Land Surface Temperature In Relation Research Articles

Estimation of land surface temperature in relation to land-use using remote sensing technology and geographic information system (Case study: Tabriz city(

Estimation of land surface temperature in relation to land-use using remote sensing technology and geographic information system (Case study: Tabriz city(

Spatiotemporal Evolution in the Thermal Environment and Impact Analysis of Drivers in the Beijing–Tianjin–Hebei Urban Agglomeration of China from 2000 to 2020

As urbanization advances, the issue of urban heat islands (UHIs) grows increasingly serious, with UHIs gradually transitioning into regional urban heat islands. There is still a lack of research on the evolution and drivers of the thermal environment in urban agglomerations; therefore, in this study, we used trend analysis methods and spatial statistical analysis tools to investigate these issues in the Beijing–Tianjin–Hebei (BTH) urban agglomeration. The results demonstrated the following: (1) The land surface temperature (LST) exhibited low fluctuation, while the relative land surface temperature (RLST) fluctuated significantly. In Zhangjiakou and Chengde, the LST and RLST evolution trends were complex, and the results differed between daytime and nighttime, as well as between the annual and seasonal scales. In other regions, the trends of LST and RLST evolution were more obvious. (2) During the daytime, the high UHI clusters centered on “BJ–TJ–LF” and “SJZ–XT–HD” formed gradually; during the nighttime, the high UHI clusters were mainly observed in built-up areas. The distribution range and direction of UHIs showed greater degrees of evolution during the daytime in summer. (3) The total UHI area showed an increasing trend, and the intensity of heat stress suffered by the BTH agglomeration was increasing. (4) In BTH and Hebei, aerosol optical depth, surface solar radiation, population density, and gross domestic product were the dominant factors influencing UHIs; moreover, in Beijing and Tianjin, all factors showed an basically equal impact. The methodology and findings of this study hold significant implications for guiding urban construction, optimizing urban structure, and improving urban thermal comfort in the BTH urban agglomeration.

Assessment of land surface temperature in relation to built-up index: A case study on Nagaon District, Assam, India

Assessment of land surface temperature in relation to built-up index: A case study on Nagaon District, Assam, India

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.

Quantifying the cooling effect of urban green space: A case from urban parks in a tropical mega metropolitan area (India)

• Local cooling effects of urban parks were analyzed. • Cooling effect was estimated using novel ring based method and multiple greenness and thermal parameters. • Findings revealed cooling effect is largely determined by the greenness of the parks. • Conservation and management of urban parks is essential for climate mitigation in tropical cities. Green space (GS) plays a crucial role in reducing the urban heat island (UHI) effect and helps in mitigating climate change. In Indian cities, GS are highly vulnerable due to rapid urbanization and infrastructural development. This study aims to assess the cooling effect of urban parks such as GS on the thermal environment in Kolkata Metropolitan Area (KMA), India. Five urban parks were selected from different parts of KMA for the assessment during the summer season. Three greenness indices (normalized difference vegetation index, enhanced vegetation index and soil adjusted vegetation index) and two thermal indices (land surface temperature and temperature condition index) were used to find out the cooling effect on the thermal environment. Relative land surface temperature (RLST) and vegetation cooling index (VCI) was developed for a better understanding of the relationship between greenness on the thermal environment. Correlation and regression analysis was also performed to show the relationship as well the effect of greenness parameters on thermal conditions. From the result, it was found that urban parks had a substantial impact on the cooling effect. (i) Botanical Park was the coldest park with an average LST of 33.55⁰C, followed by Nicco park (34.33⁰C), Nature park (34.48⁰C), Rabindra Sarabor (34.55⁰C), and Central Park (36.65⁰C) (ii) RLST had a negative correlation with PV (R = -0.51 for Botanical Park; R= -0.65 for Nature park; R= -0.57 for Central park) and (iii) finally, greenness had a negative impact on the thermal pattern in KMA (R = -0.16). Thus, from the results, it was documented that urban parks (as GS) had CA on the surrounding areas. Therefore, the conservation of GS is essential to achieving sustainable development goals (particularly goals- 3, 11, 13, and 15).

