Land Surface Temperature Changes Research Articles

Using satellite-derived land surface temperatures to clarify the spatiotemporal warming trends of the Alborz Mountains in northern Iran

The Alborz Mountains are some of the highest in Iran, and they play an important role in controlling the climate of the country’s northern regions. The land surface temperature (LST) is an important variable that affects the ecosystem of this area. This study investigated the spatiotemporal changes and trends of the nighttime LST in the western region of the Central Alborz Mountains at elevations of 1500–4000 m above sea level. MODIS data were extracted for the period of 2000–2021, and the Mann–Kendall nonparametric test was applied to evaluating the changes in the LST. The results indicated a significant increasing trend for the monthly average LST in May–August along the southern aspect. Both the northern and southern aspects showed decreasing trends for the monthly average LST in October, November, and March and an increasing trend in other months. At all elevations, the average decadal change in the monthly average LST was more severe along the southern aspect (0.60°C) than along the northern aspect (0.37°C). The LST difference between the northern and southern aspects decreased in the cold months but increased in the hot months. At the same elevation, the difference in the lapse rate between the northern and southern aspects was greater in the hot months than in the cold months. With increasing elevation, the lapse rate between the northern and southern aspects disappeared. Climate change was concluded to greatly decrease the difference in LST at different elevations for April–July.

Evaluation of Spatio -Temporal Changes in Leaf Area Index and Land Surface Temperature in the Bioclimatic Regions of Northern Iran

Evaluation of Spatio -Temporal Changes in Leaf Area Index and Land Surface Temperature in the Bioclimatic Regions of Northern Iran

Modeling and Estimating the Land Surface Temperature (LST) Using Remote Sensing and Machine Learning (Case Study: Yazd, Iran)

The pressing issue of global warming is particularly evident in urban areas, where urban thermal islands amplify the warming effect. Understanding land surface temperature (LST) changes is crucial in mitigating and adapting to the effect of urban heat islands, and ultimately addressing the broader challenge of global warming. This study estimates LST in the city of Yazd, Iran, where field and high-resolution thermal image data are scarce. LST is assessed through surface parameters (indices) available from Landsat-8 satellite images for two contrasting seasons—winter and summer of 2019 and 2020, and then it is estimated for 2021. The LST is modeled using six machine learning algorithms implemented in R software (version 4.0.2). The accuracy of the models is measured using root mean square error (RMSE), mean absolute error (MAE), root mean square logarithmic error (RMSLE), and mean and standard deviation of the different performance indicators. The results show that the gradient boosting model (GBM) machine learning algorithm is the most accurate in estimating LST. The albedo and NDVI are the surface features with the greatest impact on LST for both the summer (with 80.3% and 11.27% of importance) and winter (with 72.74% and 17.21% of importance). The estimated LST for 2021 showed acceptable accuracy for both seasons. The GBM models for each of the seasons are useful for modeling and estimating the LST based on surface parameters using machine learning, and to support decision-making related to spatial variations in urban surface temperatures. The method developed can help to better understand the urban heat island effect and ultimately support mitigation strategies to improve human well-being and enhance resilience to climate change.

A time-continuous land surface temperature (LST) data fusion approach based on deep learning with microwave remote sensing and high-density ground truth observations

