Special Issue "Geospatial Understanding of Sustainable Urban Analytics Using Remote Sensing"

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

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editors

Dr. Soheil Sabri
E-Mail Website
Guest Editor
Centre for Spatial Data Infrastructures and Land Administration, Melbourne School of Engineering, University of Melbourne, Parkville VIC 3010, Australia
Interests: urban planning; urban analytics; geosimulation; geodesign; planning support systems
Special Issues and Collections in MDPI journals
Dr. Abbas Rajabifard
E-Mail Website
Guest Editor
Centre for Spatial Data Infrastructures and Land Administration, Melbourne School of Engineering, University of Melbourne, Parkville VIC 3010, Australia
Interests: sustainable development; resilience enhancement; spatial information; digital twin; land management
Special Issues and Collections in MDPI journals
Prof. Dr. Yiqun Chen
E-Mail Website
Guest Editor
Centre for Spatial Data Infrastructures and Land Administration, Melbourne School of Engineering, University of Melbourne, Parkville VIC 3010, Australia
Interests: sustainable development; resilience enhancement; GIS visualisation; spatial analysis; disaster management
Special Issues and Collections in MDPI journals
Prof. Nengcheng Chen
E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Interests: geospatial sensor web; urban sensing; smart city
Assoc. Prof. Hao Sheng
E-Mail Website
Guest Editor
School of Computer Science and Engineering, Beihang University, G947, New Building, No. 37 Xueyuan Road, Haidian District, Beijing 100083, China
Interests: computer vision; pattern recognition; machine learning

Special Issue Information

Dear Colleagues,

With 75% of the world’s population set to reside in cities by 2050, the imperatives of evidence-based urban management cannot be overstated in future-proofing the sustainable development of cities. In the current rapid and complex pace of urbanization, policymakers and urban planners need new predictive analytic tools that can help them understand the potential future impact of different scenarios, policies, and decisions on the urban landscape and population. New digital technologies, particularly spatial data infrastructures, and digital ICT, offer immense potential for bringing together multi-source and heterogeneous datasets—both spatial and aspatial—for spatially enabled analysis, evaluation, and ongoing management in implementing urban policies. In this regard, this Special Issue aims to understand the crucial role of remote sensing and real-time data for answering questions such as the following:

  1. How is the city arranged horizontally (2D) and vertically (3D)?
  2. How dynamic is the urban environment over time (4D)?
  3. What is the spatial distribution pattern of traffic?
  4. How are neighborhoods assessed climatologically and socially?
  5. How do cities, local governments, and neighborhoods perform to achieve sustainable development goals (SDGs)?
  6. What is the building, neighborhood, and city energy performance?
  7. What are urban land consumption rates (open spaces, green spaces, built-up densities)?
  8. How can cities perform to mitigate vulnerability and increase resilience and sustainability with respect to hazards and risks?

This Special Issue will open up a dialogue on the application of current advancements in spatial technologies and digital infrastructures in urban analytics. Those technologies include, but are not limited to, different earth observation methods and data, IoT, geo-tagged crowdsourced data, location intelligence, autonomous vehicles, and digital twins. This Special Issue will focus on how the integration of such data and technologies in a robust platform will enable policymakers and urban planners to engage in evidence-based and data-driven decision-making to address future urbanization challenges.

Dr. Soheil Sabri
Prof. Abbas Rajabifard
Dr. Yiqun Chen
Prof. Nengcheng Chen
Assoc. Prof. Hao Sheng

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 2400 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

  • Digital twin
  • Urban network analytics
  • 2D/3D city modeling
  • Urban/disaster resilience
  • Urban form
  • Sensor web
  • SDGs

Published Papers (4 papers)

