Land Surface Temperature Values Research Articles

Examining the impact of Green Riyadh Project on alleviating urban heat island effects

The increase of land surface temperature (LST) because of the urban growth and spatial expansion specially in developing country have shown many negative impacts on the health and quality of life of people, nowadays some cities experience much warmer temperatures than nearby rural areas, causes what is known as the urban heat island (UHI) phenomenon. This study investigates alterations in vegetation and land surface temperature over space and time due to the Green Riyadh Project, employing remote sensing techniques. Multi-temporal Landsat images of the study area from 2018 and 2022, were processed in order to derive The Normalized Difference Vegetation Index (NDVI) and LST are being considered. Change detection methods were used to indicate the changes between the two time periods. Based on the results of this study, NDVI values show that the vegetated areas in Riyadh have not increased between (2018 and 2022). While the LST values show that the total area of UHIs has increased between (2018 and 2022). The change detection in vegetation indicates that there was a decrease in vegetation, mostly in urban areas. Where the change detection of surface temperature indicates huge areas where temperature has increased, mostly in the rural are-as, leaving few areas where the temperature has decreased. This means that the present amount of vegetation has still not made an impact in terms of decreasing the LST. Therefore, there is a need for more effort to be made in increasing the green areas within the urban environment of Riyadh.

Evaluation Of The Relationship Between Spatial-Temporal Changes Of Land Use/Land Cover (Lulc) And Land Surface Temperature (Lst): A Case Study Of Nilüfer, Bursa

This study was carried out in Nilüfer district of Bursa in order to reveal the extent of urbanization, to monitor the changes in landscape elements such as water, vegetation and agricultural lands, and to examine the effects of this on Land Surface Temperature (LST). For this purpose, images taken by Sentinel-2 satellites in 2017 and 2022 were used. With these images, NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), mNDWI (Modified Normalized Difference Water Index) and NDBI (Normalized Difference Built-up Index), which are widely used in understanding terrain changes, were calculated. Time series analyzes were made between the relevant years The relationship between the changes in the field and the surface temperature was questioned by calculating the LST value with Landsat 8 OLI_TIRS images, and the relations between the indexes and the LST were evaluated by correlation analysis. The results show that NDVI, SAVI, and mNDWI are on a decreasing trend between 2017-2022, while NDBI is on an increasing trend. In other words, the results showed that the vegetation areas and water-covered surfaces decreased, while the built-up areas increased. It has been observed that the changes in Land Use/Land Cover (LULC) increase the LST in the west and south regions of the district.

Urbanization and its impacts on land surface temperature and sea surface temperature in a tourist region in Mexico from 1990 to 2020

The United Nations World Tourism Organization has stated that Mexico is the sixth most visited country, with nearly 40 million international tourists. However, tourism can be one of the key contributors to rapid urbanization, contributing to climate change and environmental impacts. One of the tourist areas with the highest urban growth in the Mexican Pacific is Mazatlán, receiving more than 2 million tourists each year. The goal of this study was to analyze urban expansion and its impacts on land surface temperature and sea surface temperature in a tourist region in Mexico from 1990 to 2020. A supervised classification was carried out using the support vector machine algorithm on Landsat images taken over the last 30 years, analyzing the relationship of this growth to land surface temperature (LST). We also analyze whether increases in LST on urban land are related to increases in ocean temperature (SST) adjacent to the city. It was found that in the 1990s the urban area, currently 80 km2, was 25 km2. Urban growth has been exponential, and greater than in some other coastal megacities that have 5 times the population of Mazatlán. The median LST in the 1990s was 30.8 and is currently ∼32.9 °C, but in an analysis of the city's temperatures by pixel we found areas with temperatures as high as 40 °C; e.g., malls and residential developments. These LST values are higher than those reported in other coastal cities located at similar latitudes. Median SST values were between 19.2 and 20.8 °C, so SST was not related to LST. From the 1990s to the 2020s, the city's urban area has grown by 303%. Therefore, LST values in summer could reach 40 °C, which would give this coastal city one of the highest LSTs in the world, with a non-permeable surface that would cause flooding throughout the hotel zone.

