Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Future Prediction and Scenario Analysis of Urbanization Using Remote Sensing...
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Future Prediction and Scenario Analysis of Urbanization Using Remote Sensing and GIS

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

Deadline for manuscript submissions: 15 July 2024 | Viewed by 9604

Special Issue Editors

Prof. Dr. Yuji Murayama

E-Mail Website
Guest Editor
Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba 305-8572, Japan
Interests: geospatial analysis; GIS; urbanization; urban heat island; urban geography
Special Issues, Collections and Topics in MDPI journals
Dr. Ruci Wang

E-Mail Website
Guest Editor
Center for Environmental Remote Sensing (CEReS), Chiba University, Chiba 260-8670, Japan
Interests: scenario simulation; GIS; remote sensing; urban heat island; land surface temperature
Special Issues, Collections and Topics in MDPI journals
Dr. Ahmed Derdouri

E-Mail Website
Guest Editor
Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba 305-8572, Japan
Interests: GIScience; remote sensing; land change science; urban informatics; urban geography

Special Issue Information

Dear Colleagues,

Urbanization has garnered a lot of attention from researchers across the globe due to its significant adverse effects on several aspects that touch humans directly or indirectly. These include urban climate, natural resources, infrastructure, life quality, and even social stability. These urban expansion impacts are expected to worsen further in the near and long term as the population is projected to reach over 8 billion by 2030 and 10.4 billion by the end of the century—with more than half anticipated to live in urban areas—according to the United Nations. The negative consequences of population growth and urbanization on the environment at the global level may be exacerbated by unplanned shifts in land use/cover (LULC) at the local level, which, in turn, may exacerbate climate change conditions. Therefore, planning properly based on concrete information is the best policy for minimizing such impacts in the long run.

Using GIS and Remote Sensing techniques, scenario-based prediction and simulation of future urbanization trends and their possible impacts on people and urban ecosystem services are essential from a planning perspective. Such invaluable information could aid city planners and decision-makers in determining how to conveniently manage land resources depending on past and current conditions and plan strategies for future cities to fulfill the mission of SDG 11 to "make cities inclusive, resilient, and sustainable." Technique-wise, several methods have been developed and applied successfully for simulating future LULC changes in general and urban growth in particular. One important goal for advancing the field is to make new simulation models that are more accurate, more efficient, and less demanding in terms of inputs and calculation resources. 

This Special Issue focuses on data, methods, techniques, and empirical outcomes of urbanization studies from a time and space perspective. We wish to showcase your research papers, case studies, conceptual or analytic reviews, and policy-relevant articles to help achieve urban sustainability.

Areas of interest include, but are not necessarily limited to:

  • Methodology and dataset for simulating urbanization trends in the future;
  • Impacts of future urbanization on ecosystem services;
  • Novel techniques for land use/cover monitoring and forecasting with remote sensing and GIS;
  • Spatiotemporal mapping of the urbanization process in big cities through empirical studies;
  • The spatial relationship between urban heat island intensity and land use/cover distribution in metropolitan areas;
  • Scenario simulation based on sustainable development goals (SDGs);
  • Spatial differences in land use/cover distribution between developing and developed countries;
  • Urban heat island disaster mitigation and adaptation for future urban sustainability;
  • Prediction and scenario analysis of urbanization for policy and planning.

Prof. Dr. Yuji Murayama
Dr. Ruci Wang
Dr. Ahmed Derdouri
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
  • future prediction
  • scenario simulation
  • land use/cover change
  • GIS
  • remote sensing
  • sustainable development
  • machine learning
  • urban climate
  • urban-rural gradient analysis
  • SDGs
  • time and space
  • public health
  • urban ecosystem services
  • urban living environment
  • urban heat island adaptation
  • urban planning