Land surface temperature (LST) is a crucial parameter in the circulation of water, exchange of land-atmosphere energy, and turbulence. Currently, most LST products rely heavily on thermal infrared remote sensing, which is susceptible to cloud and rain interference, leading to inferior temporal continuity. Microwave remote sensing has the advantage of being available “all-weather” due to strong penetration capability, which provides the possibility to simulate time-continuous LST data. In addition, the continuous increase in high-density station observations (>10,000 stations) provides reliable measured data for the remote sensing monitoring of LST in China. This study aims to adopt the “Earth big data” generated from high-density station observation and microwave remote sensing data to monitor LST based on deep learning (U-Net family) for the first time. Given the significant spatial and temporal variability of LST and its sensitivity to various factors according to radiation transmission equations, this study incorporated climatic, anthropogenic, geographical, and vegetation datasets to facilitate a multi-source data fusion approach for LST estimation. The results showed that the U-Net++ model with modified skip connections better minimized the semantic discrepancy between the feature maps of the encoder and decoder subnetworks for 0.1° daily LST mapping across China than the U-Net and U2-Net deep learning models. The accuracy of the LST simulation exhibited favorable outcomes in the spatial and temporal dimensions. The station density met the requirements of monitoring air-ground integration monitoring in China. Additionally, the temporal change in the simulation accuracy fluctuated in a W-shape owing to the limited simulation capability of deep learning in extreme scenarios. Anthropogenic factors had the largest influence on LST changes in China, followed by climate, geography, and vegetation. This study highlighted the application of deep learning in remote sensing monitoring against the background of “big data” and provided a scientific foundation for the response of climate change to human activities, ecological environmental protection, and sustainable social and economic development.

Evaluating the thermal environment of urban land surfaces in Yakutsk, a city located in a region of continuous permafrost

Rapid urbanization has led to changes in the urban land surface thermal environment. However, there are still much unknown about the urban land surface thermal conditions in permafrost regions. Permafrost is a unique geological environment, changes in the urban land surface thermal environment may trigger geological disasters caused by permafrost degradation. This study utilized remote sensing data and geographic detectors to identify the dynamic changes in land surface temperature (LST) and land use/land cover (LU/LC) in Yakutsk, as well as the potential factors contributing to LST variations. Between 1992 and 2020, the built-up area in Yakutsk increased by 36%, and the annual average LST in Yakutsk has risen by 6.67 °C, accompanied by an expansion of high-temperature areas. Despite ongoing greening efforts, rapid urbanization poses a threat to these green spaces. Changes in the normalized difference built-up index (NDBI) and land use transfer (LDT) were identified as the primary drivers of urban LST changes. By integrating geographic detector technology and artificial neural network models, we optimized the selection of input factors in the prediction model and used it to explore the future changes in LST in Yakutsk. The average LST in Yakutsk is expected to reach 23.4 °C and 25.1 °C in 2030 and 2040, respectively, with a further increase in high-temperature areas. This study provides a reference for ecological, hydrological, and geological assessments of cities in permafrost regions.

Mapping land use land cover (LUCL) and its influence on land surface temperature (LST): A case study in Dau Tieng district at Binh Duong province

At present, land surface temperature (LST) is an important index in monitoring the urban heat is-land phenomenon and changes in local climate, as well as global climate. By analyzing the variation in land use land cover (LULC), we can see how changes in LULC will lead to changes in LST and understand the expansion of urbanization related to LST. This study used Landsat 8’s thermal infrared band to extract and calculate LST values in DauTieng district in the period 2015-2022. In addition, this study also classified LULC by using the Support Vector Machine algorithm, then evaluated changes in LST on cropland, forest, water surface, and impervious surface areas. The results showed that in places with dense vegetation cover, LST value was low, whereas when moving to areas with low density of vegetation and residential areas, industrial parks,… LST value gradually increased. In general, changes in LULC are important factors that cause LST to increase or decrease, thereby suggesting solutions to minimize high-temperature areas

Spatiotemporal analysis of seasonal trends in land surface temperature within the distribution range of Moringa peregrina (Forssk.) in Southern and Southeastern Iran.

