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Remote Sensing and GIS for Monitoring Urbanization and Urban Health

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 17862

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Special Issue Editors

Prof. Dr. Xinhu Li

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School of Spatial Planning and Design, Zhejiang University City College, Hangzhou 31001, China
Interests: big health; spatial data analysis; urban health; remote sensing applications; temporal and spatial perception calculations

Prof. Dr. Shihong Du

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School of Earth and Space Sciences, Peking University, Beijing 100871, China
Interests: image classification; spatial analysis; deep learning; sample learning; urban landscape
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Peng Jia

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School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China
Interests: health geography; spatial epidemiology; spatial life course epidemiology; urban health; GIS; remote sensing; artificial intelligence
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Prof. Dr. Franz W. Gatzweiler

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Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Interests: urban health and wellbeing; ecological and institutional economics of social and ecological system
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Dr. Jinchao Song

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Department of Green Technology (IGT), Life Cycle Engineering, University of Southern Denmark, Odense, Denmark
Interests: urban functional zone classification; traffic congestion; traffic-related air pollution; surface energy balance; global mapping on GEE

Special Issue Information

Dear Colleagues,

Urbanization is one of the leading global trends of the 21st century and has a significant impact on health. Many people living in cities have to deal with inadequate housing and transport, poor sanitation and waste management, and environmental pollution. Noise, water and soil contamination, a lack of space for walking, urban heat island and extreme weather driven by climate change are all harmful to the health of city residents. With the increasing abundance of remote sensing data, related GIS spatial analysis technologies are emerging endlessly. These data and technologies are increasingly used in urban and health research.

The aim of this Special Issue is to present the latest research findings and ideas with respect to the application of remote sensing and GIS in monitoring urbanization and urban health to readers globally. The inclusion of these research studies on this specific theme in our journal will provide valuable information for urbanization and urban health based on the application of remote sensing and GIS. Case studies, innovative methods, system reviews and perspectives are all welcomed.

Prof. Dr. Xinhu Li
Prof. Dr. Shihong Du
Prof. Dr. Peng Jia
Prof. Dr. Franz W. Gatzweiler
Dr. Jinchao Song
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

urbanization and health both regionally and globally
urban landscape, urban ecological system and health
territorial spatial planning and health city
urbanization speed and acceleration, spatial-temporal analysis of urbanization
green space/blue space/gray space remote sensing monitoring
map of urban development and evolution
urban environment changes and health effects
remote sensing GIS big data and urban health
remote sensing and GIS monitoring, simulating and predicting of infectious diseases, such as COVID-19

Published Papers (9 papers)

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Research

24 pages, 15718 KiB

Open AccessArticle

The Assessment of Industrial Agglomeration in China Based on NPP-VIIRS Nighttime Light Imagery and POI Data

by Zuoqi Chen, Wenxiang Xu and Zhiyuan Zhao

Remote Sens. 2024, 16(2), 417; https://doi.org/10.3390/rs16020417 - 21 Jan 2024

Cited by 2 | Viewed by 821

Abstract

Industrial agglomeration, as a typical aspect of industrial structures, significantly influences policy development, economic growth, and regional employment. Due to the collection limitations of gross domestic product (GDP) data, the traditional assessment of industrial agglomeration usually focused on a specific field or region. To better measure industrial agglomeration, we need a new proxy to estimate GDP data for different industries. Currently, nighttime light (NTL) remote sensing data are widely used to estimate GDP at diverse scales. However, since the light intensity from each industry is mixed, NTL data are being adopted less to estimate different industries’ GDP. To address this, we selected an optimized model from the Gaussian process regression model and random forest model to combine Suomi National Polar-Orbiting Partnership—Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data and points-of-interest (POI) data, and successfully estimated the GDP of eight major industries in China for 2018 with an accuracy (R²) higher than 0.80. By employing the location quotient to measure industrial agglomeration, we found that a dominated industry had an obvious spatial heterogeneity. The central and eastern regions showed a developmental focus on industry and retail as local strengths. Conversely, many western cities emphasized construction and transportation. First-tier cities prioritized high-value industries like finance and estate, while cities rich in tourism resources aimed to enhance their lodging and catering industries. Generally, our proposed method can effectively measure the detailed industry agglomeration and can enhance future urban economic planning. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Monitoring Urbanization and Urban Health)

