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5 pages, 199 KiB

Open AccessEditorial

Editorial: Special Issue on Geographical Analysis and Modeling of Urban Heat Island Formation

by Yuji Murayama and Ruci Wang

Remote Sens. 2023, 15(18), 4474; https://doi.org/10.3390/rs15184474 - 12 Sep 2023

Cited by 3 | Viewed by 1769

Abstract

This Special Issue focuses on the data, methods, techniques, and empirical outcomes of urban heat island studies from a time and space perspective. We showcase research papers, empirical studies, conceptual or analytic reviews, and policy-related tasks to help achieve urban sustainability. We are [...] Read more.

This Special Issue focuses on the data, methods, techniques, and empirical outcomes of urban heat island studies from a time and space perspective. We showcase research papers, empirical studies, conceptual or analytic reviews, and policy-related tasks to help achieve urban sustainability. We are interested in target methodologies and datasets capturing urban heat island phenomena, including novel techniques for urban heat island monitoring and forecasting with the integration of remote sensing and GIS, the spatial relationship between urban heat island intensity and land use/cover distribution in metropolitan areas, the geographical patterns and processes of urban heat island phenomena in large cities, spatial differences in urban heat island intensity between developing and developed countries, urban heat island disaster mitigation and adaptation for future urban sustainability, and prediction and scenario analysis of urban heat island formation for policy and planning purposes. Full article

(This article belongs to the Special Issue Geographical Analysis and Modeling of Urban Heat Island Formation)

Research

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26 pages, 12140 KiB

Open AccessArticle

Spatial Influence of Multifaceted Environmental States on Habitat Quality: A Case Study of the Three Largest Chinese Urban Agglomerations

by Fei Liu, Yuji Murayama and Yoshifumi Masago

Remote Sens. 2023, 15(4), 921; https://doi.org/10.3390/rs15040921 - 7 Feb 2023

Cited by 5 | Viewed by 2252

Abstract

Habitat structure and quality in the urban agglomeration (UA) are subject to multiple threats and pressures due to ongoing anthropogenic activities and call for comprehensively effective solutions. Many approaches, including cartographic comparison, correlation analysis, the local entropy model, and GeoDetector, were jointly used [...] Read more.

Habitat structure and quality in the urban agglomeration (UA) are subject to multiple threats and pressures due to ongoing anthropogenic activities and call for comprehensively effective solutions. Many approaches, including cartographic comparison, correlation analysis, the local entropy model, and GeoDetector, were jointly used to clarify the interplay between habitat quality and multiple environmental issues. In response to the overlapped risks of diverse environmental systems, this study presented an integrated research framework to evaluate the spatial influences of multifaceted environmental situations on habitat quality. We conducted the case study in the three largest Chinese UAs: Beijing–Tianjin–Hebei (BTH), Greater Bay Area (GBA), and Yangtze River Delta (YRD). The evaluation results show that the three UAs shared similarities and differences in relationship/impact types and their strengths. In 2015, most of the three UAs’ landscapes delivered low–medium magnitudes of habitat quality (score <0.7) and emerged with unevenly severe consequences over space across different environmental aspects, highlighting the importance of maintaining habitat safety. Overall, habitat quality scores were synergistic with NDVI, but antagonistic to surface heat island intensity (SHII), PM2.5 concentrations, and residential support. However, locally structured relationships exhibited geographical complexity and heterogeneity between habitat quality and environmental systems. Regarding GeoDetector evaluation, PM2.5 concentrations in BTH, SHII in GBA, and NDVI in YRD played a dominant role in single-factor and interaction analysis. More importantly, the synergistic effect of various environmental issues on habitats was manifested as mutually enhanced rather than independent or weakened interactive effects, implying the aggravation of compound effects and the necessity of prioritization schemes. This study could provide beneficial insights into the interconnections between habitats’ sustainability and multifaceted environmental situations in UAs. Full article

(This article belongs to the Special Issue Geographical Analysis and Modeling of Urban Heat Island Formation)

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40 pages, 17715 KiB

Open AccessArticle

Assessing Local Climate Change by Spatiotemporal Seasonal LST and Six Land Indices, and Their Interrelationships with SUHI and Hot–Spot Dynamics: A Case Study of Prayagraj City, India (1987–2018)

by Md. Omar Sarif, Rajan Dev Gupta and Yuji Murayama

Remote Sens. 2023, 15(1), 179; https://doi.org/10.3390/rs15010179 - 28 Dec 2022

Cited by 10 | Viewed by 2842

Abstract

LST has been fluctuating more quickly, resulting in the degradation of the climate and human life on a local–global scale. The main aim of this study is to examine SUHI formation and hotspot identification over Prayagraj city of India using seasonal Landsat imageries [...] Read more.