Temperature fluctuations and related factors are among the main causes of climate change. Understanding the temporal and spatial variations in temperature can shed light on how species respond to climate change. Plants generally persist in suitable microclimates in response to environmental change; however, examining long-term temperature variations within a species' range can be challenging using field observations. Thermal remote sensing, on the other hand, provides multi-scale time-series data with good coverage and regularity to overcome the challenges associated with field observations in environmental monitoring. Although changes in land surface temperature (LST) affect climate, hydrological processes, land-atmosphere interactions, and ecological activities, this metric has not received much research attention. This study aimed to analyze changes in habitat suitability and microclimatic conditions for Moringa peregrina. Seasonal changes in LST within the distribution range of the species were also investigated. To this aim, mean seasonal LST was computed in Google Earth Engine using the daily MODIS/006/MYD13A2 product from 2003 to 2023. Subsequently, a binary habitat suitability map was created based on the true skill statistic (TSS). The Mann-Kendall test was used to analyze seasonal LST trends. Major trends in LST were quantified based on the z-score, and compatibility with habitat suitability was evaluated via GAP analysis and the Kappa index. Seasonal temperature trends were evaluated by comparing each season with the following season using binary comparison. Landforms at presence points were regarded as microclimates and the sensitivity of microclimates to LST was evaluated using two methods: Principal component analysis (PCA) was used to quantify seasonal LST heterogeneity and the random forest (RF) approach was used to evaluate the effect of environmental parameters on habitat suitability within microclimates. The Kappa index revealed a weak overlapping between suitable / unsuitable habitat and the surfaces affected by the trend of changes. Moreover, the suitable habitat of Moringa peregrina in spring, autumn and winter is spatially overlapped by areas that have shown an increasing LST trend, and the presence points have not experienced an increasing temperature trend only in the summer. The findings show that the analysis of seasonal trends in LST provides insights into the effect of LST on habitat suitability and the condition of vegetation. The current study clearly shows that seasonal changes have had a significant impact on the distribution and habitat suitability of M. peregrina, particularly during summer and winter. Improved habitat suitability and range expansion were observed throughout the year. The study also highlights the role of landforms in regulating temperature. Landforms such as local ridges with minimal temperature fluctuations and regions near the Oman Sea were identified as potential future habitats due to favorable humidity conditions.

A Remote Sensing Analysis of the Urban Heat Island Effect in Guangzhou City from 2018 to 2023

This study aims to assess the urban heat island (UHI) effect in Guangzhou City using remote sensing technology and to explore its potential impact on urban spatial layout and measures for mitigating the UHI effect. By analyzing Landsat 8 satellite data, this paper focuses on the changes in land surface temperature in Guangzhou from 2018 to 2023, using a weekly scale as the primary time analysis unit. The research method is based on the Radiative Transfer Equation (RTE) for the inversion of land surface temperature, utilizing ArcMap software for the related data calculations and processing. The research findings reveal the weekly scale distribution of land surface temperature in Guangzhou City from 2018 to 2023, showing that the UHI effect is mainly concentrated in the southwestern part of the city. In contrast, due to less economic activity, lower population density, and a larger area of green coverage, the northeastern part experiences a relatively weaker UHI effect. The discoveries of this paper provide important references and bases for spatial planning and mitigation of the UHI effect in Guangzhou City and other similar cities, emphasizing the application value of remote sensing technology in the study of urban climate change.

Dynamics of land surface temperature: Insights into vegetation, elevation, and air pollution in Bengaluru

Bengaluru, among the five fastest-growing cities of India, is facing the challenge of urban heat islands (UHI) and temperature variations across its landscape. Monitoring the temperature fluctuations is crucial for understanding the city's microclimate and the impact of urbanization on its environment. Land Surface Temperature (LST) is a significant metric for this purpose. The present study examined the relationship of LST with vegetation, elevation, and air pollutants during the summer and winter seasons for 4-years (2019–2022). Air pollutants and weather parameters data from air quality monitoring stations was used. The study found negative relationship of LST with NDVI and Digital Elevation Model (DEM) during both seasons. The relationship between LST and individual air pollutants was variable during the summer and winter seasons and R2 ranged between 0.52 and 0.79. The relationship became stronger (R2 between 0.69 and 0.82 during summer and R2 between 0.74 and 0.80 during winter season) when all the parameters were considered simultaneously. The result from the Taguchi method showed that different air pollutants had variable impact over change in LST and the order of their effect was found to be PM2.5 > SO2 > O3 > CO > NO2. The results indicate that O3 (summer) and PM2.5 (winter) were the major contributors to rising LST. The present study has applications in future scientific research as well as it can be utilized to integrate mitigation strategies for increasing temperatures.

