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Remote Sensing
Special Issues
Urban Modeling: Simulating Urban Growth and Subsequent Landscape Change
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Urban Modeling: Simulating Urban Growth and Subsequent Landscape Change

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 12335

Special Issue Editors

Dr. Rahim Aguejdad

E-Mail Website
Guest Editor
UMR TETIS, CNRS, Université de Montpellier, 500 rue Jean-François Breton, 34093 Montpellier, France
Interests: urban growth; remote sensing; urban growth modeling; scenarios
Dr. Thomas Houet

E-Mail Website
Guest Editor
LETG-Rennes COSTEL - UMR 6554 CNRS, Université Rennes 2, Place du recteur Henri Le Moal, 35043 Rennes, CEDEX, France
Interests: land use and land cover change modelling; urban growth; scenarios; remote sensing

Special Issue Information

Dear Colleagues,

Monitoring and modeling land use and cover changes (LUCC) associated with urbanization are of great importance to understanding how urban expansion and subsequent landscape changes interact with ecological and social processes. Many computational urban growth models were developed and applied in order to improve our understanding of spatial urban expansion dynamics and behavior, develop hypotheses, and predict or project future LUCC. Several reviews show that these existing models vary widely in underlying the theoretical assumptions and methodological approaches. Most of them are based on an empirical modeling framework using historical, remotely sensed images and biophysical and socio-economic data. Despite recent advances, urban growth modeling continues to raise several issues, particularly those related to the calibration and validation processes. Therefore, there is a need to continue developing new algorithms and innovative methodological approaches in order to capture the dynamic and non-linear human–environment processes that drive complex urban growth changes.

This Special Issue aims at collecting new developments and methodologies about your recent research on urban growth modeling. Accordingly, we would like to invite you to submit articles that provide the community with the most recent advancements, practices, and applications on all aspects of urban growth modeling and simulation, including, but not limited to, the following:

  • New approaches and models for urban growth modeling and simulation;
  • Developing robust methods and algorithms for model calibration and validation;
  • 3D modeling of urban growth;
  • Uncertainty, remote sensing data, and scales requirements for urban growth and subsequent landscape changes modeling;
  • Non-stationarity of land use changes in calibrating and validating urban growth models;
  • Emerging and innovative methods of urban growth scenarios development;
  • Urban growth models to support adaptation decision, strategic planning, and sustainability assessment of urban land-use policy;
  • Comparative studies of urban growth models;
  • Comparative applications of urban growth simulations to various urban contexts.

Dr. Rahim Aguejdad
Dr. Thomas Houet
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
  • urban growth
  • modeling and simulation
  • model calibration and validation
  • uncertainty
  • non-stationary urban growth
  • urban remote sensing
  • land use and cover change
  • landsacpe change
  • sustainable urban development scenarios

Published Papers (3 papers)

