Affect Land Surface Temperature Research Articles

Analysing Thermal Land Surface Characteristics and Urban Expansion in Kenitra City, Morocco, using Remote Sensing and GIS

Abstract This research investigates how land use and cover the affect land surface temperature in Kenitra. It employs Geographic Information System techniques and remote sensing methods to monitor and control surface temperature changes. The present study integrates remote sensing and geographic information system to process satellite images. Furthermore, it focuses, also, on analyzing land surface temperature and its correlation with land cover changes over three time periods (1989, 2000, 2020) using landsat satellite data. Thus, supervised classification is employed to map these changes and derive urban heat islands from thermal band data. This study provides a comprehensive evaluation of the relationship between land use and land surface temperature. It utilizese a landscape dynamics assessment based on multi-source and multi-sensor remote sensing technologies. Specifically, the study uses landsat satellite data (TM for 1989 and OLI-8 for 2002 and 2020) to assess the effects of land use and land cover changes on land surface temperature distribution in the Kenitra city. Spatial and statistical analyses are performed by comparing maps from remotely sensed data generated using geographic information system. Therefore, these analyses reveal various changes in the kenitra region between 1989 and 2020. The primary changes observed include an increase in built-up areas and bare ground with a decrease alongside in natural areas (vegetation, etc.). The average temperatures in 1989, 2002, and 2020 were 29°C, 31.05°C, and 32°C, respectively. The obtained results are valuable for planning and managing climate scientists, land-use planners, and researchers focusing on sustainable urbanization areas. The study’s findings will assist urban planners and policymakers in adopting appropriate measures for sustainable planning in the city to mitigate the adverse effects of land surface temperature.

How do heat waves affect the relationship between built environment patches of different compactness and land surface temperature?

The compactness of the urban built environment significantly affects land surface temperature (LST), especially during heat waves (HW). However, the mechanisms by which the configuration of key building patches in built environments of varying compactness drives LST are unclear. This study proposes a new research framework combining local climate zones (LCZ), spatial pattern type (SPT) and landscape index (LI) to reveal the impacts of key building patches on LST. Taking Shenyang as an example, we utilized the geographically weighted regression (GWR) method to reveal the non-stationary relationship between building patches with different compactness and LST during heat and non-heat waves, and an optimal parameters-based geographical detectors model (OPGDM) to explore the mechanisms by which the configuration of key building patches drives LST. The results show that HW enhances the spatially non-stationary effects of different types of building patches on LST. The configuration of key building patches in the open built environment drives LST more strongly than those in the compact built environment. The relationship between LIs and LST in key building patches exhibits diverse characteristics during heat and non-heat waves, so differentiated configuration optimization strategies are required for built environments of different compactness. The interactions of patch configurations also require emphasis, especially the patch complexity. The research findings help to formulate urban planning strategies from a climate adaptation and mitigation perspective to cope with the increasing frequency of extreme heat events.

Land use inspection technique affects land surface temperature from satellite data

This research aims to present a land use inspection technique that affects land surface temperature from satellite data by using Maha Sarakham province, Thailand as a case study. Procedures: 1) Analyze land use from Sentinel-2 Satellite data that can be divided into 4 categories such as water, agriculture, forest, and urban, 2) Analyze land surface temperature from Sentinel-3 Satellite data. The study results found that urban area has the highest average surface temperature followed by forest area, water area, and agricultural area respectively. The analyzed data from the satellite found the highest average surface temperature is 32.20°C. The result shows that this inspection technique can analyze land surface temperature in other areas of the countryThis research aims to present a land use inspection technique that affects land surface temperature from satellite data by using Maha Sarakham province, Thailand as a case study. Procedures: 1) Analyze land use from Sentinel-2 Satellite data that can be divided into 4 categories such as water, agriculture, forest, and urban, 2) Analyze land surface temperature from Sentinel-3 Satellite data. The study results found that urban area has the highest average surface temperature followed by forest area, water area, and agricultural area respectively. The analyzed data from the satellite found the highest average surface temperature is 32.20°C. The result shows that this inspection technique can analyze land surface temperature in other areas of the country.