Innovation and digitalization in environmental education: the case study of climate change adaptation and analysis of land surface temperature


 
 
 The purpose of the article is to present the research results on the land surface temperature change in the frame of the Edu- cational and Professional Program «Ecology and Environmental Protection» developed at the Department of Environmental Sciences at National Aviation University, where the importance of the development of education in the noosphere paradigm is emphasized, as the most integral bio-adequate basis for the interaction of society and the environment in the conditions of today’s challenges. The climate’s change and the land surface temperature, as an integral part of it, are highlighted to tackle the environmental challenges in- cluding those connected with the warfare activities in the country. Development of the remote sensing technique within the framework of the Educational and Professional Program provides students with the opportunity to perform the environment changes assessment due to long-term anthropogenic stress, and also acquire knowledge and skills from the new course of «Adaptation to Climate Change», introduced to strengthen the ability to survive and effectively work and study in conditions of global climate change. The land surface temperature assessment is considered on the examples of the highly technologically loaded areas within the central part of the Ukrainian Shield, in particular the mining area and thermal power plant. Land surface temperature is one of the key features that could represent environmental changes caused by anthropogenic influence, for example, urbanization. It makes land surface temperature an appropriate Earth’s attribute that combines remote sensing and environmental sciences in the sense of environmental education. It is clear, that its influence on environment is intuitive for students and it could be represented using geoinformation systems. Land surface estimation is still a challenging task for non-specialists but its ecological significance makes land temperature mapping useful and important in environmental education.
 
 

Land use land cover and land surface temperature changes and their relationship with human modification in Islamabad Capital Territory, Pakistan.

Human activities are altering the existing patterns of Land Use Land Cover (LULC) and Land Surface Temperature (LST) on a global scale. However, long-term trends of LULC and LST are largely unknown in many remote mountain areas such as the Karakorum. . The objective of our study therefore was to evaluate the historical changes in land use and land cover (LULC) in an alpine environment located in Islamabad Capital Territory, Pakistan. We used Landsat satellite pictures (namely Landsat 5 TM and Landsat 8 OLI) from the years 1988, 2002, and 2016 and applied the Maximum Likelihood Classification (MLC) approach to categorize land use classes. Land Surface Temperatures (LST) were calculated using the thermal bands (6, 10, and 11) of Landsat series data. The correlation between the Human Modification Index (HMI) and LULC as well as LST was evaluated by utilizing data from Google Earth Engine (GEE). Over the study period, the urbanized area increased by 9.94%, whilst the agricultural and bare soil areas decreased by 3.81% and 3.94%, respectively. The findings revealed a significant change in the LULC with a decrease of 1.99% in vegetation. The highest LST class exhibited a progressive trend, with an increase from 12.27% to 48.48%. Based on the LST analysis, the built-up area shows the highest temperature, followed by the barren, agricultural, and vegetation categories. Similarly, the HMI for different LST categories indicates that higher LST categories have higher levels of human alteration compared to lower LST categories, with a strong correlation (R-value = 0.61) between HMI and LST. The findings can be utilized to promote sustainable urban management and for biodiversity conservation efforts. The work also has the potential of utilizing it to protect delicate ecosystems from human interference and to formulate strategies and regulations for sustainable urban growth, including aspects of land utilization and zoning, reduction of urban heat stress, and urban infrastructure.

