Land Surface Temperature Values Research Articles

Spatio-temporal pattern of urban vegetation in the central business district of the Wa municipality of Ghana

Increasing physical infrastructure development influences the spatial distribution of vegetation and temperature in many cities around the globe. However, in emerging cities such as Wa Municipality in Ghana and elsewhere, research attention has focused on urban expansion or urban sprawl and the impacts thereof on the peri‑urban vegetation. Considering the importance of vegetation in regulating temperature, reducing windstorm, carbon sequestration and creating beauty, this paper concentrates on the threat posed by urban retrofitting activities on vegetation in open spaces in the Wa Central Business District (CBD). The study area was gridded into 1 ha plots using the Fishnet tool in ArcMap. One hundred and fifty-seven full plots and 75 plots of various sizes less than 1 ha were obtained from the fishnet gridding. One hundred and thirteen full plots were sampled for the counting of the trees. Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) maps were prepared from 2014, 2017 and 2020 Landsat 8 satellite images. Point NDVI and LST values were extracted for 600 random points and used to analyze the spatial distribution using Global and Local Moran I tests. Two-sample t-test and Pearson correlation statistical tests were performed to assess the significance of any differences in the NDVI, LST and the tree density for 2014, 2017 and 2020. The results revealed that the average tree densities for 2017 and 2020 in the Wa CBD were 4 and 3 trees ha−1, respectively which were classified as very low. The spatial distribution of the NDVI values showed low, high and insignificant clusters. The NDVI values were within the range of 0.019–0.219. The clusters of high values were found around state institutional structures, where large open spaces existed while clusters of low values were found around the market, transport stations and densely populated areas where open spaces were very few. The land surface temperature trend revealed increasing minimum and maximum values. The minimum LST values were 32 °C, 33 °C and 36 °C in 2014, 2017 and 2020, respectively and the maximum LST values were 36 °C, 37 °C and 40 °C for 2014, 2017 and 2020, respectively. It is concluded that tree density and NDVI values in the CBD are low, signifying isolated trees and patches of vegetation in the area. Also, it was revealed that as greenness reduces LST increases. The Wa Municipal Assembly and the Regional Forestry Commission should intensify public education on tree planting and maintenance in the city.

Spatial and temporal analysis of the increasing effects of large-scale infrastructure construction on the surface urban heat island

With the rapid development of China's economy and the continuous improvement of people's living standards, large-scale infrastructure constructions (LSICs) are also increasing rapidly. LSICs with impervious surfaces have increasingly resulted in replacing natural landscapes, altering surface radiation, thermal properties, and humidity in urban areas. To study the environmental thermal changes of Beijing Daxing International Airport before and after its construction and operation, four Landsat-8 images (from the year of 2014, 2017, 2019, and 2021) were used to calculate the land surface temperature (LST). Then the LST values of four images covering the study area were compared and analyzed using the urban heat island ratio index (URI). Results show that the URI value of this area increased from 0.120 of 2014 to 0.185 of 2017 after the construction of Daxing Airport, indicating that the urban surface heat island effect in this area greatly increased. Additionally, the URI value of this area increased from 0.153 of 2019 to 0.206 of 2021 after the operation of Daxing Airport, indicating that the surface urban heat island effect in this area further increased. Therefore, we infer that this effect is closely related to airport construction and operation. Afterward, the random forest classification algorithm is used to classify land types based on pixels, and then the relationship between URI and land classification types was discussed. It is found that before the construction of Daxing Airport, both dark buildings and bare land contribute significantly to the thermal environment of the airport. After the completion of Daxing Airport, the contribution model was changed to high-reflectivity buildings and bright soil. The thermal pollution generated by the airport has a greater impact on the ground objects within the range of 7.5 km, and a relatively smaller impact on the ground objects outside the range of 9.5 km. Our results can provide a valuable reference for the study of the thermal environment caused by human activities.

