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Geospatial Analysis of Urban Heat Island Phenomena in Megacities

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (30 April 2020) | Viewed by 25713

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Special Issue Editors

Prof. Dr. Yuji Murayama

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Guest Editor

Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba 305-8572, Japan
Interests: human geography; GIScience; geospatial analysis; spatial modeling; urban geography
Special Issues, Collections and Topics in MDPI journals

Dr. Courage Kamusoko

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Guest Editor

Global Business, Development Division, Asia Air Survey Co., Ltd, Kawasaki, Kanagawa, Japan
Interests: remote sensing; land use/ cover classification and analysis; land use/cover change modeling; urban growth modeling; GIS; machine learning; deep learning

Special Issue Information

Dear Colleagues,

The United Nations has projected that the world will have 43 megacities by 2030. Most of the fastest-growing urban agglomerations or megacities will be in developing countries in Asia and Africa. The high rate of urbanization is expected to induce a further expansion of built-up surfaces, and hence, alterations of land surface temperatures (LST). The surface urban heat island (SUHI), which refers to LST differences between urban and non-urban areas, has been shown to affect local climate variations, vegetation growth, and water and air quality. Given the continued horizontal and vertical urban developments, megacities in developing countries are expected to be affected by SUHI and its associated effects in the near future.

Remotely sensed data have provided valuable insights into SUHI, particularly in developed countries. However, SUHI is still poorly understood in most megacities of Africa and Asia. This is mainly attributed to the fact that urban growth dynamics exhibit high degrees of spatial and temporal complexity, especially in urban areas of developing regions. Therefore, it is critical to map and analyze SUHI in an accurate, consistent, and timely manner in order to understand the constantly evolving urban spatial developments. The main focus of this Special Issue is to contribute to SUHI science as well as discuss the challenges and future research prospects in megacities.

We are inviting submission including, but not limited to:

Developing new and robust methods for mapping surface urban heat island (SUHI);
Geospatial analysis and modeling of SUHI;
Remotely sensed data for urban growth and SUHI analysis;
Urban–rural gradient analysis;
Exploring high-resolution optical/radar sensors for modeling SUHI’
Future SUHI effects and urban growth modeling.

Prof. Dr. Yuji Murayama
Dr. Courage Kamusoko
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Surface urban heat island (SUHI)
Land surface temperature
Land use/cover
Urbanization
Geo-health and disaster
Green urban structure
Developing countries
Thermal remote sensing
Megacities

Published Papers (5 papers)

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Research

17 pages, 3829 KiB

Open AccessArticle

Effects of Different Urbanization Levels on Land Surface Temperature Change: Taking Tokyo and Shanghai for Example

by Zhenhua Chao, Liangxu Wang, Mingliang Che and Shengfang Hou

Remote Sens. 2020, 12(12), 2022; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12122022 - 24 Jun 2020

Cited by 24 | Viewed by 3631

Abstract

The influence of different urbanization levels on land surface temperature (LST) has attracted extensive attention. Though both are world megacities, Shanghai and Tokyo have gone through different urbanization processes that urban sprawl characterized by impervious surfaces was more notable in Shanghai than in Tokyo over the past years. Here, annual and seasonal mean LST in daytime (LSTday), in nighttime (LSTnight), and LSTdiff (annual and seasonal mean difference of LST in daytime and nighttime) were extracted from the MODIS LST product, MYD11A2 006, for 9 typical sites in Shanghai and Tokyo from 2003 to 2018, respectively. Then the effects of the urbanization levels were analyzed through Mann-Kendall statistics and Sen’s slope estimator. The trends of change in LSTday and LSTdiff for most sites in Shanghai, an urbanizing region, rose. In addition, there was no obvious regularity when considering seasonal factors, which could be due to the increasing fragmentized landscapes and scattered water bodies produced by urbanization. By comparison, the change in LST in Tokyo, a post-urbanizing region, was regular, especially in the spring. In other seasons, there was no obvious trend in temperature change regardless of whether the land cover was impervious surface or mountain forest. On the whole, vegetation cover and water bodies can mitigate the urban heat island (UHI) effect in urban regions. For more scientific urban planning, further analysis about the effect of urbanization on LST should focus on the compound stress from climate change and urbanization. Full article

(This article belongs to the Special Issue Geospatial Analysis of Urban Heat Island Phenomena in Megacities)

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31 pages, 8248 KiB

Open AccessArticle

Impacts of Land Cover/Use on the Urban Thermal Environment: A Comparative Study of 10 Megacities in China

by Fei Liu, Xinmin Zhang, Yuji Murayama and Takehiro Morimoto

Remote Sens. 2020, 12(2), 307; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12020307 - 17 Jan 2020

