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Remote Sensing
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Remote Sensing based Urban Development and Climate Change Research
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Remote Sensing based Urban Development and Climate Change Research

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

Deadline for manuscript submissions: closed (31 July 2020) | Viewed by 39882

Special Issue Editors

Dr. Cristina Milesi

E-Mail Website
Guest Editor
CropSnap LLC, Sunnyvale, CA, USA
Interests: land use/cover change; satellite-based urbanization monitoring; satellite-based agriculture monitoring; ecosystem modeling; global change research
Dr. Galina Churkina

E-Mail Website
Guest Editor
Potsdam Institute for Climate Impact Research, Potsdam, Germany
Interests: urbanization; biogeochemical cycles; climate change; urban heat island; numerical modeling; remote sensing of environment

Special Issue Information

Dear Colleagues,

Urban development plays a critical role in mitigation and adaptation to climate change. This is because urban areas host more than half of the growing global population and are responsible for 70% of the global greenhouse gas emissions.

In addition to global warming trends, urban areas experience a local heat island effect resulting from the high density of impervious surfaces, modification of air ventilation patterns from built-up structures, as well as waste heat emissions from residential and industrial sources. Furthermore, high air temperatures amplify air pollution and influence intensity and frequency of rainfall.

As cities are getting warmer, they also experience growing population. Under changing climate, urban development must adapt to increasing pressure on resources, such as water and energy, as their demand increases with warmer temperatures. At the same time, the supply and storage of these resources may be impacted by changes in regional precipitation pattern or early snow melt.

A majority of cities are coastal and are already facing the challenge of adaptation to sea level rise and enhanced flooding. Cities located in floodplains are at increased risk of flooding from the intensification of storm events.

A wide range of remote sensing technologies such as optical, thermal infrared, microwave, as well as light detection and ranging (LiDAR) are used to observe the urban environment and its changes. These technologies can contribute to monitoring, testing, and exploring solutions for evolving urban development to adapt to the changing climate. Furthermore, remote sensing observations can also help to understand past urban expansion and its influence on climate.

We are requesting papers for a Special Issue of Remote Sensing on remote sensing based urban development and climate change research. Specific topics include, but are not limited to

  • The use of remote sensing to understand the evolution of the urban heat island, its interaction with global warming trends, and its impacts on air quality, energy or water use, and urban vegetation
  • The use of remote sensing to identify urban development at risk of sea level rise, coastal and inland flooding
  • The effect of urban development and climate change on water availability and quality
  • Monitoring urban emissions of waste heat and greenhouse gases
  • Novel remote sensing techniques including new sensors, new methodology, new datasets, etc., for monitoring urban development in response to climate change research
  • Novel remote sensing applications for parameterization of urban areas in climate models.

We especially encourage submissions that combine different methodologies such as remote sensing, urban climatology, downscaled climate projections, air quality models, spatial analysis, etc., to understand the overarching topic.

Dr. Cristina Milesi
Dr. Galina Churkina
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

  • urban remote sensing
  • urban climate models
  • urban air quality
  • urban water quality and availability
  • urban heat emissions
  • urban greenhouse gas emission monitoring
  • urban adaptation to climate change
  • sustainable urban development

Published Papers (5 papers)

