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Geospatial Understanding of Sustainable Urban Analytics Using Remote Sensing

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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 (1 February 2022) | Viewed by 40237

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Special Issue Editors

Dr. Soheil Sabri

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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
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Prof. Dr. Abbas Rajabifard

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Guest Editor

Centre for Spatial Data Infrastructures and Land Administration, Melbourne School of Engineering, University of Melbourne, Parkville, VIC 3010, Australia
Interests: digital twin; land administration; cadastre; spatial data infrastructure
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Dr. Yiqun Chen

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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
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Prof. Nengcheng Chen

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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
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Prof. Dr. Hao Sheng

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School of Computer Science and Engineering, Beihang University, Beijing 100191, China
Interests: computer vision; pattern recognition; machine learning
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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:

How is the city arranged horizontally (2D) and vertically (3D)?
How dynamic is the urban environment over time (4D)?
What is the spatial distribution pattern of traffic?
How are neighborhoods assessed climatologically and socially?
How do cities, local governments, and neighborhoods perform to achieve sustainable development goals (SDGs)?
What is the building, neighborhood, and city energy performance?
What are urban land consumption rates (open spaces, green spaces, built-up densities)?
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. Dr. Abbas Rajabifard
Dr. Yiqun Chen
Prof. Nengcheng Chen
Prof. Dr. 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 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.

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Keywords

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

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Published Papers (10 papers)

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Editorial

Jump to: Research

4 pages, 180 KiB

Open AccessEditorial

Editorial: Geospatial Understanding of Sustainable Urban Analytics Using Remote Sensing

by Soheil Sabri, Abbas Rajabifard, Yiqun Chen, Nengcheng Chen and Hao Sheng

Remote Sens. 2022, 14(12), 2748; https://doi.org/10.3390/rs14122748 - 8 Jun 2022

Viewed by 1768

Abstract

The increasing trend of urbanization has challenged the traditional ways of urban planning, design, and management [...] Full article

(This article belongs to the Special Issue Geospatial Understanding of Sustainable Urban Analytics Using Remote Sensing)

Research

Jump to: Editorial

21 pages, 10237 KiB

Open AccessArticle

A Geospatial Platform to Manage Large-Scale Individual Mobility for an Urban Digital Twin Platform

by Ahyun Lee, Kang-Woo Lee, Kyong-Ho Kim and Sung-Woong Shin

Remote Sens. 2022, 14(3), 723; https://doi.org/10.3390/rs14030723 - 3 Feb 2022

Cited by 40 | Viewed by 4588

Abstract

Urban digital twin (UDT) technology can be used to digitize physical urban spaces. Previous UDT or smart city research reconstructed the three-dimensional topography of urban spaces, buildings, and facilities. They collected various multimodal sensor data from cities and monitored conditions such as temperature, humidity, fine dust, and real-time road traffic. However, these studies lacked ways to manage individual mobility data, such as those of the vehicles and pedestrians, which constitute major components of a city. Here, we propose a geospatial platform based on the universal game engine Unity3D, which manages large-scale individual mobility data for a UDT platform. The proposed platform stores and manages individual vehicles or pedestrians using information from public closed-circuit television. It also allows the generation of long-term route information for a unique vehicle based on its license plate. We also propose methods to anonymize license plates, to ensure the security of individuals, and to compress individual mobility data. Unique UDT models with individual mobility functionalities can be built and visualized using our proposed geospatial platform. Full article

(This article belongs to the Special Issue Geospatial Understanding of Sustainable Urban Analytics Using Remote Sensing)

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17 pages, 6858 KiB

Open AccessArticle

Business Circle Identification and Spatiotemporal Characteristics in the Main Urban Area of Yiwu City Based on POI and Night-Time Light Data

by Liangliang Zhou, Yishao Shi and Jianwen Zheng

Remote Sens. 2021, 13(24), 5153; https://doi.org/10.3390/rs13245153 - 18 Dec 2021

Cited by 19 | Viewed by 4029

Abstract

The activity of the urban night-time economy is one of the most important indicators reflecting the prosperity of an urban economy. The business circle is an important carrier of urban commercial activities and the core area of urban nightlife. This paper takes the main urban area of Yiwu city as the research object. Based on POI data and night-time light remote sensing data, two-factor mapping, kernel density analysis, DBSCAN clustering, and local contour tree methods are adopted to identify the business circle structure of the main urban area of Yiwu city and analyse the relationship between business circle characteristics and the night-time economy. The following conclusions can be drawn. (1) The spatial superimposition relationship between the night-time remote sensing data and points of interest (POI) data in the main urban area of Yiwu city is good, and the overall coupling results show obvious circle structure characteristics. (2) The spatial distribution of different business combinations has obvious regularity: comprehensive shopping business shows a multicentre distribution pattern and has a hierarchical feature. In contrast, professional food and beverage and leisure and entertainment businesses are close to urban residential areas, and different groups of people live in different places with their own characteristics. (3) From 2015 to 2019, the brightness value of each business circle showed a continuously increasing trend. In 2020, due to the impact of COVID-19, most of them declined. (4) Overall, the difference in business circle tiers reflects the difference in the level of night-time economic activities. Full article