LST has been fluctuating more quickly, resulting in the degradation of the climate and human life on a local–global scale. The main aim of this study is to examine SUHI formation and hotspot identification over Prayagraj city of India using seasonal Landsat imageries of 1987–2018. The interrelationship between six land indices (NDBI, EBBI, NDMI, NDVI, NDWI, and SAVI) and LST (using a mono-window algorithm) was investigated by analyzing correlation coefficients and directional profiling. NDVI dynamics showed that the forested area observed lower LST by 2.25–4.8 °C than the rest of the city landscape. NDBI dynamics showed that the built-up area kept higher LST by 1.8–3.9 °C than the rest of the city landscape (except sand/bare soils). SUHI was intensified in the city center to rural/suburban sites by 0.398–4.016 °C in summer and 0.45–2.24 °C in winter. Getis–Ord G_i* statistics indicated a remarkable loss of areal coverage of very cold, cold, and cool classes in summer and winter. MODIS night-time LST data showed strong SUHI formation at night in summer and winter. This study is expected to assist in unfolding the composition of the landscape for mitigating thermal anomalies and restoring environmental viability. Full article

(This article belongs to the Special Issue Geographical Analysis and Modeling of Urban Heat Island Formation)

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18 pages, 7593 KiB

Open AccessArticle

Exploring the Spatio-Temporal Characteristics of Urban Thermal Environment during Hot Summer Days: A Case Study of Wuhan, China

by Weifang Shi, Jiaqi Hou, Xiaoqian Shen and Rongbiao Xiang

Remote Sens. 2022, 14(23), 6084; https://doi.org/10.3390/rs14236084 - 30 Nov 2022

Cited by 7 | Viewed by 2070

Abstract

An urban thermal environment is an area receiving special attention. In order to effectively explore its spatio-temporal characteristics during hot summer days, this study introduced the standard deviational ellipse (SDE) to construct an urban heat island index to describe the general spatial character [...] Read more.

An urban thermal environment is an area receiving special attention. In order to effectively explore its spatio-temporal characteristics during hot summer days, this study introduced the standard deviational ellipse (SDE) to construct an urban heat island index to describe the general spatial character of an urban thermal environment, and then used local Moran’s I to identify its local spatial cluster characteristics. Finally, the regressions of ordinary least squares (OLS) and spatial lag model (SLM) were adopted to explore the effect of woodland, water body and impervious surface on the thermal environment. Taking the city of Wuhan as a study area and using the air temperature on seven consecutive days, from 17 July to 23 July in 2018, from the China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0), the results show that the urban heat island index can effectively represent the general characteristics of the thermal environment. The general trends of heat island intensity decrease first and then increase from 00:00 to 24:00. The heat island intensity is at its minimum from 10:00 to 16:00, and at its maximum from 22:00 to 4:00 the next day. Local Moran’s I values indicate that the clusters of high air temperature at 06:00 and at 22:00 are associated with the impervious surface and the water body. This is further illustrated by the regression analysis of OLS, which can explain 50–60% of the spatial variation of the air temperature. Then, the fitness of the SLM is greatly improved; the coefficients of determination at 06:00 and at 22:00 are all not less than 0.97. However, the explanation of the local land uses accounting for the spatial variation of the air temperature becomes lower. The regression analysis also shows that the woodland always has the effect of decreasing air temperature at 06:00, 14:00 and 22:00, implying that increasing the vegetation may be the most effective way to mitigate the adverse circumstance of the urban thermal environment. Full article

(This article belongs to the Special Issue Geographical Analysis and Modeling of Urban Heat Island Formation)

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19 pages, 6442 KiB

Open AccessArticle

Reverse Thinking: The Logical System Research Method of Urban Thermal Safety Pattern Construction, Evaluation, and Optimization

by Chunguang Hu and He Li

Remote Sens. 2022, 14(23), 6036; https://doi.org/10.3390/rs14236036 - 29 Nov 2022

Cited by 12 | Viewed by 2391

Abstract

The acceleration of urbanization has significantly impacted the changing regional thermal environment, leading to a series of ecological and environment-related problems. A scientific evaluation of the urban thermal security pattern (TSPurban) strongly benefits the planning and layout of sustainable development and the construction [...] Read more.

