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Recent Advances of Urban Development Scenarios Simulation Using Remote Sensing and GIS

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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 (15 May 2023) | Viewed by 22649

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Special Issue Editors

Dr. Kaifang Shi

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Guest Editor

School of Geographical Sciences, Southwest University, Chongqing 400715, China
Interests: urban remote sensing; nighttime light remote sensing; urban geography; environmental sustainability
Special Issues, Collections and Topics in MDPI journals

Dr. Yuanzheng Cui

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Guest Editor

College of Hydrology and Water Resources, Hohai University, Nanjing 210098, China
Interests: remote sensing; urban geography; sustainable development
Special Issues, Collections and Topics in MDPI journals

Dr. Hankui Zhang

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Guest Editor

Geospatial Sciences Center of Excellence (GSCE), 1021 Medary Ave, Wecota Hall 115, Box 506B, Brookings, SD 57007, USA
Interests: remote sensing; machine vision; GIS development
Special Issues, Collections and Topics in MDPI journals

Dr. Zuoqi Chen

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Guest Editor

The Academy of Digital China, Fuzhou University, Fuzhou 350002, China
Interests: nighttime light remote sensing; urban remote sensing; GIS development
Special Issues, Collections and Topics in MDPI journals

Dr. Bin Wu

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Guest Editor

School of Geographic Sciences, East China Normal University, Shanghai 200241, China
Interests: LiDAR; urban remote sensing; spatial–temporal analysis

Dr. Jingwei Shen

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Guest Editor

School of Geographical Sciences, Southwest University, Chongqing 400715, China
Interests: spatiotemporal data mining; environment and health
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urban growth is a spatial and social evolutionary process associated with urban spatial changes, shifts in people’s lifestyles, and demographic changes. Urban development scenario simulation plays a significant role in urban planning and management. The increasing advances in remote sensing (RS) and geographic information system (GIS) technology are changing people’s understanding of urban development. GIS can integrate spatial data from different sources as the input data for urban development simulation, and RS obtains information regarding dynamic urban changes in a high spatial and temporal resolution.

Recently, various models and methods have been employed to predict the urban growth process, such as linear regression models, cellular automata models, system dynamics models, etc. In addition, a series of remote sensing images, including nighttime light (NTL) data, light detection and ranging (LIDAR) data, Landsat Thematic Mapper (TM)/Operational Land Imager and Thermal Infrared Sensor (OLI-TIRS) data, etc., have brought unique perspectives and opportunities for urban simulation research. This Special Issue entitled “Recent Advances of Urban Development Scenarios Simulation using Remote Sensing and GIS” is focused on advances in geo-technologies such as new remote sensing technologies, big geospatial data collections, new sensors, advanced GIS technology, and processing methodologies applied to urban development simulation, and to guiding the management of sustainable urban development. We welcome submissions from urban remote sensing, GIS, urban studies areas, but not limited to:

New methodology in urban development scenario simulation;
Big geospatial data in urban development scenario simulation;
Human activity multidimensional representation in the process of urbanization;
Environmental impact assessment in the process of urbanization;
VGI (volunteered geographic information) in urban development scenario simulation;
Urban development and human health.

Dr. Kaifang Shi
Dr. Yuanzheng Cui
Dr. Hankui Zhang
Dr. Zuoqi Chen
Dr. Bin Wu
Dr. Jingwei Shen
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

Remote sensing
GIS
Scenario simulation
Urban development
Nighttime light data
Urbanization
Urban environmental sustainability
Spatiotemporal dynamics
LiDAR

Related Special Issue

Recent Advances of Urban Development Scenarios Simulation Using Remote Sensing and GIS II in Remote Sensing

Published Papers (10 papers)

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Research

19 pages, 4713 KiB

Open AccessArticle

A New Method for Identifying the Central Business Districts with Nighttime Light Radiance and Angular Effects

by Na Jie, Xin Cao, Jin Chen and Xuehong Chen

Remote Sens. 2023, 15(1), 239; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15010239 - 31 Dec 2022

