Land Surface Temperature Changes Research Articles

Analyzing land surface temperature changes in the Danjiang River Basin: a MODIS-based reconstruction and assessment before and after the middle route of the South-to-North Water Diversion Project

Analyzing land surface temperature changes in the Danjiang River Basin: a MODIS-based reconstruction and assessment before and after the middle route of the South-to-North Water Diversion Project

Spatio−Temporal Changes and Key Driving Factors of Urban Green Space Configuration on Land Surface Temperature

Changes in land cover by rapid urbanization have diminished the cooling effect of urban green spaces (UGS), exacerbating the upward trend of land surface temperature (LST). A thorough and precise understanding of the spatio-temporal characteristics of UGS and LST is essential for mitigating localized high temperatures in cities. This study identified the spatio-temporal changes in UGS configuration and LST in Shanghai from 2003 to 2022. The correlation between UGS configuration and LST was explored using spatial autocorrelation analysis and causal inference. The results show that (1) the high-temperature space had grown from 721 km2 in 2003 to 3059 km2 in 2022; (2) in suburbs, the largest area of UGS tended to decrease, while the number of patches tended to increase, indicating a distinct feature of suburbanization; (3) changes in the largest area of UGS had more significant spatial correlation, indicating that urban sprawl primarily impacts large UGSs; and (4) compared to the number and shape of UGS, changes in the largest area are the key factor influencing regional LST. These findings enrich the knowledge of the spatio−temporal relationship between the UGS configuration and its cooling effect in urbanization, offering valuable insights for building cooler cities.

Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during the summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation (R2 = 0.879) compared to Landsat (R2 = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09 °C and 2.16 °C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30 °C and 2.24 °C, with the bare land class showing the highest fluctuation 2.46 °C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p > 0.05 for both monthly and annual datasets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as the warmest classes at 39.51 °C and 38.20 °C, respectively, and classifies water at 35.96 °C, dense vegetation at 35.52 °C, and sparse vegetation 37.71 °C as the coldest, which is a trend that is consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects.

Remote sensing of hazards: The spatio-temporal evolution of land surface temperature over tailings flows and related drivers

Since mining activities began in Brazil some 300 years ago, disasters related to tailings dams have been recorded. In 2019, the B1 dam located at Vale, S.A.'s Córrego do Feijão mine in the municipality of Brumadinho (state of Minas Gerais) collapsed, releasing a vast amount of iron ore tailings into the Ferro-Carvão stream, which spread to the Paraopeba River located 10 km downstream. This tragedy devastated around 294 hectares of the Ribeirão Ferro-Carvão watershed, including mining infrastructure, farmland and housing, and is among the biggest environmental disasters ever recorded in Brazil. The torrent of mud and debris and the substances released by the collapse, namely metals (e.g. iron, manganese), in addition to completely flooding the bed and banks of the Ribeirão Ferro-Carvão and remaining on them for several years as a thick blanket, caused significant changes in land surface temperature (LST). The aim of this study was to spatially analyze the LST in the period before and after the dam burst (2018 - 2021), and to find its relationship with the granulometric and chemical parameters of the deposited materials, as well as with the changes in land use and land cover that occurred with and after the dam burst. Landsat 8 satellite images processed on the Google Earth Engine platform were used to estimate the LST. The results showed a relationship between this parameter and the characteristics of the tailings, with lower temperatures being associated with sectors of the impacted area covered with fine fractions (presumably with greater heat dissipation capacity). A reduction in temperature was also detected over the period analyzed, which was related to the tailings removal and revegetation works in the impacted area. The integrated results indicate that LST is an effective parameter for characterizing disasters involving tailings dams, as well as for monitoring natural recovery or recovery as a result of mitigation measures implemented in the impacted areas. We therefore recommend its use in remote sensing of mining activities in general and tailings dam disasters in particular.