Exploring Adaptive UHI Mitigation Solutions by Spatial Heterogeneity of Land Surface Temperature and Its Relationship to Urban Morphology in Historical Downtown Blocks, Beijing

Heat stress brought on by the intensification of urban heat island (UHI) has caused many negative effects on human beings, which were found to be more severe in highly urbanized old towns. With the inconsistent findings on how urban spatial morphological characteristics influence land surface temperature (LST) and gaps between design practices being found, we chose Beijing Old Town (BOT) as the study area and took the basic planning implementation module “block” as a study to reveal the spatial heterogeneity of LST and its relationship to multiple urban morphological characteristics with higher spatial resolution calculated via WorldView3. Our results have shown that (1) UHI effect was significant and spatially heterogeneous in BOT, and significant hot areas with high LST value and small LST differences were found, as cold areas were the exact opposite. (2) The proportion of vegetated area, water, impervious surface, and urban spatial structure indicators i.e., building coverage ratio, mean height, highest building index, height fluctuation degree, space crowd degree and sky view factor were identified as significantly affecting the LST of blocks in BOT. (3) The effects of GBI components and configuration on LST varied within different block types; generally, blocks with GBI with larger patches that were more complex in shape, more aggregated, and less fragmented were associated with lower LST. Finally, in the context of integrating our study results with relevant planning and design guidelines, a strategy sample of adaptive GBI planning and vegetation design for blocks with different morphological features was provided for urban planners and managers to make a decision on UHI mitigation in the renewal process of BOT.

Identifying the effect of monsoon floods on vegetation and land surface temperature by using Google Earth Engine

Flood is one of the most devastating climatic disasters around the world. The physical and infrastructural damage of floods is uncontrollable and challenging to recover. Though the mismanagement of the water system is one cause of flood, some countries face a seasonal natural flood, which is impossible to avoid. The history of flood affection in these countries are long, and people don't have any other choice but to adapt to this circumstance every year. The continuous flood event has several climatic impacts that are not broadly documented and remain in the shadows of severe infrastructural damage. This research aims to identify the effect of monsoon floods on vegetation and land surface temperature (LST). The study area is the northeast part of Bangladesh, a highly flood-prone area. The research incorporates Google Earth Engine (GEE) to manage the satellite image data related to this research which are Sentinel-1 SAR imagery and Landsat-8 imagery. Six years of data from 2015 to 2020 have been taken to continuously monitor the flooded area, vegetation, and LST dynamics. Primary results indicate a yearly increase and decrease of the flooded area with 57.3% highest increase rate in 2019. A continuous increase of Enhanced Vegetation Index (EVI) value and decrease of LST has a changing pattern similarity with flooded area fluctuation over the year. Also, the flooded areas have around 50% less mean EVI value than the non-flooded areas, eventually rising average LST in flooded areas. 10,024 grids of 1 km × 1 km have been used to extensively analyze the relationship of flood and EVI through correlation and linear regression. The final result reveals a clear negative correlation value (less than 0.56 for all the years) of EVI with flooded areas, having the highest R-squared value of 0.4325 in 2017.

Investigation of Potential Prevailing Wind Impact on Land Surface Temperature at Gas Flaring Sites in the Niger Delta, Nigeria.