Application of MODIS land surface temperature data on ENSO-based analysis in Kalimantan

El Nino Southern Oscillation as climate anomaly has been impacted Land Surface Temperature (LST) in Indonesia. Higher temperature during El Nino has been considered to give more risk in fire hazards in Kalimantan, thus, analysis of spatial and temporal distribution of LST became crucial. This study aims to analyze the spatial and temporal distribution of land surface temperature in Kalimantan based on the ENSO phases from 2014 to 2020. MODIS MOD11A1 images and ONI index were used as analysis materials. Image processing was conducted by selecting pixels using the QC band and then processing the LST values using a scale using RStudio software. The result shows the distribution of higher LST values on the southern side of Kalimantan, influenced by low elevation, peat soil, and land use change. The temporal distribution shows a tendency for higher temperatures in the dry months of August to October, with an increase of 4.5 percent during El Nino and a decrease of 0.9 percent during La Nina from 2014 to 2020. This study could contribute to management and mitigation for fire risk in certain area and period as ENSO occurred in Kalimantan.

Resposta da vegetação semiárida à seca agrícola determinada por índices derivados do satélite MODIS

ABSTRACT Indices based on data from remote sensors play an important role in the characterization, mapping and monitoring of a drought event. Thus, the objective of this study was to monitor the dynamics of agricultural drought, through the response of vegetation, in the semi-arid region that comprises the state of Paraíba, Brazil, through MODIS satellite products applied to the VCI (vegetation condition index), TCI (temperature condition index) and VHI (vegetation health index) indices, and to evaluate the correspondence of VHI index with standardized precipitation index (SPI) and agricultural standardized precipitation index (aSPI). In this study, values of NDVI (normalized difference vegetation index) and LST (land surface temperature) - covering the period between 2010 and 2020 - were used to estimate VCI, TCI and VHI. In addition, the correspondence of VHI with SPI and aSPI was evaluated at the 12-month time scale, conducted using Pearson’s correlation analysis. Characteristics of a stressed vegetation predominated in the study region, due to irregularity of precipitation and high temperatures, confirming the possibility of detection of droughts through VHI, VCI and TCI, and that the indices detected remotely and through local data are strongly correlated in drought detection.

Modeling the impact of urban land cover features and changes on the land surface temperature (LST): The case of Jordan

Higher land surface temperature (LST) in cities than their surrounding areas presents a major sustainability challenge for cities. Decision-makers and planners use the LST measurements to monitor the urban environment to reduce the urban climate’s main challenges. Therefore, there is an urgent need to examine the impacts of urban features and changes on the LST. This study focused on the relationship between LST and urban land changes and the impact of these changes on LST during different periods. Although a set of studies explored the relationship between landscape features and urban LST, several aspects still need further discussion. Here, the study aims to explore the influence of landscape features and land cover patterns on land surface temperature LST in two cities (Amman and Zarqa) in Jordan, and identify which of these features (vegetation cover, built-up and population density) has the most effective and influence on LST values. Therefore, this paper is the first study about land surface temperature LST and its relations with these aspects in Jordan. This study used a mixed method approach using quantitative method (GIS) and qualitative method (comparative case studies). This study revealed that the most important features affecting the LST values were: (1) Population density; (2) Built-up; and (3) Vegetation, and in descending order from the strongest to the least effective. It is also concluded that in the city in which the population density is high, the effect of built-up areas on the values of LST is as high as possible and positive more than the cities with medium and low population density. As for the city in which the population density is medium to low, the effect of vegetation cover on the values of LST is greater, and this can be more positive than in cities with a high population density. Therefore, the study contributes to improving the planners' and policymakers’ suitable future decisions for making sustainable cities.