The acceleration of urbanization has significantly impacted the changing regional thermal environment, leading to a series of ecological and environment-related problems. A scientific evaluation of the urban thermal security pattern (TSPurban) strongly benefits the planning and layout of sustainable development and the construction of comfortable human settlements. To analyze the characteristics of the TSPurban under cross-regional differences and provide targeted solutions to mitigate the urban heat island effect in later stages, the logical system research framework of the TSPurban based on the “construction–evaluation–optimization” model was explored using reverse thinking. This study selected the Wuhan metropolitan area in China as the research object. First, a morphological spatial pattern analysis (MSPA) model was used to extract the top 30 core heat island patches, and Conefor 2.6 software was used for connection analysis to evaluate their importance. Second, based on the characteristics of various land cover types, the friction (cost) map of surface urban heat island (SUHI) diffusion was simulated. The spatial attributes of the heat island resistance surface were examined using a standard deviation ellipse and hot spot analysis. Finally, this paper used circuit theory to find 56 low-cost heat island links (corridors) and circuit scape software to find widely distributed vital nodes. The optimization of the TSPurban network was then investigated using a reverse thinking process. Heat island patches, corridors, and vital nodes are among the crucial components of the TSPurban. By obstructing corridor links and disturbing important nodes, it is possible to appropriately and proficiently reduce the TSPurban network’s connection efficiency and stability, which will have a positive influence on regional climate mitigation and the heat island effect. Full article

(This article belongs to the Special Issue Geographical Analysis and Modeling of Urban Heat Island Formation)

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20 pages, 50477 KiB

Open AccessArticle

Spatial Effects of Landscape Patterns of Urban Patches with Different Vegetation Fractions on Urban Thermal Environment

by Yu Zhang, Yuchen Wang and Nan Ding

Remote Sens. 2022, 14(22), 5684; https://doi.org/10.3390/rs14225684 - 10 Nov 2022

Cited by 18 | Viewed by 2550

Abstract

The landscape patterns of urban green spaces have been proven to be important factors that affect urban thermal environments. However, the spatial effect of the landscape patterns of urban patches with different vegetation densities on land surface temperature (LST) has not been investigated [...] Read more.

The landscape patterns of urban green spaces have been proven to be important factors that affect urban thermal environments. However, the spatial effect of the landscape patterns of urban patches with different vegetation densities on land surface temperature (LST) has not been investigated in detail. In this study, the built-up area of Xuzhou City was taken as the study region, and the four phases of Landsat 8 images and their corresponding ground observations from 2014 to 2020 were selected as the basic data. Normalized spectral mixture analysis and an improved mono-window algorithm were used to invert the vegetation component fraction (VF) and LST maps of the study area, respectively, and the surface patches were classified into five levels according to the VF values, from low to high. Four landscape-level and five class-level metrics were then selected to represent the landscape characteristics of each VF-level patch. The tested values of 60 and 780 m were regarded as the best grain size and spatial extent, respectively, in the calculation of all landscape metrics of ALL VF-level patches (VFLM) using the moving-window method. The results of bivariate Moran’s I for VFLM and LST showed the following: (1) for landscape-level metrics, only the Shannon diversity index and patch diversity have substantial negative spatial correlations with LST (with average |Moran’s I| < 0.2), indicating that the types of VF levels and the number of patches exert weak negative effects on the thermal environment for a certain area; (2) for class-level metrics such as percentage of landscape, patch cohesion index, largest patch index, landscape shape index, and aggregation index, only the class-level metrics of sub-high VF (LV4) and extreme-high (LV5) VF levels patches have significant negative spatial correlations with LST (with high Moran’s I value, and high–high and low–high distributions in local indications of spatial association cluster maps), indicating that only the patches of high VF levels can effectively alleviate LST and that patch proportion, natural connectivity degree, predominance degree, shape complexity, and aggregation degree are important landscape factors for regulating the thermal environment. Principal component analysis and multiple linear regression were applied to determine the impact weights of the class-level VFLMs of LV4 and LV5 patches on LST, which revealed the contributions of these landscape metrics to mitigating the urban heat island effect (UHI). These results signify the importance of and differences in the spatial patterns of various VF-level patches for UHI regulation; these patterns can provide new perspectives and references for urban green space planning and climate management. Full article

