Land Surface Temperature Values Research Articles

Urban agriculture for environmentally just cities: the case of urban community gardens in Cape Town, South Africa

ABSTRACT Cities in the global South confront a multitude of environmental challenges, including air pollution, waste management issues, and biodiversity loss. Addressing these issues is vital for the welfare of residents in rapidly urbanising cities. Urban agriculture emerges as a proposed solution with the potential to mitigate some of these environmental challenges. There is a lack of research exploring the relationship between urban agriculture and environmental justice in the global South, particularly in marginalised neighbourhoods that bear the brunt of environmental injustice. To fill this research gap, the study examines how community gardens contribute to advancing environmental justice in low-income neighbourhoods of Cape Town, South Africa. To achieve this goal, a mixed-methods approach is adopted. Remote sensing techniques are utilised to compare land surface temperature values of selected gardens in Cape Flats with other suburbs in Cape Town. Additionally, semi-structured interviews and observations are conducted in 34 urban community gardens, purposively selected, to delve into how these gardens can address environmental injustices in the areas. The research findings uncover an unequal distribution of green spaces between wealthier and less affluent neighbourhoods in Cape Town. While the current benefits of gardening may not be widespread, the results indicate that urban community gardens offer numerous advantages, such as environmental education, promotion of agro-ecological practices, and the utilisation of land for green purposes. As repositories of knowledge promoting environmental justice, urban community gardens require institutional support aligned with their objectives to foster a fairer and more sustainable urban future.

Ilha de calor de superfície e indicadores geoespaciais: avaliação decadal comparativa por sensoriamento remoto

Abstract Surface Urban Heat Islands (SUHI) are formed through processes that include decreasing vegetation and increasing materials that are more conductive to heat. To investigate this phenomenon, the objective of this work is to comparatively verify the surface temperature and geospatial indicators to analyze environmental variation in the city of São Paulo, Brazil. The study method is remote sensing, with data from the Landsat 8 satellite over a ten-year timeframe. The variables include land cover, the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Built-up Index (NDBI), and land surface temperature (LST), thereby obtaining the Urban Thermal Field Variation Index (UTFVI) and estimating SUHI formation. The results allowed for the presentation of a progression of variables, highlighting the recurrent formation of SUHI in the central and eastern regions of the city of São Paulo, Brazil. Conversely, the southern and extreme northern districts exhibited the best NDVI indices and low LST values.

Spatial and Temporal Analysis of Thermal Comfort Before the Pandemic, Lockdown, and the Covid-19 Pandemic Transition Period in Yogyakarta City, Indonesia Using Modified Temperature-Humidity Index

Yogyakarta as a city of education and a tourism city has the potential to experience a very significant change in the concentration of anthropogenic activity between before the pandemic and when the lockdown policy was implemented. Also, during the pandemic transition period, it allows for changes in fluctuating thermal comfort. This study aims to analyse thermal comfort before the pandemic, lockdown, and transition of the Covid-19 pandemic in Yogyakarta City. This study utilised Landsat 8 images before the pandemic, lockdown, and the transition to the Covid-19 pandemic. Next, the land surface temperature (LST) and Normalized Difference Moisture Index (NDMI) were extracted. The LST and NDMI values are then processed to obtain thermal comfort in the form of the Modified Temperature-Humidity Index (MTHI). There was no clear pattern regarding MTHI (as a proxy of thermal comfort) before the pandemic, during the lockdown, and during the Covid 19 pandemic transition in the City of Yogyakarta. This study confirmed that there is a need of a better spectral index that reflects thermal comfort condition of a region.

Examining the nexus of social vulnerability, land cover dynamics, and heat exposure in Reno, Nevada, USA