Filling gaps in cloudy Landsat LST product by spatial-temporal fusion of multi-scale data

Land surface temperature (LST) is an important factor in studies of surface energy fluxes between the Earth's surface and atmosphere. The Landsat LST product has been applied widely due to its fine spatial resolution and high data quality. Frequent cloud coverage, however, results in different degrees of gaps in the Landsat LST images, restricting greatly their application in practical cases requiring continuous LST time-series. Gap filling is an important technique used to reconstruct the missing data in the Landsat LST images. Using only temporally neighboring data from the same sensor, however, current gap filling methods struggle to deal with large spatial gaps and large temporal changes in LST. To solve these challenging issues, in this paper, multi-scale data (i.e., the 2 km, all-sky, hourly GOES LST product) were proposed for gap filling of Landsat LST images, and a spatial-temporal random forest (STRF) method was developed. The GOES LST product can provide effective information on LST in the gaps of the target Landsat LST data, with great potential to decreasing the uncertainty in gap filling when large LST change exists or the gap size is large. Through experiments in six regions of the USA, the proposed STRF method was shown to be more accurate than three typical gap filling and spatio-temporal fusion methods, and particularly advantageous for cases involving large temporal changes or large spatial gaps in LST. Specifically, STRF produces an average root mean square error (RMSE) of 1.6444 K and average correlation coefficient (CC) of 0.8653 for the six regions, which are 0.1133 K smaller and 0.0176 larger than the most competitive benchmark method (i.e., the spatial random forest (SRF) method). Conceptually, by spatial-temporal fusion of multi-scale data in gap filling, STRF provides a new paradigm for both spatial-temporal fusion and gap filling. Furthermore, the proposed STRF has the potential to be applied to various gap filling tasks in other situations.

The contrasting trend of global urbanization-induced impacts on day and night land surface temperature from a time-series perspective

Increasing impervious surface area (ISA) is a major contributor to the rising land surface temperature (LST). Understanding the warming effect of ISA quantitively is crucial to alleviate the urban heat island effect. However, the quantitative analyses of urbanization-induced LST change globally remain to be determined over past decades. Here, we first characterized the response of LST to ISA (i.e., δLST) in 369 global cities using Terra MODIS LST product (1km) and the global artificial impervious area dataset (30m). We then investigated the spatiotemporal changes of δLST and its associated driving factors, including enhanced vegetation index (1km), albedo (500m), population (100m), air temperature (0.1°), and precipitation (0.1°). The analysis was conducted using linear regression models from 2000 to 2010 at the pixel level. The results illustrate that (1) the δLST in urban areas shows a negative trend during daytime (–0.2971°C/% and –0.0870°C/%) and a negative trend at night (0.0938°C/% and 0.0048°C/%) in arid and temperate regions, respectively. While in tropical regions, the δLST remains positive throughout the day, but it exhibits an opposite trend in snow regions. (2) The magnitude of nighttime δLST is stronger compared to daytime globally, which is more significant in arid regions with the largest ΔδLST values (i.e., diurnal variations) in urban (0.3909°C/%) and rural (0.3262°C/%) areas. Besides, the impervious surface has a distinct impact on LST in summer and winter, particularly at night. (3) In temperate regions, δALB (negative) and δAT (positive) are dominant factors in regulating δLST during daytime and nighttime. However, in arid regions, the daytime and nighttime δLSTs are negatively correlated by urban population and vegetation, respectively. These findings provide a quantitative understanding on long-term LST variations and driving factors behind at the pixel level, suggesting that urban greening and increasing surface albedo are effective strategies to mitigate the urbanization-induced surface warming.

Evaluation of Factors Influencing Changes in Land Surface Temperature at Gas Flaring Sites in the Niger Delta, Nigeria

Evaluation of Factors Influencing Changes in Land Surface Temperature at Gas Flaring Sites in the Niger Delta, Nigeria

Evaluating the thermal environmental alterations due to photovoltaic installations in the kushida river basin, Japan

The construction of Photovoltaic (PV) provides renewable energy, but it is also impacting the basins environment. The study simulates the annual construction status of PV installations within the research area over a time series from 2013 to 2023, and to understanding of the trends in Land Surface Temperature (LST) changes due to the PVs. The results revealed that the number of PV installations in the study area increased most significantly between 2018 and 2019, with the newly installed area during this period being approximately 5.2 times larger than 2013. Although large-scale construction slowed after 2020, small and medium-sized PV installations continued. The study found that the LST around PV which built from 2013 to 2023 increased by an average of 2.85 °C. Also, LST-difference (LST-D) changes with the seasons. The analysis of the relationship between geographical conditions and LST difference revealed that in hilly and mountainous areas with higher elevations, the impact of PV construction on LST was lower. And found that the distance of the PV from the watercourse and the surrounding land cover also had an impact to LST changes, with spatial variability between the different impact variables as analysed by the Geographically Weighted Regression (GWR) model.