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

24 pages, 10745 KiB  
Article
Modeling Land Use Transformations and Flood Hazard on Ibaraki’s Coastal in 2030: A Scenario-Based Approach Amid Population Fluctuations
by Mohammadreza Safabakhshpachehkenari and Hideyuki Tonooka
Remote Sens. 2024, 16(5), 898; https://doi.org/10.3390/rs16050898 - 03 Mar 2024
Viewed by 847
Abstract
Coastal areas, influenced by human activity and natural factors, face major environmental shifts, including climate-induced flood risks. This highlights the importance of forecasting coastal land use for effective flood defense and ecological conservation. Japan’s distinct demographic path necessitates flexible strategies for managing its [...] Read more.
Coastal areas, influenced by human activity and natural factors, face major environmental shifts, including climate-induced flood risks. This highlights the importance of forecasting coastal land use for effective flood defense and ecological conservation. Japan’s distinct demographic path necessitates flexible strategies for managing its urban development. The study examines the Ibaraki Coastal region to analyze the impacts of land-use changes in 2030, predicting and evaluating future floods from intensified high tides and waves in scenario-based forecasts. The future roughness map is derived from projected land-use changes, and we utilize this information in DioVISTA 3.5.0 software to simulate flood scenarios. Finally, we analyzed the overlap between simulated floods and each land-use category. The results indicate since 2020, built-up areas have increased by 52.37 sq. km (39%). In scenarios of constant or shrinking urban areas, grassland increased by 28.54 sq. km (42%), and urban land cover decreased by 7.47 sq. km (5.6%) over ten years. Our research examines two separate peaks in water levels associated with urban flooding. Using 2030 land use maps and a peak height of 4 m, which is the lower limit of the maximum run-up height due to storm surge expected in the study area, 4.71 sq. km of residential areas flooded in the urban growth scenario, compared to 4.01 sq. km in the stagnant scenario and 3.96 sq. km in the shrinkage scenario. With the upper limit of 7.2 m, which is the extreme case in most of the study area, these areas increased to 49.91 sq. km, 42.52 sq. km, and 42.31 sq. km, respectively. The simulation highlights future flood-prone urban areas for each scenario, guiding targeted flood prevention efforts. Full article
(This article belongs to the Special Issue Future Prediction and Scenario Analysis of Urbanization Using Remote Sensing and GIS)
►▼ Show Figures

Figure 1

25 pages, 18948 KiB  
Article
Regulatory Effect Evaluation of Warming and Cooling Factors on Urban Land Surface Temperature Based on Multi-Source Satellite Data
by Yuchen Wang, Yu Zhang and Nan Ding
Remote Sens. 2023, 15(20), 5025; https://doi.org/10.3390/rs15205025 - 19 Oct 2023
Viewed by 761
Abstract
Various physical characteristics of urban impervious surfaces (ISAs) and urban green spaces (UGSs) collectively regulate environmental temperatures through heating and cooling processes. However, current research often analyzes each regulating factor as an independent variable when examining its relationship with land surface temperature (LST), [...] Read more.
Various physical characteristics of urban impervious surfaces (ISAs) and urban green spaces (UGSs) collectively regulate environmental temperatures through heating and cooling processes. However, current research often analyzes each regulating factor as an independent variable when examining its relationship with land surface temperature (LST), with limited studies considering the combined contribution weights of all regulating factors. Based on multi-source remote sensing data and ground observations from the near summers of 2014, 2016, 2017, and 2018 in the built-up area of Xuzhou City, numerical values and spatial distributions of 15 regulating factors, including ISA density (fi), land surface albedo (Albedo), population density (Population), anthropogenic heat flux (AHF), maximum ISA patch index (LPIISA), natural connectivity of ISA patches (COHESIONISA), aggregation index of ISA patches (AIISA), average shape index of ISA patches (SHAPE_MNISA), UGS density (fv), evapotranspiration (ET), UGS shading index (UGSSI), maximum UGS patch index (LPIUGS), natural connectivity of UGS patches (COHESIONUGS), aggregation index of UGS patches (AIUGS), and average shape index of UGS patches (SHAPE_MNUGS), were separately extracted within the study area. Using geographically weighted regression models and bivariate spatial autocorrelation models, we separately obtained the quantitative and spatial correlations between the 15 regulating factors and LST. The results revealed that all selected regulating factors exhibited high goodness-of-fit and significant spatial correlations with LST, which led to their categorization into eight warming factors and seven cooling factors. The factor detection of the Geographic Detector further reveals the combined contribution of all regulating factors to LST. The results indicate that cooling factors collectively have higher explanatory power for LST compared to warming factors, with UGSSI contributing the most to LST, while Population contributed the least. Furthermore, the interaction detection results of the Geographic Detector have highlighted variations in the explanatory power of different factor combinations on LST. Ultimately, it has identified factor combinations that have proven to be most effective in mitigating the urban heat environment across three scenarios: warming factors alone, cooling factors alone, and a combination of both warming and cooling factors. The suggested factor combinations are as follows: fi ∩ Albedo, fi ∩ LPIISA, UGSSI ∩ fv, UGSSI ∩ LPIUGS, fi ∩ UGSSI, and Albedo ∩ UGSSI. Therefore, our findings hold the potential to provide a valuable reference for urban planning and climate governance. Tailoring factor combinations to the local context and selecting the most effective ones can enable cost-effective mitigation of the urban heat environment. Full article
(This article belongs to the Special Issue Future Prediction and Scenario Analysis of Urbanization Using Remote Sensing and GIS)
►▼ Show Figures