This research examines the effects of South prevailing wind on Land Surface Temperature (LST) retrieved from Earth Observation (EO) Satellites at 11 gas flaring sites in Rivers State, Niger Delta region, Nigeria. 7 Landsat 5 Thematic Mapper (TM) and 18 Landsat 7 Enhanced Thematic Mapper Plus (ETM+) from 17/01/1986 to 08/03/2013 with < 5 % cloud contamination were considered. All sites are located within a single Landsat scene (Path 188, Row 057). The atmospherically corrected reflectance was used for the classification of 4 land cover (LC) types at each site. The emissivity (ε) for each site is estimated by using standard values for determined LC from Look Up Table (LUT). The surface-leaving radiance (Lλ) is computed from the atmospherically corrected thermal band 6 (High gain) and the emissivity (ε) values. The Planck equation was inverted using Landsat calibration constants to derive LST. Geospatial analysis of LST results using ArcGIS show 6 ranges of LST values for all sites. For both sensors LST retrieved for the flare stack sources are the highest values compared to other locations within the sites. Wind directions and wind speeds for Landsat data acquisitions dates and the South prevailing wind were applied to the LST for assessing their effects on it. The results show that for Eleme I and II, and Onne, the p-values results showed that no statistically significant relationships between δLST values in different directions (δLSTNE, δLSTNS and δLSTNW) existed. For Obigbo site, the wind direction (South) for data acquisition date combined with the South prevailing wind to generate a noticeable impact on the LST towards the North-East and the North-West directions. For Alua, Bonny, Chokocho, Rukpokwu, Umurolu and Sara sites, the p-value obtained is statistically significant for all the 3 (δLSTNE, δLSTNS and δLSTNW) relationships; therefore, producing a circle flare δLST footprint. For Umudioga site, only δLSTN versus δLSTW is statistically significant, causing a noticeable effect on the flare δLST in the North-West direction. Based on these results, it can be concluded that the volume and rate of burning gas, and the speed of the South wind at the time of satellite overpass are major factors that determine the influence of the South prevailing wind on the LST retrieved at the flaring sites in the Niger Delta.

Determining the influence of population density on the land surface temperature based on remote sensing data and GIS techniques: application to Prizren, Kosovo

The whole study was conducted for the Municipality of Prizren and aims to to determine the effect that the population density has on land surface temperature (LST). All this was achieved through the connection of land surface temperature (LST) and population density. The free Landsat 8 satellite image downloaded from the United States Geological Survey website was used and then processed using GIS and remote sensing techniques. To understand the relationship between population density and LST, we performed a regression analysis. This analysis showed a strong positive relationship with a value of r = 0.8206, emphasizing the important role that the population has in creating empowering areas that generate surface urban heat island (SUHI) effect. The results of the study clearly showed that in the northern, central, and western parts there are pixels with high LST values. This presentation corresponds with the population density, which means that it is precisely the actions of the population that help generate, display, and strengthen the harmful effect of the SUHI. The map with areas of high LST pixels are of great importance to the policymakers and urban planners of Prizren so that they can orient themselves in these areas and take all actions necessary to minimize this harmful effect which is worrying citizens. If it continues with unplanned development, the peripheral parts of Prizren are seriously endangered by the damage of the spaces which offer protection (green spaces) from the SUHI phenomenon.

Land Surface Temperature and its Relationship to Population Density

Population density due to urbanization contributes to the SUHI phenomenon and urban climate change. Understanding the SUHI phenomenon that brings enormous negative impacts to the environment and human life, Land Surface Temperature (LST) assessment is essential for creating a feasible and livable city. By utilizing the data of 1999 and 2018, this study aims to assess the LST value and its relationship to population density in Tanjungpinang city over two decades. As an island, Tanjungpinang has a vulnerability to SUHI and the climate change phenomenon. This study applied GIS and remote sensing models based on the mathematical formula of digital remote sensing images to calculate the LST value, and the relationship between LST and population density was examined using correlation analyses with Microsoft Excel. The results showed that Tanjungpinang city had increased 3.5oC in LST and 94.80% in density population over two decades. SUHI phenomenon has occurred during this period. It also indicated that there was a significant relationship between population density and LST. The LST spatial pattern spread from west to east of Tanjungpinang city was in line with the population density distribution pattern. The area with the highest percentage of population density addition and experienced the highest LST was Tanjungpinang Barat District. This study considers local governments to create effective population control and adaptive planning strategies for SUHI phenomenon mitigation.