Cited by 2 | Viewed by 2376

Abstract

Central business districts (CBDs) play a crucial role in urban economic activities. Thus, the location and boundaries of CBDs identified by the unified standards are essential for comparative analyses in urban geography. However, past research mainly applied specific data or sensitive methods to delimitate CBDs within local knowledge in the case study, there remains no automated standardization technique for identifying and delimitating CBDs across the globe. This paper proposed a new method for identifying CBDs based on nighttime lights (NTL) to overcome the above limitations. The main advantages of this method include (1) the use of available high-quality global Black Marble products, which are the basis of a standardized delineation of CBDs and (2) the use of more characteristics of CBD (i.e., the brightness) and NTL negative angular effects that can reflect high-rise building. The proposed method was employed in 14 cities in China and the U.S., and the results showed that China cities needed five NTL indexes and U.S. cities needed two NTL indexes to distinguish CBD and non-CBD successfully. Therefore, our approach is recommended for CBD detection and delineation over large areas. Full article

(This article belongs to the Special Issue Recent Advances of Urban Development Scenarios Simulation Using Remote Sensing and GIS)

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20 pages, 2770 KiB

Open AccessArticle

Spatial Downscaling of NPP-VIIRS Nighttime Light Data Using Multiscale Geographically Weighted Regression and Multi-Source Variables

by Shangqin Liu, Xizhi Zhao, Fuhao Zhang, Agen Qiu, Liujia Chen, Jing Huang, Song Chen and Shu Zhang

Remote Sens. 2022, 14(24), 6400; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14246400 - 19 Dec 2022

Cited by 4 | Viewed by 2347

Abstract

Remote sensing images of nighttime lights (NTL) were successfully used at global and regional scales for various applications, including studies on population, politics, economics, and environmental protection. The Suomi National Polar-orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data has the advantages of high temporal resolution, long coverage time series, and wide spatial range. The spatial resolution of the monthly and annual composite data of NPP-VIIRS NTL is only 500 m, which hinders studies requiring higher resolution. We propose a multi-source spatial variable and Multiscale Geographically Weighted Regression (MGWR)-based method to achieve the downscaling of NPP-VIIRS NTL data. An MGWR downscaling framework was implemented to obtain NTL data at 120 m resolution based on auxiliary data representing socioeconomic or physical geographic attributes. The downscaled NTL data were validated against LuoJia1-01 imagery based on the coefficient of determination (R²) and the root-mean-square error (RMSE). The results suggested that the spatial resolution of the data was enhanced after downscaling, and the MGWR-based downscaling results demonstrated higher R² (R² = 0.9141) and lower RMSE than those of Geographically Weighted Regression and Random Forest-based algorithms. Additionally, MGWR can reveal the different relationships between multiple auxiliary and NTL data. Therefore, this study demonstrates that the spatial resolution of NPP-VIIRS NTL data is improved from 500 m to 120 m upon downscaling, thereby facilitating NTL-based applications. Full article

(This article belongs to the Special Issue Recent Advances of Urban Development Scenarios Simulation Using Remote Sensing and GIS)

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26 pages, 7021 KiB

Open AccessArticle

Tracking Spatiotemporal Patterns of Rwanda’s Electrification Using Multi-Temporal VIIRS Nighttime Light Imagery

by Yuanxi Ru, Xi Li and Wubetu Anley Belay

Remote Sens. 2022, 14(17), 4397; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174397 - 04 Sep 2022

Cited by 2 | Viewed by 1477

Abstract

After recovering from the Rwanda Genocide in the last century, Rwanda is experiencing rapid economic growth and urban expansion. With increasing demand for electricity and a strong desire to achieve the Sustainable Development Goals (SDGs), it is significant to further investigate the electrification progress in Rwanda. This study analyzes the characteristics of electrification in Rwanda from 2012 to 2020 using VIIRS nighttime light imagery. Firstly, by analysis of the nighttime light change patterns on a national scale, we find that the electrification in Rwanda is seriously unbalanced, as electrification progress in Kigali is much faster than that in the rest of the country. Secondly, there is a common phenomenon where power grid expansion in Rwanda fails to keep pace with rapid urbanization, especially in areas with an inadequate electricity infrastructure foundation. Quantitatively, original electricity infrastructure level shows a positive impact on the grid access of new settlements, with an R² value of 0.695 in the linear regression. In addition, new settlements inside the urban boundary tend to achieve more extensive grid access compared to those outside the boundary. Finally, the grid access rates are calculated on multi-spatial scales. By comparing the calculated results with the official electricity access rate data, we analyze the development of off-grid access in Rwanda. The results imply that, since 2016, off-grid access has rapidly developed in Rwanda, especially in the rural areas, playing an important role in achieving the SDGs. Full article