Intense heat is a persistent urban challenge whose impacts are detrimental to human health. Heat-related effects disproportionately impact underserved populations. Modification of urban landscapes through increased imperviousness intensifies surface temperatures, leading to heightened heat exposure risks. While climate adaptation efforts have advanced, they are inadequate in addressing the uncertainties of climate change and the long-term risks of climate-related hazards. In addition, despite the numerous heat vulnerability studies across U.S. cities, the City of Reno is largely understudied. To address these gaps, the research examined the relationship between the spatiotemporal patterns of social vulnerability, changes in biophysical properties, and the heat hazard in Reno, Nevada. We utilized CDC census data to map the Social Vulnerability Index (SVI) and Landsat satellite data from 1990 to 2023 to analyze Land Surface Temperature (LST) trends for a temporal comparative study of heat patterns. Additionally, we employed the Normalized Difference Vegetation Index (NDVI) for vegetation extent. The zonal statistics tool helped assess the influence of different land use features on surface temperatures. The results showed that regions identified as social vulnerability hotspots often coincided with areas highly exposed to extreme temperatures and vice versa. Our findings also revealed an extension of heat vulnerability hotspots from the urban core to suburban regions. We observed a decline in mean LST values in regions covered by vegetation and a rise in mean surface temperatures in regions encompassed with imperviousness. These findings underscore the need for increased vegetation for heat mitigation.

The Preliminary Study of Environmental Variations Around the Du-Ku Highway Since 2000

Highways and their surrounding areas in mountainous and plateau regions are particularly susceptible to environmental changes, which can significantly impact their safety. In the context of global warming, the magnitude of environmental changes around highways has been further amplified. These environmental disturbances pose substantial risks to highway infrastructure in mountainous regions. By using satellite data and remote sensing techniques, this study focused on the environmental variations of the Du-Ku Highway (DKH) in the Tianshan Mountains and the preliminary revealed shifts in surface water, land surface temperature (LST), normalized difference vegetation index (NDVI), and temperature vegetation dryness index (TVDI) since 2000. The quantitative results showed that the water bodies with area between 0.1 and 0.5 ha showing the most significant growth around the DKH. The LST values are primarily distributed between 280 and 285 K, while the NDVI values are mostly below 0.4, and the TVDI is mainly concentrated at the two extremes. In the context of global warming and its amplified impact on mountainous and plateau regions, these findings offer critical insights that can directly support mountainous highway construction and maintenance strategies by identifying environmental indicators, providing a scientific foundation for making data-driven decisions.

Landscapes of thermal inequality: Exploring patterns of climate justice across multiple spatial scales in Spain

Over the last four decades, global temperatures have seen a generalized, long-term increase, and Europe is at the forefront of this trend, with temperatures rising by over twice the global average in the past 30 years. And the problem of excessive heat exposure is disproportionately more serious urban areas than it is in rural areas. This study investigates the issue of thermal inequality in Southern Europe, a largely understudied region exposed to substantial heat stress. We selected five major Spanish cities that reflect different socio-demographic and environmental contexts of the country, using the city’s administrative boundaries as our unit of analysis. We used bespoke neighborhoods to investigate heat inequality at multiple spatial scales. To illustrate the relationship between the thermal inequality patterns and the socio-demographic characteristics of neighborhoods, we estimated three separate generalized least squares regression models for each city, with the outcome variable being the average land surface temperature values in bespoke neighborhoods across the three spatial scales: 300 m, 1000 m, and 2000 m. The actual link between heat exposure and neighborhood characteristics appears to be sensitive to the local social, economic, institutional, historical and geographical context. Nonetheless, the connection between heat exposure and residents’ socioeconomic status, as well as the association of neighborhood population density with land surface temperature, seems to maintain consistent significance, often retaining their importance despite the spatial scale of analysis and employed zoning method.

A GIS-Based Framework to Analyze the Behavior of Urban Greenery During Heatwaves Using Satellite Data

This work proposes a new unsupervised method to evaluate the behavior of urban green areas in the presence of heatwave scenarios by analyzing three indices extracted from satellite data: the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Moisture Index (NDMI), and Land Surface Temperature (LST). The aim of this research is to analyze the behavior of urban vegetation types during heatwaves through the analysis of these three indices. To evaluate how these indices characterize urban green areas during heatwaves, an unsupervised classification method of the three indices is proposed that uses the Elbow method to determine the optimal number of classes and the Jenks classification algorithm. Each class is assigned a Gaussian fuzzy set and the green urban areas are classified using zonal statistics operators. The membership degree of the corresponding fuzzy set is calculated to assess the reliability of the classification. Finally, for each type of greenery, the frequencies of types of green areas belonging to NDVI, NDMI, and LST classes are analyzed to evaluate their behavior during heatwaves. The framework was tested in an urban area consisting of the city of Naples (Italy). The results show that some types of greenery, such as deciduous forests and olive groves, are more efficient, in terms of health status and cooling effect, than other types of urban green areas during heatwaves; they are classified with NDVI and NDMI values of mainly High and Medium High, and maximum LST values of Medium Low. Conversely, uncultivated areas show critical behaviors during heatwaves; they are classified with maximum NDVI and NDMI values of Medium Low and maximum LST values of Medium High. The research results represent a support to urban planners and local municipalities in designing effective strategies and nature-based solutions to deal with heat waves in urban settlements.

