Analysis Of Land Surface Temperature Research Articles

Multitemporal Analysis of the Relationship Between Vegetation Density and Built-Up Land to the Urban Heat Island Phenomenon in Cirebon City

Abstract The attractiveness of Cirebon City as a target for urbanization lies in its strategic location and significant development, particularly in tourism, the service industry, and culture. As a result, vegetation has been transformed into urban development, leading to a subsequent rise in land surface temperatures and causing the Urban Heat Island (UHI) phenomenon. UHI is prevalent in urban areas and diminishes in peripheral regions. This research aims to investigate the correlation between vegetation density and built-up areas to the UHI phenomenon in Cirebon City, utilizing Landsat-8 data for the years 2014 and 2023. Normalized Difference Built-Up Index (NDBI) is used to identify built-up areas, vegetation density is identified with the Normalized Difference Vegetation Index (NDVI) algorithm, and Land Surface Temperature (LST) data is obtained from thermal band extraction. The results of the LST analysis were used to describe the Urban UHI phenomenon. Findings throughout the study period revealed significant changes in built-up land and vegetation density, while dynamic changes occurred in LST and UHI. The results also show that there is a positive correlation between the built-up land index and UHI, which indicates a unidirectional relationship. Meanwhile, the vegetation density index to UHI is negatively correlated, which means it shows an inverse relationship.

Spatiotemporal Analysis of Land Surface Temperature in Response to Land Use and Land Cover Changes: A Remote Sensing Approach

Rapid urbanisation in the global south has often introduced substantial and rapid uncontrolled Land Use and Land Cover (LULC) changes, considerably affecting the Land Surface Temperature (LST) patterns. Understanding the relationship between LULC changes and LST is essential to mitigate such effects, considering the urban heat island (UHI). This study aims to elucidate the spatiotemporal variations and alterations of LST in urban areas compared to LULC changes. The study focused on a peripheral urban area of Phnom Penh (Cambodia) undergoing rapid urban development. Using Landsat images from 2000 to 2021, the analysis employed an exploratory time-series analysis of LST. The study revealed a noticeable variability in LST (20 to 69 °C), which was predominantly influenced by seasonal variability and LULC changes. The study also provided insights into how LST varies within different LULC at the exact spatial locations. These changes in LST did not manifest uniformly but displayed site-specific responses to LULC changes. This study accounts for changing land surfaces’ complex physical energy interaction over time. The methodology offers a replicable model for other similarly structured, rapidly urbanised regions utilising novel semi-automatic processing of LST from Landsat images, potentially inspiring future research in various urban planning and monitoring contexts.

Construction and characteristic analysis of background error covariance coupled with land surface temperature

Land surface temperature (LST) is the key variable in land–atmosphere interaction, having an important impact on weather and climate forecasting. However, achieving consistent analysis of LST and the atmosphere in assimilation is quite challenging. This is because there is limited knowledge about the cross-component background error covariance (BEC) between LST and atmospheric state variables. This study aims to clarify whether there is a relationship between the error of LST and atmospheric variables, and whether this relationship varies spatially and temporally. To this end, the BEC coupled with atmospheric variables and LST was constructed (LST-BEC), and its characteristics were analyzed based on the 2023 mei-yu season. The general characteristics of LST-BEC show that the LST is mainly correlated with the atmospheric temperature and the correlation decreases gradually with a rise in atmospheric height, and the error standard deviation of the LST is noticeably larger than that of the low-level atmospheric temperature. The spatiotemporal characteristics of LST-BEC on the heavy-rain day and light-rain day show that the error correlation and error standard deviation of LST and low-level atmospheric temperature and humidity are closely related to the weather background, and also have obvious diurnal variations. These results provide valuable information for strongly coupled land–atmosphere assimilation.摘要地表温度 (LST) 是涉及陆气相互作用的关键变量, 对天气气候预报具有重要影响. 不过在同化中实现LST与大气的协调分析却并不容易. 这是因为目前对跨陆气圈层的背景误差协方差 (BEC) 的了解较少. 本文旨在探究LST与大气的背景误差是否存在联系, 以及这种联系是否存在时空变化. 为此, 本研究构建了耦合大气变量和LST的BEC, 并基于2023年梅雨季分析了其特征. 总体特征表明: LST误差主要与大气温度相关; 随着大气高度的上升, 误差相关性逐渐减小; LST误差的标准差明显大于低层大气温度. “多雨日”和“少雨日”的时空特征表明, LST与低层大气温湿度误差的相关性及标准差均与天气背景密切相关, 且具有明显的昼夜变化. 上述结果可为后续陆气强耦合同化提供参考.

