Land Surface Temperature Distribution Research Articles

Impacts of Land Cover Changes on Land Surface Temperature using Landsat Imagery with the Supervised Classification Method

Land cover changes occur along with development and an increase in urbanization, affecting the region's ecology. Tarogong Kidul Sub-district, Garut District, experiences land cover changes that change land surface temperature. This study aims to determine the relationship between land cover changes and land surface temperature. This study uses Landsat 8 Surface Reflectance Tier 1 satellite imagery to extract Normalized Difference Vegetation Index information and process soil surface temperatures for three periods, 2014, 2017, and 2020 as well as supervised classification methods. Image collection and processing are done via Google Earth Engine and ArcGIS Pro software. The results were in the form of the distribution of land surface temperature correlated with changes in the value of the land cover vegetation index using simple linear regression and spatial correlation analysis. This study reveals that land cover change is closely related to the increase in land surface temperature indicated by the rise in land surface temperature in areas experiencing changes in land use. The results of linear regression analysis (84.49%) confirm that land cover changes and the greenness index of vegetation are the most critical driving factors for changes in land surface temperature.

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

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

Analysis of Land Surface Temperature Distribution in Response to Land Use Land Cover Change in Agroforestry Dominated Area, Gedeo Zone, Southern Ethiopia

This study examined LST distribution in Ethiopia's agroforestry-dominated Gedeo Zone due to Land Use Land Cover change. For 2005, 2011, 2017, and 2022, 10 m Sentinel 2A and 30 m Landsat images were used to extract and map LST and LULC distribution. The DOS1 method corrected atmospheric errors in all satellite images. LULC change was detected using SVM image classification. The study result revealed that the Agroforestry and Built-up coverage has increased by 1520 sq. km and 2600 sq. km, respectively, from 2005 to 2022. The Bare Land and Farm Land coverage decreased by 1554 sq. km and 2565 sq. km, respectively, in the same period. The LST result has shown that there has been a remarkable variation in the spatial pattern of the LST between 2005 and 2022. The average LST in Agroforestry, Bare Land, Farm Land, and Built-up area has progressively increased over the years, from 19.6°C, 26.0°C, 20.2°C, and 25.58°C in 2005 to 25°C, 32.16°C, 28.23°C, and 30.62 °C in 2022, respectively. While in 2005, the maximum recorded LST did not exceed 37.3°C, by 2022, it had increased by close to 3°C, reaching 40.6°C. The overall result revealed that the average LST in °C has increased from 2005 to 2022. From the result, it was concluded that agroforestry had contributed a lot to LST distribution. LST may not depend on the local LULC change only; other factors like urbanization and global warming could play a significant role in changing LST locally and globally.

EFFECT OF VEGETATION AND BUILDING DENSITIES TO URBAN THERMAL COMFORT (CASE STUDY OF DENPASAR CITY)

Denpasar is the capital of Bali Province, which is one of the most popular tourist destination in Indonesia. Anthropogenic activities have changed the land use and land cover (LULC) that affect the land surface temperature (LST) and thermal comfort. Good quality of thermal comfort can improve the livelihood of its citizen as well as the tourists. This research aims a) to analyses the density distribution of vegetation and buildings, b) to map the distribution of LST and thermal comfort, and c) to analyses the effect of vegetation and building density on thermal comfort in Denpasar City. LST and thermal comfort were extracted using Landsat 9. Thermal comfort was assessed using urban thermal field variance index (UTFVI). Then, Pearson correlation and simple regression were applied to investigate the relationship and effect of Effect of vegetation and building densities to urban thermal comfort. In general, high density of vegetation is located in the northern part of North Denpasar District, north and east of East Denpasar District, and south of South Denpasar District. High density buildings are scattered in the southern Denpasar District, the western eastern Denpasar District, the northern South Denpasar District, and parts of the West Denpasar District. Low UTFVI values indicate good thermal comfort. Greater vegetation and lower building densities are likely to have an impact by improving thermal comfort levels in a location. This research can be served as a guidance in urban heat island mitigation as well as ecological restoration planning.