Recent trends of land surface temperature in relation to the influencing factors using Google Earth Engine platform and time series products in megacities of India

Increasing land surface temperature (LST) is one of the significant anthropogenic issues that is threatening livelihoods in urban areas. Understanding the urban LST is essential for sustainable management of the urban landscape. Various studies focused on the spatial pattern and average trend of LST in relation to influencing factors, but little is known regarding the pixel-scale spatiotemporal trends and integration of multiple influencing factors in the different seasons using time series data. Therefore, we focused on the spatiotemporal trends of LST in relation to the influencing factors using the Google Earth Engine platform and time series products. MODIS land surface temperature data was used to estimate the annual and seasonal day-night LST from 2003 to 2020. Theil-Sen slope and Mann-Kendall statistics were used to examine the magnitude and significance of the spatiotemporal trend. All-subsets regression and hierarchical partitioning (HP) were applied to explore the driving factors of LST. Average LST and range varied between the cities, but were highest in Chennai and Delhi, respectively. Except for winter nighttime in Kolkata, all cities' annual and seasonal nighttime LST are increasing (0.027–0.075 °C/year) over a large part of the study area (33.934%–100% of areas), indicating that anthropogenic force is increasing in urban areas during nighttime. Annual and seasonal daytime LST is increasing in most parts (55.545%–94.604% of areas) of Chennai and Kolkata, but daytime LST is decreasing (33.602%–98.571% of areas) in Mumbai and Delhi due to the increasing concentration of absorbing aerosol over the city. Strength of independent factors and multiple influencing factors varied in the day-night time between the cities. But nighttime LST was well explained using the all-subsets regression compared to daytime LST, except for Mumbai. Also, nighttime light density and NDBI had consistent effects on nighttime, and daytime, respectively. These findings can help to take scientific adaptation and mitigation strategies for sustainable development.

Quantitative valuation of green roofs’ cooling effects under different urban spatial forms in high-density urban areas

In the context of global climate change, the increasing urban heat island (UHI) effect is a serious challenge to sustainable urban development. The construction of green roofs is an important means to optimize the urban thermal environment, especially in high-density urban areas. However, the cooling effect of green roofs in different urban spatial forms varies. Thus, the quantitative evaluation of the cooling effect of green roofs is significant for the scientific and effective mitigation of the UHI effect. This study took the central district of Chengdu City as a typical area, clearly classified urban spatial form types based on the LCZ theory, and quantified the relative land surface temperature (RLST) difference between before (2014) and after (2018) the implementation of green roofs based on Landsat 8 remote sensing image retrieval. Numerical analysis methods were used to statistically evaluate the cooling effect of green roofs under different urban spatial form types by GIS. The results showed that (1) the cooling effect of green roofs varied significantly among different urban spatial form types; (2) the best performance of the cooling effect of green roofs was in Compact Highrise (LCZ1), which could reduce RLST by 0.94 °C, while the worst was in Lightweight Lowrise (LCZ7). Based on the above performance of the cooling effect of green roofs, the construction of green roofs can be prioritized and zoned, and the design strategies of green roofs under different urban spatial form types can be proposed. Our study will provide a basis for developing design guidance for green roofs and scientific support for mitigating the urban thermal environment.