Spatial Assessment of Urban Heat Island (UHI) in Kütahya using Landsat-8 Satellite Data

The Urban Heat Island (UHI) effect refers to the phenomenon where urban areas experience significantly higher temperatures than their rural surroundings due to human activities and modifications, such as replacing natural land cover with impervious surfaces, reducing vegetation, and increasing heat-absorbing materials. Kütahya central district has a dense population and building layout, which contributes to the intense UHI. Geographic Information System (GIS) and its tools are widely used in assessing UHI in urban areas, providing insights for both researchers and local authorities. Landsat-8 images acquired on August 8, 2023 (Path: 179, Row: 33, Cloud Cover: 0%) were utilized to create the Urban Heat Island (UHI) map for the study area. Through spatial analysis using Landsat-8 data for Kütahya city center, six different classes were defined, with the UHI map created using land surface temperature values. The highest and lowest UHI values vary between 4.245°C and -3.457°C. It has been observed that the average UHI increases by 12% in areas lacking dense building structures, characterized by rock surfaces and limited green areas. Conversely, in areas with parks, forests, and sparse settlements, the UHI decreases by 10%. With this study, a comprehensive evaluation of UHI was conducted for Kütahya city center. While the results obtained are expected to be an important resource for researchers and local authorities, they are also anticipated to be beneficial in the fields of engineering geology, urban geology, and urban planning.

A Modified Method for Reducing the Scale Effect in Land Surface Temperature Downscaling at 10 m Resolution

Satellite-derived Land Surface Temperature (LST) plays an important role in research on natural energy balance and water cycle. Considering the tradeoff between spatial and temporal resolutions, accurate fine-resolution LST must be obtained through the use of LST downscaling (DLST) technology. Various methods have been proposed for DLST at fine resolutions (e.g., 10 m) and small scales. However, the scale effect of these methods, which is inherent to DLST processes at different extents, has rarely been addressed, thus limiting their application. In this study, a modified daily 10 m resolution DLST method based on Google Earth Engine, called mDTSG, is proposed in order to reduce the scale effect at fine spatial resolutions. The proposed method introduces a convolution-based moving window into the DLST process for the fusion of different remote sensing data. The performance of the modified method is compared with the original method in six regions characterized by various extents and landscape heterogeneity. The results show that the scale effect is significant in the DLST process at fine resolutions across extents ranging from 100 km2 to 22,500 km2. Compared with the original method, mDTSG can effectively reduce the LST value differences between tile edges, especially when considering large extents (>22,500 km2) with an average R2 improvement of 33.75%. The average MAE is 1.63 °C, and the average RMSE is 2.3 °C in the mDTSG results, when compared with independent remote sensing products across the six regions. A comparison with in situ observations also shows promising results, with an MAE of 2.03 °C and an RMSE of 2.63 °C. These findings highlight the robustness and scalability of the mDTSG method, making it a valuable tool for fine-resolution LST applications in diverse and extensive landscapes.

Is Zagreb Green Enough? Influence of Urban Green Spaces on Mitigation of Urban Heat Island: A Satellite-Based Study

The urban heat island phenomenon is a climatic condition in which urbanized areas exhibit higher temperature values than their natural surroundings. This occurs due to an unbalanced energy budget caused by the extensive use of synthetic materials. In such a scenario, urban green areas act as stressors to mitigate the intensity of the urban heat island and improve urban well-being. This study analyzes the spatial-temporal characteristics of the urban heat island in Zagreb, Croatia, aiming to examine the role of different types of green infrastructure in mitigating elevated temperature values and facilitating the definition of greener planning strategies. To achieve this, a multitemporal remote sensing- and NDVI-based analysis was conducted for the time series 1984–2014. An urban heat island intensity map was obtained for the selected 30-year period, along with thermal graphs registering land surface temperature values among different city districts. The results reveal significant heterogeneity, displaying variable behavior dependent on the city district. The role of Zagreb’s urban green areas in urban heat island mitigation is evident but largely dependent on urban morphology, construction types, and periods. Urban forests and urban parks play the most significant role in temperature reduction, followed by residential building neighborhoods and extensive neighborhoods consisting of familiar houses with gardens. Continuously built areas, such as the city center and industrial zones, are less prone to registering lower intensity values. Additionally, multitemporal intensity variations based on land use changes are registered in several districts.