Identification of geothermal potential zone associated with land surface temperature derived from Landsat 8 data using split-window algorithm

This study was conducted in the geothermal potential area of Mount Seulawah, Aceh province. Study was conducted using remote sensing methods in order to estimate and map the land surface temperature (LST). Data used in this study is Landsat 8 OLI and TIRS. Methods used in this study were split-window algorithm (SWA) to obtain the LST through NDVI analysis and landcover classification, and image enhancement to obtain the lineament density through visual interpretation referring to the results of band ratio and filter techniques. The results of the NDVI analysis show that the study area has a value in the range of -0.57 – 0.97. The results of the LST analysis show that the surface temperature in the area has a value in the range of 21.32 - 32.88 oC. Areas that have high LST values can be estimated as areas that have anomalies. Based on the results of the lineament density analysis, the higher density value in an area can indicate the better permeability where geothermal manifestations come out. The direction of the lineaments that control the study area is dominated by the northwest-southeast in accordance with the direction of the main stress, namely the Seulimum fault and the Aceh fault which also has a northwest-southeast direction. The overall results show that the Seulawah geothermal area is quite potential to be developed for the sustainable natural resource.

Analysing Sustainable Urban Growth: Spatio-Temporal Dynamics of Urban Expansion in Gampola Urban Centre

Urbanization is a dynamic process which can be considered as one of the most significant indicators of development. It occurs both horizontally and vertically on the Earth’s surface according to a pattern which has positive and negative implications. Some countries measure their development by the level of urbanization because it directly affects for a country’s economy. During past decades, Gampola urban centre was experiencing a gradual urban growth and several implications. This study focuses on investigating urban expansion pattern of Gampola during 16 years by analysing three distinct years 2006, 2013, and 2022. Geographic Information System (GIS) and Remote Sensing (RS) techniques were employed to examine spatio-temporal transformations and urban expansion patterns of Gampola and its outskirts. Landsat images for three distinct years were classified using pixel-based supervised classification to determine Land Use Land Cover (LULC) of the study area which achieved an impressive overall accuracy of approximately 90%. Four major LULC types were identified those are prominent in the area namely, water, built-up, forest, paddy lands and bare lands. Temporal changes of vegetation cover for 16 years were analysed further using Normalized Difference Vegetation Index (NDVI). Consequent implications on Land Surface Temperature (LST) across the time period were analysed using thermal band of Landsat images. NDVI value has decreased from +0.7 to +0.5 during 2006-2013 and up to +0.4 during 2013-2022. LST analysis revealed a rise in temperature, increasing from 29.89° C up to 31.11° C during 2006- 2013 and surging to 33.14° C by 2022. Quantifying the urban expansion of Gampola was performed using buffer analysis. Analysis of urban areas grown within 1km distanced five buffer zones from urban core was performed to quantify urban expansion. Fourth buffer zone exhibited the highest urban growth, while first buffer zone displayed lowest urban growth during 2006 to 2022. In the interval between 2013 and 2022, the fifth buffer zone appeared as the zone with the highest urban growth. Further, Landscape Expansion Index (LEI) was employed to identify the urban expansion patterns, and Gampola urban area displayed an Edge Expansion growth type. We recommend the development of urbanized areas in a sustainable and efficient manner to mitigate adverse consequences and promote balanced growth.
 