An Investigation of the Impact of Spatiotemporal Changes of Land Use and Land Cover and Land Surface Temperature Using RS Techniques for Selected Cities in Sri Lanka

Changes in land use and land cover (LULC) have an impact on land surface temperature (LST) locally, regionally, and globally. This research focused on the spatiotemporal dynamics of LULC and LST between 2000 and 2021 at six selected meteorological stations, belonging to the different climatic zones of Sri Lanka. The LULC were classified from Landsat ETM+ and OLI/TIRS data using the maximum likelihood classification approach, while LST was retrieved using the thermal band of both Landsat images. Resultsreveal that the built-up areas have increased in all selected locations and bare land, vegetation cover, and agricultural land have all declined in the last 20 years. In Galle and Batticaloa areas, built-up areas have grown significantly by 11.9% and 11.14% respectively, by 2021. The thermal environment spatiotemporal alterations were consistent with the urbanization trend. Since many other land use types have been converted into urban areas, the average LST in Vavuniya, Anuradhapura, Batticaloa, and Galle has been rising steadily. This showed that increasing built-up area density has played a significant role in raising LST in the study areas. The vegetation cover, agricultural land, and water bodies showed the least LST change. By examining the correlation analysis between the Normalized Difference Vegetation Index (NDVI) and LST, a somewhat positive relationship was revealed only in the areas of Galle, Batticaloa, and Hambantota. Nevertheless, the findings of this study will serve as a helpful benchmark for future landscape and urban design initiatives aimed at reducing the detrimental effects of LST on urban sustainability.

Multi-Temporal Analysis of the Impact of Summer Forest Dynamics on Urban Heat Island Effect in Yan’an City

In this study, MODIS land products and China land cover datasets were used to extract normalized difference vegetation index, land surface temperature, and vegetation cover type in Yan’an City during the summers of 2017–2022. On this basis, analysis of spatial change and correlation were carried out as a way to study the mitigation effect on urban heat islands in Yan’an City with forest. The study showed that: (1) The coverage of normalized difference vegetation index over 0.4 in summer in Yan’an City increased from 59.38% to 69.12%, and the vegetation showed good growth conditions. It has a spatial distribution pattern of more in the south and less in the north. (2) The proportion of the urban heat island in Yan’an City increased from 15.51% to 16.86%. Urban heat island intensity fluctuated year by year, with the maximum urban heat island intensity of 6.26 °C appearing in 2019. It has a spatial distribution pattern of less in the south and less in the north. The transition rate of temperature field grade from low to high is 73.32%, and the transition rate to low is only 0.31%. (3) There is a negative correlation between land surface temperature and normalized difference vegetation index in Yan’an City. Vegetation has a mitigating effect on the UHI and the best cooling effect among the vegetation is shown by forest. The cooling effect of forest in Yan’an City is attenuated by an increase in distance, and the effective range is greater than 1000 m. In this study, the regulation effect of forest on the urban heat island was obtained by digging deeper into the intrinsic connection between spatial change in vegetation cover and land surface temperature change in Yan’an City. It provides an important reference for the formulation of meteorological protection policy as well as the promotion of sustainable development of the urban ecological environment and is of guiding significance for future urban planning and ecological construction.

Integrating meteorological and geospatial data for forest fire risk assessment

Understanding the factors that cause fire is crucial for minimizing the fire risk. In this research, a comprehensive approach was adopted to recognize factors influencing forest fires. Golestan National Park (GNP) was considered as a representative area with a humid climate in this study. Initially, using the Multi-Criteria Evaluation Method, a fire risk map was created by analyzing natural and human factors, and vulnerable areas were identified. Then, the relationship between key elements such as meteorological conditions, Land Surface Temperature (LST), and precipitation, with the occurrence of fire in different years was investigated. CHIRPS and Landsat data were utilized to assess LST changes and precipitation. 23-year changes in fire occurrence areas in GNP were acquired using MODIS products. The results of the data analysis showed that the highest number of fires occurred in forest areas, and in the fire risk prediction map, the extremely high-risk class is completely consistent with the ground truth data. The assigned weights, derived from expert opinions, highlight the substantial significance of elevation, and distance from roads and settlements. Additionally, the effectiveness of the model in providing reliable forecasts for fire risks in GNP is highlighted by the ROC curve with an AUC value of 0.83. Forest fires within GNP exhibit a distinct seasonality, predominantly occurring from July to December. During the warmer months, by coinciding with summer excursions, human activities may contribute to the ignition of fires. In 2013 and 2014, rising fire incidents correlated with elevated temperatures, hinting at a potential connection. GNP fires showed an upward trend with higher monthly LST and a downward trend with increased annual precipitation. The results showed that there is a relationship between LST, precipitation, and the occurrence of fire in GNP. Approximately 176.15 ha of GNP's forest areas have been destroyed by fires over the last two decades. This research demonstrated that there is a dynamic interaction between environmental conditions and fire incidents. By considering these factors, managers and environmental planners can develop effective strategies for managing and preventing forest fire risks.