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Research

Article
Spatiotemporal Patterns of Urbanization in the Three Most Developed Urban Agglomerations in China Based on Continuous Nighttime Light Data (2000–2018)
Remote Sens. 2021, 13(12), 2245; https://doi.org/10.3390/rs13122245 - 08 Jun 2021
Viewed by 312
Abstract
Urban agglomeration is an advanced spatial form of integrating cities, resulting from the global urbanization of recent decades. Understanding spatiotemporal patterns and evolution is of great importance for improving urban agglomeration management. This study used continuous time-series NTL data from 2000 to 2018 [...] Read more.
Urban agglomeration is an advanced spatial form of integrating cities, resulting from the global urbanization of recent decades. Understanding spatiotemporal patterns and evolution is of great importance for improving urban agglomeration management. This study used continuous time-series NTL data from 2000 to 2018 combined with land-use images to investigate the spatiotemporal patterns of urbanization in the three most developed urban agglomerations in China over the past two decades: the Beijing–Tianjin–Hebei urban agglomeration (BTH), the Yangtze River Delta urban agglomeration (YRD), and the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). The NTL intensity indexes, dynamic thresholds, extracted urban areas, and landscape metrics were synthetically used to facilitate the analysis. This study found that the urbanization process in the study areas could be divided into three stages: rapid urbanization in core cities from 2000 to 2010, a fluctuating urbanization process in both core cities and surrounding cities from 2010 to 2015, and stable urbanization, mainly in surrounding cities with a medium size after 2015. Meanwhile, the urbanization level of GBA was higher than that of YRD and BTH. However, with the acceleration of urban development in YRD, the gap in the urbanization level between GBA and YRD narrowed significantly in the third stage. In addition, this study confirmed that the scattered, medium-sized cities in YRD and GBA were more developed than those in BTH. This study showed that continuous NTL data could be effectively applied to monitor the urbanization patterns of urban agglomerations. Full article
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Article
Methodology for Determining the Nearest Destinations for the Evacuation of People and Equipment from a Disaster Area to a Safe Area
Remote Sens. 2021, 13(11), 2170; https://doi.org/10.3390/rs13112170 - 01 Jun 2021
Viewed by 406
Abstract
Floods are the most frequent natural disasters in the world. In the system of warning and flood protection of areas at risk of flooding in the event of its occurrence, it seems advisable to initially work out the possibility of evacuating the population, [...] Read more.
Floods are the most frequent natural disasters in the world. In the system of warning and flood protection of areas at risk of flooding in the event of its occurrence, it seems advisable to initially work out the possibility of evacuating the population, animals, equipment, material values, etc. In this article, a methodology for determining destinations (points of destination) for the evacuation of people and equipment from a predicted flood zone (of a natural disaster) to a safe area is proposed based upon the criterion of the shortest possible distance. In the paper, a scenario is considered that involves the contours of the flood zone boundaries for several variants of the intensity of the probable development of future events (with the aid of geoinformation technologies), and the coordinates of the objects to evacuate are permanent and known in advance. With the known coordinates of the objects and the closest points of the boundary of the predicted flood zone, the shortest distances can be calculated. Based on these calculations, the appropriate destinations for evacuation are determined. The proposed methodology can be used for flood forecasting and flood zone modeling to assess the economic and social risks of their aftereffects and to allow the public, local governments, and other organizations to better understand the potential risks of floods and to identify the measures needed to save lives and avoid damage to and loss of property and equipment. This methodology, in contrast to known approaches, allows the determination of the nearest locations for the evacuation of people and equipment from a flood zone (of a natural disaster) to safe areas, to be determined for several variants, depending on the possible development of future events. The methodology is algorithm-driven and presented in the form of a flowchart and is suitable for use in the appropriate software. The proposed methodology is an introduction to the next stages of research related to the determination of safe places for evacuation of people and their property (equipment) to safe places. This is especially important in case of sudden weather events (flash floods). Full article
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Article
Flood Risk Assessment of Subway Systems in Metropolitan Areas under Land Subsidence Scenario: A Case Study of Beijing
Remote Sens. 2021, 13(4), 637; https://doi.org/10.3390/rs13040637 - 10 Feb 2021
Viewed by 640
Abstract
Flooding is one of the most destructive natural events that severely damage the ground and inundate underground infrastructure. Subway systems in metropolitan areas are susceptible to flooding, which may be exacerbated when land subsidence occurs. However, previous studies have focused on flood risk [...] Read more.