(This article belongs to the Special Issue Geographical Analysis and Modeling of Urban Heat Island Formation)

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19 pages, 6606 KiB

Open AccessArticle

Relationships between Land Surface Temperatures and Neighboring Environment in Highly Urbanized Areas: Seasonal and Scale Effects Analyses of Beijing, China

by Qingyan Meng, Wenxiu Liu, Linlin Zhang, Mona Allam, Yaxin Bi, Xinli Hu, Jianfeng Gao, Die Hu and Tamás Jancsó

Remote Sens. 2022, 14(17), 4340; https://doi.org/10.3390/rs14174340 - 1 Sep 2022

Cited by 23 | Viewed by 2891

Abstract

Urban environments have a strong influence on the land surface temperature (LST) in urban areas. Understanding the relationship between LST and urban environmental factors can help develop effective strategies to reduce high LSTs in urban areas, which is critical for mitigating the urban [...] Read more.

Urban environments have a strong influence on the land surface temperature (LST) in urban areas. Understanding the relationship between LST and urban environmental factors can help develop effective strategies to reduce high LSTs in urban areas, which is critical for mitigating the urban heat island effect. Previous studies have focused on the correlation between LST and the environmental factors that drive its formation, without considering the influences of the neighboring environment and the vertical expansion of highly urbanized areas. Notably, the correlation between LST and its neighboring environment in different seasons remains unclear. In this study, we selected central Beijing in China as our study area and employed the moving window method to characterize the environmental factors of the neighboring environment of the central LST cell. We explored eight environmental factors from three layers: normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), modified normalized difference water index (MNDWI), building density (BD), building height (BH), building volume (BV), sky view factor (SVF), and road density (RD). The Pearson correlation and extreme gradient boosting (XGB) regression methods were applied to measure the correlation between LST and the different factors in moving windows of different sizes. The results indicated that the correlation between NDVI, MNDWI, and LST was considerably different in the winter and other seasons. However, NDBI was positively correlated with LST in all seasons, although the correlation was strongest/weakest in summer/winter. Among building-related factors, BD and BH were more strongly correlated with LST, and the positive/negative correlation between BD/BH and LST was stronger in summer/winter. The correlation between LST and its neighboring environment varied with increasing window size, and this variation differs significantly between winter and other seasons. In spring, summer, and autumn, the strength of the correlation between LST and its neighboring environment showed an “inverted V” pattern with increasing window size. The optimal spatial scales to explore the influence of neighboring environments on the LST of 30-m cells were 210 m and 270 m. This study revealed the seasonal correlation between LST and its neighboring environment while explaining the variation at a spatial scale. Notably, this study can provide a new perspective for understanding the driving mechanism of the urban thermal environment, while contributing to its scientific optimization and management. Full article

(This article belongs to the Special Issue Geographical Analysis and Modeling of Urban Heat Island Formation)

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17 pages, 3525 KiB

Open AccessArticle

Temporal and Spatial Variation of Land Surface Temperature and Its Driving Factors in Zhengzhou City in China from 2005 to 2020

by Shenghui Zhou, Dandan Liu, Mengyao Zhu, Weichao Tang, Qian Chi, Siyu Ye, Siqi Xu and Yaoping Cui

Remote Sens. 2022, 14(17), 4281; https://doi.org/10.3390/rs14174281 - 30 Aug 2022

Cited by 22 | Viewed by 3135

Abstract

Rapid urbanization is an important factor leading to the rise in surface temperature. How to effectively reduce the land surface temperature (LST) has become a significant proposition of city planning. For the exploration of LST and the urban heat island (UHI) effect in [...] Read more.

Rapid urbanization is an important factor leading to the rise in surface temperature. How to effectively reduce the land surface temperature (LST) has become a significant proposition of city planning. For the exploration of LST and the urban heat island (UHI) effect in Zhengzhou, China, the LST was divided into seven grades, and the main driving factors of LST change and their internal relations were discussed by correlation analysis and gray correlation analysis. The results indicated that LST showed an upward trend from 2005 to 2020, and a mutation occurred in 2013. Compared with 2005, the mean value of LST in 2020 increased by 0.92 °C, while the percentage of LST-enhanced areas was 22.77. Furthermore, the spatial pattern of UHI was irregularly distributed, gradually spreading from north to south from 2005 to 2020; it showed a large block distribution in the main city and southeast in 2020, while, in the areas where woodlands were concentrated and in the Yellow River Basin, there was an obvious “cold island” effect. In addition, trend analysis and gray correlation analysis revealed that human factors were positively correlated with LST, which intensified the formation of the UHI effect, and the influence of Albedo on LST showed obvious spatial heterogeneity, while the cooling effect of vegetation water was better than that of topography. The research results can deepen the understanding of the driving mechanism of the UHI effect, as well as provide scientific support for improving the quality of the urban human settlement environment. Full article

