship between FVC and radiance. Nadir radiance and LST are obtained by using FVC data at nadir. The validity and accuracy of the FRAN method are tested by using simulated dataset. Results show that 60%–70% of the angular effect of LST can be corrected, and the maximum LST correction reaches 2.0 K. The LST biased error distribution changes to unbiased distribution after angular normalization. The method sensitivity to the vertex of FVC-radiance space and the parameter in FVC calculation is analyzed. The FRAN method is applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data, and obtains the maximum correction of LST as 1.2 K. Viewing zenith angle and land cover affect the corrected LST. The FRAN method has the potential for practical angular normalization of LST with single-angle data.

Urban expansion impacts on agricultural land and thermal environment in Larkana, Pakistan

Urban area expansion and the increase in the built-up area are major threats to agriculture, the natural environment, and ecology throughout the world. The population of the city of Larkana is rapidly increasing due to natural growth and uncontrolled migration from the surrounding areas, which have resulted in a haphazard increase in the built-up area over the fertile agricultural land and affected the city’s thermal environment. This research aims to evaluate the impacts of urban area expansion on agricultural land and the thermal environment of the study area by assessing the change in land use/ land cover (LULC) from 1990 to 2020 and land surface temperature (LST). For this purpose, the Random Forest (RF) algorithm was applied, and LST was calculated in Google Earth Engine (GEE) using Landsat imageries for the years 1990, 2000, 2010, and 2020. For the RF classifier algorithm, 3500 reference random points were generated for each year, which were then randomly divided into two datasets, i.e., a training sample consisting of 70% of the points and a validation dataset (30% of the points). After LULC classification, the results were validated for 1990, 2000, 2010, and 2020, and the accuracy was 88.3%, 89%, 90.01%, and 90.8%, respectively. The study results showed that the built-up area increased from 12.31 to 43.83 km2while the barren land in the study region decreased from 56.51 km2to 11.62 km2. The agricultural land was 66.66 km2in 1990, expanded to 101.38 km2, and then decreased to 79.49 km2in 2020. The results also revealed that most of the urban expansion in the last decade (2010-2020) took place on agricultural land. The urban thermal environment also showed a gradual increase in surface temperatur,e as recorded by the LST results. The LST results revealed that the maximum LST was 33.4°C in 1990, which increased to 36.1°C in 2020, and similarly, the minimum LST was 25.1°C, which also increased to 26.6°C in 2020. This study provides useful results for stakeholders to devise better policies and plans to control further haphazard urban expansion on fertile agricultural land in the study area.

Climate Change Impacts on LULC in the Jarmet Wetland and its Surrounding Areas in Western Ethiopia

The concerns over land use and land cover (LULC) change have emerged on the global stage due to realization that changes occurring on the land surface also influence climate, ecosystem and its services. This study aimed to map the temporal dynamic of LULC patterns and LST in the Jarmet wetland in Ethiopia. The dynamics and pattern of changes for a period of 21 years (2000-2021) were analyzed using geospatial techniques. Multi-temporal satellite images from Landsat ETM+ and Landsat-8 OLI sensor data were used to extract land-cover maps. The Land Surface Temperature (LST) trend of the study areas was computed using MODIS satellite imagery (2000-2021). Supervised classification using a Maximum Likelihood Classifier (MLC) was applied to prepare LULC maps of the watershed. The accuracy of the classified map was assessed using high-resolution data, and ground realities have been verified and ascertained through field observations. The results revealed a decreased trend in wetland, forest, shrubland and grassland in the period of 21 years (2000-2021) by -1148.71ha, -1073.26 ha, -1480.1 ha, and -87.73 ha, respectively. On the other hand, farmland and plantation areas followed an increasing trend. LST revealed decreasing trend in terms of mean and minimum with a fraction change of -0.018 and -0.073, whereas the maximum LST value shows an increasing trend with 0.021. The overall accuracy was 84.41%, with Kappa index of 76.13%. The analysis and findings of the study highlight important policy implications for sustainable LULC management in the study area. The study suggests the design and implementation of a guided natural resource policy, stopping the illegal expansion of farmland and educating society about the value of the sustainable management of habitat reserves.