Time Series Analysis of Landsat Data for Investigating the Relationship between Land Surface Temperature and Forest Changes in Paphos Forest, Cyprus

This study aims to investigate how alternations of the land surface temperature (LST) affects the normalized difference vegetation index (NDVI) in Paphos forest, Cyprus, using Landsat-5 and Landsat-8 imagery for the time periods 1993–2000 and 2013–2018, respectively. A total of 262 Landsat images were processed to compute the mean monthly NDVI and LST values and create a time series. Using the Cook’s distance, the effect of missing values in the analysis of the time series were examined. Results from the cross-correlation and cross-variograms, decomposition model, and the BFAST algorithm were compared to produce reliable conclusions on forest changes and satellite, meteorological, and environmental data were combined to interpret the changes that occurred inside the forest. The decomposition analysis showed a decrease of 2.7% in the LST for the period 1993–2000 and an increase of 4.6% in the LST during the period 2013–2018. The NDVI trend is negatively correlated to the LST trend for both time periods. An increase in the LST trend was identified in November 1998 as well as in the NDVI trend in October 1994 and May 2014 that was caused by favorable climatic conditions. An increase in the NDVI trend from May 2014 to December 2015 may be related to reduced pityocampa attacks. An abrupt decrease was detected in December 2015 that was probably caused by the locust invasion that occurred in the island earlier that year. A positive correlation appears for LST and NDVI variables for time lags 4, 5, 6, 7, and 8 months. Overall, it was shown that LST and NDVI analysis is very promising for identifying potential forest decline.

A global seamless 1 km resolution daily land surface temperature dataset (2003–2020)

Abstract. Land surface temperature (LST) is one of the most important and widely used parameters for studying land surface processes. Moderate Resolution Imaging Spectroradiometer (MODIS) LST products (e.g., MOD11A1 and MYD11A1) can provide this information with moderate spatiotemporal resolution with global coverage. However, the applications of these data are hampered because of missing values caused by factors such as cloud contamination, indicating the necessity to produce a seamless global MODIS-like LST dataset, which is still not available. In this study, we used a spatiotemporal gap-filling framework to generate a seamless global 1 km daily (mid-daytime and mid-nighttime) MODIS-like LST dataset from 2003 to 2020 based on standard MODIS LST products. The method includes two steps: (1) data pre-processing and (2) spatiotemporal fitting. In the data pre-processing, we filtered pixels with low data quality and filled gaps using the observed LST at another three time points of the same day. In the spatiotemporal fitting, first we fitted the temporal trend (overall mean) of observations based on the day of year (independent variable) in each pixel using the smoothing spline function. Then we spatiotemporally interpolated residuals between observations and overall mean values for each day. Finally, we estimated missing values of LST by adding the overall mean and interpolated residuals. The results show that the missing values in the original MODIS LST were effectively and efficiently filled with reduced computational cost, and there is no obvious block effect caused by large areas of missing values, especially near the boundary of tiles, which might exist in other seamless LST datasets. The cross-validation with different missing rates at the global scale indicates that the gap-filled LST data have high accuracies with the average root mean squared error (RMSE) of 1.88 and 1.33∘, respectively, for mid-daytime (13:30) and mid-nighttime (01:30). The seamless global daily (mid-daytime and mid-nighttime) LST dataset at a 1 km spatial resolution is of great use in global studies of urban systems, climate research and modeling, and terrestrial ecosystem studies. The data are available at Iowa State University's DataShare at https://doi.org/10.25380/iastate.c.5078492 (T. Zhang et al., 2021).

Improving land surface temperature prediction using spatiotemporal factors through a genetic-based selection procedure (Case Study: Tehran, Iran)

In recent decades, land surface temperature (LST) has been considered as an important satellite product for thermal monitoring of the environment. Despite the great development of LST retrieval technology from satellite data, especially from Landsat images, up-to-date thermal information cannot always be obtained. Predicting LST values for future times is a pressing issues to understand and mitigate the effects of climate change, especially in urban environments. This highlights the importance of LST forecasting methods. Accordingly, in this study a combination of Genetic Algorithm and Geographically and Temporally Weighted Regression (GTWR) is presented to predict LST based on nine selected spatiotemporal factors affecting LST in the urban areas. The genetic algorithm is used to determine weights of different factors in the LST prediction with respect to the GTWR as the regression model. Then, these weights are used to obtain predicted LST values for experimental points. Landsat-derived LST bands is used to train the genetic algorithm and extract the factors’ weights. Tehran city in the summer is selected as the case study in our work. Landsat LST data from 2013 to 2016 are used for training. To assess the performance of our scheme, we apply it in 2017. We find a correlation coefficient of 0.99 and a root-mean-square error of 0.5 K with respect to the Landsat data. The results of this study also indicate that LST prediction using weighted factors is superior to using all factors with equal contributions.

