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Global Urban Observation for SDG Goal 11: Sustainable Cities and Communities

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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 (10 August 2023) | Viewed by 27135

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Special Issue Editors

Dr. Hua Liu

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Guest Editor

Department of Political Science & Geography, Old Dominion University, Norfolk, VA 23529, USA
Interests: remote sensing; GIS; urban environmental changes; public health
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Qihao Weng

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Guest Editor

Center for Urban and Environmental Change, Department of Earth and Environmental Systems, Indiana State University, Terre Haute, IN 47809, USA
Interests: remote sensing; GIS; land use and land cover change; urban environment and ecosystem
Special Issues, Collections and Topics in MDPI journals

Dr. Umamaheshwaran Rajasekar

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Guest Editor

National Institute of Urban Affairs, 1st Floor, Core 4B, India Habitat Centre, Lodhi Road, New Delhi, India
Interests: remote sensing; urban resilience

Prof. Dr. Li Zhang

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 10094, China
Interests: remote sensing; coast; drylands carbon
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Urbanization has become a widespread issue across the globe, with over half of the human population living in cities. A rising population and migration have led to a rapid growth of mega-cities. In recent decades, public attention has been focused on the sustainability and resilience of cities and communities, which have been reenergized by the United Nations’ initiative for Sustainable Development Goals (SDGs) commencing in 2015. A sustainable city/community is expected to contain sufficient business and career opportunities and provide a safe and affordable living environment and sturdy societal and economic development. Sustainability itself is directly linked to a broad range of activities, including optimizing public transportation, creating/reserving green living spaces, and improving natural resource management and urban planning, which are closely linked to physical and built environments, social–economic development, characteristics of human population, and public policy. Geospatial technologies have been widely used in assessing natural resources and environmental conditions, evaluating urbanization and associated impacts and analysis and modeling of sustainability of cities and communities. Since 2012, the Group on Earth Observations have developed numerous programs and initiatives (e.g., Global Urban Observation and Information Initiative) to coordinate the activities of participating organizations and countries to cope with the environmental and societal challenges of sustainability concerns. Numerous user engagement efforts have been made to facilitate the dissemination of EO-based data products, models, systems, tools, and services in support of the UN’s SDGs.

This Special Issue invites manuscripts that demonstrate state-of-the-art geospatial technologies, in particular, global urban observation methods and techniques, in addressing issues related to sustainable cities and communities. Potential topics include but are not limited to the following:

SDG Goal 11: Sustainable Cities and Communities;
Natural resources and environmental sustainability;
Land use, infrastructure, and transportation assessment;
Urban natural disaster assessment and public safety;
Urban exposure, disease outbreak, and environmental and public health;
Urban energy conservation, biodiversity, and green cities;
Affordable and healthy living environment, and public participation in planning.

Assoc. Prof. Hua Liu
Prof. Qihao Weng
Dr. Umamaheshwaran Rajasekar
Dr. Li Zhang
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.

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Published Papers (7 papers)

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Research

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23 pages, 9458 KiB

Open AccessArticle

Satellite Monitoring of the Urban Expansion in the Pearl River–Xijiang Economic Belt and the Progress towards SDG11.3.1

by Shuyue Liu, Yan Yan and Baoqing Hu

Remote Sens. 2023, 15(21), 5209; https://doi.org/10.3390/rs15215209 - 2 Nov 2023

Cited by 3 | Viewed by 1062

Abstract

Quantitative analysis of the spatiotemporal pattern of urban expansion and forecasting of the progress towards SDG11.3.1 are of great significance for the promotion of sustainable urban development. This study employed the spatiotemporal normalized threshold method to extract urban built-up areas in the Pearl River–Xijiang Economic Belt based on night-time light data and investigated the intricate patterns of urban expansion from 2000 to 2020. Then, the historical trends of the SDG11.3.1 indicators within the economic belt were evaluated, and future urban built-up areas were predicted based on the SSP1 scenario. The results indicate the following: (1) Built-up area extraction has an overall accuracy that exceeds 97% and G-mean values that all surpass 82%, indicating the high accuracy of the method. (2) The Pearl River–Xijiang Economic Belt demonstrates evident urban expansion trends, albeit with uneven development. The urban area of the economic belt has expanded from 1020.29 km² to 3826.87 km², the expansion direction of each city is different, and the center of gravity of the economic belt has moved to the southeast. (3) During the period from 2008 to 2020, the entire economic belt experienced a situation where the urban expansion rate was lower than the population growth rate, and there was an imbalance in urban development (LCRPGR = 0.33). However, looking ahead to the period from 2020 to 2030, the average LCRPGR for the entire economic belt shows a significant upward trend, approaching the ideal state of sustainable development (LCRPGR ≈ 1). Full article