(This article belongs to the Special Issue Recent Advances of Urban Development Scenarios Simulation Using Remote Sensing and GIS)

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20 pages, 6523 KiB

Open AccessArticle

The Regional Disparity of Urban Spatial Expansion Is Greater than That of Urban Socioeconomic Expansion in China: A New Perspective from Nighttime Light Remotely Sensed Data and Urban Land Datasets

by Zhijian Chang, Shirao Liu, Yizhen Wu and Kaifang Shi

Remote Sens. 2022, 14(17), 4348; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174348 - 01 Sep 2022

Cited by 8 | Viewed by 1538

Abstract

The regional disparity of urban expansion varies significantly in China’s different regions, hindering sustainable socioeconomic development. However, most studies to date have focused on a single aspect of urban expansion, e.g., urban spatial expansion (USS) disparity. This study attempts to define urban expansion from USS and urban socioeconomic expansion (USE) based on nighttime light remotely sensed (NTL) data and urban land datasets. Then, taking China’s 241 prefecture-level cities within different provinces as experimental subjects, the Dagum Gini (DG) coefficient and stochastic convergence test are employed to assess the disparity of urban expansion from two different dimensions. The results show that, on the national scale, the regional disparity of USS is always greater than that of USE and has a converging trend. Additionally, regional disparity is the main factor causing the difference between USS and USE, with average contribution rates of 55% and 45%, respectively. The average difference between USS and USE in the eastern region (ER) is greater than 10%, while it is the lowest in the northeastern region (NER) and shows a significant expansion trend in performance convergence with a regression coefficient of 0.0022, followed by the central (CR), eastern, and western (WR) regions. Through the panel unit root test, we found that urban expansion in China in terms of USS and USE has internal random convergence in certain regions under the premise of global random divergence, and there may be differentiation and formation of one or more convergence clubs in the future. Using this novel perspective to define urban expansion, this study quantifies the contributions of USS and USE to regional disparity and provides a scientific basis for governments to implement appropriate approaches to sustainable urban development in different regions. Full article

(This article belongs to the Special Issue Recent Advances of Urban Development Scenarios Simulation Using Remote Sensing and GIS)

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20 pages, 6474 KiB

Open AccessArticle

Assessment of Socioeconomic Dynamics and Electrification Progress in Tanzania Using VIIRS Nighttime Light Images

by Changjun Zhu, Xi Li and Yuanxi Ru

Remote Sens. 2022, 14(17), 4240; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14174240 - 28 Aug 2022

Cited by 2 | Viewed by 1474

Abstract

Tanzania is one of the fastest-growing countries in the world, but it still faces many challenges of unbalanced development. However, Tanzania’s economic assessment studies based on traditional statistics are mostly conducted at the national level, which leaves the details of regional economic disparity and electrification unknown. Despite experiencing one of the fastest urbanizations in the world, there is a lack of research on the match between urbanization and electrification in Tanzania. This study accesses the socioeconomic dynamics in Tanzania using nighttime light images from the Visible Infrared Imaging Radiometer Suite (VIIRS), providing spatiotemporal details for Tanzania’s development. We examined the ability of nighttime light data to evaluate the socioeconomic dynamics in Tanzania and studied regional economic disparity based on the total nighttime light (TNL). Furthermore, the land electrification rate (LER) was defined to study the relationship between urbanization and electrification in Tanzania’s major cities. We found that the LER was less than 0.9 in 2019 and had decreased from 2015 to 2019 in most cities, indicating that the power infrastructure gaps were widespread and growing in major cities. Additionally, we found a negative correlation between the change rate of land electrification and the urban expansion rate, indicating that the construction of power infrastructure has lagged behind the urbanization. We concluded that nighttime light data can effectively provide spatiotemporal details for socioeconomic dynamics in Tanzania. Additionally, our data mining method may be applied to other data-poor countries. Full article

(This article belongs to the Special Issue Recent Advances of Urban Development Scenarios Simulation Using Remote Sensing and GIS)