Long-Term Spatiotemporal Patterns and Evolution of Regional Heat Islands in the Beijing–Tianjin–Hebei Urban Agglomeration

With the continuous development of urbanization, the urban heat island (UHI) phenomenon is becoming increasingly prominent. Especially with the development of various large urban agglomerations and the shrinking distance between cities, the regional thermal environment has attracted extensive attention. Therefore, we used Modis land surface temperature (LST) data and employed least squares, standard deviation and spatial autocorrelation analysis methods to analyze the spatiotemporal patterns and characteristics of summer daytime regional urban heat islands (RHI) in the Beijing–Tianjin–Hebei (BTH) urban agglomeration. Our results indicated that the relative land surface temperature (RLST) in the southeastern part of BTH with a relatively high level of urbanization showed a significant and continuous upward trend. With the continuous development of the level of urbanization in the southeast, the center of gravity (GC) of RHI gradually moved to the southeast, and the development direction of RHI changed from northwest–southeast to northeast–southwest. The area transfer of RHI was concentrated in no change and little change, indicating that the evolution trend of RHI was relatively stable. The high-high aggregation areas were mainly located in the more developed areas in the southeast. In addition, the methods and results of this study can provide reasonable and effective insights into the future development and planning of the BTH.

Effects of urbanization on the relationship between greenspace patterns and evolution of regional heat island in cities of Ethiopia

Green space (GS) is a central element of urban ecosystems while, urbanization is substantially changed green land into impervious ecosystems, which has then troubled the equilibrium of the surface thermal energy and the ecosystem services fluxes. This study was intended to explore the response of relative land surface temperature (RLST) intensities to the urban ecosystem- agglomeration dynamics as well as regional thermal environment (RTE) effects on green space in four major cities (i.e., Addis Ababa-Hawassa-Adama-Bahir Dar) of Ethiopia from 1990-2020, using a set of remote sensing images, single-channel algorism, and geospatial analyses. The results showed the trends of GS evolution were: GS loss > GS gain > GS exchange. Besides, the distinct ecosystems as high-temperature zones (RLST more than 2°C) were gradually augmented and significantly intensified between 2010-2020 in all cities, while the proportion of green space was significantly decreased. Due to this, the segregated urban heat islands were gradually inter-weaved and interacted with each other and forming regional heat islands. However, the newly established green space has fewer cooling effects than urban forest and greenery, because RLST of green space loss and gain are substantially disparate. We also confirmed that the regional thermal environment is not only prejudiced by ecosystem patterns and processes besides substantially conquered by the particular GS change processes as well as RHI dynamics and evolution rate.

Determination of Land Surface Temperature in Relation to Changing Land-Use / Land Cover Pattern at Urban Area of Bilaspur City [CG] through Temporal Remote Sensing & GIS

Determination of Land Surface Temperature in Relation to Changing Land-Use / Land Cover Pattern at Urban Area of Bilaspur City [CG] through Temporal Remote Sensing & GIS

Spatiotemporal analysis land surface temperature in relation to earthquake occurrence around the cimandiri fault

Cimandiri Fault is one of the faults that have high tectonic activity. Several earthquake events around the Cimandiri Fault have caused impacts and losses on humans. Efforts to detect earthquake events need to be done as disaster mitigation. Detection of earthquake events in this study by looking at deviations in surface temperature. This study was conducted to investigate the relationship between changes in land surface temperature (LST) in relation to the earthquake that occurred on 7 July 2018 (ML = 4.73) and 11 October 2018 (ML = 3.77). Landsat 8 satellite imagery are used in this study to get the value of the land surface temperature using the LST algorithm. To find out the deviation that occurred researchers used a statistical test x ± σ with a 66% confidence level and compared with the average LST for five years. The results of the study are an increase in land surface temperature due to the earthquake occurrence on July 7, 2011, an increase in surface temperature of land was 6.56 oC on the day before the earthquake, and October 11, 2011, there was an increase of 7.02°C the day before the earthquake.