LST-based threshold method for detecting UHI in a complex urban landscape

Urban Heat Island (UHI) explains the occurrence of excessive heat that spread unevenly in urban areas. Intense and complex human activities in urban areas are mainly causing the formation of UHI. This study aims to identify the spatial characteristics of UHI in a complex urban feature, like the Universitas Indonesia campus area and its surroundings in Depok City. The campus and its surroundings might represent a complex urban area and can be used as a reference to understand the widespread impact of UHI. UHI was determined based on the threshold value, calculated from the average and standard deviation of the surface temperature. This study utilized Landsat 8 OLI-TIRS imagery to extract Land Surface Temperature (LST) value. The LST observations were collected for three different periods, including 2013, 2017, and 2021. The LST value tends to be increased during these times, with the LSTmax recorded at 34.68 °C, 35.47 °C, and 36.4 °C for 2013, 2017, and 2021, respectively. Based on the number of the accommodated grid, the UHI was getting larger every year. Started from 313 grids in 2013, the UHI has been expanded to more than 550 grids in 2021.

Multiple linear and non-linear regression analyses of various soil and terrain indices with regard to their efficiency in the determination of temporal changes in LST values within Trichy district of Tamil Nadu, India.

Land surface temperature (LST) of the terrain is the result of thermal energy received from the sun as the source. The study site was Trichy district of Tamil Nadu state in India wherein the LST values pertaining to all the belonging pixels have been calculated using generalized split window (GSW) algorithm from the Landsat-8 satellite images on two different dates with a gap of 5years between them. Along with the LST calculation on these 2 dates, corresponding NDWI, NDVI, NDBI, NDMI, BSI, DBI, DBSI, LSE, and albedo values have also been calculated. Then, changes in the dependent variable (LST) and in the each of the respective explanatory or independent variables have been undergone through the process of multiple linear and non-linear regression analysis to find out the best-fitting model set of explanatory variables that best describes the variation in LST values at a location. In this research, it has been found that a non-linear model set comprising five independent variables like change in BSI, LSE, NDWI, NDBI, and albedo seem to be the best-fitting model for predicting the variations in the dependent variable to the maximum degree and also with acceptable redundancy. Also, it has been observed that changes in LSE or albedo values at any place within the study-site could play the most significant role in having an influence in the LST value change that could be recorded in the same over any given time period, thus proving the vastness of the effect that could be made by an increase in urbanization or structural development on the extent of positive change in LST.

The Extreme Heat Wave over Western North America in 2021: An Assessment by Means of Land Surface Temperature