Modeling the Impact and Risk Assessment of Urbanization on Urban Heat Island and Thermal Comfort Level of Beijing City, China (2005–2020)

Rapid urbanization poses a threat to various ecosystem services. Beijing has undergone extensive infrastructure development in recent years. The study aims to extract land surface temperature (LST) and land use cover (LUC) data from satellite imagery, identify urban heat island (UHI) areas in Beijing, and determine the correlation between LST, LUC, NDVI, and BUI. It will also investigate the relationship between UHI and built/unbuilt areas, evaluate thermal comfort in Beijing using UTFVI, and assess the ecological quality of different land use types using the Ecological Evaluation Index (EEI). The results can inform urban planning and management in rapidly urbanizing and climate-changing regions. Changes in LUC and other activities affect the distribution of LST. For the study years (2005–2020), the estimated mean LST in Beijing was 24.72 °C, 27.07 °C, 26.22 °C, and 27.03 °C, respectively. A significant positive correlation (r = 0.96 p > 0.005) was found between LST and urban areas with other infrastructures. Geographically weighted regression (GWR) outperformed with Adj R2 > 0.74, suggesting that the extent of an urban heat island (UHI) is strongly dependent on the settlements, LUC composition, size, and terrain of surrounding communities. Urban hotspots in the city were identified and validated using Google Earth imagery. The Ecological Evaluation Index (EEI) value was relatively low compared to other ecosystem-related units. EEI showed a continuous increase of six percent in the most negative categories, indicating an unstable environment. This study concludes that urbanization affects the city’s environment, and study findings would help to regulate the urban ecosystem in Beijing.

MAPPING OF FOREST AND LAND FIRE HAZARDOUS USING LANDSAT 8 SATELLITE IMAGERY WITH LAND SURFACE TEMPERATURE (LST) AND NORMALIZED BURN RATIO (NBR) METHODS

This study aims (1) to determine the distribution of Land Surface Temperature (LST) in the Baso District in 2022 (2) to determine the Normalized Burn Ratio (NBR) in Baso District in 2022 (3) to map areas prone to forest and land fires by utilizing the Land Surface Temperature (LST) and Normalized Burn Ratio (NBR) algorithms in Baso District in 2022.
 This study uses the Land Surface Temperature (LST) method to determine the distribution of land surface temperatures in the Baso District in 2022. The Normalized Burn Ratio (NBR) method is used to identify areas that are burned and then weighted overlay using Arcgis to obtain data on land and forest fire vulnerability. in Baso District.
 The results of this study are (1) showing a minimum temperature value of 13.6oC maximum temperature of 34.5oC and an average temperature of 26oC (2) showing the results of the distribution of areas with a value of -1 which are identified as burnt or those with bad vegetation of 2.5 and areas with a value of 0 indicating vegetation a good area of ​​7,636 Ha (3) on the mapping of areas prone to forest and land fires after the Weighted Overlay was carried out found 4 classes of vulnerability levels not prone to forest and land fires, moderately prone, prone and very prone to forest and land fires.

Distinguishing Dominant Drivers on LST Dynamics in the Qinling-Daba Mountains in Central China from 2000 to 2020

Land surface temperature (LST) is an important driving factor in the land-atmosphere energy cycle. To examine the spatiotemporal patterns of LST changes and the internal mechanisms driven by multiple factors, we used a trend analysis method on TRIMS LST data from 2000 to 2020 in the Qingling-Daba Mountains. The optimal parameter geographic detector (OPGD) model was used to detect the influence of twelve factors, including elevation, precipitation, albedo, relative humidity (RH) and normalized difference vegetation index (NDVI), on the spatial distribution of LST, as well as to explore the dominant factors affecting LST differentiation in the study area. The results showed that: (1) From 2000 to 2020, the average annual LST of the Qinling-Daba Mountains was 18.17 °C. The warming trend was obvious (0.034 °C/a), and the warming effect at nighttime (0.066 °C/a) was stronger than that during daytime (0.0004 °C/a). The difference between day and night temperature (DIF) was decreasing. (2) The seasonal changes in LST and DIF in the Qinling-Daba Mountains were significant, and the spatial distribution of their average values in the summer was slightly larger and fluctuated more than in the other seasons. (3) Elevation was the main driving factor affecting the spatial distribution of LST, with the contribution scores of 62.9% in the daytime and 92.7% in the nighttime. The controlling effects of these factors were generally stronger in the nighttime than in the daytime. (4) Nighttime elevation had the strongest interaction with precipitation (contribution score of 95%), while daytime elevation had the strongest interaction with albedo (contribution rate of 83%). We revealed the temporal and spatial variation in LST in the Qinling-Daba Mountains since 2000 and explored the main driving factors involved, thereby improving our understanding of LST changes in the Qinling-Daba Mountains. This study can provide a scientific basis for distinguishing dominant drivers of LST dynamics in the Qinling-Daba Mountains.