Evaluating Land Surface Temperature Trends and Explanatory Variables in the Miami Metropolitan Area from 2002–2021

Physical and climatic variables such as Tree Canopy coverage, Normalized Difference Vegetation Index (NDVI), Distance to Roads, Distance to the Coast, Impervious Surface, and Precipitation can affect land surface temperature (LST). This paper examines the relationships using linear regression models and explores LST trends in the Miami Statistical Area (MSA) between 2002 and 2021. This study evaluates the effect of dry and wet seasons as well as day and night data on LST. A multiscale investigation is used to examine LST trends at the MSA scale, the individual county level, and at the pixel level to provide a detailed local perspective. The multiscale results are needed to understand spatiotemporal LST distributions to plan mitigation measures such as planting trees or greenery to regulate temperature and reduce the impacts of surface urban heat islands. The results indicate that LST values are rising in the MSA with a positive trend throughout the 20-year study period. The rate of change (RoC) for the wet season is smaller than for the dry season. The pixel-level analysis suggests that the RoC is primarily in rural areas and less apparent in urban areas. New development in rural areas may trigger increased RoC. This RoC relates to LST in the MSA and is different from global or regional RoC using air temperature. Results also suggest that climatic explanatory variables have different impacts during the night than they do in the daytime. For instance, the Tree Canopy variable has a positive coefficient, while during the day, the Tree Canopy variable has a negative relationship with LST. The Distance to the Coast variable changes from day to night as well. The increased granularity achieved with the multiscale analysis provides critical information needed to improve the effectiveness of potential mitigation efforts.

How do 2D/3D urban landscapes impact diurnal land surface temperature: Insights from block scale and machine learning algorithms

Urban landscapes significantly affect land surface temperature (LST) and are considered crucial factors affecting urban heat island (UHI). The impacts of urban landscapes on LST have been extensively explored, mainly focusing on grid scale and the daytime. However, how 2D/3D urban landscapes affect diurnal LST at the block scale is unclear. Therefore, taking 1, 536 blocks (including low-rise blocks (LRB), middle-rise blocks (MRB), and high-rise blocks (HRB)) in Beijing as samples, the performances of boosted regression tree (BRT) and random forest (RF) were first evaluated, and the impacts of 2D/3D urban landscapes on diurnal LST across different block types were explored. The results showed that the mean LST was the highest in MRB (daytime) and HRB (nighttime). BRT performed better than RF in investigating diurnal impacts at the block scale. Vegetation and buildings are the domain factors influencing daytime and nighttime LST in LRB and MRB, while buildings are the domain factor in HRB except at 03:09 (impervious surface). The relationships between the key 2D/3D urban landscape metrics and block diurnal LST are nonlinear. The findings can serve as f basis for UHI mitigation and urban renewal strategies by urban planners to develop thermal comfort.

A new framework quantifying the effect of morphological features on urban temperatures

Urban morphology varies within and among cities. Previous literature well discussed how 2D/3D morphology affects land surface temperature (LST) from physical form, scale and time. However, they focused on ranking the contributions. How quantifying the heating/cooling efficiency and conducting multi-cities comparisons is challenging. This study proposed a new framework to process this issue by integrating cities, scales, and seasons. Among 24 cities close to China's Eastern Seaboard, building coverage ratio (BCR), building height (BH), surface developed ratio (SDR), and tree height (TH) were dominant contributors to LST over seasons. Among them, TH had the highest cooling efficiency, although it varied from cities. For every 1 m increase, LST decreased about 0.3 °C in northern cities during summer and tiny during winter, while LST decreased about 0.2∘C in southern cities during both summer and winter. Economic activities significantly affected the heating/cooling efficiency of dominant building features. High economic levels and population aggregation cause a significant increase in the heating effect of BCR, but a decrease in the cooling effect of BH and SDR, which collectively leads to a warmer environment. According to the results, this study offered several recommendations for better local adaptation strategies of urban transformation, with a focus on heat management.