In our current global warming climate, the growth of record-breaking heat waves (HWs) is expected to increase in its frequency and intensity. Consequently, the considerably growing and agglomerated world’s urban population becomes more exposed to serious heat-related health risks. In this context, the study of Surface Urban Heat Island (SUHI) intensity during HWs is of substantial importance due to the potential vulnerability urbanized areas might have to HWs in comparison to their surrounding rural areas. This article discusses Land Surface Temperatures (LST) reached during the extreme HW over Western North America during the boreal summer of 2021 using Thermal InfraRed (TIR) imagery acquired from TIR Sensor (TIRS) (30 m spatial resolution) onboard Landsat-8 platform and Moderate Resolution Imaging Spectroradiometer (MODIS) (1 km spatial resolution) onboard Terra/Aqua platforms. We provide an early assessment of maximum LSTs reached over the affected areas, as well as impacts in terms of SUHI over the main cities and towns. MODIS series of LST from 2000 to 2021 over urbanized areas presented the highest recorded LST values in late June 2021, with maximum values around 50 °C for some cities. High spatial resolution LSTs (Landsat-8) were used to map SUHI intensity as well as to assess the impact of SUHI on thermal comfort conditions at intraurban space by means of a thermal environmental quality indicator, the Urban Field Thermal Variance Index (UFTVI). The same high resolution LSTs were used to verify the existence of clusters and employ a Local Indicator of Spatial Association (LISA) to quantify its degree of strength. We identified the spatial distribution of heat patterns within the intraurban space as well as described its behavior across the thermal landscape by fitting a polynomial regression model. We also qualitatively analyze the relationship between both UFTVI and LST clusters with different land cover types. Findings indicate that average daytime SUHI intensity for the studied cities was typically within 1 to 5 °C, with some exceptional values surpassing 7 °C and 9 °C. During night, the SUHI intensity was reduced to variations within 1–3 °C, with a maximum value of +4 °C. The extreme LSTs recorded indicate no significant influence of HW on SUHI intensity. SUHI intensity maps of the intraurban space evidence hotspots of much higher values located at densely built-up areas, while urban green spaces and dense vegetation show lower values. In the same manner, UTFVI has shown “no” SUHI for densely vegetated regions, water bodies, and low-dense built-up areas with intertwined dense vegetation, while the “strongest” SUHI was observed for non-vegetated dense built-up areas with low albedo material such as concrete and pavement. LST was evidenced as a good marker for assessing the influence of HWs on SUHI and recognizing potential thermal environmental consequences of SUHI intensity. This finding highlights that remote-sensing based LST is particularly suitable as an indicator in the analysis of SUHI intensity patterns during HWs at different spatial resolutions. LST used as an indicator for analyzing and detecting extreme temperature events and its consequences seems to be a promising means for rapid and accurate monitoring and mapping.

Assessing the intraurban differences in vegetation coverage and surface climate in a heterogeneous area

Urban trees play a critical role in alleviating land surface temperatures in cities. In remote sensing studies, vegetation indices are widely used to examine the relationship between Land Surface Temperature (LST) and vegetation cover. The vegetation cover can be measured using the Normalised Difference Vegetation Index (NDVI). In this study, the LST-NDVI relationship was assessed in each of the seven city-regions (A-G) in Johannesburg using Moderate-Resolution Imaging Spectroradiometer (MODIS) datasets to provide a basis for urban ecological planning and environmental protection. This study's specific objective was to determine the intraurban differences in vegetation coverage and LST in the seven city-regions over 19 years. The relationship between LST and NDVI was also examined over the years of study. The results showed a significant intraurban difference in LST and NDVI values in the city-regions with a negative correlation between them, ranging from −0.03 to −0.76. The LST values increased in all the city-regions with the highest value of 20.1°C in city-region G, followed by 19.6°C in city-region E. The vegetation cover decreased over the years, with the lowest NDVI value in city-region G (0.39), followed by city-regions F (0.43) and D (0.48). The city-regions with high LST and low vegetation cover include the city-centre and highly populated suburbs. This indicates that areas with greater vegetation cover have low LSTs and vice versa. These findings provide useful information for municipal authorities and other stakeholders to undertake appropriate decisions to tackle Urban Heat Island (UHI) effects by adopting effective urban planning and management interventions.

Spatiotemporal Fusion Network for Land Surface Temperature Based on a Conditional Variational Autoencoder

High spatiotemporal resolution land surface temperature (LST) data are essential for dynamic monitoring and prediction in climate change research. Due to the limitations of remote sensing instruments, the current platforms have difficulty achieving a compromise between high spatial and temporal resolutions for LST products. In this study, we propose a spatiotemporal fusion network for land surface temperature based on a conditional variational autoencoder (CVAE-LSTFM). First, an improved network is designed based on the CVAE by reconstructing an encoder and a decoder. To generate fine LST images based on dense time series, a variational inference model is formulated to integrate coarse and fine LST image pairs in variational autoencoded latent space. In addition, a new compound loss function for the proposed training method is designed to reduce the effects of noise and outliers. Then, a pretraining mechanism is adopted to optimize the network training process, and the parameters can be transferred to the training network of the CVAE to accelerate network convergence. Finally, a novel weighting strategy that considers spatiotemporal variations in LST (LST consistency weighting) is employed to solve the spatiotemporal heterogeneity problem caused by the rapid changes in LST. The method is quantitatively tested and evaluated in the Heihe River Basin using FY-4A LST and MODIS LST from September 2019. Compared with two traditional models and two deep learning-based models, CVAE-LSTFM yields lower RMSE (average<1.26 K) and LPIPS (learning perceptual image patch similarity, average<0.13) values and higher SSIM (structural similarity, average>0.96). In practice, CVAE-LSTFM can generate high spatiotemporal resolution LST values (hourly LST with a 1 km spatial resolution) with high accuracy, quality, and robustness.