基于MSPA模型的新型城市热景观连通性分析

PDF HTML阅读 XML下载 导出引用 引用提醒 基于MSPA模型的新型城市热景观连通性分析 DOI: 10.5846/stxb202109172619 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 湖南省科技创新计划湖湘青年英才项目（2021RC3104）；湖南省研究生科研创新基金项目（CX20210895）；株洲云龙示范区生态廊道专项规划项目（HNHXZZ-20171109） Analysis connectivity of urban heat island in a new-type urbanization based on MSPA model Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:基于土地利用景观格局对城市热岛效应的影响研究较多，但准确识别对城市热岛效应具有重要影响的关键斑块和节点方面的研究亟待开展。以新型中小城市株洲云龙示范区为例，以城市热岛网络构建为切入点，应用形态学空间格局分析（Morphological Spatial Pattern Analysis，MSPA）与景观连通性模型识别城市发展进程中核心热景观斑块和关键节点，并分析其时空演变特征。研究结果表明：（1）2007-2018年云龙示范区热岛斑块趋于集中，热岛面积较研究初期有明显增加，从地表温度空间分布上体现为城区与周边农村的温度对比更加鲜明，表明城市化进程中建设用地呈聚集扩散趋势。（2）研究期间热岛核心区占热景观面积比最大，且比例有明显上升趋势。而边缘、支线、孤岛、桥接、环线以及孔隙形态类型面积占比均呈现出不同程度的下降趋势，表明热岛斑块之间聚集程度不断提高。（3）识别出景观连通度重要性值较高的前20热景观斑块，其中前3斑块的重要性值和面积占比较大，属于极重要与重要两个热岛核心斑块重要性等级，值得重点关注与优化。（4）对重要性值高的热岛核心区域可优先采取降温措施，可以打断节点之间连接减缓整个区域的热岛效应。本研究结果对我国中小城市规划和可持续发展具有重要的实践指导意义。 Abstract:Many previous studies have concerned with the effects of land use/land cover changes and landscape patterns on the urban heat island. However, accurate identification of key patches and nodes with high contribution to urban heat island need to be urgently studied. Using Yunlong Demonstration Zone of Zhuzhou, a new-type city, as the case study area based on morphological spatial pattern analysis (MSPA) and landscape connectivity model, this study constructed an urban heat island network to identify the spatio-temporal dynamic characteristics of core thermal landscape patches and nodes in process of urban development. The results showed that:(1) from 2007 to 2018, heat island patches tended to be concentrated in Yunlong Demonstration Zone, and the area of heat island increased significantly compared with the initial stage of the study. From the spatial distribution of land surface temperature, the contrast between urban area and surrounding countryside was more distinct. (2) During the study period, the area of heat island core class was the largest, and the area proportion showed an obviously rising trend. While the area proportion of edge class, branch class, islet class, bridge class, loop class and perforation class showed a decreasing trend to varying degrees, which was largely related to the increase of aggregation degree between heat island patches. (3) The top 20 thermal landscape patches with higher importance value of landscape connectivity were identified, and the importance value and area of the top 3 patches were relatively large, which should be deserved more attention and optimization. (4) Patch-based cooling measures adopted to avoid the connection between node could effectively alleviate the heat island effects in the whole region. The results of this study have important practical significance for the planning and sustainable development of small and medium-sized cities in China. 参考文献 相似文献 引证文献

Determining UHI Effect by Remote Sensing Method in Bolu City Centre, Turkey

Urban Heat Island (UHI) has been described by authors as the UHI effect is among the most common forms of human origin (anthropogenic) local climate change. The increasing UHI effect with the differences in land use and landscape pattern varies depending on surface soil, watery field presence and vegetation. In this study, using Landsat 5 TM of 1994 and Landsat 7 ETM+ images of 2019, the ArcGIS 10.6.1 program and the remote sensing methods have identified surface temperature and vegetation distribution. Surface temperature values of the land-use in Bolu province of 2019, 1/5000 Urban Development Plan land uses and average temperature values were determined. The study revealed the change between urban development and the effect of land surface temperature over the course of 25 years, and discussed the UHI effect in the Bolu province. The effects of the historical process in Bolu city center on land surface temperature (with LST differences) and vegetation distribution (Normalized Difference Vegetation Index; NDVI) have been surveyed. The constraints of this study are that the spatial resolution in orthophotos of 1994 is low, and the type of land-use temperature data cannot be compared to 2019. For this reason, LST and NDVI analyzes were conducted in 1994 orthophotos, classifying all parcels with structure and related area in the form of manually constructed areas (built Environment). One of the findings of this study are surface temperatures of areas used as farmland in the year 1994 data reached higher values after they quickly began to urbanize in Bolu. The main reason for the high surface temperature in the Bolu province over the 25-year period is that agricultural areas are impurized and increasing population density and the albedo effect. It has been concluded that the lack of green space and lack of vegetation in the continuous urban area has increased the UHI effect.