 Keywords: GIS and RS, Urban expansion, LEI, LST, Sustainable development

Establishing the relationship between land use land cover, normalized difference vegetation index and land surface temperature: A case of Lower Son River Basin, India

The study explores the intricate interplay between land use land cover (LULC), normalized difference vegetation index (NDVI), and land surface temperature (LST) within the Lower Son River Basin in India from 1991 to 2020. The region’s ecological balance has been increasingly strained due to rapid urbanization and changing land use patterns. Through a combination of Landsat TM & OLI/TIRS satellite imageries and geospatial analysis techniques, this study unveils the intricate connection between land use and land cover changes, vegetation, and land surface temperature variations. The study area is classified into three altitudinal zones (Zone I: 39–300 m, Zone II: 301–600 m and Zone III: 601–1,247 m) to examine the changes in depth. The area has seen significant changes in LULC, vegetation and LST in all the three altitudinal zones. The findings hold significant implications for sustainable land management and environmental conservation strategies in the Lower Son River Basin. As per the result, 103,438 ha of vegetation was converted into agriculture land and 82,572 ha of agricultural land was transformed into settlements from 1991 to 2020. This trend shows human pressure on the land resource in the study area. Minor increase in water body is seen which is attributed to commissioning of Bansagar dam. Zone I has seen highest settlement growth while Zone III experienced severe deforestation of around 15%. Zone II and III needs attention for holistic sustenance. Analysis of LST shows that it has increased by 0.82 °C from 1991 to 2020 which is a red flag. The study underscores the critical importance of balanced land use practices to preserve ecological integrity and mitigate the adverse effects of urbanization and climate change.

Seasonal analysis of land surface temperature using local climate zones in peak forest basin topography: A case study of Guilin

Understanding the characteristics of Land Surface Temperature (LST) is crucial to mitigate the Surface Urban Heat Island (SUHI) effect. Local Climate Zones (LCZs) framework can be applied in LST analysis and SUHI quantification. However, this framework primarily focuses on evaluating detailed local-scale SUHI, thereby failing to provide a comprehensive assessment of the overarching SUHI trends at the urban level. Additionally, existing LST analyses ignored geographic factors, resulting in partial analysis of LST, especially in areas with complex geography. To fill these gaps, the study develops a SUHI assessment method combining an improved surface urban heat intensity ratio index with LCZs-based SUHII, enabling a dual evaluation of both overall and detailed SUHI effect. The impact of geographic factors, both topography and natural features, on seasonal LST patterns is investigated, especially within built LCZs. Additionally, the spatio-temporal image fusion model is employed to address issues of cloud effects or image mutilations in LST results. Guilin, with unique peak forest basin topography, is selected as a case study to represent complex geographical areas. The results reveal the following: (1) An abnormal heat island phenomenon occurs during winter due to Guilin's distinctive geomorphological conditions. (2) Cooling effects influenced by mountains and water bodies on different built LCZs exhibit seasonal variation, with the strongest cooling effect observed within a 50-m distance. LCZ 2 consistently exhibits the strongest cooling effect. The study offers valuable insights for guiding decisions in sustainable urban planning and design, especially in cities influenced by complex local geography.

Co-seismic characterization analysis in PWV and land-atmospheric observations associated with Luding Ms 6.8 earthquake occurrence in China on September 5, 2022

The Sichuan Luding earthquake that struck on September 5, 2022 is one of the strongest earthquakes in China in recent years. The analysis of precipitable water vapor (PWV) retrieved from the ground-based global navigation satellite system (GNSS), surface pressure (SP), surface latent heat flux (SLHF), and land surface temperature (LST) from the reanalysis dataset was carried out in the epicenter and the nearby areas. The results show that PWV decreases distinctly and reaches the trough at the outburst with significant minimums of 43.21 mm and 37.84 mm over the nearest SCSM and SCTQ station from the epicenter. SLHF also has the same trend, and SP increased. Additionally, the LST analysis from two-temporal series was conducted to reveal that the Luding event accompanies by a low-temperature anomaly. Based on the background field established from the same period of the last ten years, LST at the epicenter on the day of occurrence was 5.68 °C lower than in previous years. Furthermore, the strongest low-temperature anomalies were observed from September 4 to 6, with the anomaly index of −1.95, −1.71, and −1.60, respectively. It is plain that the parameters from the land and atmosphere perform the anomalies at the minimum during the Luding earthquake.

Exploring the land-use urban heat island nexus under climate change conditions using machine learning approach: A spatio-temporal analysis of remotely sensed data

Urbanization strongly correlates with land use land cover (LULC) dynamics, which further links to changes in land surface temperature (LST) & urban heat island (UHI) intensity. Each LULC type influences UHI differently with changing climate, therefore knowing this impact & connection is critical. To understand such relations, long temporal studies using remote sensing data play promising role by analysing the trend with continuity over vast area. Therefore, this study is aimed at machine learning centred spatio-temporal analysis of LST and land use indices to identify their intra-urban interaction during 1991–2021 (summer) in Imola city (specifically representing small urban environment) using Landsat-5/8 imageries. It was found that LST in 2021 increased by 38.36% from 1991, whereas average Normalised Difference Built-up Index (NDBI) increased by 43.75%, associating with increased thermal stress area evaluated using ecological evaluation index. Major LULC transformations included green area into agricultural arable-land and built-up. Finally, the modelled output shows that built-up & vegetation index have strongly impacted LST. This study, help to understand the relative impact of land-use dynamics on LST at intra-urban level specifically with respect to the small urban settings. Further assisting in designing and regenerating urban contexts with stable configuration, considering sustainability and liveable climate, for benefit of health of public and fragile population in particular.

Adoption of Climate Smart Agricultural Technologies among Smallholder Farmers in Semi-Arid Ghana

The evidence of climate change and variability in semi-arid Ghana is glaring and the adverse impact is being felt mostly by smallholder farmers because of their over dependence on agriculture for livelihood and subsistence. As a solution to building the resilience of the smallholder farmers, the Climate Smart Agriculture (CSA) concept was introduced a decade ago by the Food and Agriculture Organization, guided by three key principles of adaptation to climate change, greenhouse gas emissions reduction and promotion of food security. The paper sought to assess the level of awareness of climate smart agriculture practices and the respective rate of adoption of these practices. Moreso, this paper established how Normalised Difference Water Index (NDWI) and Land Surface Temperature (LST) affects the adoption rate of CSA practices or technologies. The study employed the explanatory sequential mixed research methods. A semi-structured questionnaire was used to collect data from 300 smallholder farmers and 16 focus group discussions were conducted, with a total of 180 persons taking part in the focus group discussions. Key informant interviews were also conducted for 11 relevant stakeholders from governmental and non-governmental institutions. Findings from this study reveal that CSA practices such as intercropping, manure management and mulching had a 100% adoption rate, and the least adopted practice was irrigation followed by dry season gardening. The NDWI and LST analysis concluded that Nandom is the most viable among the two municipals to support irrigation projects since it has more capacity to retain surface water during the dry and wet seasons.

Monitoring of Land Surface Temperature from Landsat Imagery: A Case Study of Al-Anbar Governorate in Iraq

Land surface temperature (LST) estimation is a crucial topic for many applications related to climate, land cover, and hydrology. In this research, LST estimation and monitoring of the main part of Al-Anbar Governorate in Iraq is presented using Landsat imagery from five years (2005, 2010, 2015, 2016 and 2020). Images of the years 2005 and 2010 were captured by Landsat 5 (TM) and the others were captured by Landsat 8 (OLI/TIRS). The Single Channel Algorithm was applied to retrieve the LST from Landsat 5 and Landsat 8 images. Moreover, the land use/land cover (LULC) maps were developed for the five years using the maximum likelihood classifier. The difference in the LST and normalized difference vegetation index (NDVI) values over this period was observed due to the changes in LULC. Finally, a regression analysis was conducted to model the relationship between the LST and NDVI. The results showed that the highest LST of the study area was recorded in 2016 (min = 21.1°C, max = 53.2°C and mean = 40.8°C). This was attributed to the fact that many people were displaced and had left their agricultural fields. Therefore, thousands of hectares of land which had previously been green land became desertified. This conclusion was supported by comparing the agricultural land areas registered throughout the presented years. The polynomial regression analysis of LST and NDVI revealed a better coefficient of determination (R2) than the linear regression analysis with an average R2 of 0.423.