Cited by 42 | Viewed by 4080

Abstract

Satellite-derived land surface temperature (LST) reveals the variations and impacts on the terrestrial thermal environment on a broad spatial scale. The drastic growth of urbanization-induced impervious surfaces and the urban population has generated a remarkably increasing influence on the urban thermal environment in China. This research was aimed to investigate land surface temperature (LST) intensity response to urban land cover/use by examining the thermal impact on urban settings in ten Chinese megacities (i.e., Beijing, Dongguan, Guangzhou, Hangzhou, Harbin, Nanjing, Shenyang, Suzhou, Tianjin, and Wuhan). Surface urban heat island (SUHI) footprints were scrutinized and compared by magnitude and extent. The causal mechanism among land cover composition (LCC), population, and SUHI was also identified. Spatial patterns of the thermal environments were identical to those of land cover/use. In addition, most impervious surface materials (greater than 81%) were labeled as heat sources, on the other hand, water and vegetation were functioned as heat sinks. More than 85% of heat budgets in Beijing and Guangzhou were generated from impervious surfaces. SUHI for all megacities showed spatially gradient decays between urban and surrounding rural areas; further, temperature peaks are not always dominant in the urban core, despite extremely dense impervious surfaces. The composition ratio of land cover (LCC%) negatively correlates with SUHI intensity (SUHII), whereas the population positively associates with SUHII. For all targeted megacities, land cover composition and population account for more than 63.9% of SUHI formation using geographically weighted regression. The findings can help optimize land cover/use to relieve pressure from rapid urbanization, maintain urban ecological balance, and meet the demands of sustainable urban growth. Full article

(This article belongs to the Special Issue Geospatial Analysis of Urban Heat Island Phenomena in Megacities)

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21 pages, 15620 KiB

Open AccessArticle

Multi-Scale Relationship between Land Surface Temperature and Landscape Pattern Based on Wavelet Coherence: The Case of Metropolitan Beijing, China

by Qiong Wu, Jinxiang Tan, Fengxiang Guo, Hongqing Li and Shengbo Chen

Remote Sens. 2019, 11(24), 3021; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11243021 - 15 Dec 2019

Cited by 25 | Viewed by 3917

Abstract

The relationship between urban landscape pattern and land surface temperature (LST) is one of the core issues in urban thermal environment research. Although previous studies have shown a significant correlation between LST and landscape pattern, most were conducted at a single scale and rarely involve multi-scale effects of the landscape pattern. Wavelet coherence can relate the correlation between LST and landscape pattern to spatial scale and location, which is an effective multi-scale correlation method. In this paper, we applied wavelet coherence and Pearson correlation coefficient to analyze the multi-scale correlations between landscape pattern and LST, and analyzed the spatial pattern of the urban thermal environment during the urbanization of Beijing from 2004 to 2017 by distribution index of high-temperature center (HTC). The results indicated that the HTC of Beijing gradually expands from the main urban zone and urban function extended zone to the new urban development zone and far suburb zone, and develops from monocentric to polycentric spatial pattern. Land cover types, such as impervious surfaces and bare land, have a positive contribution to LST, while water and vegetation play a role in mitigating LST. The wavelet coherence and Pearson correlation coefficients showed that landscape composition and spatial configuration have significant effects on LST, but landscape composition has a greater effect on LST in Beijing metropolitan area. Landscape composition indexes (NDBI and NDVI) showed significant multi-scale characteristics with LST, especially at larger scales, which has a strong correlation on the whole transect. There was no significant correlation between the spatial configuration indexes (CONTAG, DIVISION, and LSI) and LST at smaller scales, only at larger scales near the urban area has a significant correlation. With the increase of the scale, Pearson correlation coefficient calculated by spatial rectangle sampling and wavelet coherence coefficient have the same trend, although it had some fluctuations in several locations. However, the wavelet coherence coefficient diagram was smoother and less affected by position and rectangle size, which more conducive to describe the correlation between landscape pattern index and LST at different scales and locations. In general, wavelet coherence provides a multi-scale method to analyze the relationship between landscape pattern and LST, helping to understand urban planning and land management to mitigate the factors affecting urban thermal environment. Full article

(This article belongs to the Special Issue Geospatial Analysis of Urban Heat Island Phenomena in Megacities)

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20 pages, 5378 KiB

Open AccessArticle

Spatial Analysis of Surface Urban Heat Islands in Four Rapidly Growing African Cities

by Matamyo Simwanda, Manjula Ranagalage, Ronald C. Estoque and Yuji Murayama

Remote Sens. 2019, 11(14), 1645; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11141645 - 10 Jul 2019

Cited by 106 | Viewed by 10016

Abstract

Africa’s unprecedented, uncontrolled and unplanned urbanization has put many African cities under constant ecological and environmental threat. One of the critical ecological impacts of urbanization likely to adversely affect Africa’s urban dwellers is the urban heat island (UHI) effect. However, UHI studies in African cities remain uncommon. Therefore, this study attempts to examine the relationship between land surface temperature (LST) and the spatial patterns, composition and configuration of impervious surfaces/green spaces in four African cities, Lagos (Nigeria), Nairobi (Kenya), Addis Ababa (Ethiopia) and Lusaka (Zambia). Landsat OLI/TIRS data and various geospatial approaches, including urban–rural gradient, urban heat island intensity, statistics and urban landscape metrics-based techniques, were used to facilitate the analysis. The results show significantly strong correlation between mean LST and the density of impervious surface (positive) and green space (negative) along the urban–rural gradients of the four African cities. The study also found high urban heat island intensities in the urban zones close (0 to 10 km) to the city center for all cities. Generally, cities with a higher percentage of the impervious surface were warmer by 3–4 °C and vice visa. This highlights the crucial mitigating effect of green spaces. We also found significant correlations between the mean LST and urban landscape metrics (patch density, size, shape, complexity and aggregation) of impervious surfaces (positive) and green spaces (negative). The study revealed that, although most African cities have relatively larger green space to impervious surface ratio with most green spaces located beyond the urban footprint, the UHI effect is still evident. We recommend that urban planners and policy makers should consider mitigating the UHI effect by restoring the urban ecosystems in the remaining open spaces in the urban area and further incorporate strategic combinations of impervious surfaces and green spaces in future urban and landscape planning. Full article

(This article belongs to the Special Issue Geospatial Analysis of Urban Heat Island Phenomena in Megacities)

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19 pages, 4248 KiB

Open AccessArticle

Night Thermal Unmixing for the Study of Microscale Surface Urban Heat Islands with TRISHNA-Like Data

by Carlos Granero-Belinchon, Aurelie Michel, Jean-Pierre Lagouarde, Jose A. Sobrino and Xavier Briottet

Remote Sens. 2019, 11(12), 1449; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11121449 - 18 Jun 2019

Cited by 8 | Viewed by 2895

Abstract

Urban Heat Islands (UHIs) at the surface and canopy levels are major issues in urban planification and development. For this reason, the comprehension and quantification of the influence that the different land-uses/land-covers have on UHIs is of particular importance. In order to perform a detailed thermal characterisation of the city, measures covering the whole scenario (city and surroundings) and with a recurrent revisit are needed. In addition, a resolution of tens of meters is needed to characterise the urban heterogeneities. Spaceborne remote sensing meets the first and the second requirements but the Land Surface Temperature (LST) resolutions remain too rough compared to the urban object scale. Thermal unmixing techniques have been developed in recent years, allowing LST images during day at the desired scales. However, while LST gives information of surface urban heat islands (SUHIs), canopy UHIs and SUHIs are more correlated during the night, hence the development of thermal unmixing methods for night LSTs is necessary. This article proposes to adapt four empirical unmixing methods of the literature, Disaggregation of radiometric surface Temperature (DisTrad), High-resolution Urban Thermal Sharpener (HUTS), Area-To-Point Regression Kriging (ATPRK), and Adaptive Area-To-Point Regression Kriging (AATPRK), to unmix night LSTs. These methods are based on given relationships between LST and reflective indices, and on invariance hypotheses of these relationships across resolutions. Then, a comparative study of the performances of the different techniques is carried out on TRISHNA synthesized images of Madrid. Since TRISHNA is a mission in preparation, the synthesis of the images has been done according to the planned specification of the satellite and from initial Aircraft Hyperspectral Scanner (AHS) data of the city obtained during the DESIREX 2008 capaign. Thus, the coarse initial resolution is 60 m and the finer post-unmixing one is 20 m. In this article, we show that: (1) AATPRK is the most performant unmixing technique when applied on night LST, with the other three techniques being undesirable for night applications at TRISHNA resolutions. This can be explained by the local application of AATPRK. (2) ATPRK and DisTrad do not improve significantly the LST image resolution. (3) HUTS, which depends on albedo measures, misestimates the LST, leading to the worst temperature unmixing. (4) The two main factors explaining the obtained performances are the local/global application of the method and the reflective indices used in the LST-index relationship. Full article

(This article belongs to the Special Issue Geospatial Analysis of Urban Heat Island Phenomena in Megacities)

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