Spatio-temporal analysis of urbanization effects: unravelling land use and land cover dynamics and their influence on land surface temperature in Aligarh City

ABSTRACT Rapid urbanization causes significant changes in land use and land cover (LULC), contributing to Urban Heat Islands (UHIs) and environmental challenges. This study analyzes two decades of Landsat satellite imagery to examine LULC changes and their impact on Land Surface Temperature (LST) in Aligarh City, India. Supervised classification using maximum likelihood techniques produced LULC maps with 85% accuracy. The results show a 21.10% increase in built-up areas, from 2338.83 hectares in 2001 to 2832.66 hectares in 2021, alongside a 24.55% decline in vegetation, from 620.28 hectares to 467.91 hectares. LST increased by 11.35°C, with a total maximum rise of 10.70°C between 2001 and 2021. Regression analysis revealed strong correlations between LST and spectral indices, with NDVI showing a negative correlation (R² = 0.89, 0.83, 0.78) and NDBI a positive one (R² = 0.87, 0.89, 0.79). The Aligarh main city area consistently recorded higher LST values than the AMU campus, with 88.60% of the city area in the 40–45°C range by 2021, compared to 52.87% for the campus. This study underscores the need for sustainable urban strategies to mitigate UHI effects and promote climate resilience.

Analysis of LST, NDVI, and UHI patterns for urban climate using Landsat-9 satellite data in Delhi

The present study is based on remote sensing techniques focusing on Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) to investigate their influence on land use and land cover dynamics, and the assessment of the Urban Heat Island (UHI) effect in Delhi, India. The objective of this study is to calculate LST, NDVI, and UHI values to understand the changes in LULC patterns, urbanization, and temperature increase within the city. Unlike previous studies conducted with Landsat-8, the present study employs Landsat-9 data, ensuring a higher level of authenticity in the results. Landsat-9, equipped with state-of-the-art sensors and instrumentation, provides superior data quality, enhanced image resolution, and advanced capabilities for precise monitoring and analysis. The methodology encompasses six steps for LST retrieval, enabling the calculation of UHI values and intensity. Ground data from 32 meteorological stations validate the LST results. Pearson correlation coefficients between LST and NDVI exhibit correlations ranging from −0.58 to −0.68 for three dates. On Dec 8, 2023, there is a weak negative correlation of −0.004. The analysis of changing land cover with variation in NDVI and LST unveils a diverse landscape, primarily characterised by green cover (47.34%), followed by built-up area (44.57%), barren land (7.57%), and water (0.52%). The study identifies the minimum value of UHI intensity for Delhi to be 8.13 °C on 26-Feb 2023 and the maximum value of UHI was estimated 10.29 °C on 2-June 2023. The study of Urban Heat Island (UHI) patterns revealed distinctive seasonal trends. The urban areas exhibited relatively cooler temperatures compared to surrounding rural regions on Dec 8, 2023. The conclusion drawn from this comprehensive analysis is that rapid urbanization in Delhi has significantly contributed to the increase in LST and UHI values. This rise can largely be attributed to the extensive use of concrete in construction activities, which exacerbates the UHI effect. Moreover, this analysis signifies the dynamic nature of UHI and emphasizes the urgency for strategic urban planning and climate-sensitive design approaches. Implementing such measures can create more sustainable and resilient urban environments.

Time series decomposition of land surface temperature for long-term trend forecasting and impact on nesting sea turtle habitats in the Arabian Gulf

Abstract Improving Land Surface Temperature (LST) modeling is vital for mitigating climate change effects on various ecosystems and marine habitats such as on important sea turtle habitats. Over the past decade, extreme temperatures have likely significantly affected nesting sea turtle habitats in the Arabian Gulf, with predominantly female hatchlings creating an imbalance in the sex ratio. Such shifts have profound implications for these habitats' long-term survival and conservation management. This study leverages statistical machine learning models to measure ongoing temporal variations in LST. We break down the LST time series into trend, seasonal, and noise components using classical decomposition methods like X11, SEATS, and the Seasonal and Trend decomposition using Loess (STL) approach. The long-term trends in LST data are driven by climate change rather than seasonal fluctuations. We employed Neural Network Auto Regression (NNAR), BaggedETS, Exponential Smoothing models, and STL method to project future LST values. We also explored advanced forecasting models like Dynamic Harmonic Regression, TBATS, and SARIMA for comparative performance analysis. Extended warm periods were identified for Abu Ali Island between 2017 and 2018 through several decomposition methods, likely linked to the 2015-2016 El Niño event. We also conducted a Marine Heat Wave (MHW) analysis from 2010-2020, establishing a pronounced impact of the 2015-2016 El Niño on the Arabian Gulf. In nesting beach environments with high LST, marine heatwaves could have a significant impact on sea turtle populations without human intervention such as artificially cooling the nest temperature. SARIMA model showed higher forecasting precision for in-situ weather data while NNAR model demonstrated superior performance with remotely sensed data.

Spatiotemporal Patterns of Land Surface Temperature, Urban Heat Island, and Potential Heat Stress Risk Areas Assessment for Tropical Inland City and Coastal City in Thailand

Global warming and rapid urban growth, compounded by the urban heat island (UHI) effect, are posing substantial challenges for urban populations and ecological well-being due to increased heat stress risks. This study aimed to analyze the spatiotemporal patterns of land surface temperature (LST), UHI, and potential heat stress risk areas (PHSRA) in two distinct cities, an inland city, Nakhon Ratchasima, and a coastal city, Si Racha, during the summer. Geoinformatics techniques and Crichton’s Risk Triangle method were employed, using updated demographic data and Sentinel-3 data from 2017 to 2022. The findings indicate that the inland city daytime average LST values were higher than in the coastal city, while the opposite was true at nighttime. Additionally, high daytime UHI patterns in the inland city were observed on the outskirts, and nighttime UHI concentrated in the urban center. The coastal city exhibited a strong daytime and nighttime UHI compared to the inland city, with daytime UHI peaking in the inner area and nighttime UHI near the coastline. The PHSRA was categorized into five risk levels (very high: > 0.8, high: 0.8–0.6, moderate: 0.6–0.4, low: 0.4–0.2 and very low: ≤ 0.2). During the daytime the highest risk areas, the high and very high levels of PHSRA in the inland city (23.84%) were greater than those in the coastal city (16.46%). Conversely, at nighttime, the coastal city (16.46%) exhibited higher levels than the inland city (5.26%). In nighttime conditions, all levels of PHSRA occurred more significantly than during the daytime in these cities. Understanding the spatiotemporal variations in PHSRA is vital for pinpointing places at risk of heat stress accidents in summer. This data is a goldmine for city planners and healthcare authorities, helping them plan interventions and effective measures for the future.

Assessing the impacts of vegetation loss and land surface temperature on Surface Urban Heat Island (SUHI) in Gazipur District, Bangladesh

Rapid replacement of vegetated land with impermeable land (built-up areas) is a major factor in the increase in Land Surface Temperature (LST), while increased LST worsens the temperature in cities and creates the Surface Urban Heat Island (SUHI) effect. The study aims to measure vegetation loss and Land Surface Temperature of the Gazipur district between 2000 and 2020 and explore the relationship among Normalized Difference Vegetation Index (NDVI), LST, and Urban Thermal Field Variance Index (UTFVI). The Landsat TM/OLI images with minimum cloud coverage have been used to derive different indices. The mean NDVI values are 0.21, 0.16, and 0.22 in 2000, 2010, and 2020 respectively which indicates a general improvement in the health of the vegetation. Besides, the highest LST values throughout 20 years, represent a general increasing trend. As a consequence, different land covers have experienced fluctuations in mean temperature. The result shows that the mean temperature of bare land, buildup, vegetation, and waterbody has increased by 4.77, 2.01, 2.25, and 2.23 °C respectively from 2000 to 2020. The strongest SUHI zone’s area grew by about 28% between 2000 and 2020. Additionally, the highest index value of UTFVI was 0.39 in 2000 and grew to 0.43 in 2010. It changed to 0.49 in 2020, or ten years later. Thus, the SUHI effect’s increasing intensity is visible. Also, regression analysis has been used to explore the correlation between the derived indices. Stakeholders from different sectors like urban planners and policymakers may take insights from this study to work to promote greenery for a healthy urban environment.