Dynamics of Land Surface Temperature in Response to Land Use Land Cover Change in Phewa watershed, Kaski, Nepal

Geospatial tools play an important role in monitoring Land Use Land Cover Change (LULCC) and Land Surface Temperature (LST). This study assessed the extent of LULCC during 1990, 2007 and 2020 using temporal satellite imageries, and estimates LST during the study periods in relation to LULCC and explores various adaptation practices adopted in response to LST change in Phewa watershed. Landsat imageries were used for LULC classification and LST estimation. Purposive household survey (N=150), key informant survey (N=4), focus group discussion (N=4) and direct field observations were carried out to explore various adaptation practices. LULC maps was generated by using supervised classification followed by post classification change detection technique for LULCC analysis. Agricultural land and urban areas were found to have increased by 6.6% and 0.32% respectively while forest area, barren land and water bodies were found to have decreased by 1.88%, 4.41% and 0.63%, respectively, between 1990 and 2007. Forest area, urban areas and barren land had increased by 5.54%, 1.32% and 1.48%, respectively, while agricultural land and water bodies were observed to have decreased by 8.2% and 0.13%, respectively, between 2007 and 2020. The mean LST were 22.59° C, 22.81° C and 24.56° C in March-April and 14.65° C, 14.97° C and 15.27° C in January-December, respectively. Urban areas and water bodies exhibit highest and lowest mean values of LST, respectively, during study periods. LULCC due to urban growth and infrastructural developments had contributed to increase in LST. The highest rank to adaptation practices were for changes in cropping patterns and species, followed by agroforestry, use of improved seeds, shift to other income generation activities, use of more fertilizers, irrigation practices and growing vegetables in poly house which were statistically significant. This study provides scientific insights for policy makers and urban planners in improving urban planning and management.

CHARACTERIZATION OF THE PERI-URBAN AREA OF TIMISOARA MUNICIPALITY BASED ON THE LST INDEX

The study aimed to characterize a peri-urban area through the LST index. 12 localities were studied, which fall within the peri-urban area of Timisoara, Timis County, Romania. In relation to the intended purpose, a satellite scene was used, Landsat 8, with a component of 11 spectral bands. The satellite scene was subjected to a processing in order to extract the information necessary to determine the LST (Land Surface Temperature). The authors built a logical scheme, as a synthetic model for determining LST, based on consecrated formulas. The analyzed LST index presented values between LST = 26.857 in the case of Ghiroda locality and LST = 30.198 in the case of Sanandrei locality. According to PCA, PC1 explained 51.41% of variance, and PC2 explained 48.59% of variance. It was found an independent positioning of the localities Dumbravita (Dum), Ghiroda (Ghi), Remetea Mare (Rem M) and Mosnita Noua (Mos N) in relation to LST as biplot, due to the lower values for this index. Cluster analysis led to the grouping of the localities studied, based on Euclidean distances, in relation to LST values, in statistical safety conditions (Coph. Corr = 0.826). The highest level of similarity, in relation to LST, was registered in the case of Dudestii Noi and Sanandrei localities (Dud N_San), sub-cluster C2-2 (SDI = 0.025). In ascending order of SDI values followed the localities Becicherecu Mic and Giroc (Bec M_Gir), sub-cluster C2-1, SDI = 0.026; Giroc and Sag localities (Gir_Sag), subcluster C2-1, SDI = 0.040; Becicherecu Mic and Sag localities (Bec M_Sag), sub-cluster C2-1, SDI = 0.066.