Analysis of the relationship among land surface temperature (LST), land use land cover (LULC), and normalized difference vegetation index (NDVI) with topographic elements in the lower Himalayan region

Land Surface Temperature (LST) affects exchange of energy between earth surface and atmosphere which is important for studying environmental changes. However, research on the relationship between LST, Land Use Land Cover (LULC), and Normalized Difference Vegetation Index (NDVI) with topographic elements in the lower Himalayan region has not been done. Therefore, the present study explored the relationship between LST and NDVI, and LULC types with topographic elements in the lower Himalayan region of Pakistan. The study area was divided into North-South, West-East, North-West to South-East and North-East to South-East directions using ArcMap 3D analysis. The current study used Landsat 8 (OLI/TIRS) data from May 2021 for LULC and LST analysis in the study area. The LST data was obtained from the thermal band of Landsat 8 (TIRS), while the LULC of the study areas was classified using the Maximum Likelihood Classification (MLC) method utilizing Landsat 8 (OLI) data. TIRS collects data for two narrow spectral bands (B10 and B11) with spectral wavelength of 10.6 μm–12.51 μm in the thermal region formerly covered by one wide spectral band (B6) on Landsat 4–7. With 12-bit data products, TIRS data is available in radiometric, geometric, and terrain-corrected file format. The effect of elevation on LST was assessed using LST and elevation data obtained from the USGS website. The LST across LULC types with sunny and shady slopes was analyzed to assess the influence of slope directions. The relationship of LST with elevation and NDVI was examined using correlation analysis. The results indicated that LST decreased from North-South and South-East, while increasing from North-East and South-West directions. The correlation coefficient between LST and elevation was negative, with an R-value of −0.51. The NDVI findings with elevation showed that NDVI increases with an increase in elevation. Zonal analysis of LST for different LULC types showed that built-up and bare soil had the highest mean LST, which was 35.76 °C and 28.08 °C, respectively, followed by agriculture, vegetation, and water bodies. The mean LST difference between sunny and shady slopes was 1.02 °C. The correlation between NDVI and LST was negative for all LULC types except the water body. This study findings can be used to ensure sustainable urban development and minimize urban heat island effects by providing effective guidelines for urban planners, policymakers, and respective authorities in the Lower Himalayan region. The current thermal remote sensing findings can be used to model energy fluxes and surface processes in the study area.

Integration of multiple earthquakes precursors before large earthquakes: A case study of 25 April 2015 in Nepal

In this work, we analyze magnetic data, Land Surface Temperature (LST), Outgoing Longwave Radiation (OLR), Air Temperature (AT) and Relative Humidity (RH) around the epicenter before and after the large EQs occurred on April 25, 2015 in Nepal. Daytime and nighttime LST values were retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra satellite. We used the magnetic data acquired by Swarm satellites in the top side ionosphere, and daily OLR data was obtained from the United States National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites. Moreover, AT and RH values were provided from the nearest meteorological ground station to the epicenter located at Katmandu airport. As data analysis, short-term (15 days before and 5 days after the earthquakes) and medium-term (2 months before and 1 month after the earthquakes) evaluations were carried out. For short-term analysis, the results of the magnetic field in the topside ionosphere detected an interesting anomaly on April 12, 2015. Besides, nighttime LST anomaly was observed on April 13, 2015, 14 days before the earthquakes, which was also an anomalous day for AT and RH analysis. Moreover, in the OLR analysis, April 12, 2015 was anomalous day based on the time series of the epicentral pixel that coincides with the magnetic field anomaly. Concerning the medium-term nighttime LST analysis, 13th April was the day, which may be associated with the earthquakes. For the medium-term OLR analysis, April 12, 2015 was found to be related to the seismic activity based on the epicentral pixel. Besides, the RH anomaly on 13 April was the only mutual day with the anomalies of LST and OLR for the medium-term analysis. Based on the short-term and medium-term results, these two days, 12 and April 13, 2015, may be associated with the preparation for the earthquake occurrence. Overall, it is concluded that these methods could be effective and reliable in detecting anomalies that precede upcoming EQs with further investigations.