(This article belongs to the Special Issue Global Urban Observation for SDG Goal 11: Sustainable Cities and Communities)

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25 pages, 7452 KiB

Open AccessArticle

Multi-Scale Spatiotemporal Pattern Analysis and Simulation (MSPAS) Model with Driving Factors for Land Cover Change and Sustainable Development Goals: A Case Study of Nepal

by Wenqi Jia, Xingfa Gu, Xiaofei Mi, Jian Yang, Wenqian Zang, Peizhuo Liu, Jian Yan, Hongbo Zhu, Xuming Zhang and Zhouwei Zhang

Remote Sens. 2022, 14(24), 6295; https://doi.org/10.3390/rs14246295 - 12 Dec 2022

Cited by 3 | Viewed by 1823

Abstract

In pursuit of Sustainable Development Goals (SDGs), land cover change (LCC) has been utilized to explore different dynamic processes such as farmland abandonment and urban expansion. The study proposed a multi-scale spatiotemporal pattern analysis and simulation (MSPAS) model with driving factors for SDGs. With population information from the census, multi-scale analysis criteria were designed using the combination of administrative and regional divisions, i.e., district, province, nation and ecological region. Contribution and correlation of LCC or population were quantified between multiple scales. Different kinds of driving factors were explored in the pattern analysis and then utilized for the definition of adaptive land suitability rules using the Cellular Automata-Markov (CA-Markov) simulation. As a case study of the MSPAS model, Nepal entered into a new era by the establishment of a Federal Republic in 2015. The model focused on four specific land cover classes of urban, farmland, forest and grassland to explore the pattern of Nepal’s LCC from 2016 to 2019. The result demonstrated the performance of the MSPAS model. The spatiotemporal pattern had consistency, and characteristics between multiple scales and population were related to LCC. Urban area nearly doubled while farmland decreased by 3% in these years. Urban areas expanded at the expense of farmland, especially in Kathmandu and some districts of the Terai region, which tended to occur on flat areas near the existing urban centers or along the roads. Farmland abandonment was relatively intense with scattered abandoned areas widely distributed in the Hill region under conditions of steep topography and sparse population. The MSPAS model can provide references for the development of sustainable urbanization and agriculture in SDGs. Full article

(This article belongs to the Special Issue Global Urban Observation for SDG Goal 11: Sustainable Cities and Communities)

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20 pages, 10852 KiB

Open AccessArticle

The Spatial Relationship and Evolution of World Cultural Heritage Sites and Neighbouring Towns

by Yihan Xie, Ruixia Yang, Yongqi Liang, Wei Li and Fulong Chen

Remote Sens. 2022, 14(19), 4724; https://doi.org/10.3390/rs14194724 - 21 Sep 2022