Assessment of Landscape Change and its Relationships with Surface Temperature and Geo-spatial Indices during 1992 - 2022 at Haldia Industrial Region, West Bengal, India

Unplanned local development and alterations in Land Use and Land Cover (LULC) are an early alert for towns and cities around the world. The population pressure has resulted in the conversion of natural land into impermeable areas, which has altered the surface energy budget and created microclimatic differences locally. The main objective of the present study is to estimate LULC changes and its relation to increases Land Surface Temperature (LST) in the Haldia Industrial Region (HIR) of West Bengal using multi-temporal Landsat 5 Thematic Mapper (TM) of 1992 and Landsat 8 Operational Land Imager (OLI) of 2022 with 30-m spatial resolution images were obtained from the USGS website. The supervised classification method was applied by using Maximum Likelihood classification (MLC) to classify the satellite images into various LULC classes such as settlement, waterbody, vegetation, agriculture land, and fallow land, respectively. The result suggested an overall increase of settlement area from 13.49 km2 in 1992 to 44.54 km2 in 2022. On the other hand, the agriculture land decreased from 214.26 km2 to 152.34 km2 due to fast urbanization, decline green lands, and expansion of barren lands. The outcome result from LST was exhibited that the LST value has increased 3.77°C during 1992 to 2022 in this study area. The Normalized Difference Vegetation Index (NDVI) was showed a strong negative relationship with LST with a determination coefficient (R2) values found as 0.491 (1992) and 0.356 (2022). The positive determination coefficient between LST with Normalized Difference Water Index (NDWI) was found as 0.0002 (1992), and 0.0417 (2022). R2 values were estimated as 0.174 (1992), and 0.0347 (2022) between LST with Normalized Difference Built-up Index (NDBI) that indicated a positive determination coefficient. The findings enhanced understanding of the relationship between urban LST and LULC in developing an inclusive climate resilience policy and making the HIR more sustainable to the effects of climate change.

Spatiotemporal changes in LULC and associated impact on urban Heat Islands over Pakistan using geospatial techniques

Urban Heat Islands arise when the natural land cover changes with dense concentrations of pavement, buildings, roads, and other surfaces that absorb and preserve heat. The current study aimed to assess Land Use Land Cover(LULC) changes and associated impact on Urban Heat Island (UHI) from 2000 to 2020 using Geospatial technologies for four metropolitan cities (Islamabad, Lahore, Multan and Muzaffarabad) of Pakistan. A Supervised Classification algorithm was performed on satellite imageries using ArcGIS software for analyzing LULC changes. The study areas were classified into 4 classes such as built-up, barren land, vegetation, and water bodies. Thermal bands of Landsat 4–5 and 8 were used for retrieved Land Surface Temperature (LST) and UHI. The findings of this study indicate that the urban area in all four districts i.e. Islamabad, Lahore, Multan and Muzaffarabad has increased by 21.3 %, 20.9 %, 6.1 % and 15 % respectively during 2000–2020. The built-up and barren land revealed the highest recorded LST, while vegetation and water bodies showed the lowest measured LST values. From 2000 to 2020, the annual maximum LST in Islamabad rose by 1.1 °C, Lahore by 2.1 °C, and Multan and Muzaffarabad by 1.9 °C and 1.5 °C. The expansion of built-up areas and the reduction in vegetation contribute to a favorable contribution to UHI. The study's findings showed that appropriate action plans are required to manage urban heat and promote sustainable city development.

Relating Urban Land Surface Temperature to Vegetation Leafing using Thermal Imagery and Vegetation Indices