An IoT System and MODIS Images Enable Smart Environmental Management for Mekong Delta

The smart environmental management system proposed in this work offers a new approach to environmental monitoring by utilizing data from IoT stations and MODIS satellite imagery. The system is designed to be deployed in vast regions, such as the Mekong Delta, with low building and operating costs, making it a cost-effective solution for environmental monitoring. The system leverages telemetry data collected by IoT stations in combination with MODIS MOD09GA, MOD11A1, and MCD19A2 daily image products to develop computational models that calculate the values land surface temperature (LST), 2.5 and 10 (µm) particulate matter mass concentrations (PM2.5 and PM10) in areas without IoT stations. The MOD09GA product provides land surface spectral reflectance from visible to shortwave infrared wavelengths to determine land cover types. The MOD11A1 product provides thermal infrared emission from the land surface to compute LST. The MCD19A2 product provides aerosol optical depth values to detect the presence of atmospheric aerosols, e.g., PM2.5 and PM10. The collected data, including remote sensing images and telemetry sensor data, are preprocessed to eliminate redundancy and stored in cloud storage services for further processing. This allows for automatic retrieval and computation of the data by the smart data processing engine, which is designed to process various data types including images and videos from cameras and drones. The calculated values are then made available through a graphic user interface (GUI) that can be accessed through both desktop and mobile devices. The GUI provides real-time visualization of the monitoring values, as well as alerts to administrators based on predetermined rules and values of the data. This allows administrators to easily monitor the system, configure the system by setting alerting rules or calibrating the ground stations, and take appropriate action in response to alerts. Experimental results from the implementation of the system in Dong Thap Province in the Mekong Delta show that the linear regression models for PM2.5 and PM10 estimations from MCD19A2 AOD values have correlation coefficients of 0.81 and 0.68, and RMSEs of 4.11 and 5.74 µg/m3, respectively. Computed LST values from MOD09GA and MOD11A1 reflectance and emission data have a correlation coefficient of 0.82 with ground measurements of air temperature. These errors are comparable to other models reported in similar regions in the literature, demonstrating the effectiveness and accuracy of the proposed system.

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.

Disproportionate exposure to surface-urban heat islands across vulnerable populations in Lima city, Peru

Climate change constitutes an unprecedented challenge for public health and one of its main direct effects are extreme temperatures. It varies between intra-urban areas and this difference is called surface urban heat island (SUHI) effect. We aimed to assess SUHI distribution among socioeconomic levels in Lima, Peru by conducting a cross-sectional study at the block-level. The mean land surface temperature (LST) from 2017 to 2021 were estimated using the TIRS sensor (Landsat-8 satellite [0.5 km scale]) and extracted to block level. SUHI was calculated based on the difference on mean LST values (2017–2021) per block and the lowest LST registered in a block. Socioeconomic data were obtained from the 2017 Peruvian census. A principal component analysis was performed to construct a socioeconomic index and a mixture analysis based on quantile g-computation was conducted to estimate the joint and specific effects of socioeconomic variables on SUHI. A total of 69 618 blocks were included in the analysis. In the Metropolitan Lima area, the mean SUHI estimation per block was 6.44 (SD = 1.44) Celsius degrees. We found that blocks with high socioeconomic status (SES) showed a decreased exposure to SUHI, compared to those blocks where the low SES were predominant (p-value < 0.001) and that there is a significant SUHI exposure variation (p-value < 0.001) between predominant ethnicities per block (Non-White, Afro-American, and White ethnicities). The mixture analysis showed that the overall mixture effect estimates on SUHI was −1.01 (effect on SUHI of increasing simultaneously every socioeconomic variable by one quantile). Our study highlighted that populations with low SES are more likely to be exposed to higher levels of SUHI compared to those who have a higher SES and illustrates the importance to consider SES inequalities when designing urban adaptation strategies aiming at reducing exposure to SUHI.