Integrating random forest and morphological spatial pattern analysis for predicting future land surface temperature dynamics: insights from urbanizing Saudi Arabia

In the face of rapid urbanization in Saudi Arabia, understanding the impact of landscape changes on land surface temperature (LST) is crucial for sustainable urban planning. This study assesses the influence of landscape morphology on LST and predicts future LST changes. A multi-temporal land use and land cover (LULC) analysis using random forest (RF) quantified urban expansion and its ecological impacts. Accuracy assessment using the kappa coefficient illustrated the precision of the classification techniques. Morphological Spatial Pattern Analysis (MSPA), developed in Python, analyzed the structural evolution of urban areas, vegetation and exposed rocks. Polynomial regression models established the relationship between landscape morphology and LST and predicted future temperature trends. Results for Asir show a significant improvement in accuracy of the LULC models over three decades, with overall accuracy increasing from 89.99% in 1990 to 91.72% in 2020. The bootstrapping trend analysis showed an urban expansion with a positive slope of 9.27 and a decline in water bodies with a negative slope of −0.03. The MSPA analysis reflected a significant urban expansion, with the core area growing from 45.19 km2 in 2001 to 230.33 km2 in 2021. The vegetated areas showed resilience and dynamic connectivity despite slight reduction and fragmentation. The polynomial regression predicted an increase in future average LST by 2030, with urban core areas reaching 58.47 °C, vegetated cores 42.19 °C and exposed rock cores 55.79 °C. These results highlight the link between urban expansion and LST rise and make the case for integrating green infrastructure and cooling strategies into urban development.

Impacts of urban landscape pattern changes on land surface temperature in Southeast Brazil

Urban sprawl is a global concern, considering the current climate change scenario. The conversion of natural habitats or other land uses to urban areas increases the Land Surface Temperature (LST), impacting people mainly in tropical regions, such as Brazil, where the temperature is aggravated by high air humidity. In this context, we assessed the seasonal variation of LST in response to the land-use/land-cover (LULC) changes over a large city in southwest Brazil during the last 36 years (1985–2020). We used Mapbiomas LULC data and LST retrieved from the Landsat series with the Google Earth Engine platform. The methodology involved the change detection analysis from 1985 to 2020 for all LULC types, a non-parametric Dwass-Steel-Critchlow-Fligner test to estimate the LST spatio-temporal variability over different LULC types, and a non-parametric Sperman's rho test and scatter plots to relate LST changes to the LULC patterns. The results presented areas exchanging under an intense urbanization process, wherein nearly 13% of the transitions were from agricultural to urban areas. These transitions occurred jointly with an increase in temperature for all LULC types. Forest formation registered the lowest LST and a moderate to strong negative correlation with LST in both seasons. On the other hand, urban areas showed the highest temperatures and a moderate to strong positive correlation with LST in summer. From 1985 to 2020, the mean temperature in urban (built-up) areas during the summer increased by 7 °C. In 2020, the mean temperature in urban areas was 32.5 °C and 27.8 °C in forested areas during the summer. The main contribution of this study is to provide evidence-based support for establishing LULC governance at the city scale. It offers essential scientific information about the detrimental impact of urban sprawl against the climate benefits of green infrastructure on the city scale, even in tropical regions with high forest fragmentation, temperatures, and long periods with high air humidity.

The Global Land Surface Temperature Change in the 21st Century—A Satellite Remote Sensing Based Assessment

The Global Land Surface Temperature Change in the 21st Century—A Satellite Remote Sensing Based Assessment

Monitoring and Prediction of Siberian Silk Moth Dendrolimus sibiricus Tschetv. (Lepidoptera: Lasiocampidae) Outbreaks Using Remote Sensing Techniques.