(This article belongs to the Special Issue Geospatial Understanding of Sustainable Urban Analytics Using Remote Sensing)

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20 pages, 22329 KiB

Open AccessArticle

Fractal Characteristic Analysis of Urban Land-Cover Spatial Patterns with Spatiotemporal Remote Sensing Images in Shenzhen City (1988–2015)

by Luxiao Cheng, Ruyi Feng and Lizhe Wang

Remote Sens. 2021, 13(22), 4640; https://doi.org/10.3390/rs13224640 - 18 Nov 2021

Cited by 12 | Viewed by 3015

Abstract

Understanding the urban land-cover spatial patterns is of particular significance for sustainable development planning. Due to the nonlinear characteristics related to the spatial pattern for land cover, it is essential to provide a new analysis method to analyze them across remote sensing imagery. This paper is devoted to exploring the fractals and fractal dimension properties of land-cover spatial patterns in Shenzhen city, China. Land-cover information was extracted using a supervised classification method with ArcGIS technology from cloud-free Landsat TM/ETM+/OLI imagery, covering 1988–2015. The box-counting method and the least squares regression method are combined to estimate fractal dimensions of the land-cover spatial pattern. The information entropy was used to verify our fractal dimension results. The results show the fractal dimension changes for each land cover type from 1988 to 2015: (1) the land-cover spatial form of Shenzhen city has a clear fractal structure, but fractal dimension values vary in different land cover types; (2) the fractal dimension of build-up land increases and reaches a stable value, while grassland and cultivated land decrease; The fractal structure of grassland and bare land showed a bifractals trend increasing year by year; (3) the information entropy dimension growth is approaching its maximum capacity before 2011. We integrated the information entropy index and fractal dimension to analyze the complexity in land-cover spatial evolution from space-filling, space balance, and space complexity. It can be concluded that driven by policies, the land-cover spatial form in Shenzhen experienced a process from a hierarchical spatial structure with a low evolution intensity to a higher evolution intensity with multiscale differential development. The fractal dimension has been becoming better through self-organization, and its land resources are reaching the growth limits. Full article

(This article belongs to the Special Issue Geospatial Understanding of Sustainable Urban Analytics Using Remote Sensing)

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21 pages, 5341 KiB

Open AccessArticle

How Do Two- and Three-Dimensional Urban Structures Impact Seasonal Land Surface Temperatures at Various Spatial Scales? A Case Study for the Northern Part of Brooklyn, New York, USA

by Wen He, Shisong Cao, Mingyi Du, Deyong Hu, You Mo, Manqing Liu, Jianghong Zhao and Yuee Cao

Remote Sens. 2021, 13(16), 3283; https://doi.org/10.3390/rs13163283 - 19 Aug 2021

Cited by 12 | Viewed by 3119

Abstract

Identifying the driving factors of urban land surface temperatures (U-LSTs) is critical in improving urban thermal environments and in supporting the sustainable development of cities. Previous studies have demonstrated that two- and three-dimensional (2D and 3D) urban structure parameters (USPs) largely influence seasonal U-LSTs. However, the effects of 2D and 3D USPs on seasonal U-LSTs at different spatial scales still await a general explanation. In this study, we used very-high-resolution remotely sensed data to investigate how 2D and 3D USPs impact seasonal U-LSTs at different spatial scales (including pixel and city block scales). In addition, the influences of various functional zones on U-LSTs were analyzed. The results show that, (1) generally, the links between USPs and U-LSTs at the city block scale were more obvious than those at the pixel scale, e.g., the Pearson correlation coefficient (r) between U-LST and the mean building height at the city block scale (summer: r = −0.156) was higher than that at the pixel scale (summer: r = −0.081). Tree percentage yielded a considerable cooling effect on summer U-LSTs on both the pixel (r = −0.199) and city block (r = −0.369) scales, and the effect was more obvious in regions with tall trees. (2) The independently total explained variances (R²) of 3D USPs on seasonal U-LSTs were considerably higher than those of 2D USPs in most urban functional zones (UFZs), suggesting the distinctive roles of 3D USPs in U-LST regulation at the local scale. Three-dimensional USPs (R² value = 0.66) yielded more decisive influences on summer U-LSTs than 2D USPs did (R² value = 0.48). (3) Manufacturing zones yielded the highest U-LST, followed by residential and commercial zones. Notably, it is found that the explained variances of the total study area for seasonal U-LSTs were significantly lower than those of each UFZ, suggesting the different roles of 2D and 3D USPs played in various UFZs and that it is critical to explain U-LST variations by using UFZs. Full article