Interactions among spatial configuration aspects of urban tree canopy significantly affect its cooling effects

Increasing urban tree canopy (UTC) has been widely recognized as an effective means for urban heat mitigation and adaptation. While numerous studies have shown that both percent cover of UTC and its spatial configuration can significantly affect urban temperature, the pathways governing these relationships are largely unexplored. Here we present a cross-city comparison aiming to fill this gap by explicitly quantifying the pathways on which percent cover of UTC and its spatial configuration affect land surface temperature (LST) using structural equation modeling (SEM), based on UTC mapped from high resolution imagery and LST derived from Landsat thermal bands. We found: 1) Although both the direct and indirect pathways significantly affected LST regardless of scales and cities, the direct pathway played a more important role in affecting LST in Baltimore, Beijing, and Shenzhen. In contrast, an opposite result was found in Sacramento, likely due to the effects of buildings and their interactions with UTC. 2) Similarly, the direct pathway of mean patch size (MPS) and mean shape index (MSI) played a more important role in affecting LST than their indirect effects via altering edge density (ED). Our results highlighted the necessity for discomposing the effects of different spatial configuration variables on LST. Understanding the pathways through which UTC affects LST can provide insights into urban heat mitigation and adaptation.

Urban heat island phenomenon in majalengka regency

The Majalengka Regency considering by The Government of West Java Province is one of the priorities of infrastructure development to sustain the accelerate of development. The conversion function of agricultural land into non-agricultural or industrial will affect land surface temperature, which triggers the urban heat island phenomenon. The purpose of this study was to analyze the spatial and temporal distribution of urban heat island and the assosiation of land cover, building density, and vegetation greenness with land surface temperatures in Majalengka in 2013, 2016, and 2019. Variables used in this study were land cover, vegetation greenness, building density, and land surface temperature obtained from the processing of Landsat Image 8 are validating survey at 98 points selected by random sampling method. The results showed that the phenomenon of urban heat island dominates the north to the Majalengka Regency. The Areas with low land surface temperatures are found in areas with land cover in the form of built land, low greenness, and high building density. The southern of Majalengka Regency of the land surface temperature is getting lower with vegetation land cover, high vegetation greenness, and low building density. Low land surface temperature is associated with high vegetation greenness and low building density while the highest surface temperature is associated with built-up area with high building density. The difference in temperature between 2013-2019 is around 6°C with a reduction in vegetation area around 109 km2.

Detection of geothermal anomalies in the Northern Lake Abaya geothermal field, Main Ethiopian Rift

The detection of geothermal anomalies using Thermal Infrared (TIR) remote sensing data is challenging because of how sensor specifications (such as the infrared wavelength used for the measurement, spectral dependence of the emissivity, angle at which the measurement is made, state of the surface and height of the sensor above the surface) and physical parameters (such as solar radiation, topography, albedo, soil compaction and coherence of rocks) affect Land Surface Temperature (LST) retrieval and analysis. This work tests whether TIR remote sensing measurements with thorough spatial and temporal sampling can improve LST retrievals. Multi-temporal TIR data from 2000 through 2019 from Landsat 7 and 8 TIR instruments and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were used to detect geothermal areas in the geologically active region of the southern Main Ethiopian Rift. In addition, field-based temperature data from 19 sites were evaluated for comparison to the remotely detected geothermal anomaly areas. We have used the single-channel algorithm and Normalized Difference Vegetation Index (NDVI) method of emissivity retrieval to derive LST for each year. The result shows that the mean LST is highest in 2003 (320.1 K) and lowest in 2019 (303.1 K). The change in mean LST was between −9 K to 13 K. These LST results from ASTER images were validated with MODIS LST products and showed a correlation coefficient >0.6. LST of the year 2003 has been much closer to the actual temperature value from field data. Fifteen sites (79%) fit with the identified geothermal anomaly areas. LST values in known geothermal activity sites show no correlation (< 0.5) with time attesting. That is, even though LST varies with time (e.g., day and night and seasonal changes), the LST of areas with geothermal potential remain more or less constant on yearly basis.