The feasibility of risk assessment of a Siberian silk moth (Dendrolimus sibiricus Tschetv.) outbreak was analyzed by means of landscape and weather characteristics and tree condition parameters. Difficulties in detecting forest pest outbreaks (especially in Siberian conditions) are associated with the inability to conduct regular ground surveillance in taiga territories, which generally occupy more than 2 million km2. Our analysis of characteristics of Siberian silk moth outbreak zones under mountainous taiga conditions showed that it is possible to distinguish an altitudinal belt between 400 and 800 m above sea level where an outbreak develops and trees are damaged. It was found that to assess the resistance of forest stands to pest attacks, researchers can employ new parameters: namely, characteristics of a response of remote sensing variables to changes in land surface temperature. Using these parameters, it is possible to identify in advance (2-3 years before an outbreak) forest stands that are not resistant to the pest. Thus, field studies in difficult-to-access taiga forests are not needed to determine these parameters, and hence the task of monitoring outbreaks of forest insects is simplified substantially.

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.

RESEARCH ON THERMAL ENVIRONMENT ANALYSIS OF PHOTOVOLTAIC POWER PLANTS BASED ON REMOTE SENSING IMAGES

Abstract. As a representative of new energy, photovoltaic power stations have developed rapidly in China, with annual installed capacity increasing. At present, research on photovoltaic power plants mainly concentrates on carbon neutrality based on the whole life cycle, and there are few studies on the thermal environment problems that may be caused by photovoltaic power plants. This paper takes the area where the photovoltaic power station is located in Quyang County, Baoding City, Hebei Province, and the surrounding mountains as the research area. It uses Gaofen-1 and Landsat 8 remote sensing images to study the changes in land cover and surface temperature before and after the construction of mountain photovoltaic power stations over a period of 8 years from 2013 to 2020.Given the lack of quantitative research on the impact of mountain photovoltaic power stations on the surrounding environment using remote sensing technology, this paper focuses on the influence of the distribution and proportion of mountain photovoltaic power stations on the spatial pattern of the thermal environment in the region. Through experimental verification and result analysis, it can be seen that under the geographical location and time conditions of the study area, the proportion of photovoltaic power stations shows a strong correlation with the average surface temperature, which is one of the main factors causing the temperature rise in mountainous areas. It is a land cover type with heat island effects in the traditional sense.

Semi-Supervised Detection of Detailed Ground Feature Changes and Its Impact on Land Surface Temperature

This paper presents a semi-supervised change detection optimization strategy as a means to mitigate the reliance of unsupervised/semi-supervised algorithms on pseudo-labels. The benefits of the Class-balanced Self-training Framework (CBST) and Deeplab V3+ were exploited to enhance classification accuracy for further analysis of microsurface land surface temperature (LST), as indicated by the change detection difference map obtained using iteratively reweighted multivariate alteration detection (IR-MAD). The evaluation statistics revealed that the DE_CBST optimization scheme achieves superior change detection outcomes. In comparison to the results of Deeplab V3+, the precision indicator demonstrated a 2.5% improvement, while the commission indicator exhibited a reduction of 2.5%. Furthermore, when compared to those of the CBST framework, the F1 score showed a notable enhancement of 6.3%, and the omission indicator exhibited a decrease of 8.9%. Moreover, DE_CBST optimization improves the identification accuracy of water in unchanged areas on the basis of Deeplab V3+ classification results and significantly improves the classification effect on bare land in changed areas on the basis of CBST classification results. In addition, the following conclusions are drawn from the discussion on the correlation between ground object categories and LST on a fine-scale: (1) the correlation between land use categories and LST all have good results in GTWR model fitting, which shows that local LST has a high correlation with the corresponding range of the land use category; (2) the changes of the local LST were generally consistent with the changes of the overall LST, but the evolution of the LST in different regions still has a certain heterogeneity, which might be related to the size of the local LST region; and (3) the local LST and the land use category of the corresponding grid cells did not show a completely consistent correspondence relationship. When discussing the local LST, it is necessary to consider the change in the overall LST, the land use types around the region, and the degree of interaction between surface objects. Finally, future experiments will be further explored through more time series LST and land use data.