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15 pages, 7109 KiB

Open AccessArticle

Spatio-Temporal Heterogeneous Impacts of the Drivers of NO₂ Pollution in Chinese Cities: Based on Satellite Observation Data

by Yuanzheng Cui, Hui Zha, Yunxiao Dang, Lefeng Qiu, Qingqing He and Lei Jiang

Remote Sens. 2022, 14(14), 3487; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14143487 - 21 Jul 2022

Cited by 6 | Viewed by 1590

Abstract

Rapid urbanization in China has led to an increasing problem of atmospheric nitrogen dioxide (NO₂) pollution, which negatively impacts urban ecology and public health. Nitrogen dioxide is an important atmospheric pollutant, and quantitative spatio-temporal analysis and influencing factor analysis of Chinese cities can help improve urban air pollution. In this study, the spatio-temporal analysis methods were used to explore the variations of NO₂ pollution in Chinese cities from 2005 to 2020. The findings are as follows. In more than half of Chinese cities, NO₂ levels remarkably decreased between 2005 and 2020. The effective NO₂ reduction strategies contributed to the significant NO₂ reduction during the 13th Five-Year Plan (2016–2020). Moreover, we found that the pandemic of COVID-19 alleviated NO₂ pollution in China since it reduced the traffic, industrial, and living activities. The NO₂ pollution in Chinese cities was found highly spatially clustered. The geographically and temporally weighted regression model was used to analyze the spatio-temporal heterogeneity of NO₂ pollution influencing factors in Chinese cities, including natural meteorological and socio-economic factors. The results showed that the GDPPC, population densities, and ambient air pressure were positively correlated with NO₂ pollution. In contrast, the ratio of the tertiary to the secondary industry, temperature, wind speed, and relative humidity negatively impacted the NO₂ pollution level. The findings of this research contribute to the improvement of urban air quality, stimulating the achievements of the sustainable development goals of Chinese cities. Full article

(This article belongs to the Special Issue Recent Advances of Urban Development Scenarios Simulation Using Remote Sensing and GIS)

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15 pages, 6015 KiB

Open AccessArticle

A Framework for Assessing the Dynamic Coastlines Induced by Urbanization Using Remote Sensing Data: A Case Study in Fujian, China

by Wenting Wu, Yiwei Gao, Chunpeng Chen, Yu Sun and Hua Su

Remote Sens. 2022, 14(12), 2911; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14122911 - 17 Jun 2022

Cited by 7 | Viewed by 1647

Abstract

The coastline plays an important role in indicating the conditions of social-economic development in the coastal zone. In this study, an integrated assessment framework was proposed to address the provincial and county-level spatiotemporal dynamics of continental coastlines from the perspectives of length, position, composition, and anthropogenic utilization quantitatively, and to explore the exact impacts of urbanization on coastline changes in the Fujian Province over the period from 1985 to 2020. Results showed that the total length of coastlines decreased first and then increased due to the different patterns of economic development. The proportion of artificial coastlines and the index of coastal utilization degree increased rapidly during the same period. Moreover, the seaward movement of coastlines due to the coastal reclamation projects resulted in a considerable increment in land areas. The pressure brought by the continuous concentration of population, built-up areas, and industrial districts under the rapid urbanization was the primary factor that increased the degree of anthropogenic disturbances in the coastal zone. Furthermore, the policies issued by the local or central government can be critical tipping points for coastline changes in different periods. Full article

(This article belongs to the Special Issue Recent Advances of Urban Development Scenarios Simulation Using Remote Sensing and GIS)

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24 pages, 7973 KiB

Open AccessArticle

Three-Dimensional Simulation Model for Synergistically Simulating Urban Horizontal Expansion and Vertical Growth

by Linfeng Zhao, Xiaoping Liu, Xiaocong Xu, Cuiming Liu and Keyun Chen

Remote Sens. 2022, 14(6), 1503; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14061503 - 20 Mar 2022