Linkages between urban growth and land surface temperature variations in the Seoul metropolitan area: A spatial first-order difference approach

This study investigates the effects of urban development and expansion on Land Surface Temperature (LST) changes from 2000 to 2018, using the Seoul Metropolitan area (SMA) as a case study area. Along with descriptive analysis, this study uses a spatial first-order difference model to statistically analyze the longitudinal relationship between land use features and LST, including impervious surface (IS), enhanced vegetation index (EVI), anthropogenic factors, and urban spatial and topographical factors as well as urban development types as influential factors on LST variations. Our findings support prior research indicating the cooling effects of vegetation and warming effects of imperviousness. We also found a positive association between detached housing units, manufacturing activities and LST changes and a negative relationship between apartment units, urban parks, service activities and LST changes. More importantly, substantial LST reduction was found in the large-scale high-rise apartment complexes redeveloped from the low-rise, detached housing sites in inner-city infill areas, contrasting with significant LST rise in the suburban new town development. We suggest redevelopment of inner-city low-rise compact areas into high-rise but greener blocks as a LST mitigation strategy, equipped with cooling strategies such as green infrastructure and climate-sensitive design considering local microclimate and natural ventilation.

Spatiotemporal Land Use/Land Cover Changes and Impact on Urban Thermal Environments: Analyzing Cool Island Intensity Variations

This study pioneers the comprehensive evaluation of the spatiotemporal evolution of land use/land cover (LULC) in Hangzhou city, introducing the novel water body shape index (WBSI) to analyze its seasonal impacts on the urban thermal environment and urban cool island (UCI) effects, uncovering distinct patterns of thermal regulation. It particularly investigates how distance gradients and the water body shape index (WBSI) influence land surface temperature (LST) in the urban core. The region’s climate, featuring hot summers and cold winters, highlights significant seasonal LST variations. Addressing a gap in existing UCI research, the analysis extends beyond the typical large-scale planning focus to include small-scale, high-resolution aspects. Employing remote sensing and geographic information system (GIS) analysis techniques, this study analyzes the seasonal dynamics in Hangzhou’s central urban area. High-resolution LST data, obtained through single-channel inversion and resolution enhancement algorithms, are crucial to this analysis. This study employs the maximum likelihood classification method to analyze land use and land cover changes from 1990 to 2020. This analysis reveals potential drivers of urban thermal environment changes, such as the expansion of residential and commercial areas and the reduction in green spaces. Different regions in LST data are delineated to assess the cool island effect, and the complexity of water body boundaries is quantified using the water body shape index. Spatial and temporal patterns of LST changes are investigated using multivariate regression and time-series analysis models. We identified significant changes in LULC over the past 30 years in Hangzhou, closely correlating with a continuous rise in LST. This observation underscores a clear finding: the strategic importance of blue–green infrastructure in mitigating urban heat, a novel insight that extends the current understanding of urban thermal dynamics. A clear and novel finding of this study is that the intensity of the cool island effect from large water bodies not only diminishes with distance but is intricately influenced by the complexity of their shapes, as quantified by the WBSI, whereas the complexity of their boundaries enhances this effect. Additionally, the regulatory role of the cool island effect is observed to vary seasonally, being most pronounced in summer and less so in autumn and winter, thereby demonstrating a positive impact. In conclusion, our findings innovatively highlight how the specific shapes of water bodies, quantified through the water body shape index (WBSI), emerge as critical, yet previously underappreciated, drivers in modulating the urban thermal environment. This underscores a new avenue for urban planning, advocating for the strategic design of water bodies within urban landscapes. It also finds that spatial factors and seasonal variations significantly affect the intensity of the cool island effect. These findings offer valuable evidence for urban planning and climate change adaptation, emphasizing balancing natural elements with the built environment in urban design.