Land-Use/Land-Cover Changes and Its Contribution to Urban Heat Island: A Case Study of Islamabad, Pakistan

One of the essential anthropogenic influences on urban climate is land-use/land-cover (LULC) change due to urbanization, which has a direct impact on land surface temperature (LST). However, LULC changes affect LST, and further, urban heat island (UHI) still needs to be investigated. In this study, we estimated changes in LULC from 1993 to 2018, its warming (positive) and cooling (negative) effect, and their contribution to relative LST (RLST) in the city of Islamabad using satellite remote-sensing data. The LULC was classified using a random forest (RF) classifier, and LST was retrieved by a standardized radiative transfer equation (RTE). Our results reveal that the impervious surfaces has increased by 11.9% on the cost of declining barren land, forest land, grass/agriculture land, and water bodies in the last 26 years. LULC conversion contributed warming effects such as forest land, water bodies, and grass/agriculture land transformed into impervious surfaces, inducing a warming contribution of 1.52 °C. In contrast, the replacement of barren land and impervious surfaces by forest land and water bodies may have a cooling contribution of −0.85 °C to RLST. Furthermore, based on the standardized scale (10%) of LULC changes, the conversion of forest land into impervious surfaces contributed 1% compared to back conversion by −0.2%. The positive contribution to UHI due to the transformation of a natural surface to the human-made surface was found higher than the negative (cooler) contribution due to continued anthropogenic activities. The information will be useful for urban managers and decision makers in land-use planning to control the soaring surface temperature for a comfortable living environment and sustainable cities.

Modeling the spatial variation of urban land surface temperature in relation to environmental and anthropogenic factors: a case study of Tehran, Iran

ABSTRACTSpatial variation of Urban Land Surface Temperature (ULST) is a complex function of environmental, climatic, and anthropogenic factors. It thus requires specific techniques to quantify this phenomenon and its influencing factors. In this study, four models, Random Forest (RF), Generalized Additive Model (GAM), Boosted Regression Tree (BRT), and Support Vector Machine (SVM), are calibrated to simulate the ULST based on independent factors, i.e., land use/land cover (LULC), solar radiation, altitude, aspect, distance to major roads, and Normalized Difference Vegetation Index (NDVI). Additionally, the spatial influence and the main interactions among the influential factors of the ULST are explored. Landsat-8 is the main source for data extraction and Tehran metropolitan area in Iran is selected as the study area. Results show that NDVI, LULC, and altitude explained 86% of the ULST °C variation. Unexpectedly, lower LST is observed near the major roads, which was due to the presence of vegetation along the streets and highways in Tehran. The results also revealed that variation in the ULST was influenced by the interaction between altitude – NDVI, altitude – road, and LULC – altitude. This indicates that the individual examination of the underlying factors of the ULST variation might be unilluminating. Performance evaluation of the four models reveals a close performance in which their R2 and Root Mean Square Error (RMSE) fall between 60.6–62.1% and 2.56–2.60 °C, respectively. However, the difference between the models is not statistically significant. This study evaluated the predictive performance of several models for ULST simulation and enhanced our understanding of the spatial influence and interactions among the underlying driving forces of the ULST variations.

Assessing land surface temperature and land use change through spatio-temporal analysis: a case study of select major cities of India

Globalisation and opening up of markets has seen large-scale unplanned urbanisation in previous decade especially in developing countries like India. Dynamic changing policies influenced by global entities to set up profitable, industry-oriented cities have powered extensive migration with population upwelling contributing to the fast-growing urban expansion that needs monitoring and planning to design urban cities of the future. Research agenda here is to understand the change in land use (LU) temporally and model the land use change based on cellular automata through slope, land use, excluded, urban, transport and hillshade (SLEUTH) approach. Scientific studies carried out to address LU, its linkage with rising surface temperature and prediction of urban growth are limited or conducted only for smaller geographical extents and lack study especially in the most active and diverse country like India. This paper proposes implementation of integrated method of LU, land surface temperature dynamics, and urban modelling for four major cities: Delhi, Mumbai, Kolkata and Hyderabad. Of the four cities, Delhi, the capital of India, has shown enormous change in terms of increasing paved surface from 21.63% in 2003 to 31.56% in 2017 with corresponding mean surface temperature surge of 25.93 °C and 36.51 °C, respectively, suggesting one of the worst affected cities. Predicted LU envisions the possibility of a spurt in unplanned growth across the city boundary that would hinder developing basic amenities and would cause increased thermal discomfort amongst residents. This was understood through analysis of land surface temperature in relation to changing land use, indicating a need for sustainable development strategies to be implemented to avoid business-as-usual scenario. The analysis aims to help planners and city managers to understand region-specific issues in urban growth and to improve the cityscape with specific interventions.

