Impact Of Land Surface Temperature Research Articles

Impact of Land Surface Temperature (LST) and Ground Air Temperature (Tair) on Land Use and Land Cover (LULC): An Investigative Study

The Land Surface Temperature (LST) is the fundamental concept of measuring the temperature of the ground or soil using remote sensing technologies. LST is an essential environmental parameter that has several uses in meteorology, climatology, agriculture, urban planning, and environmental monitoring. Remote sensing instruments, such as thermal infrared sensors aboard satellites, provide the means to capture LST data on a global scale. LST and ground air temperature ( ) are two related but distinct measures that provide valuable insights into different aspects: LST refers to the temperature of the Earth's surface, and it provides information about the temperature of the soil or crop canopy, which can impact plant growth and development, on the other hand measures the temperature of the surrounding air, which influences the rate of evaporation, transpiration, and photosynthesis in plants. In this study, a linear regression equation was developed to correlate the LST and using air temperature data of 19th March 2017, and 14th March 2021 from the two meteorological stations namely Veganpur and Main Maize Research Station (MMRS) of the Godhra region. The Landsat 8 thermal and optical bands were used to estimate LST, whereas Sentinel 2 optical data was used for the Land Use and Land Cover (LULC) purpose. To correlate LST with , eight ground truth (GT) points were collected from both stations, namely the minimum and maximum air temperatures in 2017 and 2021. The satellite derived LST data and the maximum ground air temperature ( ) were found to strongly concur, with an R2 value of 0.97. It was noted that a temperature difference of 2.9°C was found between the maximum ground air temperature and the Land Surface Temperature (LST). Study showed that from 2017 to 2021, the area used for agriculture, forestry, water bodies, and barren land decreased from 75.62 to 71.51%, 15.17 to 14.30%, 1.60 to 1.38%, and 0.03 to 0.02%, respectively, however the area used for build-up increased from 7.59 to 12.79%. During a five-year period, the study identified an enhancement in Land Surface Temperature (LST) due to an increase in built-up area. A temperature change was also seen throughout the course of the five-year period as a result of variations in LULC. Overall, it came to light that, under specific conditions and speculation, LST estimated using thermal data from Landsat 8 can be closely associated with ground air maximum temperature.

A geographic information systems and remote sensing-based approach to assess urban micro-climate change and its impact on human health in Bartin, Turkey.

Increasing land surface temperature (LST) is one of the major urban climatology problems arising in urban development. In this paper, the impact of vegetation and built-up areas on the LST and impact of LST on human health are assessed using the Landsat thermal data in Bartin, Turkey. The results show that there is a constant change in the share of vegetation and built-up areas due to rapid urbanization in Bartin. Strong positive correlation has been found between NDBI and LST while strong negative correlation has been found between NDVI and LST, suggesting their strong impacts on land surface temperatures. Similarly, a strong positive correlation has been observed between LST, sleep deprivation, and heat stress. This study provides precise information on effects of urbanization and man-made activities, which cause major changes in micro-climate and human health in the city. This study can assist decision-makers or planners to plan future developments sustainably.

Assessing spatiotemporal variations in land surface temperature and SUHI intensity with a cloud based computational system over five major cities of India

• The study showed that all cities have undergone a change in LULC pattern. • Concurrent data from space-based sensors for effective disaster response. • The impact of LST and SUHI over the vegetation cover with a cloud-based computing. • Biophysical parameters like NDVI, NDBI, and MNDWI are used for analysis and results derivations. The surface urban heat island (SUHI) is one of the most evident climate phenomena in low- and mid-latitude cities. Extensive growth and intense urbanization has brought a phenomenal change to the urban landscape. The rapid expansion of cities have the potential to generate distinctive local urban climates by increasing land surface temperature (LST) and the intensities of surface urban heat islands (SUHI). The study uses Landsat data in a cloud based computational system to investigate the spatiotemporal variation of land use land cover (LULC) for five major cities (Mumbai, New Delhi, Bangalore, Hyderabad, and Ahmadabad) from 2007 to 2020. Selected biophysical parameters like Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) are used. The interrelationship with the dynamics of LST to analyze the SUHI profiles reveals that the mean LST has a positive correlation for impermeable surfaces and a negative correlation for green surfaces, which are common for all Indian cities and results in degradation of environmental quality. The results obtained from this study showed that all cities have undergone a change in LULC pattern, which induced a rise in overall temperature and SUHI intensity. The expansion of the urban landscape along with the changing vegetation and cropping patterns due to the rise in anthropogenic activities has resulted in susceptible climatic conditions and necessitates further environmental investigations. The findings of the study can be recommended to monitor the impacts of SUHI intensity at different locations for future studies to adapt sustainable urban planning of cities.