Assessment of Intra-Urban Heat Island in a Densely Populated City Using Remote Sensing: A Case Study for Manila City

Changes in the environment occur in cities due to increased urbanization and population growth. Sustainable Development Goal (SDG) 11 is intrinsically linked to the environment, one facet of which is the need for universal access to secure, inclusive, and accessible green and public places. As urban heat islands (UHI) have the potential to negatively influence cities and their residents, existing resources and data must be used to identify and quantify these effects. To address this, we present the use of satellite-derived (2013–2022) and meteorological data (2014–2020) to assess intra-urban heat islands in Manila City, Philippines. The assessment includes (a) understanding the temporal variability of air temperature measurements and outdoor thermal comfort based on meteorological data, (b) comparative and correlative analysis between common Land-Use Land Cover indicators (Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Built-up Index (NDBI)) and Land Surface Temperature (LST), (c) spatial and temporal analysis of LST using spatial statistics techniques, and (d) generation of an intra-urban heat island (IUHI) map with a recommended class of action using a suitability analysis model. Finally, the areas that need intervention are compared to the affected population, and suggestions to enhance the thermal characteristics of the city and mitigate the effects of UHI are established.

Application of Land Surface temperature from Landsat series to monitor and analyze forest ecosystems: A bibliometric analysis

Aim of study: Land surface temperature (LST) is an essential variable to monitor and characterize forest ecosystems. This variable has been consistently captured for almost four decades by the Landsat program. The current study aimed at identifying trends, knowledge gaps and opportunity areas in the use of Landsat derived LST for the monitoring and analysis of forest ecosystems.
 Materials and methods: A bibliometric analysis of scientific articles indexed in Scopus in the period 1995-2020 was conducted.
 Main results: Annual increase rate in the number of publications on the topic analyzed was 22.58%. The journal with more publications on the topic was Proceedings of SPIE, followed by Remote Sensing. The authors with the highest productivity on this topic were C. Quintano, I. Vorovencii, O. E. Yakubailik and M. A. Zoran. Regarding productivity by country, 38 countries with publications on this topic were identified, with the highest productivity located in China, USA and India. This group of countries also represented the most solid network of cooperation between countries. Forest ecosystems more frequently analyzed were temperate forests, followed by tropical forests. The analysis of keywords highlighted topics such as remote sensing, NDVI, MODIS and evapotranspiration. The analysis of thematic evolution indicated that areas of research and interpretation of LST data has evolved in parallel with remote sensing areas.
 Research highlights: Landsat LST analysis is an evolving topic with potential to contribute to improve ecosystem knowledge and to support diverse challenges in forest resources decision-making.

Impact of land use/land cover on land surface temperature and its relationship with spectral indices in Dakahlia Governorate, Egypt

Land surface temperature (LST) is a direct impact of urbanization and a crucial factor in global climate and land cover changes. In this research, we aim to identify the impact of land use/land cover (LULC) on LST as well as analyze the relationship between LST and three spectral indices using linear, polynomial and multiple regression models. The LST was first retrieved from Landsat imagery using single-channel algorithm. Afterwards, LULC maps were developed using maximum likelihood (ML) classifier and three spectral indices, namely Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI). Finally, regression analysis was conducted to model the relationship between LST and the three spectral indices. Landsat 8 OLI/TIRS imagery of year 2019 of Dakahlia Governorate in Egypt was processed for LST retrieval as well as LULC classification. The ML classifier achieved an overall accuracy and kappa coefficient of 95.14% and 0.857, respectively, while of those based on spectral indices were 94.86% and 0.777, respectively. The results demonstrated an average temperature of 35.8°C, 31.2°C and 27.6°C for urban, vegetation and water, respectively. The LST statistics difference between classification methods of the three land covers was less 2°C. Based on the regression analysis, the NDVI and NDWI indicated a negative correlation with LST, while the NDBI indicated a positive correlation with LST. The polynomial regression analysis of LST against NDVI and NDWI demonstrated a better coefficient of determination (R2) than linear regression analysis of 0.341 and 0.305, respectively. For NDBI, linear and polynomial regression analysis demonstrated very close R2 of 0.624 and 0.628, respectively. The multiple regression analysis of LST against NDVI, NDBI and NDWI revealed R2 of 0.699. Consequently, the three spectral indices can be used as effective indicators for separating terrain into different classes, and hence relate their LST.