Monitoring land use land cover changes and its impacts on land surface temperature over Mardan and Charsadda Districts, Khyber Pakhtunkhwa (KP), Pakistan.

Land use/land cover (LULC) changes due to urban growth on the regional scale affect land surface temperature (LST). The present study aims to assess the LULC changes and their impact on LST over Mardan and Charsadda districts of Khyber Pakhtunkhwa (KP), Pakistan, in the period from 1990 to 2019. Landsat satellite (TM& ETM +) datasets in the period from 1990 to 2010 and Sentinel-2 images from 2016 to 2019 were used in this study. All the datasets were pre-processed and the LULC types were classified by maximum likelihood classification algorithm. The vegetation degradation was computed from normalized difference vegetation index (NDVI), and the LST was derived based on the LULC changes. The results showed that the overall accuracy of LULC classification was 87.84%. Dramatic LULC changes were observed during the last three decades, where the vegetation degradation area was decreased from 1307.8 (59.27%) to 1147.6 km2 (52.1%) and the barren land area increased from 816.6 (37.07%) to 961.4 km2 (42.64%). Similarly, the built-up area has also increased from 57.2 (2.5%) to 104.3 km2 (4.73%) in the years 1990 and 2019, respectively. These variations in LULC types have significantly influenced the LST from 1990 to 2019; specifically, the LST of built-up area, barren land, and vegetation cover increased from 20.1 to 32.1°C, 21.5 to 35.5°C, and 17.1 to 28.2°C, respectively. The regression line plotted defines that the LST has a negative correlation with NDVI and a positive correlation with normalized difference of built-up index (NDBI). In particular, the vegetation and land covers dramatically transformed to barren land and/or to urban development over the study area in the period from 1990 to2019, which has severely affected the LST and the natural resources of the study area. Therefore, our study will be very helpful for managing the rapid environmental changes and urban planning.

Quantifying how urban landscape heterogeneity affects land surface temperature at multiple scales

BackgroundLandscape metrics have been widely applied to quantifying the relationship between land surface temperature and urban spatial patterns and have received acceptable verification from landscape ecologists but some studies have shown their inaccurate results. The objective of the study is to compare landscape metrics and texture-based measures as alternative indices in measuring urban heterogeneity effects on LST at multiple scales.ResultsThe statistical results showed that the correlation between urban landscape heterogeneity and LST increased as the spatial extent (scale) of under-study landscapes increased. Overall, landscape metrics showed that the less fragmented, the more complex, larger, and the higher number of patches, the lower LST. The most significant relationship was seen between edge density (ED) and LST (r = − 0.47) at the sub-region scale. Texture measures showed a stronger relationship (R2 = 34.84% on average) with LST than landscape metrics (R2 = 15.33% on average) at all spatial scales, meaning that these measures had a greater ability to describe landscape heterogeneity than the landscape metrics.ConclusionThis study suggests alternative measures for overcoming landscape metrics shortcomings in estimating the effects of landscape heterogeneity on LST variations and gives land managers and urban planners new insights into urban design.

Impact of Land Cover Changes on Land Surface Temperature and Human Thermal Comfort in Dhaka City of Bangladesh

Urbanization leads to the construction of various urban infrastructures in the city area for residency, transportation, industry, and other purposes, which causes major land use change. Consequently, it substantially affects Land Surface Temperature (LST) by unbalancing the surface energy budget. Higher LST in city areas decreases human thermal comfort for the city dwellers and affects the urban environment and ecosystem. Therefore, a comprehensive investigation is needed to evaluate the impact of land use change on the LST. Remote Sensing (RS) and Geographic Information System (GIS) techniques were used for the detailed investigation. RS data for the years 1993, 2007 and 2020 during summer (March–May) in Dhaka city were used to prepare land cover maps, analyze LST, generate hazard maps and relate the land cover change with LST by using GIS. The results show that the built-up area in Dhaka city increased by 67% from 1993 to 2020 by replacing lowland mainly, followed by vegetation, bare soil and water bodies. LSTs found in the study area were ranged from 23.26 to 39.94 °C, 23.69 to 43.35 °C and 24.44 to 44.58 °C for the years 1993, 2007 and 2020, respectively. The increases of spatially distributed maximum and mean LST were found 4.62 °C and 6.43 °C, respectively, for the study period of 27 years while the change in minimum LST was not substantial. LST increased by around 0.24 °C per year and human thermal discomfort shifted from moderate to strong heat stress for the total study period due to the increase of built-up and bare lands. This study also shows that normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were negatively correlated with LST while normalized difference built-up Index (NDBI) and normalized difference built-up Index (NDBAI) were positively correlated with LST. The methodology developed in this study can be adapted to other cities around the globe.