(This article belongs to the Special Issue Geographical Analysis and Modeling of Urban Heat Island Formation)

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23 pages, 8132 KiB

Open AccessArticle

Assessing Surface Urban Heat Island Related to Land Use/Land Cover Composition and Pattern in the Temperate Mountain Valley City of Kathmandu, Nepal

by Siri Karunaratne, Darshana Athukorala, Yuji Murayama and Takehiro Morimoto

Remote Sens. 2022, 14(16), 4047; https://doi.org/10.3390/rs14164047 - 19 Aug 2022

Cited by 8 | Viewed by 3342

Abstract

Rapid urban growth has coincided with a substantial change in the environment, including vegetation, soil, and urban climate. The surface urban heat island (UHI) is the temperature in the lowest layers of the urban atmosphere; it is critical to the surface’s energy balance [...] Read more.

Rapid urban growth has coincided with a substantial change in the environment, including vegetation, soil, and urban climate. The surface urban heat island (UHI) is the temperature in the lowest layers of the urban atmosphere; it is critical to the surface’s energy balance and makes it possible to determine internal climates that affect the livability of urban residents. Therefore, the surface UHI is recognized as one of the crucial global issues in the 21st century. This phenomenon affects sustainable urban planning, the health of urban residents, and the possibility of living in cities. In the context of sustainable landscapes and urban planning, more weight is given to exploring solutions for mitigating and adapting to the surface UHI effect, currently a hot topic in urban thermal environments. This study evaluated the relationship between land use/land cover (LULC) and land surface temperature (LST) formation in the temperate mountain valley city of Kathmandu, Nepal, because it is one of the megacities of South Asia, and the recent population increase has led to the rapid urbanization in the valley. Using Landsat images for 2000, 2013, and 2020, this study employed several approaches, including machine learning techniques, remote sensing (RS)-based parameter analysis, urban-rural gradient analysis, and spatial composition and pattern analysis to explore the surface UHI effect from the urban expansion and green space in the study area. The results revealed that Kathmandu’s surface UHI effect was remarkable. In 2000, the higher mean LST tended to be in the city’s core area, whereas the mean LST tended to move in the east, south, north, and west directions by 2020, which is compatible with urban expansion. Urban periphery expansion showed a continuous enlargement, and the urban core area showed a predominance of impervious surface (IS) on the basis of urban-rural gradient analysis. The city core had a lower density of green space (GS), while away from the city center, a higher density of GS predominated at the three time points, showing a lower surface UHI effect in the periphery compared to the city core area. This study reveals that landscape composition and pattern are significantly correlated with the mean LST in Kathmandu. Therefore, in discussing these findings in order to mitigate and adapt to prominent surface UHI effects, this study provides valuable information for sustainable urban planning and landscape design in mountain valley cities like Kathmandu. Full article

(This article belongs to the Special Issue Geographical Analysis and Modeling of Urban Heat Island Formation)

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21 pages, 5184 KiB

Open AccessArticle

The Seasonality of Surface Urban Heat Islands across Climates

by Panagiotis Sismanidis, Benjamin Bechtel, Mike Perry and Darren Ghent

Remote Sens. 2022, 14(10), 2318; https://doi.org/10.3390/rs14102318 - 11 May 2022

Cited by 25 | Viewed by 3636

Abstract

In this work, we investigate how the seasonal hysteresis of the Surface Urban Heat Island Intensity (SUHII) differs across climates and provide a detailed typology of the daytime and nighttime SUHII hysteresis loops. Instead of the typical tropical/dry/temperate/continental grouping, we describe Earth’s climate [...] Read more.