Dynamic Impact of Urban Built Environment on Land Surface Temperature Considering Spatio-Temporal Heterogeneity: A Perspective of Local Climate Zone

Thermal environment deterioration has seriously threatened urban habitat quality and urban sustainable development. The evolution of the urban built environment (UBE) is an important cause for urban thermal environment variation. However, the dynamic effect of the UBE on the land surface temperature (LST) is rarely studied by combining the local climate zone (LCZ) theory and spatio-temporal heterogeneity. Based on a case study of Beilin District in Xi’an, China, this paper identified LCZ types of Beilin District in 2010, 2015, and 2020 using the GIS method. It also analyzed the spatial–temporal characteristics of the LST in summer based on the remote sensing retrieval method and explored the effects of the built environment on the LST by Geodetector and geographically weighted regression (GWR). The results showed the following: (1) The area share of dense building zones in Beilin District was greater than that of open building zones and natural surface zones, while the share of mid- and high-rise dense building zones continued to increase and the share of low-rise dense building zones continued to decrease during the study period. (2) The LST of different LCZ types in Beilin District was obviously different, and the LST of dense building zones was generally higher than that of open building zones and natural surface zones. Meanwhile, the LST of mid- and low-rise dense building zones increased gradually, and the LST of high-rise open building zones decreased gradually, but the overall warming area was obviously more than the cooling area. (3) The effects of the UBE factors on the LST varied greatly, with their interaction having an enhancement effect. The direct and interactive influence of the two-dimensional (2D) UBE indicators on the LST were greater than those of the three-dimensional (3D) indicators, but there was a gradual decrease in the force of the 2D indicators and a simultaneous diminution, enhancement, and invariance of the force of the 3D indicators. (4) Vegetation cover (VC) and floor area ratio (FAR) acted negatively, and the building height (BH) was changing from a positive to a negative role, with the average action intensity of VC changing from −0.27 to −0.15, FAR from −0.20 to −0.16, and BH from 0.05 to −0.04. The impervious surface area (ISA), building area (BA), and space congestion (SC) acted positively, with the average action intensity of the ISA changing from 0.12 to 0.20, BA from 0.12 to 0.19, and SC was stable at 0.04. The framework enables a deeper portrayal of LST changes in different LCZs, reflecting the direct and interactive effects of different UBE indicators on LST, as well as local variations in the impact effects and provides a basis for urban managers or planners to improve urban heat resilience.

Monitoring Land Surface Temperature in the Karajae Watershed as an influential ecosystem for Pare-Pare City

Temperature is an indicator that is often used to assess environmental conditions in an area because it affects food and air resources, hydrology, and all ecosystems on earth. Monitoring the temperature conditions of an area is also carried out from time to time to see trends that occur. Land surface temperature monitoring using remote sensing which has spatial resolution, temporal resolution, and spectral resolution. Monitoring is only in urban areas without looking at the surrounding conditions. However, temperature is influenced by several factors such as geographic location, position relative to the sun, altitude, and biophysical conditions such as land. This study looks at changes in land surface temperature in the Karajae watershed area as an ecosystem that affects urban areas, Pare-Pare City or vice versa. Analysis of land surface temperature monitoring using remote sensing methods in the form of multi-temporal Landsat Imagery data 2004-2021. The land image displays changes in land surface temperature with a single channel algorithm. In addition, land images are the basis for analyzing land cover changes which are the basis for estimating land surface temperature changes with normalized fractional vegetation cover relationships. The results show that changes in land cover must occur, which are estimated to be twice as large in the future. Settlements have experienced a very large increase in area, while the closure of forest land and dry agricultural land has experienced a reduction in area which will cause disruption to ecosystems and food. The trend of increasing land surface temperature continues to increase every period. The increase in settlements as built-up land, rice fields, dry land agriculture as a form of community activity causes an increase in land surface temperature. In addition, agricultural activities have an influence on increasing land surface temperatures in vegetated land cover. Indirectly, the city of Pare-Pare has an impact on increasing land surface temperatures in the upstream area of the Karajae watershed. This is a concern in the future, if urban activities, transportation, to industrial activities are much faster, and will cause a greater increase in land surface temperature.