Land surface temperature distribution in Mahanadi delta: impact of land use land cover change

Land surface temperature distribution in Mahanadi delta: impact of land use land cover change

Modeling spatio-temporal assessment of land use land cover of Lahore and its impact on land surface temperature using multi-spectral remote sensing data.

Urban sprawl, also widely known as urbanization, is one of the significant problems in the world. This research aims to assess and predict the urban growth and impact on land surface temperature (LST) of Lahore as well as land use and land cover (LULC) with a cellular automata Markov chain (CA-Markov chain). LULC and LST distributions were mapped using Landsat (5, 7, and 8) data from 1990, 2004, and 2018. Long-term changes to the landscape were simulated using a CA-Markov model at 14-year intervals from 2018 to 2046. Results indicate that the built-up area was increased from 342.54 (18.41%) to 720.31 (38.71%) km2. Meanwhile, barren land, water, and vegetation area was decreased from 728.63 (39.16%) to 544.83 (29.28%) km2, from 64.85 (3.49%) to 34.78 (1.87%) km2, and from 724.53 (38.94%) to 560.63 (30.13%) km2, respectively. In addition, urban index, a non-vegetation index, accurately predicted LST, showing the maximum correlation R2 = 0.87 with respect to retrieved LST. According to CA-Markov chain analysis, we can predict the growth of built-up area from 830.22 to 955.53 km2 between 2032 and 2046, based on the development from 1990 to 2018. As urban index as the predictor anticipated that the LST 20-23°C and 24-27°C, regions would all decline in coverage from 5.30 to 4.79% and 15.79 to 13.77% in 2032 and 2046, while the temperature 36-39°C regions would all grow in coverage from 15.60 to 17.21% of the city. Our results indicate severe conditions, and the authorities should consider some strategies to mitigate this problem. These findings are significant for the planning and development division to ensure the long-term usage of land resources for urbanization expansion projects in the future.

Analysis of spatially varying relationships between urban environment factors and land surface temperature in Mashhad city, Iran

Land Surface Temperature (LST), particularly for the urban environment, is a crucial indicator of urban heat emission, urban climate, global environmental change, and human-environment interactions. In the present study, the spatial distribution of the mean LST was evaluated in association with the 16 explanatory indices at the neighbourhood's level in Mashhad City, Iran. To identify the main variables contributing to the LST variations, Principal Components Analysis (PCA), Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) models were employed to explore the spatially varying relationships and identify the model's efficiency at the neighborhood's scale. Our findings showed the five most important components contributing to LST variances, explaining 86.2 % of the variability. The most negative relationship was observed between LST and the morphological features of neighborhoods (PC3). In contrast, the landscape configuration of the green patches (PC5) exhibited the lowest negative impacts on LST changes. Moreover, road and traffic density characteristics of the neighborhoods (PC4) were the only effective components to alert the average LST positively. With R2 = 0.687, AICc = 1545.848, and Moran's I = 0.021, the results revealed that the GWR model had better efficiency than the corresponding non-spatial OLS model in terms of the goodness of fits. It suggests that the GWR model has more ability than the OLS one to predict LST intensities and characterize spatial non-stationary. Therefore, it can be applied to adapt more effective strategies in planning and designing the urban neighborhoods for mitigation of the adverse heat effects.

Modelling the impacts of land use/land cover changing pattern on urban thermal characteristics in Kuwait