In this work, we investigate how the seasonal hysteresis of the Surface Urban Heat Island Intensity (SUHII) differs across climates and provide a detailed typology of the daytime and nighttime SUHII hysteresis loops. Instead of the typical tropical/dry/temperate/continental grouping, we describe Earth’s climate using the Köppen–Geiger system that empirically maps Earth’s biome distribution into 30 climate classes. Our thesis is that aggregating multi-city data without considering the biome of each city results in temporal means that fail to reflect the actual SUHII characteristics. This is because the SUHII is a function of both urban and rural features and the phenology of the rural surroundings can differ considerably between cities, even in the same climate zone. Our investigation covers all the densely populated areas of Earth and uses 18 years (2000–2018) of land surface temperature and land cover data from the European Space Agency’s Climate Change Initiative. Our findings show that, in addition to concave-up and -down shapes, the seasonal hysteresis of the SUHII also exhibits twisted, flat, and triangle-like patterns. They also suggest that, in wet climates, the daytime SUHII hysteresis is almost universally concave-up, but they paint a more complex picture for cities in dry climates. Full article

(This article belongs to the Special Issue Geographical Analysis and Modeling of Urban Heat Island Formation)

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23 pages, 5935 KiB

Open AccessArticle

Heterogeneous Urban Thermal Contribution of Functional Construction Land Zones: A Case Study in Shenzhen, China

by Han Wang, Bingxin Li, Tengyun Yi and Jiansheng Wu

Remote Sens. 2022, 14(8), 1851; https://doi.org/10.3390/rs14081851 - 12 Apr 2022

Cited by 14 | Viewed by 3556

Abstract

Anthropogenic interferences through various intensive social-economic activities within construction land have induced and strengthened the Urban Heat Island (UHI) effects in global cities. Focused on the relative heat effect produced by different social-economic functions, this study established a general framework for functional construction [...] Read more.

Anthropogenic interferences through various intensive social-economic activities within construction land have induced and strengthened the Urban Heat Island (UHI) effects in global cities. Focused on the relative heat effect produced by different social-economic functions, this study established a general framework for functional construction land zones (FCLZs) mapping and investigated their heterogeneous contribution to the urban thermal environment, and then the thermal responses in FCLZs with 12 environmental indicators were analyzed. Taking Shenzhen as an example city, the results show that the total contribution and thermal effects within FCLZs are significantly different. Specifically, the FCLZs contribution to UHI regions highly exceeds the corresponding proportions of their area. The median warming capacity order of FCLZs is: Manufacture function (3.99 °C) > Warehousing and logistics function (3.69 °C) > Street and transportation function (3.61 °C) > Business services function (3.06 °C) > Administration and public services function (2.54 °C) > Green spaces and squares function (2.40 °C) > Residential function (2.21 °C). Both difference and consistency coexist in the responses of differential surface temperature (DST) to environmental indicators in FCLZs. The thermal responses of DST to biophysical and building indicators in groups of FCLZs are approximately consistent linear relationships with different intercepts, while the saturation effects shown in location and social-economic indicators indicate that distance and social-economic development control UHI effects in a non-linear way. This study could extend the understanding of urban thermal warming mechanisms and help to scientifically adjust environmental indicators in urban planning. Full article

(This article belongs to the Special Issue Geographical Analysis and Modeling of Urban Heat Island Formation)

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17 pages, 4422 KiB

Open AccessArticle

The Influence of Green Space Patterns on Land Surface Temperature in Different Seasons: A Case Study of Fuzhou City, China

by Liuqing Yang, Kunyong Yu, Jingwen Ai, Yanfen Liu, Lili Lin, Lingchen Lin and Jian Liu

Remote Sens. 2021, 13(24), 5114; https://doi.org/10.3390/rs13245114 - 16 Dec 2021

Cited by 22 | Viewed by 3829

Abstract

Background: Urban green space (UGS) has been shown to play an important role in mitigating urban heat island (UHI) effects. In the context of accelerating urbanization, a better understanding of the landscape pattern mechanisms affecting the thermal environment is important for the improvement [...] Read more.

Background: Urban green space (UGS) has been shown to play an important role in mitigating urban heat island (UHI) effects. In the context of accelerating urbanization, a better understanding of the landscape pattern mechanisms affecting the thermal environment is important for the improvement of the urban ecological environment. Methods: In this study, the relationship between land surface temperature (LST) and the spatial patterns of green space was analyzed using a bivariate spatial autocorrelation and spatial autoregression model in three seasons (summer, transition season (spring), and winter) with different grid scales in Fuzhou city. Results: Our results indicated that the LST in Fuzhou City has a significant spatial autocorrelation. The percentage of landscape and patch density area were negatively correlated with surface temperature. The results of our indicators differed according to the season, with population density and distance to the water indicators not being significant in the winter. The coefficient of determination was higher at the 510 m grid scale on this study’s scale. Conclusion: This study extends our understanding on the influence of UHI effects after accounting for different seasonal and spatial scale factors. It also provides a reference for urban planners to mitigate heat islands in the future. Full article