Cited by 11 | Viewed by 2620

Abstract

The past few decades have witnessed unprecedented global urbanisation, with direct or indirect impacts on global cultural heritage sites. Research on the spatial relationship between cultural heritage sites and urban areas has provided a new perspective for understanding the impact processes between them, which have previously been discussed at the regional scale. In this article, we analyse the spatial relationship between world cultural heritage sites and neighbouring towns through systematic observations at the global scale and attempt to model change processes and identify impact mechanisms. We adopt spatial analysis and spatial statistics to analyse the changing characteristics of the spatial relationship between world cultural heritage sites and neighbouring towns from 1990 to 2018 and to analyse the impact processes at different spatial and temporal scales by combining indicators, such as income levels and urbanisation rates, at the national scale. The results show that 8.52% of world cultural heritage sites have been incorporated into urban areas over the aforementioned 28 years, with a certain aggregation in the spatial distribution of these sites, and that the growth rate can be divided into three phases, including two periods of rapid growth. The spatial relationship between towns and the 523 world cultural heritage sites that were previously located outside towns has not yet changed substantially, but the distances between most of the towns and these sites have been decreasing, with 81% of the world cultural heritage sites having a variation in distance from the corresponding town of 7.60 km or less. We also analysed the variation in distance between cultural heritage sites and neighbouring towns and found a relationship with indicators, such as the income level and urbanisation rate of the country to which a site belongs. Among the indicators, variation in national urbanisation rates most greatly affected the distance between heritage sites and towns. This study shows that world cultural heritage sites are affected by urbanisation and that particular attention should be given to the relationship between cultural heritage sites and neighbouring towns, especially in countries undergoing rapid urbanisation, so that the authenticity and integrity of cultural heritage are not compromised. This article provides a basis for development plans and policies in urban design, especially those that are sensitive to cultural heritage, and may also provide ideas and references for heritage conservation against the background of urbanisation. Full article

(This article belongs to the Special Issue Global Urban Observation for SDG Goal 11: Sustainable Cities and Communities)

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28 pages, 7316 KiB

Open AccessArticle

EO4Migration: The Design of an EO-Based Solution in Support of Migrants’ Inclusion and Social-Cohesion Policies

by Mariella Aquilino, Cristina Tarantino, Eleni Athanasopoulou, Evangelos Gerasopoulos, Palma Blonda, Giuliana Quattrone, Silvana Fuina and Maria Adamo

Remote Sens. 2022, 14(17), 4295; https://doi.org/10.3390/rs14174295 - 31 Aug 2022

Cited by 2 | Viewed by 1973

Abstract

The purpose of this research is to demonstrate the strong potential of Earth-observation (EO) data and techniques in support of migration policies, and to propose actions to fill the existing structural gaps. The work was carried out within the “Smart URBan Solutions for air quality, disasters and city growth” (SMURBS, ERA-PLANET/H2020) project. The novelties introduced by the implemented solutions are based on the exploitation and synergy of data from different EO platforms (satellite, aerial, and in situ). The migration theme is approached from different perspectives. Among these, this study focuses on the design process of an EO-based solution for tailoring and monitoring the SDG 11 indicators in support of those stakeholders involved in migration issues, evaluating the consistency of the obtained results by their compliance with the pursued objective and the current policy framework. Considering the city of Bari (southern Italy) as a case study, significant conclusions were derived with respect to good practices and obstacles during the implementation and application phases. These were considered to deliver an EO-based proposal to address migrants’ inclusion in urban areas, and to unfold the steps needed for replicating the solution in other cities within and outside Europe in a standardized manner. Full article

(This article belongs to the Special Issue Global Urban Observation for SDG Goal 11: Sustainable Cities and Communities)

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26 pages, 8372 KiB

Open AccessArticle

Contributing Factors and Trend Prediction of Urban-Settled Population Distribution Based on Human Perception Measurement: A Study on Beijing, China

by Junnan Qi, Qingyan Meng, Linlin Zhang, Xuemiao Wang, Jianfeng Gao, Linhai Jing and Tamás Jancsó

Remote Sens. 2022, 14(16), 3965; https://doi.org/10.3390/rs14163965 - 15 Aug 2022

Cited by 5 | Viewed by 2418

Abstract

Population migration, accompanied by urbanization, has led to an increase in the urban-settled population. However, quantitative studies on the distribution of urban-settled population, especially at fine scale, are limited. This study explored the relationship between characteristics of human perceived environment and the distribution of settled population, and proposed a quantitative method to predict the distribution trend of settled population. Through the semantic segmentation of street view images and accessibility calculation based on traffic isochrone and points-of-interest, we determined human perception factors. The influence of human perception factors was quantified using the geographic detector method, and the settlement intention index (SII) was constructed combining the analytic hierarchy process to predict the distribution trend of settled population. The results indicated the following. (1) Human perception was one of the important factors influencing the distribution of urban-settled population, and the cycling accessibility to traffic facilities was closely related to the distribution of settled population. (2) The accessibility and visibility of green space with low independent influence portrayed a strong enhancement on the interactive effect of other perception factors. (3) The SII mapping of Beijing showed that the SII was reliable. This study analyzes the role of human perception in shaping the environment, and provides reference for population-related urban planning problems. Full article