• Changes of LULC, summer LST, UHI and UTFVI shift in Kuwait were analyzed. • Reduction of vegetation cover (47%) significantly increases the UHI effect. • LULC vs UTFVI relationship better explain the impacts of different land cover on thermal environment. • Summer UHI and UTFVI prediction exhibit a gradual decrease in overall thermal characteristics. • Predicted UTFVI demonstrated the highest UTFVI concentration in the built-up area. Rapid urbanization owing to population growth and economic development has made thermal environment-related studies increasingly prominent. This study aims to monitor and predict the changes in land use/land cover (LULC) and their impacts on land surface temperature (LST), urban heat island (UHI), and urban thermal field variance Index (UTFVI) in Kuwait from 1991 to 2021 for the very first time. Support Vector Machine (SVM) and Artificial Neural Network (ANN)machine learning algorithms were used to analyze and predict, respectively, using Landsat 4-5 and 8 images from 1991 to 2021 at 10 years interval. Results illustrated transformation of 27.24% bare land and 5.43% vegetation into the built-up area increased the mean LST by 5°C, resulting in an upsurge of UHI values by 0.861 and the strongest UFTVI effects by 108% from 1991 to2021. Predicted LULC, LST, UHI and UTFVI distribution modelled using the ANN-based Cellular Automata technique for 2031 shows the declination of vegetation cover (44%) and water bodies (57%) during 2021–2031, resulting upsurge in built-up areas (38%) and higher LST (10°C). Moreover, by 2031, 41% of the total study area will be covered by the strongest UTFVI affected zones. All the prediction was performed with a higher accuracy, and validated by RMSE and R 2 values with predicted and real datasets. Assessing the predicted LULC, LST, UHI, and UTFVI distribution maps will help prospective policymakers and city planners to take the necessary actions by reducing heat stress impacts and make cities sustainable.

Spatial analysis of land surface temperature distribution: case of the Greater Beirut Area

Spatial analysis of land surface temperature distribution: case of the Greater Beirut Area

SPATIOTEMPORAL ANALYSIS OF URBAN GROWTH AND LAND SURFACE TEMPERATURE: A CASE STUDY OF LAHORE, PAKISTAN

Abstract. Cities around the world are facing tremendous pressure due to rapid urbanization. They are being extended haphazardly especially in developing countries, putting strain on already depleting natural recourses. The land-use conversion to built-up areas harms the urban environment significantly. The most immediate implications of this land-use/land cover (LULC) conversion are the transition of Land Surface Temperature (LST) and the creation of Urban Heat Islands. This research investigates the spatial distribution of LST and LULC and their interrelation using satellite images from Landsat 5 (TM) and 8 (OLI/TRS) for the years 1998, 2010, and 2021. The built-up area in Lahore has grown enormously over the last two decades. Our results indicate that each year 1.26% of the land is being transformed into built-up area. Consequently, the prevailing urban development trends have also influenced the LST. In particular, we observed an average upsurge of 0.47°C per year between 1998 and 2021. If our cities continue to expanqd in the same manner, this would have serious ecological implications in the future. Thus, urban planners and policymakers need to incorporate climate-adaptive design at the community and building levels to improve the situation.

Seasonal and Diurnal Variation of Land Surface Temperature Distribution and Its Relation to Land Use/Land Cover Patterns.

The surface urban heat island (SUHI) affects the quality of urban life. Because varying urban structures have varying impacts on SUHI, it is crucial to understand the impact of land use/land cover characteristics for improving the quality of life in cities and urban health. Satellite-based data on land surface temperatures (LST) and derived land use/cover pattern (LUCP) indicators provide an efficient opportunity to derive the required data at a large scale. This study explores the seasonal and diurnal variation of spatial associations from LUCP and LST employing Pearson correlation and ordinary least squares regression analysis. Specifically, Landsat-8 images were utilized to derive LSTs in four seasons, taking Berlin as a case study. The results indicate that: (1) in terms of land cover, hot spots are mainly distributed over transportation, commercial and industrial land in the daytime, while wetlands were identified as hot spots during nighttime; (2) from the land composition indicators, the normalized difference built-up index (NDBI) showed the strongest influence in summer, while the normalized difference vegetation index (NDVI) exhibited the biggest impact in winter; (3) from urban morphological parameters, the building density showed an especially significant positive association with LST and the strongest effect during daytime.

Evaluation of earth observation datasets for LST trends over India and its implication in global warming