An Analysis on the Impact of Normalized Difference Vegetation Index (NDVI) Changes on the Land Surface Temperature (LST) using Satellite Imagery in the West Bank, Palestine

Abstract
 This study investigated the impact of the Normalized Difference Vegetation Index (NDVI) on the Land Surface Temperature (LST) in the West Bank, Palestine, using multitemporal satellite imagery. Landsat 5 TM and Landsat 8 OLI data were used in the study. The Landsat 5 TM data was acquired on March 29, 2001, while the Landsat 8 OLI data was acquired on April 05, 2021. The NDVI and the LST for the two images were then calculated from the two images using ArcGIS 10.8 software. Results of the study showed that the NDVI of the 2021 image was much higher than that of the 2001 image. The mean NDVI of the 2001 image was 0.25, while that of the 2021 image was 0.32. As for the LST, the mean LST of the 2001 image was 29.85 °C, while that of the 2021 image was 26.98 °C. Around 74.7% of the West Bank lands recorded an inverse relationship between the NDVI change and the LST change between 2001 and 2021. Around 25.3% of the West Bank lands recorded a positive relationship. In other words, the study depicted the impact of the NDVI on the LST in the West Bank. The study recommends that policymakers need to increase the green areas in the West Bank to reduce the LST as this is seen to affect the climate conditions in the area.
 Keywords: LST, NDVI, West Bank, Palestine, Landsat data.

Monitoring Spatiotemporal Changes in Land Surface Temperature and Urban Heat Islands of Sharkia Governorate Districts Using Remote Sensing Technology

Monitoring Spatiotemporal Changes in Land Surface Temperature and Urban Heat Islands of Sharkia Governorate Districts Using Remote Sensing Technology

Land Surface Temperature Changes in Different Urbanization Increments in China since 2000

In the rapidly urbanizing world, as one of the distinct anthropogenic alterations of global climate change, global warming has attracted rising concerns due to its negative effects on human well-being and biodiversity. However, existing studies mostly focused on the difference in temperature elevation among urbanized areas and non-urbanized areas, i.e., rural or suburban areas. The allometric urban warming at intra-urban scales was overlooked. This research aimed to expand our understanding of urbanization–temperature relationships by applying a concept of a “previous-new” dichotomy of urbanized areas. To quantify the land surface temperature (LST) dynamics of 340 cities in China, we analyzed the LST of different land use types through trend analysis and absolute change calculation models. The urban heat island (UHI) effect of two spatial units, i.e., newly expanded urbanized area (“new UA” hereinafter) during 2000–2015 and previously existing urbanized area (“previous UA” hereinafter) in 2000, were compared and discussed. Our findings reveal that urban growth in China coincided with an LST increase of approximately 0.68 °C across the entire administrative boundary, with higher increases observed in regions between the Yellow River and Yangtze River and lower increases in other areas. Moreover, the new UA exhibited significantly greater LST increases and urban heat island intensity (HUII) compared to the previous UA. The dynamics of LST corresponded to the speed and scale of urban growth, with cities experiencing higher growth rates and percentages exhibiting more pronounced LST increases. This study reveals the impact of the underlying surface on human settlements on a large scale.

QGIS-based modeling and analysis of urban dynamics affecting land surface temperature towards climate hazards in coastal zones of Portugal

AbstractClimate risk creates considerable concern due to the density of natural and socio-economic assets in coastal areas. Monitoring land use/cover changes, detecting population growth, and analyzing their impact on land surface temperature (LST) are necessary for effective urban management. In this study, land use/land cover (LULC), population, and LST changes in coastal regions of Portugal. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery was examined using World Bank population and CORINE data. Changes in land use types and LST values from 1990 to 2018 were analyzed. At the same time, LULC predictions were made using the Modules for Land Use Change Simulation (MOLUSCE) plug-in included in the QGIS software, and population projections were analyzed with LULC predictions in 2046. The results show the significant impact of land use on temperatures. It has been demonstrated that green and water areas can effectively cool cities. In the LULC changes between 1990 and 2018, the Leiria region stands out, with an annual increase of 4.04% in built areas from 121.58 to 259.06 km2. According to the simulations between 2018 and 2046, it was predicted that 18.74% of agricultural areas and 14.43% of forest areas would be transformed into built environments. The study is also essential as it confirms that the MOLUSCE plug-in can be effectively applied to land cover simulation on a large regional scale.