(This article belongs to the Special Issue Global Urban Observation for SDG Goal 11: Sustainable Cities and Communities)

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17 pages, 6471 KiB

Open AccessArticle

Uncertainty-Aware Interpretable Deep Learning for Slum Mapping and Monitoring

by Thomas Fisher, Harry Gibson, Yunzhe Liu, Moloud Abdar, Marius Posa, Gholamreza Salimi-Khorshidi, Abdelaali Hassaine, Yutong Cai, Kazem Rahimi and Mohammad Mamouei

Remote Sens. 2022, 14(13), 3072; https://doi.org/10.3390/rs14133072 - 26 Jun 2022

Cited by 19 | Viewed by 6326

Abstract

Over a billion people live in slums, with poor sanitation, education, property rights and working conditions having a direct impact on current residents and future generations. Slum mapping is one of the key problems concerning slums. Policymakers need to delineate slum settlements to make informed decisions about infrastructure development and allocation of aid. A wide variety of machine learning and deep learning methods have been applied to multispectral satellite images to map slums with outstanding performance. Since the physical and visual manifestation of slums significantly varies with geographical region and comprehensive slum maps are rare, it is important to quantify the uncertainty of predictions for reliable and confident application of models to downstream tasks. In this study, we train a U-Net model with Monte Carlo Dropout (MCD) on 13-band Sentinel-2 images, allowing us to calculate pixelwise uncertainty in the predictions. The obtained outcomes show that the proposed model outperforms the previous state-of-the-art model, having both higher AUPRC and lower uncertainty when tested on unseen geographical regions of Mumbai using the regional testing framework introduced in this study. We also use SHapley Additive exPlanations (SHAP) values to investigate how the different features contribute to our model’s predictions which indicate a certain shortwave infrared image band is a powerful feature for determining the locations of slums within images. With our results, we demonstrate the usefulness of including an uncertainty quantification approach in detecting slum area changes over time. Full article

(This article belongs to the Special Issue Global Urban Observation for SDG Goal 11: Sustainable Cities and Communities)

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Review

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20 pages, 416 KiB

Open AccessReview

Remote Sensing of Urban Poverty and Gentrification

by Li Lin, Liping Di, Chen Zhang, Liying Guo and Yahui Di

Remote Sens. 2021, 13(20), 4022; https://doi.org/10.3390/rs13204022 - 9 Oct 2021

Cited by 21 | Viewed by 7482

Abstract

In the past few decades, most urban areas in the world have been facing the pressure of an increasing population living in poverty. A recent study has shown that up to 80% of the population of some cities in Africa fall under the poverty line. Other studies have shown that poverty is one of the main contributors to residents’ poor health and social conflict. Reducing the number of people living in poverty and improving their living conditions have become some of the main tasks for many nations and international organizations. On the other hand, urban gentrification has been taking place in the poor neighborhoods of all major cities in the world. Although gentrification can reduce the poverty rate and increase the GDP and tax revenue of cities and potentially bring opportunities for poor communities, it displaces the original residents of the neighborhoods, negatively impacting their living and access to social services. In order to support the sustainable development of cities and communities and improve residents’ welfare, it is essential to identify the location, scale, and dynamics of urban poverty and gentrification, and remote sensing can play a key role in this. This paper reviews, summarizes, and evaluates state-of-the-art approaches for identifying and mapping urban poverty and gentrification with remote sensing, GIS, and machine learning techniques. It also discusses the pros and cons of remote sensing approaches in comparison with traditional approaches. With remote sensing approaches, both spatial and temporal resolutions for the identification of poverty and gentrification have been dramatically increased, while the economic cost is significantly reduced. Full article

(This article belongs to the Special Issue Global Urban Observation for SDG Goal 11: Sustainable Cities and Communities)

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