The influence of El Niño Southern Oscillation on urban heat island formation at tropical city: Case of Kuching City, Sarawak

El-Niño Southern Oscillation (ENSO) is an opposing natural climate phenomenon in which the El Niño event causes droughts and the La Niña event causes floods. Literature studies stated there is still a lack of studies on the application of remote sensing in studying the effect of ENSO on Urban Heat Island (UHI) distribution patterns. The effect of UHI amplifies temperature, especially during El Niño. The objective of this study is to examine the influence of ENSO on UHI formation in tropical cities on a local scale. This study uses MODIS data and Landsat satellite data from 1988 to 2019. This study uses a combination of both data to investigate the influence of ENSO on UHI formation. The data must undergo pre-processing before the digital number is converted to Land Surface Temperature (LST). This study found that La Niña and El Niño events influenced the pattern of UHI variation. The strength of El Niño and La Niña differences influenced the value of LST and the total UHI area with a temperature of 30 °C. The result discovered that during the El Niño event, the maximum, minimum and mean temperature values of LST were higher than during the La Niña event and the total of the area hot spots that have temperatures above 30 ºC is higher during El Niño than during La Niña. The result of the study is very important to the community, local governments, and urban planners because remote sensing provides spatial information that represents the hot spot and cold spot. Keywords: ENSO, UHI, remote sensing, spatial pattern.

Time Series Analysis of Land Surface Temperature and Drivers of Urban Heat Island Effect Based on Remotely Sensed Data to Develop a Prediction Model

ABSTRACT The local climate of cities is changing, and one of the primary reasons for this change is rapid urbanization. The Lahore district is situated in the Punjab province of Pakistan and is mainly comprised of Lahore city. This city is among the fastest expanding cities in Pakistan. Due to this rapid urbanization, the natural land surfaces are being altered, harming the local environment and thus causing the urban heat island (UHI) effect. For the analysis of the UHI effect, the fundamental and essential step is assessing the land surface temperature (LST). Therefore, the current investigation assessed LST to evaluate the UHI effect of the Lahore district. This study used the remote sensing data retrieved from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) and Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor. Different new generation algorithms were initially used, but a convolutional neural network (CNN) model was used based on the accuracy. The model was developed by utilizing the past 19 years’ LST values along with elevation, road density (RD), and enhanced vegetation index (EVI) as input parameters for analyzing and predicting the LST. The LST data of the year 2020 was used for the validation of the outcomes of the CNN model. Among the model predicted LST and observed LST, a high correlation was noticed. The mean absolute percentage error (MAPE), mean absolute error (MAE), and mean squared error (MSE) for the considered two different periods (January and May) were also computed for both the training and validation processes. The prediction error for most parts of the district was within 0.1 K of the observed values. Hence, the formulated CNN model can be utilized as an essential tool for analyzing and predicting LST and thus for the evaluation of the UHI effect at any location.

Assessing the impact of land cover changes on land surface temperature and the relation to urban heat island in Makassar City, South Sulawesi

Makassar City, the capital of South Sulawesi Province, is the largest metropolitan city in the eastern part of Indonesia, with a population development rate of 1.19% in 2019. An increase in population impacts city development and results in land use and land cover changes. Changes in land use and land cover pattern bring impact to Land Surface Temperature (LST). This study examines land cover’s influence on land surface temperature in Makassar City using multi-temporal satellite data. Land cover and LST data were extracted using Landsat 7 and Landsat 8 over the period of 1999, 2009, and 2019. The result shows that the highest increase in land cover changed was a built-up area of 13.1%, and vegetation decreased by 8.6%. The change in average LST value in the last 20 years was 0.39°C with the highest LST distribution areas was in 30-32°C and 32-34°C classes. The result of LST analysis in 2019 shows that the Urban Heat Island phenomenon has occurred in Makassar in the downtown area and several areas with the densely built-up area. With an overview of the UHI phenomenon in Makassar, the government is expected to raise public awareness of this phenomenon so that preventive actions can be taken, so the effects of UHI do not spread more widely.