Analysis of Surface Temperature in Buru District Using Cloud Computing on Google Earth Engine

Monitoring land surface temperature in Buru Regency using Google Earth Engine cloud computing-based geospatial technology can help in understanding global climate and weather change, as well as provide important information for scientists, governments, and non-governmental organizations in making decisions related to climate change mitigation and natural disaster management. This research aims to analyze land surface temperature in Buru Regency using MODIS satellite image data based on the cloud computing google earth engine. This research uses Moderate Resolution Imaging Spectroradiometer (MODIS) Terra Land Surface Temperature and Emissivity 8-Day Global image data analyzed on Google Earth Engine. The results show that the lowest land surface temperature value in Buru Regency is 12, 7438ᵒ C, and the highest value is 31, 9582ᵒ C. The area that has a land surface temperature (LST) in the very high class has an area of 96,604.46 ha or 19.90%, the LST area in the high class is 139,606.47 ha or 28.76%, the LST area in the medium class is 140,853.38 ha or 29.02%, the LST area in the low class is 79,896.56 ha or 16.46% and the LST area in the very low class is 28,458.57 ha or 5.86%. The land surface temperature analysis in Buru Regency can provide important information for the local government in making policies and planning for sustainable regional development.

Reconstruction of all-weather land surface temperature based on a combined physical and data-driven model.

At present, the remote sensing (RS) thermal infrared (TIR) images that are commonly used to obtain land surface temperature (LST) are contaminated by clouds and thus cannot obtain spatiotemporal integrity of LST. To solve this problem, this study combined a physical model with strong interpretability with a data-driven model with high data adaptability. First, the physical model (Weather Research and Forecast (WRF) model) was used to generate LST source data. Then, combined with multisource RS data, a data-driven method (random forest (RF)) was used to improve the accuracy of the LST, and a model framework for a data-driven auxiliary physical model was formed. Finally, all-weather MODIS-like data with a spatial resolution of 1km were generated. Beijing, China, was used as the study area. The results showed that in cases of more clouds and fewer clouds, the reconstructed all-weather LST had a high spatial continuity and could restore the spatial distribution details of the LST well. The mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (ρ) in the case of more (fewer) clouds were ranked as follows: MAE < 1K (< 2K), RMSE < 2K (< 2K), and ρ > 0.9. The errors obeyed an approximately normal distribution. The total MAE, RMSE, and ρ were 0.80K, 1.09K, and 0.94K, respectively. Generally, the LST reconstructed in this paper had a high accuracy, and the model could provide all-weather MODIS-like LST to compensate for the disadvantages of satellite TIR images (i.e., contamination by clouds and inability to obtain complete LST values).

Investigating the relationship between COVID-19 shutdown and land surface temperature on the Anatolian side of Istanbul using large architectural impermeable surfaces.

Artificial impermeable surfaces are becoming more prevalent, especially in urban areas, as a result of shifting land use and cover, roads, roofs, etc. The modification of land surface temperature (LST) can also be accomplished through artificially impermeable surfaces. Large artificial impermeable surfaces, such as rooftops, parking lots, and other areas of use, can be found in industrial zones, shopping malls, industrial airports, and other locations. For the Anatolian side of Istanbul, 14 Landsat8 OLI/TIRS imagery images over the years 2016-2022 were investigated. To evaluate how well the study's images could be utilized, correlation and cosine similarity approaches were employed. A total of 12 images may be employed for research LST studies, it was discovered. We looked at closure dates during the COVID-19 epidemic to find out how human migration affected the LST. In addition, the LST value was estimated using the ordinary least squares (OLS) method employing LST and other biophysical indices. A decrease in LST values was seen as a result of the investigation. High levels of similarity and correlation were found between the images used. Results from the Google Mobility Index also provide support to the study. All of these facts provide support to Istanbul's Anatolian side experiencing lower surface temperature values, which consequently affects the city's massive structures.

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.

Multi-temporal assessment of land surface temperatures as an indicator of land use/cover changes and climate variability in the Develi Basin, Turkey.