(This article belongs to the Special Issue Geospatial Understanding of Sustainable Urban Analytics Using Remote Sensing)

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21 pages, 8705 KiB

Open AccessArticle

Integrating Aerial LiDAR and Very-High-Resolution Images for Urban Functional Zone Mapping

by Siji Sanlang, Shisong Cao, Mingyi Du, You Mo, Qiang Chen and Wen He

Remote Sens. 2021, 13(13), 2573; https://doi.org/10.3390/rs13132573 - 1 Jul 2021

Cited by 19 | Viewed by 4380

Abstract

This study presents a new approach for Urban Functional Zone (UFZ) mapping by integrating two-dimensional (2D) Urban Structure Parameters (USPs), three-dimensional (3D) USPs, and the spatial patterns of land covers, which can be divided into two steps. Firstly, we extracted various features, i.e., spectral, textural, geometrical features, and 3D USPs from very-high-resolution (VHR) images and light detection and ranging (LiDAR) point clouds. In addition, the multi-classifiers (MLCs), i.e., Random Forest, K-Nearest Neighbor, and Linear Discriminant Analysis classifiers were used to perform the land cover mapping by using the optimized features. Secondly, based on the land cover classification results, we extracted 2D and 3D USPs for different land covers and used MLCs to classify UFZs. Results for the northern part of Brooklyn, New York, USA, show that the approach yielded an excellent accuracy of UFZ mapping with an overall accuracy of 91.9%. Moreover, we have demonstrated that 3D USPs could considerably improve the classification accuracies of UFZs and land covers by 6.4% and 3.0%, respectively. Full article

(This article belongs to the Special Issue Geospatial Understanding of Sustainable Urban Analytics Using Remote Sensing)

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19 pages, 3902 KiB

Open AccessArticle

Spatiotemporal Patterns of Urbanization in the Three Most Developed Urban Agglomerations in China Based on Continuous Nighttime Light Data (2000–2018)

by Yu Li, Haipeng Ye, Xing Gao, Dongqi Sun, Zehong Li, Ninghui Zhang, Xuejing Leng, Dan Meng and Ji Zheng

Remote Sens. 2021, 13(12), 2245; https://doi.org/10.3390/rs13122245 - 8 Jun 2021

Cited by 26 | Viewed by 3382

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 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

(This article belongs to the Special Issue Geospatial Understanding of Sustainable Urban Analytics Using Remote Sensing)

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18 pages, 8973 KiB

Open AccessArticle

Methodology for Determining the Nearest Destinations for the Evacuation of People and Equipment from a Disaster Area to a Safe Area

by Volodymyr Korolov, Krystyna Kurowska, Olha Korolova, Yaroslav Zaiets, Igor Milkovich and Hubert Kryszk

Remote Sens. 2021, 13(11), 2170; https://doi.org/10.3390/rs13112170 - 1 Jun 2021

Cited by 6 | Viewed by 3793

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, 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

(This article belongs to the Special Issue Geospatial Understanding of Sustainable Urban Analytics Using Remote Sensing)

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33 pages, 8258 KiB

Open AccessArticle

Flood Risk Assessment of Subway Systems in Metropolitan Areas under Land Subsidence Scenario: A Case Study of Beijing

by Guangpeng Wang, Yong Liu, Ziying Hu, Guoming Zhang, Jifu Liu, Yanli Lyu, Yu Gu, Xichen Huang, Qingyan Zhang and Lianyou Liu

Remote Sens. 2021, 13(4), 637; https://doi.org/10.3390/rs13040637 - 10 Feb 2021

Cited by 34 | Viewed by 5132

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 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

(This article belongs to the Special Issue Geospatial Understanding of Sustainable Urban Analytics Using Remote Sensing)

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24 pages, 5342 KiB

Open AccessArticle

Spatial Configuration and Extent Explains the Urban Heat Mitigation Potential due to Green Spaces: Analysis over Addis Ababa, Ethiopia

by Berhanu Keno Terfa, Nengcheng Chen, Xiang Zhang and Dev Niyogi

Remote Sens. 2020, 12(18), 2876; https://doi.org/10.3390/rs12182876 - 4 Sep 2020

Cited by 22 | Viewed by 4159

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 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

(This article belongs to the Special Issue Geospatial Understanding of Sustainable Urban Analytics Using Remote Sensing)

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