Influence of vegetation index to the rainfall intensity in Pasuruan Area, East Java Province, Indonesia

An increase in population increases the rate of urbanization. This results in changes in land cover from vegetation to artificial material. As a result, much of the land surface reflects the sun's energy. Consequently, this increases the surface temperature of the land. An increase in land surface temperature (LST) will increase the intensity of rainfall. Therefore, the present study aimed to investigate the relationship between the increase in LST and rainfall intensity. Changes in land cover can be detected by normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI) parameters. Landsat satellite imagery was used to detect NDVI, NDBI, and LST. Image processing was done for imageries scanned in 1995, 2015, 2017, and 2021. Two areas in the East Java Province of Indonesia, namely Malang City and Pasuruan Area, were selected. The daily rainfall intensity data were collected from related rainfall stations in the same year. The Mononobe method was applied to analyze hourly and minute rainfall intensity. IDF curves were drawn from the analyzed results. The relationship between both parameters was analyzed by comparing the LST and hourly rainfall intensity from the IDF curve. The studied results showed that the maximum temperature increase from 1995 to 2021 for the Malang City and Pasuruan Area was 2.60 C and 7.60 C, respectively. For rain, the maximum rainfall intensity increased by 58 mm for Malang City and 18 mm for the Pasuruan Area. LST and rainfall intensity change trends of the two areas had a positive coefficient of regression. The findings can be used to predict the rainfall intensity and floods based on the LST data.  

Analysis of Land Cover Changes to Increase Land Surface Temperature in Surabaya using Landsat Satellite

Surabaya has experienced very significant development in the last few decades. Changes in land use will cause the Urban Heat Island phenomenon. This study aims to determine how far the impact of land cover changes on the increase in surface temperature in the Surabaya. The use of Landsat satellite imagery is considered very effective in describing land cover and surface temperature because it has good spatial resolution and long data availability. During 1991 – 2020 there was a significant decrease in the amount of vegetation by 24.3%, decrease in the number of water bodies by 4.9%, and increase in the number of buildings by 29.2%. The average increase in Land Surface Temperatures was 1.40°C between decades 2 and 1, and an increase of 2.19°C between decades 3 and 2. The development of Surabaya began in the city center and then developed mainly in the west and east. The urban development model is consistent with the pattern of land surface temperature changes. Each type of land cover has special characteristics on the value of NDVI, NDBI, and surface temperature. Changes in cover from water bodies to buildings have the highest contribution to increasing the and surface temperature. There was a significant increase in hotspots in decade 3 in Surabaya which indicated an increasingly severe UHI phenomenon.

Remote sensing-based analysis of land use, land cover, and land surface temperature changes in Jammu District, India

ABSTRACT The study conducted in Jammu District, India, investigates land use and land cover (LULC) transformations over the past three decades using satellite data and remote sensing techniques. Analyzing data from 1990 to 2020, significant changes were observed. Agricultural land expanded by 157.76 km2 (6.71%), barren land by 151.69 km2 (6.45%), and settlements by 96.97 km2 (4.12%). However, vegetation decreased by 389.77 km2 (16.57%), while water bodies experienced minimal changes. Land Surface Temperature (LST) analysis, utilizing MODIS data (2000-2020), revealed warming trends, with temperatures ranging from 15.92°C to 42.77°C in 2010 and 14.04°C to 37.01°C in 2020. Notably, NDVI values peaked in 2020 (0.759) and were lowest in 1990 (−0.243), indicating healthier vegetation and lower surface temperatures. This inverse correlation highlights NDVI's potential as an indicator for assessing vegetation health and its impact on local temperature conditions. Man-Kendall Z statistics indicated negative trends for Tmax and Tmin, while rainfall data showed significant positive trend. Population growth, urbanization, climate change and agricultural intensification emerged as principal drivers of the LULC changes in the region. This study underscores the importance of geospatial tools in monitoring LULC changes, providing valuable insights for policymakers and planners to formulate sustainable land use planning and management strategies.