(This article belongs to the Special Issue Geographical Analysis and Modeling of Urban Heat Island Formation)

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24 pages, 9560 KiB

Open AccessArticle

A Remote Sensing Approach for Surface Urban Heat Island Modeling in a Tropical Colombian City Using Regression Analysis and Machine Learning Algorithms

by Julián Garzón, Iñigo Molina, Jesús Velasco and Andrés Calabia

Remote Sens. 2021, 13(21), 4256; https://doi.org/10.3390/rs13214256 - 22 Oct 2021

Cited by 11 | Viewed by 5353

Abstract

The Surface Urban Heat Islands (SUHI) phenomenon has adverse environmental consequences on human activities, biophysical and ecological systems. In this study, Land Surface Temperature (LST) from Landsat and Sentinel-2 satellites is used to investigate the contribution of potential factors that generate the SUHI [...] Read more.

The Surface Urban Heat Islands (SUHI) phenomenon has adverse environmental consequences on human activities, biophysical and ecological systems. In this study, Land Surface Temperature (LST) from Landsat and Sentinel-2 satellites is used to investigate the contribution of potential factors that generate the SUHI phenomenon. We employ Principal Component Analysis (PCA) and Multiple Linear Regression (MLR) techniques to model the main temporal and spatial SUHI patterns of Cartago, Colombia, for the period 2001–2020. We test and evaluate the performance of three different emissivity models to retrieve LST. The fractional vegetation cover model using Sentinel-2 data provides the best results with R² = 0.78, while the ASTER Global Emissivity Dataset v3 and the land surface emissivity model provide R² = 0.27 and R² = 0.26, respectively. Our SUHI model reveals that the factors with the highest impact are the Normalized Difference Water Index (NDWI) and the Normalized Difference Build-up Index (NDBI). Furthermore, we incorporate a weighted Naïve Bayes Machine Learning (NBML) algorithm to identify areas prone to extreme temperatures that can be used to define and apply normative actions to mitigate the negative consequences of SUHI. Our NBML approach demonstrates the suitability of the new SUHI model with uncertainty within 95%, against the 88% given by the Support Vector Machine (SVM) approach. Full article

(This article belongs to the Special Issue Geographical Analysis and Modeling of Urban Heat Island Formation)

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25 pages, 10610 KiB

Open AccessArticle

Driving Factors of Land Surface Temperature in Urban Agglomerations: A Case Study in the Pearl River Delta, China

by Wenxiu Liu, Qingyan Meng, Mona Allam, Linlin Zhang, Die Hu and Massimo Menenti

Remote Sens. 2021, 13(15), 2858; https://doi.org/10.3390/rs13152858 - 21 Jul 2021

Cited by 32 | Viewed by 4469

Abstract

Land surface temperature (LST) in urban agglomerations plays an important role for policymakers in urban planning. The Pearl River Delta (PRD) is one of the regions with the highest urban densities in the world. This study aims to explore the spatial patterns and [...] Read more.

Land surface temperature (LST) in urban agglomerations plays an important role for policymakers in urban planning. The Pearl River Delta (PRD) is one of the regions with the highest urban densities in the world. This study aims to explore the spatial patterns and the dominant drivers of LST in the PRD. MODIS LST (MYD11A2) data from 2005 and 2015 were used in this study. First, spatial analysis methods were applied in order to determine the spatial patterns of LST and to identity the hotspot areas (HSAs). Second, the hotspot ratio index (HRI), as a metric of thermal heterogeneity, was developed in order to identify the features of thermal environment across the nine cities in the PRD. Finally, the geo-detector (GD) metric was employed to explore the dominant drivers of LST, which included elevation, land use/land cover (LUCC), the normalized difference vegetation index (NDVI), impervious surface distribution density (ISDD), gross domestic product (GDP), population density (POP), and nighttime light index (NLI). The GD metric has the advantages of detecting the dominant drivers without assuming linear relationships and measuring the combined effects of the drivers. The results of Moran’s Index showed that the daytime and nighttime LST were close to the cluster pattern. Therefore, this process led to the identification of HSAs. The HSAs were concentrated in the central PRD and were distributed around the Pearl River estuary. The results of the HRI indicated that the spatial distribution of the HSAs was highly heterogeneous among the cities for both daytime and nighttime. The highest HRI values were recorded in the cities of Dongguan and Shenzhen during the daytime. The HRI values in the cities of Zhaoqing, Jiangmen, and Huizhou were relatively lower in both daytime and nighttime. The dominant drivers of LST varied from city to city. The influence of land cover and socio-economic factors on daytime LST was higher in the highly urbanized cities than in the cities with low urbanization rates. For the cities of Zhaoqing, Huizhou, and Jiangmen, elevation was the dominant driver of daytime LST during the study period, and for the other cities in the PRD, the main driver changed from land cover in 2005 to NLI in 2015. This study is expected to provide useful guidance for planning of the thermal environment in urban agglomerations. Full article