Assessing the impact of land surface temperature on urban net primary productivity increment based on geographically weighted regression model

Urbanization had a huge impact on the regional ecosystem net primary productivity (NPP). Although the urban heat island (UHI) caused by urbanization has been found to have a certain promoting effect on urban vegetation NPP, the factors on the impact still are not identified. In this study, the impact of urbanization on NPP was divided into direct impact (NPPdir) and indirect impact (NPPind), taking Kunming city as a case study area. Then, the spatial heterogeneity impact of land surface temperature (LST) on NPPind was analyzed based on the geographically weighted regression (GWR) model. The results indicated that NPP, LST, NPPdir and NPPind in 2001, 2009 and 2018 had significant spatial autocorrelation in Kunming based on spatial analytical model. LST had a positive impact on NPPind in the central area of Kunming. The positively correlation areas of LST on NPPind increased by 4.56%, and the NPPind caused by the UHI effect increased by an average of 4.423 gC m−2 from 2009 to 2018. GWR model can reveal significant spatial heterogeneity in the impacts of LST on NPPind. Overall, our findings indicated that LST has a certain role in promoting urban NPP.

Land Cover Influences on LST in Two Proposed Smart Cities of India: Comparative Analysis Using Spectral Indices

Elucidating the impact of Land Surface Temperature (LST) is an important aspect of urban studies. The impact of urbanization on LST has been widely studied to monitor the Urban Heat Island (UHI) phenomenon. However, the sensitivity of various urban factors such as urban green spaces (UGS), built-up area, and water bodies to LST is not sufficiently resolved for many urban settlements. By using remote sensing techniques, this study aimed to quantify the influence of urban factors on LST in the two traditional cities (i) Panaji and (ii) Tumkur of India, proposed to be developed as smart cities. Landsat data were used to extract thematic and statistical information about urban factors using the Enhanced Built-up and Bareness Index (EBBI), Modified Normalized Difference Water Index (MNDWI), and Soil Adjusted Vegetation Index (SAVI). The multivariate regression model revealed that the value of adjusted R2 was 0.716 with a standard error of 1.97 for Tumkur city, while it was 0.698 with a standard error of 1.407 for Panaji city. The non-parametric correlation test brought out a strong negative correlation between MNDWI and LST with a value of 0.83 for Panaji, and between SAVI and LST with a value of 0.77 for Tumkur. The maximum percentage share of cooling surfaces are water bodies in Panaji with 35% coverage and green spaces in Tumkur with 25% coverage. Apparently, the UGS and water bodies can help in bringing down the LST, as well as facilitating healthy living conditions and aesthetic appeal. Therefore, the significance of ecosystem services (green spaces and water bodies) should be given priority in the decision-making process of sustainable and vibrant city development.

Land surface temperature and energy expenditures of households in the Netherlands: Winners and losers

This study aims to examine the associations between land surface temperature (LST) and annual household energy expenditure (HEE) in urbanized zones of the Netherlands. To do so, satellite images of 96 days at four different overpassing local times (10:30 a.m., 1:30 p.m., 10:30 p.m., 1:30 a.m.) are retrieved, and annual average maximum (LST-Max) and average minimum (LST-Min) LST at each zone are calculated. Employing geographically weighted regression, controlling for 11 control variables, the results indicate that the impact of LST on HEE could not be enhanced unless the interactions between LST and location-specific circumstances are taken into consideration. In this line, four types of socio-spatial characteristics are distinguished: (1) losers, where higher levels of both LST-max and LST-Min are associated with higher HEE, characterised by high population density; (2) peak losers and trough winners, where LST-Max is associated with higher HEE and LST-Min with lower HEE, characterised by large households and elderly citizens; (3) peak winners and trough losers, where LST-Max is associated with lower HEE and LST-Min with higher HEE, characterised by private-rental dwellings and large building surface to volume ratio; (4) winners, where both LST-Max and LST-Min are associated with lower HEE, characterised by old buildings.

Land surface temperature and households’ energy consumption: Who is affected and where?