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24 pages, 14771 KiB  
Article
Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery
by Paria Ettehadi Osgouei, Sinasi Kaya, Elif Sertel and Ugur Alganci
Remote Sens. 2019, 11(3), 345; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11030345 - 10 Feb 2019
Cited by 90 | Viewed by 17616
Abstract
In this research work, a multi-index-based support vector machine (SVM) classification approach has been proposed to determine the complex and morphologically heterogeneous land cover/use (LCU) patterns of cities, with a special focus on separating bare lands and built-up regions, using Istanbul, Turkey as [...] Read more.
In this research work, a multi-index-based support vector machine (SVM) classification approach has been proposed to determine the complex and morphologically heterogeneous land cover/use (LCU) patterns of cities, with a special focus on separating bare lands and built-up regions, using Istanbul, Turkey as the main study region, and Ankara and Konya (in Turkey) as the independent test regions. The multi-index approach was constructed using three-band combinations of spectral indices, where each index represents one of the three major land cover categories, green areas, water bodies, and built-up regions. Additionally, a shortwave infrared-based index, the Normalized Difference Tillage Index (NDTI), was proposed as an alternative to existing built-up indices. All possible index combinations and the original ten-band Sentinel-2A image were classified with the SVM algorithm, to map seven LCU classes, and an accuracy assessment was performed to determine the multi-index combination that provided the highest performance. The SVM classification results revealed that the multi-index combination of the normalized difference tillage index (NDTI), the red-edge-based normalized vegetation index (NDVIre), and the modified normalized difference water index (MNDWI) improved the mapping accuracy of the heterogeneous urban areas and provided an effective separation of bare land from built-up areas. This combination showed an outstanding overall performance with a 93% accuracy and a 0.91 kappa value for all LCU classes. The results of the test regions provided similar findings and the same index combination clearly outperformed the other approaches, with 92% accuracy and a 0.90 kappa value for Ankara, and an 84% accuracy and a 0.79 kappa value for Konya. The multi-index combination of the normalized difference built-up index (NDBI), the NDVIre, and the MNDWI, ranked second in the assessment, with similar accuracies to that of the ten-band image classification. Full article
(This article belongs to the Special Issue Remote Sensing based Urban Development and Climate Change Research)
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24 pages, 8499 KiB  
Article
Analyzing the Relationship between Developed Land Area and Nighttime Light Emissions of 36 Chinese Cities
by Hui-min Li, Xiao-gang Li, Xiao-ying Yang and Hao Zhang
Remote Sens. 2019, 11(1), 10; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11010010 - 20 Dec 2018
Cited by 27 | Viewed by 3893
Abstract
The satellite-observed nighttime light emission (NTLE) data provide a new method for scrutinizing the footprint of human settlements. Changing NTLEs can be attributed to the direct/indirect influences of highly complex factors that are beyond the ability of simple statistical models to distinguish. Besides, [...] Read more.
The satellite-observed nighttime light emission (NTLE) data provide a new method for scrutinizing the footprint of human settlements. Changing NTLEs can be attributed to the direct/indirect influences of highly complex factors that are beyond the ability of simple statistical models to distinguish. Besides, the relatively coarse resolution of the NTLE products combined with light from human settlements may produce misleading results, as the relationship between spatiotemporal heterogeneity in the growth of developed land (e.g., urban and rural residences, shopping centers, industrial parks, mining plants, and transportation facilities) and the associated NTLEs has not been adequately analyzed. In this study, we developed a total nighttime brightness index (TotalNTBI) to measure the NTLEs with the defense meteorological satellite program/operational linescan system (DMSP/OLS) nighttime light data enhanced by sharpening the edges of the pixels. Thirty-six key cities in China were selected to investigate the relationship between the total developed land area and the associated TotalNTBI from 2000 to 2013 using panel regression and a simplified structural equation model (SEM). The results show that the overall trend in TotalNTBI agreed well with that of the total developed land area (mean adjusted R2 = 0.799). The panel regression models explained approximately 71.8% of the variance of total developed land area and 92.4% of the variance in TotalNTBI. The SEM revealed both the direct and indirect influences of independent variables on the total developed land area and the associated TotalNTBI. This study may provide useful information for decision-makers and researchers engaged in sustainable land development, urban management, and regional developmental inequality, focusing on recent issues, such as retrospective analysis of human footprint with sharpened nighttime NTLE products, the loss of natural and semi-natural land due to the sprawling developed land area indicated by intensively lit area, and the low efficiency of land development indicated by the anomalies of developed land area and associated NTBIs. Full article
(This article belongs to the Special Issue Remote Sensing based Urban Development and Climate Change Research)
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25 pages, 4064 KiB  
Review
Measuring and Monitoring Urban Impacts on Climate Change from Space
by Cristina Milesi and Galina Churkina
Remote Sens. 2020, 12(21), 3494; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12213494 - 24 Oct 2020
Cited by 15 | Viewed by 6406
Abstract
As urban areas continue to expand and play a critical role as both contributors to climate change and hotspots of vulnerability to its effects, cities have become battlegrounds for climate change adaptation and mitigation. Large amounts of earth observations from space have been [...] Read more.