Effect of urban tree diversity and condition on surface temperature at the city block scale

Urban forests affect land surface temperature (LST) within a city due to the cooling effect of transpiration. The latter depends on tree health, but it can also be affected by the structure and composition of forest, as a mono-species environment may potentially worsen the health of urban forest. The following hypotheses are therefore proposed: a) greater tree diversity within urban forest results in lower LST at the city block scale; and b) the state of biotic disturbance of urban forest is negatively correlated with LST. The present research explores the relationship between urban forest tree diversity and health based on a survey of 38,950 individuals in the district of Providencia in the city of Santiago, Chile, and compares this information against LST data from the ASTER satellite instrument at the city block scale. The health of the urban forest was determined by expert knowledge means of a field survey that collected data concerning growth stage, phytosanitary state, and state of biotic disturbance. The first hypothesis could not be tested by the lack of urban tree diversity which showed strong domination of three species with more than 52 % of abundance (Robinia pseudoacacia, Platanus orientalis and Acer negundo). The second hypothesis was proved since the results revealed a positive and significant correlation between urban forest diversity and LST, with a Spearman’s correlation coefficient of between 0.56 and 0.7. A positive and significant correlation of 0.55 was found between mean biotic disturbance (BDSm) and median LST (Med), indicating a direct relationship between higher LST and poorer urban forest health. A possible explanation is that, among the trees surveyed within the urban forest, the effect of biotic disturbance is greater than that of species diversity. As such, it may be concluded that planting of trees on city streets as a means of temperature moderation is made less effective if specimens are maintained in a poor general condition of health.

Investigating the diversity of land surface temperature characteristics in different scale cities based on local climate zones

The concept of Local Climate Zones (LCZs) are effective tools to quantify the urban heat island effect. However, the study of LCZs mainly focuses on the meso-micro scale, and lacks regional perspective. In order to explore the thermal environment laws of different sized cities, we selected the Pearl River Delta urban agglomerations as the study area, and used multi-source data and spatial analysis methods to obtain the thermal environment characteristics of each city based on LCZs. Our study revealed that the distribution of buildings and the relief slope affects land surface temperature (LST). Additionally, the proportions of various LCZs are different in cities of different sizes; each city had a temperature difference in the LCZs. The highest temperature among the built LCZs was in LCZ 7 (Industry; 21.810 °C), whereas the temperature of LCZ A (Dense trees) was the lowest (15.670 °C), and larger cities had more high-temperature LCZs. In cities of different sizes, the temperature of LCZs showed a hierarchy effect, i.e., super cities > megacities > type I large cities > type II large cities. Therefore, in order to alleviate the urban heat island effect, it is of great significance to discover the thermal environment characteristics in cities of different sizes.

Do Urban Functional Zones Affect Land Surface Temperature Differently? A Case Study of Beijing, China

The non-uniformity of the relationships between urban temperature and landscape has attracted board attention. The non-uniformity in urban areas is reflected in the spatial landscape’s heterogeneity and the difference of socio-economic functions. The former is shown as the spatial differentiation of land-cover, land-use, landscape composition, and configuration, while the latter leads to the difference of the intensity of human activities and population density, which are closely related with anthropogenic heat emission. Therefore, this study introduces urban functional zones (UFZs) to express urban spatial heterogeneity. This study also attempts to comprehend urban heat island (UHI) effects and discloses the variability of urban surface temperature (LST)–landscape relationships in different kinds of UFZs. There are two main technical difficulties—how to characterize the spatial heterogeneity of UFZs and how to quantify non-uniform LST effects. A three-level variable system is established from their attributes, inner structures, and interrelationships to characterize UFZs and their LST effects hierarchically. Considering the multi-collinearity among high-dimensional variables, the Elastic Net regression method is selected for quantitative analysis. The experimental results reveal the deficiency of uniform LST analysis for heterogeneous urban areas and verify the variable relationships of LST-landscaped with different kinds of UFZs.