Using Moran's I and GIS to study the spatial pattern of land surface temperature in relation to land use/cover around a thermal power plant in Singrauli district, Madhya Pradesh, India

Fast emerging energy hub of India – Singrauli, Madhya Pradesh is undergoing a drastic transformation in its land use and land cover (LU/LC) owing to the mega power projects coming up in the area. Once densely covered with forests, the study area has changed through cleared land to thermal power plant and related infrastructure from 2005 to 2015. A sharp descent is observed in the forest cover while wasteland has increased during the study period. With the upsurge of thermal power plants in the area, the ambient temperatures have also increased leading to urban heat island effect. In the present study, changes in Land Surface Temperature (LST) around 10 km2 of Sasan Ultra Mega Thermal Power Plant is studied for three years 2005, 2010 and 2015, respectively using Moran's I geostatistics in GIS environment. Spatial autocorrelation is a characteristic property of most ecological parameters and can be seen at all spatial scales. Spatial clustering of LST in an ecosystem can play a vital role in evaluating land use/cover dynamics. The Moran's I reveal a high positive spatial autocorrelation or highly clustered pattern of obtained LST values for all the three years. The spatial variation of LST is also observed to be related to the LU/LC of the study site during the different study periods.

Estimate Land Surface Temperature in Relation to Land Use Types and Geological Formations Using Spectral Remote Sensing Data in Northeast Jordan

Land Surface Temperature (LST) is one of the important indicators to understand the spatial changes and surface processes on the earth surface that leads to actual assessment of environmental quality from local to global scales. The relation between spatial analysis of the land surface temperature and existing land use/land cover changes is important to evaluate the climate processes. Monitoring of this relation in the arid and semi-arid regions is necessary to make an appropriate decision about Land surface temperature and environmental status. In this paper, generally the split-window algorithm is used to estimate LST from thermal bands of the Landsat Operational Land Imager (OLI) and the Thermal Infrared Sensor (TIRS) using remote sensing and Geographic Information System (GIS) techniques as well as meteorological data through Moderate Resolution Imaging Spectroradiometer (MODIS). The results show the relationships between land use types and land surface temperature. MODIS data were analyzed. The relationship between MODIS and Landsat data temperature is moderate relation and the (R2 = 0.5109) according on 200 random points were selected. This research concludes that the maximum temperatures of the land use types in MODIS and Landsat data for the rock formation are 59° and 45° respectively, whereas the maximum temperatures of the geological formation in MODIS and Landsat data for the basalt are 59° and 45° respectively. In conclusion, the MODIS and Landsat OLI and TIRS Data have high ability to distinguish the land use types. The correlation coefficient of the relation between the surface temperature with rock was (R2 = 0.6197). Therefore, it is found that there is an ability to monitor the spatial and temporal changes for land surface temperature and thus it can be useful to environmental studies.