Past and future impacts of urbanisation on land surface temperature in Greater Cairo over a 45 year period

Rapid and unplanned urbanisation can lead to altered local climate by increasing land surface temperature (LST), particularly in summer months. This study investigates the Urban Heat Island (UHI) in Greater Cairo, Egypt, using remote sensing techniques to estimate LST of summer months over 45 years (1986, 2000, 2017, and predicted year 2030). The research objectives and steps were, 1- mapped land use/ land cover (LULC), 2- conducted spatiotemporal analysis of LST, with a comparison of change in LST across different land cover types, 3- predicted future LST for 2030, and 4- examined this temporal change for a hot-spot area (ring road) and a cool-spot area (the River Nile). The results showed that urban areas have increased over the last 30 years by 179.9 km2 (13 %), while agriculture areas decreased by 148 km2 (12 %) and water bodies decreased by 6 km2 (0.5 %). The mean LST over Greater Cairo increased over time, from 31.3 °C (1986) to 36.0 °C (2017) and is predicted to reach 37.9 °C in 2030. While a notable rise of mean LST in the Cairo ring road buffer zone (88 km2), where it was 31.1 °C (1986), and 37 °C (2017) due to the triple increase of urban areas on account of agriculture areas, and the LST it may reach 38.9 °C by 2030. The mean LST increased slightly more in urban hot-spot areas than in cooler cultivated areas. UHI may induce a modification in the local climate that can negatively affect agricultural land, and human thermal comfort and unfortunately lead to a less sustainable environment.

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.

Machine learning algorithm based prediction of land use land cover and land surface temperature changes to characterize the surface urban heat island phenomena over Ahmedabad city, India

Rapid urbanization over the world's dense urban centers cause an enormous change in the land use land cover (LULC) over a metropolitan area, which adversely affects the land surface temperature (LST) and intensify the urban heat island phenomena. The present study emphasizes the prediction of LULC, seasonal LST, and urban thermal field variance (UTFVI) over Ahmedabad city, India using multi date Landsat data. Artificial Neural Network (ANN) based Cellular Automata (CA) model is use to predict the LULC, while the XGB Regression model is used to predict seasonal LST with input data from 2010, 2015, and 2020 to predict future scenarios of 2025 and 2030. The result suggests an addition in the built-up area of about 5.77% and 13.08%, while a deduction in cropland area of about 4.15% and 12.54% is manifest in the year 2025 and 2030, respectively. This excessive urban growth in the study area will cause to face higher LST ranges of greater than 45 °C (24.9 km2 in 2025 and 24.2 km2 in 2030) in summer and 35 °C (19.9 km2 in 2025 and 43.6 km2 in 2030) in winter. The concentration of higher LST zones is evident in the rural areas than urban areas, witnessing cool urban islands. The predicted LST analysis suggests a dominant occurrence of none UTFVI zone in the city area and the strongest UTFVI zone in the surrounding rural area during both the seasons. An increase in green space area and avoidance of non-impermeable surfaces is suggest in future scenarios to mitigate UHI. Furthermore, the outcome of the research can help urban planners and policymakers while formulating urban heat island related mitigation strategies in the near-future scenario.

Land surface temperature (LST) and soil moisture index (SMI) to identify slope stability

Scientists widely use satellite images for scientific purposes, including investigation on earth science and environmental issues. Developing of many environmental models is due to replicating the natural process. Landslide is a known natural process controlled by slope stability which incorporates many parameters such as soil water content, morphology, and meteorological factor. Both LST and SMI were derived from satellite images, while SMI was the derivation of LST, meanwhile the use of both parameters in determining slope stability was rarely done. This research explores the use of LST and SMI in slope stability modeling. The LST analysis was calculated based on SEBAL (Surface Energy Balance Algorithms) using Landsat 8 imagery. The LST was then used to construct the SMI. Slope stability (FS) was calculated using the Selby model. All those variables were then cross-plotted in a regression to find the R2 value. The result shows a weak connection between FS-LST and FS-SMI with the R2 value of 9,09% and 8,16%. A stronger connection is only demonstrated in FS-Slope regression with a value of 70,98%. The weak R2 indicates that the model is not fit to calculate the FS of the Selby model. The LST and SMI were derived from satellite images and did not directly correspond to the soil characteristic as SMI was derived from LST and vegetation indices. Further empirical data collection needs to be used to build a better model on FS.