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16 pages, 3276 KiB

Open AccessArticle

Highway Ecological Environmental Assessment Based on Modified Remote Sensing Index—Taking the Lhasa–Nyingchi Motorway as an Example

by Xinghan Wang, Qi Liu, Pengfei Jia, Xifeng Huang, Jianhua Yang, Zhengjun Mao and Shengyu Shen

Remote Sens. 2024, 16(2), 265; https://doi.org/10.3390/rs16020265 - 10 Jan 2024

Viewed by 650

Abstract

The Lhasa to Nyingchi Expressway in Xizang made efforts to protect the ecological environment during its construction, but it still caused varying degrees of damage to the fragile ecosystems along the route. Accurately assessing the process of change in the ecological environment quality in this region holds significant research value. This study selected the Linzhi-to-Gongbo’gyamda section of the Lhasa-to-Nyingchi Expressway as the research area. Firstly, based on the remote sensing ecological index (RSEI), this study constructed an ecological environmental quality evaluation system for the Xizang region. Subsequently, using the Google Earth Engine (GEE) platform, sub-indicators were extracted, and the combination weighting method of game theory was employed to determine indicator weights. This process resulted in the calculation of the MRSEI for the study area from 2012 to 2020. Finally, by utilizing the spatial distribution of the MRSEI, monitoring the level of MRSEI changes, and employing the transition matrix, this study analyzed the changing trend of the ecological environmental quality from 2012 to 2020. The results indicate that the MRSEI are 0.5885, 0.5951, 0.5296, 0.6202, 0.59, 0.5777, 0.5898, 0.5703, and 0.5987, showing a gradual increasing trend with an initial decrease followed by an ascent. This trend is mainly attributed to concentrated road construction and subsequent ecological restoration, leading to an improvement in the restoration effect. Simultaneously, the ecological environmental quality remains relatively stable, with 69.5% of the region showing no change, and the remaining 30.5% experiencing improvement exceeding degradation. Specifically, there were significant improvements in the land with ecological quality levels categorized as poor, fair, moderate, and good. The types of degradation primarily involved lands originally classified as excellent and good degrading to good and moderate levels, respectively. The above results serve as a theoretical reference for the ecological restoration project of the Lhasa-to-Nyingchi Expressway. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Monitoring Urbanization and Urban Health)

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18 pages, 6951 KiB

Open AccessEditor’s ChoiceArticle

Exploring the Relationship between Temporal Fluctuations in Satellite Nightlight Imagery and Human Mobility across Africa

by Grant Rogers, Patrycja Koper, Cori Ruktanonchai, Nick Ruktanonchai, Edson Utazi, Dorothea Woods, Alexander Cunningham, Andrew J. Tatem, Jessica Steele, Shengjie Lai and Alessandro Sorichetta

Remote Sens. 2023, 15(17), 4252; https://doi.org/10.3390/rs15174252 - 30 Aug 2023

Cited by 3 | Viewed by 1436

Abstract

Mobile phone data have been increasingly used over the past decade or more as a pretty reliable indicator of human mobility to measure population movements and the associated changes in terms of population presence and density at multiple spatial and temporal scales. However, given the fact mobile phone data are not available everywhere and are generally difficult to access and share, mostly because of commercial restrictions and privacy concerns, more readily available data with global coverage, such as night-time light (NTL) imagery, have been alternatively used as a proxy for population density changes due to population movements. This study further explores the potential to use NTL brightness as a short-term mobility metric by analysing the relationship between NTL and smartphone-based Google Aggregated Mobility Research Dataset (GAMRD) data across twelve African countries over two periods: 2018–2019 and 2020. The data were stratified by a measure of the degree of urbanisation, whereby the administrative units of each country were assigned to one of eight classes ranging from low-density rural to high-density urban. Results from the correlation analysis, between the NTL Sum of Lights (SoL) radiance values and three different GAMRD-based flow metrics calculated at the administrative unit level, showed significant differences in NTL-GAMRD correlation values across the eight rural/urban classes. The highest correlations were typically found in predominantly rural areas, suggesting that the use of NTL data as a mobility metric may be less reliable in predominantly urban settings. This is likely due to the brightness saturation and higher brightness stability within the latter, showing less of an effect than in rural or peri-urban areas of changes in brightness due to people leaving or arriving. Human mobility in 2020 (during COVID-19-related restrictions) was observed to be significantly different than in 2018–2019, resulting in a reduced NTL-GAMRD correlation strength, especially in urban settings, most probably because of the monthly NTL SoL radiance values remaining relatively similar in 2018–2019 and 2020 and the human mobility, especially in urban settings, significantly decreasing in 2020 with respect to the previous considered period. The use of NTL data on its own to assess monthly mobility and the associated fluctuations in population density was therefore shown to be promising in rural and peri-urban areas but problematic in urban settings. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Monitoring Urbanization and Urban Health)

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18 pages, 16009 KiB

Open AccessArticle

Landslide Hazard Assessment in Highway Areas of Guangxi Using Remote Sensing Data and a Pre-Trained XGBoost Model

by Yuze Zhang, Lei Deng, Ying Han, Yunhua Sun, Yu Zang and Minlu Zhou

Remote Sens. 2023, 15(13), 3350; https://doi.org/10.3390/rs15133350 - 30 Jun 2023

Cited by 5 | Viewed by 1013

Abstract

This study presents a novel method for assessing landslide hazards along highways using remote sensing and machine learning. We extract geospatial features such as slope, aspect, and rainfall over Guangxi, China, and apply an extreme gradient boosting model pre-trained on contiguous United States datasets. The model produces susceptibility maps that indicate landslide probability at different scales. However, the lack of accurate data on historical landslides in Guangxi challenges the model evaluation and comparison between regions. To overcome this, we calibrate the model to fit the local conditions in Guangxi. The calibrated model agrees with the observed landslide locations, implying its capability to capture regional variations in landslide mechanisms. We apply the model at a 30 m resolution along the Heba Expressway and validate it against reports from July 2021 to March 2022. The model correctly predicts five of seven landslide events in this period with a reasonable alarm rate. This framework has the potential for large-scale landslide risk management by informing transportation planning and infrastructure maintenance decisions. More data on landslide timing and human disturbance events may improve the model’s accuracy across diverse geographical areas and terrains. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Monitoring Urbanization and Urban Health)

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22 pages, 4532 KiB

Open AccessArticle

Coupling Random Forest, Allometric Scaling, and Cellular Automata to Predict the Evolution of LULC under Various Shared Socioeconomic Pathways

by Jiangfu Liao, Lina Tang and Guofan Shao

Remote Sens. 2023, 15(8), 2142; https://doi.org/10.3390/rs15082142 - 18 Apr 2023

Cited by 2 | Viewed by 1330

Abstract

Accurately estimating land-use demand is essential for urban models to predict the evolution of urban spatial morphology. Due to the uncertainties inherent in socioeconomic development, the accurate forecasting of urban land-use demand remains a daunting challenge. The present study proposes a modeling framework to determine the scaling relationship between the population and urban area and simulates the spatiotemporal dynamics of land use and land cover (LULC). An allometric scaling (AS) law and a Markov (MK) chain are used to predict variations in LULC. Random forest (RF) and cellular automata (CA) serve to calibrate the transition rules of change in LULC and realize its micro-spatial allocation (MKCA_RF-AS). Furthermore, this research uses several shared socioeconomic pathways (SSPs) as scenario storylines. The MKCA_RF-AS model is used to predict changes in LULC under various SSP scenarios in Jinjiang City, China, from 2020 to 2065. The results show that the figure of merit (FoM) and the urban FoM of the MKCA_RF-AS model improve by 3.72% and 4.06%, respectively, compared with the MKCA_ANN model during the 2005–2010 simulation period. For a 6.28% discrepancy between the predicted urban land-use demand and the actual urban land-use demand over the period 2005–2010, the urban FoM degrades by 21.42%. The growth of the permanent urban population and urban area in Jinjiang City follows an allometric scaling law with an exponent of 0.933 for the period 2005–2020, and the relative residual and R² are 0.0076 and 0.9994, respectively. From 2020 to 2065, the urban land demand estimated by the Markov model is 19.4% greater than the urban area predicted under scenario SSP5. At the township scale, the different SSP scenarios produce significantly different spatial distributions of urban expansion rates. By coupling random forest and allometric scaling, the MKCA_RF-AS model substantially improves the simulation of urban land use. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Monitoring Urbanization and Urban Health)

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16 pages, 10222 KiB

Open AccessCommunication

Time Ring Data: Definition and Application in Spatio-Temporal Analysis of Urban Expansion and Forest Loss

by Xin Liu, Xinhu Li and Haijun Bao

Remote Sens. 2023, 15(4), 972; https://doi.org/10.3390/rs15040972 - 10 Feb 2023

Viewed by 1502

Abstract

Remote sensing can provide spatio-temporal continuous Earth observation data and is becoming the main data source for spatial and temporal analysis. Remote sensing data have been widely used in applications such as meteorological monitoring, forest investigation, environmental health, urban planning, and water conservancy. While long-time-series remote sensing data are used for spatio-temporal analysis, this analysis is usually limited because of the large data volumes and complex models used. This study intends to develop an innovative and simple approach to reveal the spatio-temporal characteristics of geographic features from the perspective of remote sensing data themselves. We defined an efficient remote sensing data structure, namely time ring (TR) data, to depict the spatio-temporal dynamics of two common geographic features. One is spatially expansive features. Taking nighttime light (NTL) as an example, we generated a NTL TR map to exhibit urban expansion with spatial and temporal information. The speed and acceleration maps of NTL TR data indicated extraordinary expansion in the last 10 years, especially in coastal cities and provincial capitals. Beijing, Tianjin, Hebei Province, Shandong Province, and Jiangsu Province exhibited fast acceleration of urbanization. The other is spatially contractive features. We took forest loss in the Amazon basin as an example and produced a forest cover TR map. The speed and acceleration were mapped in two 10-year periods (2000–2010 and 2010–2020) in order to observe the changes in Amazon forest cover. Then, combining cropland TR data, we determined the consistency of the spatio-temporal variations and used a linear regression model to detect the association between the acceleration of cropland and forest. The forest TR map showed that, spatially, there was an apparent phenomenon of forest loss occurring in the southern and eastern Amazon basin. Temporally, the speed of forest loss was more drastic between 2000 and 2010 than that in 2010–2020. In addition, the acceleration of forest loss showed a dispersed distribution, except for in Bolivia, which demonstrated a concentrated regional acceleration. The R-squared value of the linear regression between forest and cropland acceleration reached 0.75, indicating that forest loss was closely linked to the expansion of cropland. The TR data defined in this study not only optimized the use of remote sensing data, but also facilitated their application in spatio-temporal integrative analysis. More importantly, multi-field TR data could be jointly applied to explore the driving force at spatial and temporal scales. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Monitoring Urbanization and Urban Health)

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19 pages, 13502 KiB

Open AccessArticle

MSL-Net: An Efficient Network for Building Extraction from Aerial Imagery

by Yue Qiu, Fang Wu, Jichong Yin, Chengyi Liu, Xianyong Gong and Andong Wang

Remote Sens. 2022, 14(16), 3914; https://doi.org/10.3390/rs14163914 - 12 Aug 2022

Cited by 14 | Viewed by 2273

Abstract

There remains several challenges that are encountered in the task of extracting buildings from aerial imagery using convolutional neural networks (CNNs). First, the tremendous complexity of existing building extraction networks impedes their practical application. In addition, it is arduous for networks to sufficiently utilize the various building features in different images. To address these challenges, we propose an efficient network called MSL-Net that focuses on both multiscale building features and multilevel image features. First, we use depthwise separable convolution (DSC) to significantly reduce the network complexity, and then we embed a group normalization (GN) layer in the inverted residual structure to alleviate network performance degradation. Furthermore, we extract multiscale building features through an atrous spatial pyramid pooling (ASPP) module and apply long skip connections to establish long-distance dependence to fuse features at different levels of the given image. Finally, we add a deformable convolution network layer before the pixel classification step to enhance the feature extraction capability of MSL-Net for buildings with irregular shapes. The experimental results obtained on three publicly available datasets demonstrate that our proposed method achieves state-of-the-art accuracy with a faster inference speed than that of competing approaches. Specifically, the proposed MSL-Net achieves 90.4%, 81.1% and 70.9% intersection over union (IoU) values on the WHU Building Aerial Imagery dataset, Inria Aerial Image Labeling dataset and Massachusetts Buildings dataset, respectively, with an inference speed of 101.4 frames per second (FPS) for an input image of size 3 × 512 × 512 on an NVIDIA RTX 3090 GPU. With an excellent tradeoff between accuracy and speed, our proposed MSL-Net may hold great promise for use in building extraction tasks. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Monitoring Urbanization and Urban Health)

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18 pages, 6450 KiB

Open AccessArticle

Air Pollution and Human Health: Investigating the Moderating Effect of the Built Environment

by Chenglong Wang, Yunliang Sheng, Jiaming Wang, Yiyi Wang, Peng Wang and Lei Huang

Remote Sens. 2022, 14(15), 3703; https://doi.org/10.3390/rs14153703 - 02 Aug 2022

Cited by 11 | Viewed by 3476

Abstract

Air pollution seriously threatens human health and even causes mortality. It is necessary to explore effective prevention methods to mitigate the adverse effect of air pollution. Shaping a reasonable built environment has the potential to benefit human health. In this context, this study quantified the built environment, air pollution, and mortality at 1 km × 1 km grid cells. The moderating effect model was used to explore how built environment factors affect the impact of air pollution on cause-specific mortality and the heterogeneity in different areas classified by building density and height. Consequently, we found that greenness played an important role in mitigating the effect of ozone (O₃) and nitrogen dioxide (NO₂) on mortality. Water area and diversity of land cover can reduce the effect of fine particulate matter (PM_2.5) and NO₂ on mortality. Additionally, gas stations, edge density (ED), perimeter-area fractal dimension (PAFRAC), and patch density (PD) can reduce the effect of NO₂ on mortality. There is heterogeneity in the moderating effect of the built environment for different cause-specific mortality and areas classified by building density and height. This study can provide support for urban planners to mitigate the adverse effect of air pollution from the perspective of the built environment. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Monitoring Urbanization and Urban Health)

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17 pages, 4710 KiB

Open AccessArticle

Evaluating the Spatial Risk of Bacterial Foodborne Diseases Using Vulnerability Assessment and Geographically Weighted Logistic Regression

by Wanchao Bian, Hao Hou, Jiang Chen, Bin Zhou, Jianhong Xia, Shanjuan Xie and Ting Liu

Remote Sens. 2022, 14(15), 3613; https://doi.org/10.3390/rs14153613 - 28 Jul 2022

Cited by 5 | Viewed by 1916

Abstract

Foodborne diseases are an increasing concern to public health; climate and socioeconomic factors influence bacterial foodborne disease outbreaks. We developed an “exposure–sensitivity–adaptability” vulnerability assessment framework to explore the spatial characteristics of multiple climatic and socioeconomic environments, and analyzed the risk of foodborne disease outbreaks in different vulnerable environments of Zhejiang Province, China. Global logistic regression (GLR) and geographically weighted logistic regression (GWLR) models were combined to quantify the influence of selected variables on regional bacterial foodborne diseases and evaluate the potential risk. GLR results suggested that temperature, total precipitation, road density, construction area proportions, and gross domestic product (GDP) were positively correlated with foodborne diseases. GWLR results indicated that the strength and significance of these relationships varied locally, and the predicted risk map revealed that the risk of foodborne diseases caused by Vibrio parahaemolyticus was higher in urban areas (60.6%) than rural areas (20.1%). Finally, distance from the coastline was negatively correlated with predicted regional risks. This study provides a spatial perspective for the relevant departments to prevent and control foodborne diseases. Full article

(This article belongs to the Special Issue Remote Sensing and GIS for Monitoring Urbanization and Urban Health)

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