As urban areas continue to expand and play a critical role as both contributors to climate change and hotspots of vulnerability to its effects, cities have become battlegrounds for climate change adaptation and mitigation. Large amounts of earth observations from space have been collected over the last five decades and while most of the measurements have not been designed specifically for monitoring urban areas, an increasing number of these observations is being used for understanding the growth rates of cities and their environmental impacts. Here we reviewed the existing tools available from satellite remote sensing to study urban contribution to climate change, which could be used for monitoring the progress of climate change mitigation strategies at the city level. We described earth observations that are suitable for measuring and monitoring urban population, extent, and structure; urban emissions of greenhouse gases and other air pollutants; urban energy consumption; and extent, intensity, and effects on surrounding regions, including nearby water bodies, of urban heat islands. We compared the observations available and obtainable from space with the measurements desirable for monitoring. Despite considerable progress in monitoring urban extent, structure, heat island intensity, and air pollution from space, many limitations and uncertainties still need to be resolved. We emphasize that some important variables, such as population density and urban energy consumption, cannot be suitably measured from space with available observations. Full article
(This article belongs to the Special Issue Remote Sensing based Urban Development and Climate Change Research)
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20 pages, 11430 KiB  
Article
Combining Measurements of Built-up Area, Nighttime Light, and Travel Time Distance for Detecting Changes in Urban Boundaries: Introducing the BUNTUS Algorithm
by Muhammad Luqman, Peter J. Rayner and Kevin R. Gurney
Remote Sens. 2019, 11(24), 2969; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11242969 - 11 Dec 2019
Cited by 17 | Viewed by 4395
Abstract
This paper introduces a new algorithm (BUNTUS—Built-up, Nighttime Light, and Travel time for Urban Size) using remote sensing techniques to delineate urban boundaries. The paper is part of a larger study of the role of urbanisation in changing fossil fuel emissions. The method [...] Read more.
This paper introduces a new algorithm (BUNTUS—Built-up, Nighttime Light, and Travel time for Urban Size) using remote sensing techniques to delineate urban boundaries. The paper is part of a larger study of the role of urbanisation in changing fossil fuel emissions. The method combines estimates of land cover, nighttime lights, and travel times to classify contiguous urban areas. The method is automatic, global and uses data sets with enough duration to establish trends. Validation using ground truth from Landsat-8 OLI images revealed an overall accuracy ranging from 60% to 95%. Thus, this approach is capable of describing spatial distributions and giving detailed information of urban extents. We demonstrate the method with examples from Brisbane, Australia, Melbourne, Australia, and Beijing, China. The new method meets the criteria for studying overall trends in urban emissions. Full article
(This article belongs to the Special Issue Remote Sensing based Urban Development and Climate Change Research)
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17 pages, 5237 KiB  
Article
Urban Heat Island Monitoring and Impacts on Citizen’s General Health Status in Isfahan Metropolis: A Remote Sensing and Field Survey Approach
by Mohsen Mirzaei, Jochem Verrelst, Mohsen Arbabi, Zohreh Shaklabadi and Masoud Lotfizadeh
Remote Sens. 2020, 12(8), 1350; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12081350 - 24 Apr 2020
Cited by 52 | Viewed by 6601
Abstract
Urban heat islands (UHIs) are one of the urban management challenges, especially in metropolises, which can affect citizens’ health and well-being. This study used a combination of remote sensing techniques with field survey to investigate systematically the effects of UHI on citizens’ health [...] Read more.
Urban heat islands (UHIs) are one of the urban management challenges, especially in metropolises, which can affect citizens’ health and well-being. This study used a combination of remote sensing techniques with field survey to investigate systematically the effects of UHI on citizens’ health in Isfahan metropolis, Iran. For this purpose, the land surface temperature (LST) over a three-year period was monitored by Landsat-8 satellite imagery based on the split window algorithm. Then, the areas where UHI and urban cold island (UCI) phenomena occurred were identified and a general health questionnaire-28 (GHQ-28) was applied to evaluate the health status of 800 citizens in terms of physical health, anxiety and sleep, social function, and depression in UHI and UCI treatments. The average LST during the study period was 45.5 ± 2.3 °C and results showed that the Zayandeh-Rood river and the surrounding greenery had an important role in regulating the ambient temperature and promoting the citizens’ health. Citizens living in the suburban areas were more exposed to the UHIs phenomena, and statistical analysis of the GHQ-28 results indicated that they showed severe significant (P < 0.05) responses in terms of non-physical health sub-scales (i.e., anxiety and sleep, social functioning, and depression). Therefore, it can be concluded that not all citizens in the Isfahan metropolis are in the same environmental conditions and city managers and planners should pay more attention to the citizens living in the UHIs. The most important proceedings in this area would be the creation and development of parks and green belts, as well as the allocation of health-medical facilities and citizen education. Full article
(This article belongs to the Special Issue Remote Sensing based Urban Development and Climate Change Research)
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