Land surface temperature (LST) is an important parameter that reflects land surface processes of water and energy balance and has been used in assessment of land use/cover changes. However, the use of LST in monitoring changes in non-urban areas such as agricultural areas and wetlands is still limited. In this study, we aim to determine the spatial and temporal changes in LST in a semi-arid agricultural basin in Turkey (Develi Basin), where land use/cover and climatic conditions showed considerable variability since 1980s. Irrigated agriculture have expanded in the basin since 1987, after the construction of a large irrigation project. The basin hosts an internationally important wetland, called the Sultan Marshes, affected negatively by irrigation expansion. The study covers a 39-year period from 1984 to 2022. Four Landsat Thematic Mapper (TM) images acquired in 1984, 1987, 2003, and 2007 and two Landsat 8 OLI/TIRS images acquired in 2014 and 2022 were used in the analyses. The land use/cover changes were evaluated based on Normalized Difference Vegetation Index (NDVI). LST was estimated through top-of-atmosphere brightness temperature from thermal bands of Landsat images. Climate variability from 2014 to 2022 was analyzed with statistical methods. The results indicated that Develi Basin faced both spatial and temporal land use/cover changes. The area covered with natural steppe vegetation and water bodies decreased in the basin. In contrast, the sparsely and densely vegetated soil covers, which mostly denote agricultural areas, increased. Changes in LST values were observed from 1984 to 2022 as a result of climatic factors and land use/cover changes. LST changes were variable across different land use/cover types; LST decreased in irrigated areas and increased in lakes that went dry over years. LST changes proved useful for evaluating land use/cover changes and climatic variations in agricultural basins.

Combined influence of soil moisture and atmospheric humidity on land surface temperature under different climatic background

Soil moisture (SM) and atmospheric humidity (AH) are crucial climatic variables that significantly affect the climate system. However, the combined influencing mechanisms of SM and AH on the land surface temperature (LST) under global warming are still unclear. Here, we systematically analyzed the interrelationships among annual mean values of SM, AH, and LST using ERA5-Land reanalysis data and revealed the role of SM and AH on the spatiotemporal variations of LST through mechanism analysis and regression methods. The results showed that net radiation, SM, and AH could well model the long-term variability of LST well and explain 92% of the variability. Moreover, SM played an essential and different role under the different LST backgrounds. The AH always displayed a greenhouse effect on the LST. This study provides essential insights into the global climate change mechanism from the surface hydrothermal processes perspective.

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.

Spatiotemporal Characterization Of Land Surface Temperature In Relation Landuse/Cover: A Spatial Autocorrelation Approach

Abstract The land use and land cover (LULC) characteristics of Ghaziabad have experienced dynamic changes because of the city’s ongoing industrialization and urbanisation processes. These shifts can be directly attributed to human actions. These shifts can be directly attributed to human actions. Thermal variation in the study area necessitates LULC analysis. Landsat and Sentinel satellite data for 2011 and 2021 were used to map LULC, estimate land surface temperature (LST) and analysis spatial autocorrelation among the variables using ArcGIS software and the Google Earth Engine (GEE) cloud platform. A sharp descent is observed in the cropland while built-up area has increased during the study period. With the increase in the built-up surface in the area, the ambient temperatures have also increased from 18.70 °C in 2011 to 21.81 °C in 2021 leading to urban heat island effect. At all spatial scales, spatial autocorrelation is a characteristic property of most ecological parameters. The spatial clustering of LST in an ecosystem can play a crucial role in determining the dynamics of LULC.The Moran’s, I show that there is a considerable level of spatial autocorrelation in the values of LST and highly clustered pattern for both the years. Monitoring and understanding the surface thermal environment is crucial to discerning the causes of climate change.

Effect of landscape pattern of urban surface evapotranspiration on land surface temperature

Land surfaces in urban areas can be classified into artificial surfaces (impervious surface such as buildings and roads) and natural surfaces (vegetation, soil and water), and the natural surfaces have important influence on the urban environmental thermal regulation. Current research on urban spatial characteristics mostly focuses on the regulation of urban green spaces to mitigate the urban heat island effect and rarely explores the effect of the landscape pattern of urban natural surface evapotranspiration (ET) on land surface temperature (LST). Xuzhou, China, was selected as the study area, and the urban RS-PM and improved mono-window algorithm were applied to invert ET and LST values based on Landsat 8 Operational Land Imager and Thermal Infrared Sensor images, respectively. The study area was divided into five levels (LV5 to LV1) from high to weak, according to the ET intensity (ETi), four landscape-level (SHDI, SHEI, PD, and CONTAGION) and five class-level landscape metrics (PLAND, LPI, LSI, AI, and COHESION) were extracted based on the moving-window approach. Bivariate Moran's I was then used to calculate the spatial correlation between each landscape metric and LST. The weighted impact of each landscape metric on LST was obtained using principal component analysis and multiple regression. The results indicate that only five class-level metrics of high (LV5) and sub-high (LV4) ETi patches had significant negative spatial correlations with LST, and the weighted impacts of the high ETi patch metrics were all greater than those of the sub-high ETi patches. The weighted impacts of all class-level metrics that can mitigate LST were ranked as COHESION_LV5 > PLAND_LV5 > LPI_LV5 ≈ AI_LV5 > LSI_LV5 > LSI_LV4 > PLAND_LV4 > COHESION_LV4 > LPI_LV4 > AI_LV4. These findings revealed the importance of ET spatial pattern for urban thermal environment regulation, which may provide a new perspective in urban planning and environmental governance.