Land surface temperature (LST) is crucial in surface energy balance, urban climatology, intensifying global change, ecological and environmental concerns. The present study examined the LST trends and spatio-temporal variation over India from 2002 to 2022. This includes comparison of LST for the summer and winter seasons over two decades. Secondly, the present study examined the LULC category wise LST variability during the day and night-time using MODIS (The Terra Moderate Resolution Imaging Spectroradiometer) derived products. This study explored the feasibility of cloud computing for big data analysis for LST distribution in seven landuse categories over India, providing a conceptual response to global warming. Results showed the existence of spatial LST variation due to changes in land-use patterns and MODIS derived vegetation indices- NDVI. Daytime LST for the summer and winter seasons of 2002 was found to be 45.17 °C and 39.13 °C, respectively. Outcomes illustrate declining trends in range (LSTmin-LSTmax) for winter seasons, initially, it was observed as 56.29 °C for 2002 while later on it was observed to be 20.21 °C and 20.87 °C for 2021 and 2022 respectively. The LSTmin (summer) has shown an increasing trend towards upper LST values, from −3.01 °C to 12.21 °C from 2002 TO 2022. LSTmin_winter has shown a rising trend towards the upper LST values from −17.16 °C to 9.15 °C in 2022. The maximum LST for the DRs was observed to be 61.56 °C, followed by UR as 56.24 °C. The findings demonstrate that daytime LSTmin and LSTmax are found to be 19.50 °C to 56.24 °C for UR, 29.5 °C to 61.56 °C for DR, 19.24 °C to 54.08 °C for SAR, −21.1 °C to 0.05 °C for SCR and 15 °C to 32.18 °C for FHR. NDVI-LST (daytime, nighttime and diurnal temperature range) feature space generates an obtuse triangle and depicts a negative correlation of vegetation for a few LULC categories. The outcomes indicated that desert and snow regions have highest LSTmax followed by urban and semiarid regions during daytime. During the nighttime, desert and urban regions have the highest temperature followed by semi-arid and forest regions. The outcomes support the efficacy of earth observation datasets and help to facilitate a better understanding of LULC and its impact on regional climate.

Spatiotemporal Analysis of Land Surface Temperature and Vegetation Changes in Duhok District, Kurdistan Region, Iraq

The temperature rise has become a serious environmental concern affected by both human and natural factors. Worldwide, rising land surface temperatures have emerged as the most pressing issue facing the twenty-first century. In the last two decades, a curious change was realized in temperature in the Duhok district of Iraq. Hence, this study examined the spatiotemporal land surface temperature distribution and Modified Soil Adjusted Vegetation Index (MSAVI2) and the correlation between them in the Duhok district in three different years 2001, 2011, and 2021 using Landsat satellite images. Air temperature data from seven weather stations were used to validate the land surface temperature results. The study's findings revealed that the Duhok district’s LST has risen during the study period. In general, the average land surface temperature has been increasing at a rate of 0.15 °C per year. Other findings showed that the vegetation cover of the Duhok district has changed dynamically. In all three years of study, the regression analysis results indicated that there was a negative correlation between LST and MSAVI2. This method of evaluation will be useful in guiding future urban management work and local government strategies.

An investigation into urban heat mitigation by adopting local climate zones and land surface temperatures in the Tokyo prefecture

AbstractThis study aimed to investigate urban forms susceptible to heightened heat intensities in the Tokyo Prefecture in Japan. Adopting Landsat 8 data at a pixel resolution of 100 m, local climate zones (LCZ) were identified. LCZs contain urban forms which are primarily defined by building compactness and height. Daytime spatial distribution of land surface temperatures (LST) was provided by MODIS 100 m resolution data from 2013 to 2021. Median LSTs for compact and super high‐rise, high‐rise, mid‐rise, and low‐rise LCZs were 34.4, 35.5, 37.3, and 38.1°C, respectively. Additionally, LSTs for open and super high‐rise, high‐rise, and mid‐rise LCZs were 37.4, 37.5, and 37.1°C, respectively. Therefore, this suggests lower‐rise and open LCZs are prone to increased urban heat intensities and higher‐rise and compact LCZs are an urban heat mitigation strategy. Open mid‐rise also offers heat reduction capabilities. Compact low‐rise and open mid‐rise spatial analysis also confirmed this trend with vegetation indices validating urban configuration as significantly influencing LSTs. Furthermore, due to LSTs constituting heat health risks, 11 municipalities comprised of predominantly compact low‐rise LCZs were identified as a priority for urban heat mitigation. Among these, Nakano, Nerima, and Suginami posed the greatest heat risks.

Analysing the spatial distribution of land surface temperature at Nghi Son Refinery and Petrochemical Complex using Landsat and Sentinel 2 satellite image data

The Landsat 5, Landsat 8 and Sentinel 2A satellite image data are used for extracting land surface temperature, thereby mapping the spatial distribution of land surface temperature at Nghi Son Refinery and Petrochemical Complex. The obtained results then can be used in monitoring land surface temperature changes, for land use planning as well as in minimising the impact of production processes to the environment.