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Research

20 pages, 3453 KiB  
Article
The Influence of the Calibration Interval on Simulating Non-Stationary Urban Growth Dynamic Using CA-Markov Model
by Rahim Aguejdad
Remote Sens. 2021, 13(3), 468; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13030468 - 29 Jan 2021
Cited by 22 | Viewed by 2626
Abstract
The temporal non-stationarity of land use and cover change (LUCC) processes is one of the main sources of uncertainty that may influence the calibration and the validation of spatial path-dependent LUCC models. In relation to that, this research aims to investigate the influence [...] Read more.
The temporal non-stationarity of land use and cover change (LUCC) processes is one of the main sources of uncertainty that may influence the calibration and the validation of spatial path-dependent LUCC models. In relation to that, this research aims to investigate the influence of the temporal non-stationarity of land change on urban growth modeling accuracy based on an empirical approach that uses past LUCC. Accordingly, the urban development in Rennes Metropolitan (France) was simulated using fifteen past calibration intervals which are set from six training dates. The study used Idrisi’s Cellular Automata-Markov model (CA-Markov) which is an inductive pattern-based LUCC software package. The land demand for the simulation year was estimated using the Markov Chain method. Model validation was carried out by assessing the quantity of change, allocation, and spatial patterns accuracy. The quantity disagreement was analyzed by taking into consideration the temporal non-stationarity of change rate over the calibration and the prediction intervals, the model ability to reproduce the past amount of change in the future, and the time duration of the prediction interval. The results show that the calibration interval significantly influenced the amount and the spatial allocation of the estimated change. In addition to that, the spatial allocation of change using CA-Markov depended highly on the basis land cover image rather than the observed transition during the calibration period. Therefore, this study provides useful insights on the role of the training dates in the simulation of non-stationary LUCC. Full article
(This article belongs to the Special Issue Urban Modeling: Simulating Urban Growth and Subsequent Landscape Change)
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15 pages, 5953 KiB  
Article
Land Use Simulation of Guangzhou Based on Nighttime Light Data and Planning Policies
by Jieying Lao, Cheng Wang, Jinliang Wang, Feifei Pan, Xiaohuan Xi and Lei Liang
Remote Sens. 2020, 12(10), 1675; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12101675 - 23 May 2020
Cited by 7 | Viewed by 3086
Abstract
With the implementation processes of strategies such as Guangdong-Hong Kong-Macau Greater Bay Area’s coordinated development and “Belt and Road Initiative” initiative, the planning policies had produced a significant influence on land use distributions in Guangzhou. In this paper, we employ nighttime light (NTL) [...] Read more.
With the implementation processes of strategies such as Guangdong-Hong Kong-Macau Greater Bay Area’s coordinated development and “Belt and Road Initiative” initiative, the planning policies had produced a significant influence on land use distributions in Guangzhou. In this paper, we employ nighttime light (NTL) information as a proxy indicator of gross domestic product(GDP), and a future land use simulation model (FLUS) to simulate the land use patterns in Guangzhou from 2015 to 2018 and 2018 to 2035 by incorporating planning policies. The results show that: (1) the accuracy of simulation result from 2015 to 2018 based on National Polar-orbiting Partnership, Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) is higher than that based on GDP; (2) by incorporating planning policies into the model can better identify the potential spatial distribution of urban land and make the simulated results more consistent with the actual urban land development trajectory. This study demonstrates that NTL is a suitable and feasible proxy indicator of GDP for the land use simulations, providing a scientific basis for the development of urban planning and construction policy. Full article
(This article belongs to the Special Issue Urban Modeling: Simulating Urban Growth and Subsequent Landscape Change)
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22 pages, 4863 KiB  
Article
Patterns of Historical and Future Urban Expansion in Nepal
by Bhagawat Rimal, Sean Sloan, Hamidreza Keshtkar, Roshan Sharma, Sushila Rijal and Uttam Babu Shrestha
Remote Sens. 2020, 12(4), 628; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12040628 - 13 Feb 2020
Cited by 45 | Viewed by 5741
Abstract
Globally, urbanization is increasing at an unprecedented rate at the cost of agricultural and forested lands in peri-urban areas fringing larger cities. Such land-cover change generally entails negative implications for societal and environmental sustainability, particularly in South Asia, where high demographic growth and [...] Read more.
Globally, urbanization is increasing at an unprecedented rate at the cost of agricultural and forested lands in peri-urban areas fringing larger cities. Such land-cover change generally entails negative implications for societal and environmental sustainability, particularly in South Asia, where high demographic growth and poor land-use planning combine. Analyzing historical land-use change and predicting the future trends concerning urban expansion may support more effective land-use planning and sustainable outcomes. For Nepal’s Tarai region—a populous area experiencing land-use change due to urbanization and other factors—we draw on Landsat satellite imagery to analyze historical land-use change focusing on urban expansion during 1989–2016 and predict urban expansion by 2026 and 2036 using artificial neural network (ANN) and Markov chain (MC) spatial models based on historical trends. Urban cover quadrupled since 1989, expanding by 256 km2 (460%), largely as small scattered settlements. This expansion was almost entirely at the expense of agricultural conversion (249 km2). After 2016, urban expansion is predicted to increase linearly by a further 199 km2 by 2026 and by another 165 km2 by 2036, almost all at the expense of agricultural cover. Such unplanned loss of prime agricultural lands in Nepal’s fertile Tarai region is of serious concern for food-insecure countries like Nepal. Full article
(This article belongs to the Special Issue Urban Modeling: Simulating Urban Growth and Subsequent Landscape Change)
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