Flooding is one of the most destructive natural events that severely damage the ground and inundate underground infrastructure. Subway systems in metropolitan areas are susceptible to flooding, which may be exacerbated when land subsidence occurs. However, previous studies have focused on flood risk evaluation on regional/watershed-scales and land subsidence monitoring in plains, instead of on subway flood risk evaluation and how land subsidence aggravates the flood risk in subway systems. Using the proposed risk indicators and field survey data, we present a method assessing the flood risk of metropolitan subway systems under a subsidence condition based on the fuzzy analytic hierarchy process (FAHP) combined with a geographic information system (GIS). We use the regional risk level within the 500 m buffer zone of the subway line to depict the flood risk of the subway system. The proposed method was used to evaluate the flood risk of the Beijing subway system. The results show that the flood risks of the Beijing subway show a ring-like distribution pattern—risk levels decreasing from the central urban area to the suburbs. Very high and high risks are mainly located within third and fourth ring roads, accounting for 63.58% (29.40 km2) and 63.83% (81.19 km2) of the total area. Land subsidence exacerbated the Beijing subway system’s flood risk level—the moderate to very high risk increased by 46.88 km2 (16.33%), indicating that land subsidence is an essential factor affecting the flood risk level of subway systems. In addition to enhancing flood warnings, future subway flooding could be reduced by elevating the height of the stations’ exit (entrance) and installing water stop plates and watertight doors. This study is of great significance for flood warning and prevention in the Beijing subway system; it provides a theoretical basis for flood risk evaluation in other metropolitan areas. Full article
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Article
Spatial Configuration and Extent Explains the Urban Heat Mitigation Potential due to Green Spaces: Analysis over Addis Ababa, Ethiopia
Remote Sens. 2020, 12(18), 2876; https://doi.org/10.3390/rs12182876 - 04 Sep 2020
Cited by 1 | Viewed by 1049
Abstract
Urban green space (UGS) is considered a mitigative intervention for urban heat. While increasing the UGS coverage is expected to reduce the urban heat, studies on the effects of UGS configuration have produced inconsistent results. To investigate this inconsistency further, this study conducted [...] Read more.
Urban green space (UGS) is considered a mitigative intervention for urban heat. While increasing the UGS coverage is expected to reduce the urban heat, studies on the effects of UGS configuration have produced inconsistent results. To investigate this inconsistency further, this study conducted a multi-spatial and multi-temporal resolution analysis in the Addis Ababa city metropolitan area for assessing the relationship between UGS patterns and land surface temperature (LST). Landsat images were used to generate land cover and LST maps. Regression models were developed to investigate whether controlling for the proportion of the green area (PGS), fragmentation, shape, complexity, and proximity distance can affect surface temperature. Results indicated that the UGS patches with aggregated, regular and simple shapes and connectivity throughout the urban landscape were more effective in decreasing the LST as compared to the fragmented and complicated spatial patterns. This finding highlighted that in addition to increasing the amount of UGS, optimizing the spatial structure of UGS, could be an effective and useful action to mitigate the urban heat island (UHI) impacts. Changing the spatial size had a significant influence on the interconnection between LST and UGS patterns as well. It also noted that the spatial arrangement of UGS was more sensitive to spatial scales than that of its composition. The relationship between the spatial configuration of UGS and LST could be changed when applying different statistical methods. This result underlined the importance of controlling the effects of the share of green spaces when calculating the impacts of the spatial configuration of UGS on LST. Furthermore, the study highlighted that applying different statistical approaches, spatial scale, and coverage of UGS can help determine the effectiveness of the association between LST and UGS patterns. These outcomes provided new insights regarding the inconsistent findings from earlier studies, which might be a result of the different approaches considered. Indeed, these findings are expected to be of help more broadly for city planning and urban heat mitigation. Full article
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: How landscape components and configurations influence seasonal urban land surface temperature in a three-dimensional space and at different spatial scales?
Authors: Shisong Cao; Wen He; Mingyi Du; You Mo; Deyong Hu
Affiliation: 1 School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing, China; 2 College of Resource Environment and Tourism, Capital Normal University, Beijing, China.
Abstract: This study aims at studying how landscape components and configuration influence urban land surface temperature (U-LST) in the three-dimensional (3D) space by using a multi-scale analysis (i.e., considering both the remotely sensed pixel and city block). In details, taking the northern part of Brooklyn, New York, USA, as the study area and using the high resolution remotely sensed data, i.e., aerial very-high-resolution data (VHR) and Light Detection and Ranging data (LiDAR), we designed and extracted a set of 2D and 3D landscape component indices, e.g., 2D: building/vegetation/tree canopy coverage and impervious surface coverage, and 3D: building volume, mean building/tree height; as well as a set of 2D and 3D landscape configuration index, e.g., 2D: mean distance between trees and buildings and mean distance between buildings/trees, and 3D: standard deviation of building/tree height, and high building/tree ratio. Further, we explored the relationship between 2/3D landscape indices and seasonal U-LST and found the approach of mitigations of U-LST. A multi-regression method will be proposed to identify the key driving landscape indices for the U-LST. The basic idea is that, if the variable has more effects on SUHI, it always would be selected as the key variable in different regression methods. Two categories of regression method were considered, (1) single-factor based approach, which includes a square of Pearson coefficient (linear relationship detection) and MIC (non-linear relationship detection); (2) multi-factor based method, which include multiple regression (i.e., Lasso and Ridge) and machine learning (i.e., random forest and RFE).

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