Quantifying Spatio-Temporal Land Surface Temperature and Biophysical Indices for Sustainable Management of Watershed: A Study of Vishwamitri Watershed of Gujarat

Spatio-Temporal Analysis of the nexus between vegetation dynamics and climatic parameters like surface temperature is essential in environmental and biophysical studies and for monitoring and management of watersheds. This study explored the spatio-temporal distribution of land surface temperature (LST), Normalised Difference Water Index (NDWI), and Normalized Difference Vegetation Index (NDVI) and the relationship between them in the Vishwamitri watershed of Gujarat for the Pre Monsoon and Post Monsoon of the Year 2001 and 2016 using Landsat dataset. The findings of the study showed that the LST of the Vishwamitri watershed. The mean LST value of the year 2001 was 46.19°C in the pre-monsoon season and 39.27°C in the post-monsoon season. Mean LST values for the year 2016 were 49.34°C in pre-monsoon and 35.21°C in the post-monsoon season as observed. The spatial distribution of NDVI and LST reflects an inverse relationship. A strong positive correlation between LST with NDVI is observed over highly dense built-up areas. In summary, the LST is greatly controlled by surface characteristics. The results of this study illustrate there has been a dynamic change in vegetation cover of the watershed in all seasons. There was also a negative correlation between LST and NDVI in the studied years. The study concludes that there has been a degradation of vegetation and intensification of LST in the year 2016 as compared with the year 2001. This study can be used as a reference for land use and environmental planning in a tropical city.

INVESTIGATION OF GREEN SPACE COOLING POTENTIAL ON LAND SURFACE TEMPERATURE IN ANTALYA CITY OF TURKEY

Abstract. The aim of this study is to investigate the potential cooling effect of urban green spaces on land surface temperature (LST). Two significant green areas and their surroundings in the Konyaaltı district of Antalya province were selected as the sample area. Antalya is one of the most important tourist destinations in Turkey and has experienced rapid population growth along with increasing construction. Because the sample area is located in the Mediterranean Climate Zone, the research datasets were limited to a 6-month time series covering May and October, as recommended by previous studies. In the study, cloudless Landsat 8 satellite images were utilized to obtain vegetation density and land surface temperature. After making atmospheric corrections to the satellite images, the researchers used a series of algorithms to calculate NDVI and LST values from Landsat 8's NIR, Red, and thermal bands. A linear cross-section was assigned from the maps, and statistical analysis was performed by applying the Pearson correlation test to the data obtained from the pixels in contact with the section. The analysis revealed that there is a strong opposite correlation between plant density and land surface temperature, and the land surface temperature decreases as the plant density increases. The cooling effect of green areas range from 0.13–1.04 °C, although it varies according to the month. The 1st sample area had an average cooling effect of 0.77 °C, while the 2nd sample area had an average of 0.28 °C cooling effect. Combining the results from both sample areas, the researchers found that urban green spaces provide a cooling effect of 0.52 °C during the hot months of the spring and summer seasons in the Mediterranean climate zone. The results suggest that green areas play an important role in cooling today's cities that have turned into concrete jungles. Therefore, it is suggested that increasing the presence of urban green areas through green infrastructure planning can not only enhance the cooling effect of green spaces in urban areas but also help combat global climate change.