Detecting the influence of temperature on urban vegetation is useful for planning urban biodiversity conservation efforts, since temperature affects several ecosystem processes. In this study, the relationships between land surface temperature (LST) and vegetation phenology events (start of growing season, SOS; end of growing season, EOS; peak phenology) was examined in native savannah woodland and grass parcels of a hot climate town. For comparison, similar woodland and grass parcels on the town’s periphery, and a wetland, were used. The vegetation parcel LST values (°C) in one calendar year (2023) were obtained from Landsat-8 (L8) and Landsat-9 (L9) thermal imagery, whose combination yielded an 8-day image frequency. Phenology changes relative to seasonal air temperature and LST were determined using vegetation index (VI) values computed from accompanying 30 m resolution L8-L9 non-thermal bands: the Normalised Difference Vegetation Index (NDVI) and one improved VI, the Soil Adjusted Vegetation Index (SAVI). Higher imaging frequency, 250 m resolution NDVI and Enhanced Vegetation Index (EVI) MOD13Q1 layers supplemented the L8-L9 VIs. LST correlated highly with air temperature (p < 0.001). On nearly all L8-L9 image dates, the urban vegetation parcel’s mean LST was higher (p < 0.001) than that at its peri-urban equivalent. Improved VIs (SAVI, EVI) detected some phenology events to have occurred slightly earlier than detected by the NDVI. Associated with the higher LST, the SOS was earlier in the urban than in the peri-urban woodland. This association has scarcely been demonstrated in savannah vegetation, necessitating proactive efforts to reduce potential biodiversity effects.

Land Surface Temperature Anomalies as Indicators of Urban Land Cover Change—A Study of Two Indian Cities

Rapid urbanisation over the years has led to the loss of natural land cover, thereby affecting Land Surface Temperature (LST) distribution in urban areas. This study aims to analyse LST anomalies (calculated as the deviation from the normal) over selected Indian cities and check if critical land cover changes can be identified. LST from Landsat Thermal Infrared (TIR) images acquired in March, April and May from 1988 to 2020 were used to estimate LST anomalies. Positive LST anomalies were observed mainly over barren and impervious areas; however, some areas showed a negative anomaly where the barren lands were converted to vegetated areas. The study has demonstrated that while some developed areas exhibit a positive anomaly indicative of significant changes or development, there are instances where the conversion of barren land to developed (i.e. built up) areas has resulted in a negative anomaly. Developed areas that are closer to the water creek or mangroves were associated with lower anomaly values indicating the cooling effect of the water body and vegetation. Conversely, the core urban areas generally exhibited higher LST values with positive anomalies indicating a warming effect. These findings can be used by city planners to identify hotspot areas and develop more effective strategies and policies to address the challenges of urban heat. They also highlight the regions that require infrastructural resources and policy changes to reduce the temperature.

Variability in land surface temperature concerning escalating urban development using thermal data of Landsat sensor: A case study of Lower Kharun Catchment, Chhattisgarh, India

Over the past few years, there has been a revitalized emphasis on comprehending the shifts in land cover and their implications for a range of environmental factors. This investigation seeks to analyze how changes in land surface temperatures (LST), normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and alterations in land cover intersect within the lower Kharun catchment area. The primary dataset utilized in this study is for 2001 and 2021 Landsat 7 and 8, part of the Landsat program managed by the United States Geological Survey (USGS), offer essential Earth observation data using their multispectral and thermal sensors which are designed to detect thermal radiation emitted from the Earth's surface. When these bands are properly processed, they enable accurate temperature measurements. Visual interpretation was conducted on these images, categorizing them into five specific classes of land cover these were vegetation, open land, settlement, waterbodies, and cultivation. Following this, spectral indices like NDVI and NDBI were calculated, and LST was derived using a single-channel algorithm. Subsequently, correlation analysis was utilized to explore the interconnectedness or mutual relationship among the spatial distribution of these parameters. Over the period from 2001 to 2021, the most significant changes in land use were observed in the settlement area and cultivation, which increased by 6.92 and 6.23 sq. km, respectively. Conversely, open land, vegetation, and waterbodies experienced decreases of 7.13, 5.56, and 0.46 sq. km, respectively. The patterns in which LST, NDBI, and NDVI are distributed, exhibited corresponding variations following changes in land cover. The observed alterations in LST, NDBI, and NDVI are believed to be primarily influenced by the expansion of built-up areas. A noticeable association suggests that as built-up areas increase, both NDBI and LST values typically rise.Furthermore, a correlation observed between LST with NDVI was negative, suggesting an inverse relationship between these parameters. On the other hand, the correlation of LST with NDBI observed was positive, indicating that these parameters exhibit a direct relationship. Overall, these findings seem to be complex and highlight the interactions between changing land cover and environmental parameters, underscoring the importance of understanding these relationships for effective land management and environmental monitoring.