Special Issue "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: 15 December 2019.

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
Yale University, USA
Tel. +1 203 432-7327
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 papers will be 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 1800 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 (2 papers)

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Open AccessArticle
Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery
Remote Sens. 2019, 11(3), 345; https://doi.org/10.3390/rs11030345 - 10 Feb 2019
Cited by 2
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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Open AccessArticle
Analyzing the Relationship between Developed Land Area and Nighttime Light Emissions of 36 Chinese Cities
Remote Sens. 2019, 11(1), 10; https://doi.org/10.3390/rs11010010 - 20 Dec 2018
Cited by 1
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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