Cited by 6 | Viewed by 2657

Abstract

Urban expansion studies have focused on two-dimensional planar dimensions, ignoring the impact of building height growth changes in the vertical direction on the urban three-dimensional (3D) spatial expansion. Past 3D simulation studies have tended to focus on simulating virtual cities, and a few studies have attempted to build 3D simulation models to achieve the synergistic simulation of real cities. This study proposes an urban 3D spatial expansion simulation model to achieve a synergistic simulation of urban horizontal expansion and vertical growth. The future land use simulation model was used to simulate urban land use changes in the horizontal direction. The random forest (RF) regression algorithm was used to predict building height growth in the vertical direction. Furthermore, the RF algorithm was used to mine the patterns of spatial factors affecting building heights. The 3D model was applied to simulate 3D spatial changes in Shenzhen City from 2014 to 2034. The model effectively simulates the horizontal expansion and vertical growth of a real city in 3D space. The crucial factors affecting building heights and the simulation results of future urban 3D expansion hotspot areas can provide scientific support for decisions in urban spatial planning. Full article

(This article belongs to the Special Issue Recent Advances of Urban Development Scenarios Simulation Using Remote Sensing and GIS)

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21 pages, 4247 KiB

Open AccessArticle

Analyzing Pixel-Level Relationships between Luojia 1-01 Nighttime Light and Urban Surface Features by Separating the Pixel Blooming Effect

by Ji Wu, Zhi Zhang, Xiao Yang and Xi Li

Remote Sens. 2021, 13(23), 4838; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234838 - 28 Nov 2021

Cited by 6 | Viewed by 2087

Abstract

Nighttime light (NTL) remote sensing data can effectively reveal human activities in urban development. It has received extensive attention in recent years, owing to its advantages in monitoring urban socio-economic activities. Due to the coarse spatial resolution and blooming effect, few studies can explain the factors influencing NTL variations at a fine scale. This study explores the relationships between Luojia 1-01 NTL intensity and urban surface features at the pixel level. The Spatial Durbin model is used to measure the contributions of different urban surface features (represented by Points-of-interest (POIs), roads, water body and vegetation) to NTL intensity. The contributions of different urban surface features to NTL intensity and the Pixel Blooming Effect (PIBE) are effectively separated by direct effect and indirect effect (pseudo-R² = 0.915; Pearson correlation = 0.774; Moran’s I = 0.014). The results show that the contributions of different urban surface features to NTL intensity and PIBE are significantly different. Roads and transportation facilities are major contributors to NTL intensity and PIBE. The contribution of commercial area is much lower than that of roads in terms of PIBE. The inhibitory effect of water body is weaker than that of vegetation in terms of NTL intensity and PIBE. For each urban surface feature, the direct contribution to NTL intensity is far less than the indirect contribution (PIBE of total neighbors), but greater than the marginal indirect effect (PIBE of each neighbor). The method proposed in this study is expected to provide a reference for explaining the composition and blooming effect of NTL, as well as the application of NTL data in the urban interior. Full article

(This article belongs to the Special Issue Recent Advances of Urban Development Scenarios Simulation Using Remote Sensing and GIS)

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15 pages, 1899 KiB

Open AccessArticle

Building Function Mapping Using Multisource Geospatial Big Data: A Case Study in Shenzhen, China

by Jionghua Wang, Haowen Luo, Wenyu Li and Bo Huang

Remote Sens. 2021, 13(23), 4751; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234751 - 23 Nov 2021

Cited by 5 | Viewed by 2487

Abstract

Building function labelling plays an important role in understanding human activities inside buildings. This study develops a method of function label classification using integrated features derived from remote sensing and crowdsensing data with an extreme gradient boosting tree (XGBoost). The classification framework is verified based on a dataset from Shenzhen, China. An extended label system for six building types (residential, commercial, office, industrial, public facilities, and others) was applied, and various social functions were considered. The overall classification accuracies were 88.15% (kappa index = 0.72) and 85.56% (kappa index = 0.69). The importance of features was evaluated using the occurrence frequency of features at decision nodes. In the six-category classification system, the basic building attributes (22.99%) and POIs (46.74%) contributed most to the classification process; moreover, the building footprint (7.40%) and distance to roads (11.76%) also made notable contributions. The result shows that it is feasible to extract building environments from POI labels and building footprint geometry with a dimensional reduction model using an autoencoder. Additionally, crowdsensing data (e.g., POI and distance to roads) will become increasingly important as classification tasks become more complicated and the importance of basic building attributes declines. Full article

(This article belongs to the Special Issue Recent Advances of Urban Development Scenarios Simulation Using Remote Sensing and GIS)

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