It is widely accepted that land surface temperature (LST) affects household energy consumption (HEC). There is, however, no previous study available that clarifies whether LST's impact is similar in each and every area, or if it varies from one location to another. Analysing the impact of LST on HEC of 2612 residential zones of the Netherlands in 2014, this study concludes that HEC of 50% of the zones is affected by LST, accounting for 0.8% of overall consumption on average. It is obtained that energy-intensive, high-income and large-size households are more likely to be affected by LST. The results show that the effect is likely to be significant in the zones with relatively milder air temperature, and higher levels of humidity and wind. It is obtained that the effect intensifies when the buildings are less compact and the zones are less urbanised. Ultimately, this study urges for a shift in the approach of the existing studies on the impact of LST by putting forward a proposition: the impact of LST on HEC could not be spatially generalised, and one cannot enhance the associations unless location-specific circumstances of the areas in question are taken into consideration.

Multi-temporal NDVI and surface temperature analysis for Urban Heat Island inbuilt surrounding of sub-humid region: A case study of two geographical regions

Rapid growing urban population has resulted in the occupancy of large proportionate of the city and its outskirts, thereby contributing factors to change in the environmental conditions. This has resulted in widespread land acquisition for built up and industrial development, covering the centre of the city while moving at the outskirts of the city as well. Land Use /Land Cover (LULC) changes causes alterations in the land use categories, mostly the concrete forests which has increased the urban temperature as compared to the rural regions due to rapidly growing urbanized environment. Urban Heat Island (UHI) is one of the human-induced environmental phenomenon affecting the urban inhabitant in many ways, such as altering and disturbing the land cover its use which changes thermal energy flow causing elevated surface and air temperature. Temporal satellite datasets (LANDSAT ETM+ image of 1989, 2000 and 2006) can be used to monitor surface temperature while vegetation indices can be used to assess the coverage of the vegetation and non-vegetation area in the region. Temporal NDVI is employed in the study area to analyse the impact of land surface temperature against NDVI in the region. Therefore, temporal remotely sensed data can be used to map LULC and its dynamic changes and other environmental phenomena such as surface temperature over a period of time. Temporal UHI has been estimated using geospatial technology to incorporate it for environmental impact assessment on the surrounding environment. The present research focuses on temporal NDVI and Surface temperature, the methodology used altogether for the assessment of resolution dynamic UHI change on environmental condition for Haridwar district, Uttrakhand India and Kanpur district, Uttar Pradesh in India. Both case study has different environmental conditions, geographical locations and demography. Hilly and forested region with almost no industrial activities for Haridwar while several industrial activities and densely populated region Kanpur located in an Indo-Gangetic plain. The research outcome demonstrates the correlation between temporal NDVI and Surface temperature exemplified with case study conducted over two different regions, geographically as well as economically. There is a need to consider the environmental dimension while making progress to urbanization.

DETERMINING THE IMPACTS OF LAND COVER/USE CATEGORIES ON LAND SURFACE TEMPERATURE USING LANDSAT8-OLI

Abstract. Due to unplanned and uncontrolled expansion of urban areas, rural land cover types have been replaced with artificial materials. As a result of these replacements, a wide range of negative environmental impacts seriously impacting human health, natural areas, ecosystems, climate, energy efficiency, and quality of living in town center. In this study, the impact of land surface temperature with respect to land cover and land use categories is investigated and evaluated for Istanbul, Turkey. Land surface temperature data was extracted from 21 October 2014 dated Landsat 8 OLI data using mono-window algorithm. In order to extract land use/cover information from remotely sensed data wetness, greenness and brightness components were derived using Tasseled Cap Transformation. The statistical relationship between land surface temperature and Tasseled Cap Transformation components in Istanbul was analyzed using the regression methods. Correlation between Land Surface Temperature and Meteorological Stations Temperature calculated %74.49.

DETERMINING THE IMPACTS OF LAND COVER/USE CATEGORIES ON LAND SURFACE TEMPERATURE USING LANDSAT8-OLI

Due to unplanned and uncontrolled expansion of urban areas, rural land cover types have been replaced with artificial materials. As a result of these replacements, a wide range of negative environmental impacts seriously impacting human health, natural areas, ecosystems, climate, energy efficiency, and quality of living in town center. In this study, the impact of land surface temperature with respect to land cover and land use categories is investigated and evaluated for Istanbul, Turkey. Land surface temperature data was extracted from 21 October 2014 dated Landsat 8 OLI data using mono-window algorithm. In order to extract land use/cover information from remotely sensed data wetness, greenness and brightness components were derived using Tasseled Cap Transformation. The statistical relationship between land surface temperature and Tasseled Cap Transformation components in Istanbul was analyzed using the regression methods. Correlation between Land Surface Temperature and Meteorological Stations Temperature calculated %74.49.

THE LAND SURFACE TEMPERATURE IMPACT TO LAND COVER TYPES

Abstract. Land cover type is an important signature that is usually used to understand the interaction between the ground surfaces with the local temperature. Various land cover types such as high density built up areas, vegetation, bare land and water bodies are areas where heat signature are measured using remote sensing image. The aim of this study is to analyse the impact of land surface temperature on land cover types. The objectives are 1) to analyse the mean temperature for each land cover types and 2) to analyse the relationship of temperature variation within land cover types: built up area, green area, forest, water bodies and bare land. The method used in this research was supervised classification for land cover map and mono window algorithm for land surface temperature (LST) extraction. The statistical analysis of post hoc Tukey test was used on an image captured on five available images. A pixel-based change detection was applied to the temperature and land cover images. The result of post hoc Tukey test for the images showed that these land cover types: built up-green, built up-forest, built up-water bodies have caused significant difference in the temperature variation. However, built up-bare land did not show significant impact at p<0.05. These findings show that green areas appears to have a lower temperature difference, which is between 2° to 3° Celsius compared to urban areas. The findings also show that the average temperature and the built up percentage has a moderate correlation with R2 = 0.53. The environmental implications of these interactions can provide some insights for future land use planning in the region.

THE LAND SURFACE TEMPERATURE IMPACT TO LAND COVER TYPES

Land cover type is an important signature that is usually used to understand the interaction between the ground surfaces with the local temperature. Various land cover types such as high density built up areas, vegetation, bare land and water bodies are areas where heat signature are measured using remote sensing image. The aim of this study is to analyse the impact of land surface temperature on land cover types. The objectives are 1) to analyse the mean temperature for each land cover types and 2) to analyse the relationship of temperature variation within land cover types: built up area, green area, forest, water bodies and bare land. The method used in this research was supervised classification for land cover map and mono window algorithm for land surface temperature (LST) extraction. The statistical analysis of post hoc Tukey test was used on an image captured on five available images. A pixel-based change detection was applied to the temperature and land cover images. The result of post hoc Tukey test for the images showed that these land cover types: built up-green, built up-forest, built up-water bodies have caused significant difference in the temperature variation. However, built up-bare land did not show significant impact at p<0.05. These findings show that green areas appears to have a lower temperature difference, which is between 2° to 3° Celsius compared to urban areas. The findings also show that the average temperature and the built up percentage has a moderate correlation with R<sup>2</sup> = 0.53. The environmental implications of these interactions can provide some insights for future land use planning in the region.

The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations

Abstract. For several years passive microwave observations have been used to retrieve soil moisture from the Earth's surface. Low frequency observations have the most sensitivity to soil moisture, therefore the current Soil Moisture and Ocean Salinity (SMOS) and future Soil Moisture Active and Passive (SMAP) satellite missions observe the Earth's surface in the L-band frequency. In the past, several satellite sensors such as the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and WindSat have been used to retrieve surface soil moisture using multi-channel observations obtained at higher microwave frequencies. While AMSR-E and WindSat lack an L-band channel, they are able to leverage multi-channel microwave observations to estimate additional land surface parameters. In particular, the availability of Ka-band observations allows AMSR-E and WindSat to obtain coincident surface temperature estimates required for the retrieval of surface soil moisture. In contrast, SMOS and SMAP carry only a single frequency radiometer and therefore lack an instrument suited to estimate the physical temperature of the Earth. Instead, soil moisture algorithms from these new generation satellites rely on ancillary sources of surface temperature (e.g. re-analysis or near real time data from weather prediction centres). A consequence of relying on such ancillary data is the need for temporal and spatial interpolation, which may introduce uncertainties. Here, two newly-developed, large-scale soil moisture evaluation techniques, the triple collocation (TC) approach and the Rvalue data assimilation approach, are applied to quantify the global-scale impact of replacing Ka-band based surface temperature retrievals with Modern Era Retrospective-analysis for Research and Applications (MERRA) surface temperature output on the accuracy of WindSat and AMSR-E based surface soil moisture retrievals. Results demonstrate that under sparsely vegetated conditions, the use of MERRA land surface temperature instead of Ka-band radiometric land surface temperature leads to a relative decrease in skill (on average 9.7%) of soil moisture anomaly estimates. However the situation is reversed for highly vegetated conditions where soil moisture anomaly estimates show a relative increase in skill (on average 13.7%) when using MERRA land surface temperature. In addition, a pre-processing technique to shift phase of the modelled surface temperature is shown to generally enhance the value of MERRA surface temperature estimates for soil moisture retrieval. Finally, a very high correlation (R2 = 0.95) and consistency between the two evaluation techniques lends further credibility to the obtained results.