(This article belongs to the Special Issue Geographical Analysis and Modeling of Urban Heat Island Formation)

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17 pages, 4908 KiB

Open AccessArticle

Quantifying the Cooling Effect and Scale of Large Inner-City Lakes Based on Landscape Patterns: A Case Study of Hangzhou and Nanjing

by Yaoyao Zheng, Yao Li, Hao Hou, Yuji Murayama, Ruci Wang and Tangao Hu

Remote Sens. 2021, 13(8), 1526; https://doi.org/10.3390/rs13081526 - 15 Apr 2021

Cited by 28 | Viewed by 4080

Abstract

The rapid urbanization worldwide has brought various environmental problems. The urban heat island (UHI) phenomenon is one of the most concerning issues because of its strong relation with daily lives. Water bodies are generally considered a vital resource to relieve the UHI. In [...] Read more.

The rapid urbanization worldwide has brought various environmental problems. The urban heat island (UHI) phenomenon is one of the most concerning issues because of its strong relation with daily lives. Water bodies are generally considered a vital resource to relieve the UHI. In this context, it is critical to develop a method for measuring the cooling effect and scale of water bodies in urban areas. In this study, West Lake and Xuanwu Lake, two famous natural inner-city lakes, are selected as the measuring targets. The scatter plot and multiple linear regression model were employed to detect the relationship between the distance to the lake and land surface temperature based on Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) and Sentinel-2 data. The results show that West Lake and Xuanwu Lake massively reduced the land surface temperature within a few hundred meters (471 m for West Lake and 336 m for Xuanwu Lake) and have potential cooling effects within thousands of meters (2900 m for West Lake and 3700 m for Xuanwu Lake). The results provide insights for urban planners to manage tradeoffs between the large lake design in urban areas and the cooling effect demands. Full article

(This article belongs to the Special Issue Geographical Analysis and Modeling of Urban Heat Island Formation)

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34 pages, 3669 KiB

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Understanding the Links between LULC Changes and SUHI in Cities: Insights from Two-Decadal Studies (2001–2020)

by Ahmed Derdouri, Ruci Wang, Yuji Murayama and Toshihiro Osaragi

Remote Sens. 2021, 13(18), 3654; https://doi.org/10.3390/rs13183654 - 13 Sep 2021

Cited by 55 | Viewed by 5748

Abstract

An urban heat island (UHI) is a serious phenomenon associated with built environments and presents threats to human health. It is projected that UHI intensity will rise to record levels in the following decades due to rapid urban expansion, as two-thirds of the [...] Read more.

An urban heat island (UHI) is a serious phenomenon associated with built environments and presents threats to human health. It is projected that UHI intensity will rise to record levels in the following decades due to rapid urban expansion, as two-thirds of the world population is expected to live in urban areas by 2050. Nevertheless, the last two decades have seen a considerable increase in the number of studies on surface UHI (SUHI)—a form of UHI quantified based on land surface temperature (LST) derived from satellite imagery—and its relationship with the land use/cover (LULC) changes. This surge has been facilitated by the availability of freely accessible five-decade archived remotely sensed data, the use of state-of-art analysis methods, and advancements in computing capabilities. The authors of this systematic review aimed to summarize, compare, and critically analyze multiple case studies—carried out from 2001 to 2020—in terms of various aspects: study area characteristics, data sources, methods for LULC classification and SUHI quantification, mechanisms of interaction coupled with linking techniques between SUHI intensity with LULC spatial and temporal changes, and proposed alleviation actions. The review could support decision-makers and pave the way for scholars to conduct future research, especially in vulnerable cities that have not been well studied. Full article

(This article belongs to the Special Issue Geographical Analysis and Modeling of Urban Heat Island Formation)

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