Strong contribution of rapid urbanization and urban agglomeration development to regional thermal environment dynamics and evolution

Urbanization has significantly transformed natural surfaces into impervious surfaces, which has subsequently disturbed the balance of the global surface thermal energy. However, key landscape dynamic transfer processes that can affect land surface temperature (LST) and regional thermal environment (RTE) remain poorly understood, especially in the context of urban agglomerations. Hence we selected one of the world's most rapidly urbanized regions – the Pearl-River-Delta Metropolitan Region (PRDR) located in southern China as the case. With the help of Google Cloud Computing, Markov model, and spatial analyses, we have further quantified the strong contributions of urbanization and urban agglomeration development to RTE dynamics and evolution. Specifically, we revealed (1) the cooling effects of ecological land loss and gain are significantly different, which provides evidence that the existing natural ecosystems (especially forests) are valuable for climatic adaptation because newly constructed ecological land does not provide the same cooling effect. (2) We found that the RTE is not only influenced by land cover patterns and process but also significantly dominated by the specific land conversion processes. (3) From 1995 to 2015 in the PRDR, built-up land increased significantly, while the ecological land was significantly reduced, and the isolated urban heat islands were gradually connected and interacted with each other, forming the regional heat island. (4) We also proposed that the relationship between urbanization rate and temperature (RLST) may conform to the Environmental Kuznets Curve. This study enhances the understanding of RTE dynamics and evolution in urban agglomeration and provides important insights into existing natural ecosystem protection and climate adaptation planning.

Investigating the effects of 3D urban morphology on the surface urban heat island effect in urban functional zones by using high-resolution remote sensing data: A case study of Wuhan, Central China

The Urban heat island (UHI) effect is an increasingly serious problem in urban areas. Information on the driving forces of intra-urban temperature variation is crucial for ameliorating the urban thermal environment. Although prior studies have suggested that urban morphology (e.g., landscape pattern, land-use type) can significantly affect land surface temperature (LST), few studies have explored the comprehensive effect of 2D and 3D urban morphology on LST in different urban functional zones (UFZs), especially at a fine scale. Therefore, in this research, we investigated the relationship between 2D/3D urban morphology and summer daytime LST in Wuhan, a representative megacity in Central China, which is known for its extremely hot weather in summer, by adopting high-resolution remote sensing data and geographical information data. The “urban morphology” in this study consists of 2D urban morphological parameters, 3D urban morphological parameters, and UFZs. Our results show that: (1) The LST is significantly related to 2D and 3D urban morphological parameters, and the scattered distribution of buildings with high rise can facilitate the mitigation of LST. Although sky view factor (SVF) is an important measure of 3D urban geometry, its influence on LST is complicated and context-dependent. (2) Trees are the most influential factor in reducing LST, and the cooling efficiency mainly depends on their proportions. The fragmented and irregular distribution of grass/shrubs also plays a significant role in alleviating LST. (3) With respect to UFZs, the residential zone is the largest heat source, whereas the highest LST appears in commercial and industrial zones. (4) Results of the multivariate regression and variation partitioning indicate that the relative importance of 2D and 3D urban morphological parameters on LST varies among different UFZs and 2D morphology outperforms 3D morphology in LST modulation. The results are generally consistent in spring, summer and autumn. These findings can provide insights for urban planners and designers on how to mitigate the surface UHI (SUHI) effect via rational landscape design and urban management during summer daytime.

Object-based image analysis to map local climate zones: The case of Bandung, Indonesia

Local climate zones (LCZs) are seen as a useful concept to investigate the relationship between land use types and urban heat island effects, which has been substantially researched for cities of the Global North. However, in the Global South, the usefulness of the concept may be questioned, as spatial patterns of urban structure types are typically highly heterogeneous (e.g., a mix of commercial, formal residential and slum/informal areas). With the increased and reasonable availability of (very) high-resolution imagery, however, the concept of LCZ may also prove meaningful to investigate land use dependent thermal patterns in Global South cities, which is the main research question investigated in this paper. We compared LCZs based on very-high-resolution imagery (using Pleiades and SPOT-6) with the standard method using Landsat imagery, and we analyzed their relationship with land surface temperature (Landsat TIR) in Bandung, Indonesia. Two different methods of image classification are employed to extract the LCZs, i.e., the random forest algorithm on Landsat imagery and the object-based image analysis (OBIA) on very-high-resolution images. The result shows that the OBIA provides higher accuracy, reaching 89% for the object-based analysis on Pleiades imagery compared to 69% for the pixel-based analysis of Landsat imagery. In addition, the results of our analysis show that not only the type of LCZs but also composition and configuration patterns (i.e., density and aggregation of LCZs) significantly affect land surface temperature. To conclude, VHR imagery and OBIA allow mapping the complex patterns of LCZs in cities of the Global South, in support of developing mitigation strategies for the urban heat island effect via urban and landscape planning.

Impact of urban spatial configuration on land surface temperature and urban heat islands: a case study of Mashhad, Iran

Urban heat islands (UHIs) are among important urban development issues in environmental and ecological climate changes. In this context, the urban spatial configuration is one of the factors that play a significant role in the formation of UHIs. This study investigates how urban spatial configuration affects land surface temperature (LST) and formation of UHI in Mashhad urban area, located in the northeast of Iran. The objectives of this study include (i) building quantitative relationships between LST and spatial configuration properties and (ii) representing the impacts of urban morphology on LST and climate changes. The methodology of this study is based on the space syntax and image processing theories and techniques. With respect to these two objectives, we (1) extracted the surface temperature (LST) by Landsat 7 ETM+ sensor image dated June 27, 2014, by ENVI 4.7 software, (2) provided an axial map of Mashhad city in Auto Cad 2007 environment, (3) analyzed spatial configuration by UCL Depthmap 10, and (4) employed transect model and gradient analysis and measured the measured the Pearson correlation coefficients and linear regression between LST and spatial configuration. The Pearson correlation coefficient between the two variables was found as R = − 0.394, which demonstrates that the LST is negatively correlated with integration. The results showed that land surfaces with high temperature are spatially segregated from the whole urban configuration and the integrated urban space has less temperature than other spaces.

The higher, the cooler? Effects of building height on land surface temperatures in residential areas of Beijing

Numerous studies have showed that landscape composition and configuration can significantly affect land surface temperature (LST). Most of these studies focus on the horizontal dimension of landscape structure. Few studies, however, have explored the effects of vertical dimension of urban landscape. This study aims to fill this gap. We focused on the residential landscapes in the central area of Beijing, and quantified the relationships between the vertical structure of buildings and LST. We delineated the boundaries of residential neighborhoods based on high resolution imagery, which were latterly used as the unit of statistical analysis. Building height, and proportional cover of buildings and vegetation were also mapped from high resolution imagery, with aid of digital maps. LST was retrieved from thermal band of TM imagery. We used Pearson correlation, partial correlation and ordinary least squares(OLS) regressions to quantify the relationships between these variables and LST. We found: 1) Land surface temperature varied greatly among residential neighborhoods, ranging from 53.5 °C to 37.0 °C, with a mean of 44.2 °C and a standard deviation of 2.4 °C. High-rise residential neighborhoods had the lowest LST, and mean LST decreased from low-rise to high-rise residential neighborhoods. 2) Building height, building density and vegetation coverage were all significantly correlated with LST. Building height and vegetation coverage has significantly negative effects on LST, but building density had a significantly positive one. 3) Among these variables, building height had greater impact on LST than the other two variables. These results have important implications for urban design and management.