Figure 1

26 pages, 11195 KiB  
Article
Assessing and Enhancing Predictive Efficacy of Machine Learning Models in Urban Land Dynamics: A Comparative Study Using Multi-Resolution Satellite Data
by Mohammadreza Safabakhshpachehkenari and Hideyuki Tonooka
Remote Sens. 2023, 15(18), 4495; https://doi.org/10.3390/rs15184495 - 12 Sep 2023
Cited by 1 | Viewed by 1496
Abstract
Reliable and accurate land-use/land cover maps are vital for monitoring and mitigating urbanization impacts. This necessitates evaluating machine learning simulations and incorporating valuable insights. We used four primary models, logistic regression (LR), support vector machine, random decision forests, and artificial neural network (ANN), [...] Read more.
Reliable and accurate land-use/land cover maps are vital for monitoring and mitigating urbanization impacts. This necessitates evaluating machine learning simulations and incorporating valuable insights. We used four primary models, logistic regression (LR), support vector machine, random decision forests, and artificial neural network (ANN), to simulate land cover maps for Tsukuba City, Japan. We incorporated an auxiliary input that used multinomial logistic regression to enhance the ANN and obtained a fifth model (ANN was run twice, with and without the new input). Additionally, we developed a sixth simulation by integrating the predictions of ANN and LR using a fuzzy overlay, wherein ANN had an additional new input alongside driving forces. This study employed six models, using classified maps with three different resolutions: the first involved 15 m (ASTER) covering a study area of 114.8 km2, for the second and third, 5 and 0.5 m (derived from WorldView-2 and GeoEye-1) covering a study area of 14.8 km2, and the models were then evaluated. Due to a synergistic effect, the sixth simulation demonstrated the highest kappa in all data, 86.39%, 72.65%, and 70.65%, respectively. The results indicate that stand-alone machine learning-based simulations achieved satisfactory accuracy, and minimalistic approaches can be employed to improve their performance. Full article
(This article belongs to the Special Issue Future Prediction and Scenario Analysis of Urbanization Using Remote Sensing and GIS)
►▼ Show Figures

Figure 1

19 pages, 4992 KiB  
Article
Estimation and Development-Potential Analysis of Regional Housing in Ningbo City Based on High-Resolution Stereo Remote Sensing
by Xiao Du, Li Wang, Feng Tang, Shiguang Xu, Shakir Muhammad, Biswajit Nath and Zheng Niu
Remote Sens. 2023, 15(16), 3953; https://doi.org/10.3390/rs15163953 - 10 Aug 2023
Cited by 1 | Viewed by 979
Abstract
With the challenges brought about by the COVID-19 pandemic, China’s real-estate market has been facing new bottlenecks. The solution lies in an in-depth understanding of regional real-estate conditions. In the study of housing, remote sensing technology can help to extract building height as [...] Read more.
With the challenges brought about by the COVID-19 pandemic, China’s real-estate market has been facing new bottlenecks. The solution lies in an in-depth understanding of regional real-estate conditions. In the study of housing, remote sensing technology can help to extract building height as well as to calculate the number of floors and estimate the total amount of housing. It is more efficient and accurate compared to conventional statistical and sampling methods. Remote sensing is widely used in real-estate research and building height estimation, whereas it is less frequently used for the total estimation of urban housing. In this context, we used Chinese satellite GF-7 stereopair images, point of interest (POI) data, and other data combined with the digital surface model (DSM) and shadow methods to calculate the height of residential buildings. An efficient and accurate method system was then established for estimating the total housing and per capita living area (PCLA). According to the calculation of the PCLA of each district in Ningbo City (China), it was found that different regions were suitable for different development paths. Based on this, the driving factor model was derived and the real-estate development potential of Ningbo city was quantitatively analyzed. The results showed that Ningbo City, a first-tier city with a large population inflow, still has potential for real-estate development. Full article
(This article belongs to the Special Issue Future Prediction and Scenario Analysis of Urbanization Using Remote Sensing and GIS)
►▼ Show Figures