Detection of Land Surface Temperature in Relation to Land Use Land Cover Change: Dire Dawa City, Ethiopia

Detection of Land Surface Temperature in Relation to Land Use Land Cover Change: Dire Dawa City, Ethiopia

Changing trends of land surface temperature in relation to land use/cover around thermal power plant in Singrauli district, Madhya Pradesh, India

India’s energy capital—Singrauli, has conglomeration of various thermal power plants and operational coal mines. Earlier, the area was densely forested, rich in biodiversity and home to various tribal communities. In the last decade, the area has been exploited by the private sector for setting up large scale power generation plants. The land use and its pattern and land surface temperature (LST) were studied for the Sasan Ultra Mega Thermal Power Plant covering an area of 10 km2 for 3 years—2005, 2010 and 2015, respectively. Chronologically, the year 2005 is the phase of pre-establishment of the thermal power plant; the year 2010 specifies the construction phase and 2015 is the operational phase. Landsat ETM and Landsat 8 data of remote sensing system were used for this study. The land use/cover (LULC) study shows a significant decrease in the forest cover and barren rocky land, and a dramatic expansion in vegetation and wastelands. These changes in LULC have together widened the range of LST with an overall rise of 6.69 °C and an overall decline in the NDVI value from the year 2005 to 2015. The results of Pearson’s correlation analysis depict an inverse co-relationship between LST and NDVI for the study area. Therefore, LST and NDVI are two important parameters to study the metropolitan ecosystem and can be used as an effective tool for evaluating environmental influence on ecosystems.

Using Landsat Thematic Mapper (TM) sensor to detect change in land surface temperature in relation to land use change in Yazd, Iran

Abstract. Land surface temperature (LST) is one of the key parameters in the physics of land surface processes from local to global scales, and it is one of the indicators of environmental quality. Evaluation of the surface temperature distribution and its relation to existing land use types are very important to the investigation of the urban microclimate. In arid and semi-arid regions, understanding the role of land use changes in the formation of urban heat islands is necessary for urban planning to control or reduce surface temperature. The internal factors and environmental conditions of Yazd city have important roles in the formation of special thermal conditions in Iran. In this paper, we used the temperature–emissivity separation (TES) algorithm for LST retrieving from the TIRS (Thermal Infrared Sensor) data of the Landsat Thematic Mapper (TM). The root mean square error (RMSE) and coefficient of determination (R2) were used for validation of retrieved LST values. The RMSE of 0.9 and 0.87 °C and R2 of 0.98 and 0.99 were obtained for the 1998 and 2009 images, respectively. Land use types for the city of Yazd were identified and relationships between land use types, land surface temperature and normalized difference vegetation index (NDVI) were analyzed. The Kappa coefficient and overall accuracy were calculated for accuracy assessment of land use classification. The Kappa coefficient values are 0.96 and 0.95 and the overall accuracy values are 0.97 and 0.95 for the 1998 and 2009 classified images, respectively. The results showed an increase of 1.45 °C in the average surface temperature. The results of this study showed that optical and thermal remote sensing methodologies can be used to research urban environmental parameters. Finally, it was found that special thermal conditions in Yazd were formed by land use changes. Increasing the area of asphalt roads, residential, commercial and industrial land use types and decreasing the area of the parks, green spaces and fallow lands in Yazd caused a rise in surface temperature during the 11-year period.

A modified vegetation water supply index (MVWSI) and its application in drought monitoring over Sichuan and Chongqing, China

Based on the principle of vegetation water supply index (VWSI), relative normalized difference vegetation index (RNDVI) and relative land surface temperature (RLST) are proposed, with which a modified vegetation water supply index (MVWSI) is constructed, i.e., MVWSI=RNDVIRLST2 Compared with VWSI, MVWSI eliminates the difference caused by the spatial and temporal distribution of VWSI. MVWSI was applied to monitor drought over Sichuan and Chongqing, China in 2006, suggesting that MVWSI can demonstrate the whole process of drought from its emergence and development to its weakening and disappearance. The relative precipitation index (RPI) is used to examine the effectiveness of MVWSI. The examination result shows that MVWSI and RPI have high consistency in spatial distribution and in-site time series analysis. Compared with vegetation condition index (VCI), temperature condition index (TCI), vegetation temperature condition index (VTCI) and temperature vegetation drought index (TVDI), MVWSI is simpler in calculation and more stable hence more easily realized in application.