Figure 1

22 pages, 12844 KiB  
Article
An Improved Future Land-Use Simulation Model with Dynamically Nested Ecological Spatial Constraints
by Chaoxu Luan, Renzhi Liu, Jing Sun, Shangren Su and Zhenyao Shen
Remote Sens. 2023, 15(11), 2921; https://doi.org/10.3390/rs15112921 - 03 Jun 2023
Cited by 1 | Viewed by 1242
Abstract
A land-use simulation model oriented toward ecological constraints is effective for evaluating the ecological impact of urban spatial planning. However, few studies have incorporated dynamically nested ecological spatial constraints into the model or fully considered the urban development, agricultural production, and ecological function [...] Read more.
A land-use simulation model oriented toward ecological constraints is effective for evaluating the ecological impact of urban spatial planning. However, few studies have incorporated dynamically nested ecological spatial constraints into the model or fully considered the urban development, agricultural production, and ecological function among the ecological spatial constraints. Therefore, this study developed an improved land-use simulation model with dynamically nested ecological spatial constraints (LSDNE). We fully considered the multilevel ecological spatial constraints from the perspectives of ecological (ecological protection red line, EPRL), production (capital farmland, CF), and living (urban development land-use suitability, UDLS). Five scenarios in terms of future land-use distribution in 2030 were set, namely, inertial development (S1), considering EPRL (S2), considering CF (S3), considering EPRL and CF (S4), and considering EPRL, CF, and UDLS (S5). This new approach was implemented in the rapidly developing provincial capital city of Changchun, China. The results show that, due to the occupation of arable land, Changchun had the largest increase in built-up land (2019.75 km2 to 3036.36 km2) from 2010 to 2020. Terrain elevation was the most significant factor in all kinds of land expansion. According to future land spatial distribution results in 2030, under S4, Changchun’s built-up land will be more compact compared with S1–S3 and S5, which showed more scattered built-up land. These predicted results show that Changchun’s spatial planning put forward high requirements for the efficient use of land and constraints in red-line areas. Due to a clear evaluation of the impact of ecological spatial constraints on future land expansion, the LSDNE model provides more accurate support for the efficient use of land resources and future territorial spatial planning. Full article
(This article belongs to the Special Issue Future Prediction and Scenario Analysis of Urbanization Using Remote Sensing and GIS)
►▼ Show Figures

Graphical abstract

23 pages, 7016 KiB  
Article
Prediction of Land Surface Temperature Considering Future Land Use Change Effects under Climate Change Scenarios in Nanjing City, China
by Lei Tian, Yu Tao, Mingyang Li, Chunhua Qian, Tao Li, Yi Wu and Fang Ren
Remote Sens. 2023, 15(11), 2914; https://doi.org/10.3390/rs15112914 - 02 Jun 2023
Cited by 4 | Viewed by 2714
Abstract
Land use and land cover (LULC) changes resulting from rapid urbanization are the foremost causes of increases in land surface temperature (LST) in urban areas. Exploring the impact of LULC changes on the spatiotemporal patterns of LST under future climate change scenarios is [...] Read more.
Land use and land cover (LULC) changes resulting from rapid urbanization are the foremost causes of increases in land surface temperature (LST) in urban areas. Exploring the impact of LULC changes on the spatiotemporal patterns of LST under future climate change scenarios is critical for sustainable urban development. This study aimed to project the LST of Nanjing for 2025 and 2030 under different climate change scenarios using simulated LULC and land coverage indicators. Thermal infrared data from Landsat images were used to derive spatiotemporal patterns of LST in Nanjing from 1990 to 2020. The patch-generating land use simulation (PLUS) model was applied to simulate the LULC of Nanjing for 2025 and 2030 using historical LULC data and spatial driving factors. We simulated the corresponding land coverage indicators using simulated LULC data. We then generated LSTs for 2025 and 2030 under different climate change scenarios by applying regression relationships between LST and land coverage indicators. The results show that the LST of Nanjing has been increasing since 1990, with the mean LST increased from 23.44 °C in 1990 to 25.40 °C in 2020, and the mean LST estimated to reach 26.73 °C in 2030 (SSP585 scenario, integrated scenario of SSP5 and RCP5.8). There were significant differences in the LST under different climate scenarios, with increases in LST gradually decreasing under the SSP126 scenario (integrated scenario of SSP1 and RCP2.6). LST growth was similar to the historical trend under the SSP245 scenario (integrated scenario of SSP2 and RCP4.5), and an extreme increase in LST was observed under the SSP585 scenario. Our results suggest that the increase in impervious surface area is the main reason for the LST increase and urban heat island (UHI) effect. Overall, we proposed a method to project future LST considering land use change effects and provide reasonable LST scenarios for Nanjing, which may be useful for mitigating the UHI effect. Full article
(This article belongs to the Special Issue Future Prediction and Scenario Analysis of Urbanization Using Remote Sensing and GIS)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop