Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
Remote Sensing and Geospatial Analysis in Urban Environments in the...
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

Remote Sensing and Geospatial Analysis in Urban Environments in the Big Data Era

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

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1725

Special Issue Editors

Dr. Wenliang Li

E-Mail Website
Guest Editor
Department of Geography, Environment, and Sustainability, The University of North Carolina at Greensboro, Greensboro, NC 27412, USA
Interests: spectral unmixing analysis; environmental planning; land use and land cover change modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoma Li

E-Mail Website
Guest Editor
College of Landscape Architecture and Art Design, Hunan Agricultural University, Changsha 410128, China
Interests: urban cooling; urban green spaces; urban planning; landscape ecology; urban heat island; air temperature estimated by remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Urbanization is one of the notable changes on the Earth’s surface. Although urban areas only cover a small portion of the global land surface, natural landscapes have gradually transformed into anthropogenic urban land uses. Such transformation has significantly modified urban environments and caused several urban issues, including traffic congestion, water pollution, air pollution, the urban heat island effect, urban flooding, etc., which have attracted the attention of many scholars.

Remote sensing techniques have experienced rapid development in recent decades, and the advancements in remote sensing technologies have accelerated our understanding of urban environments. In the era of big data, the availability of multisource remote sensing data with diverse spectral, spatial, and temporal resolutions presents opportunities and challenges for geospatial analysis and the comprehensive understanding of urban environments.

The objective of this Special Issue is to explore the challenges and opportunities in exploiting the potential of remote sensing and geospatial analysis applications in urban environments in the big data era.

Topics of interest include, but are not limited to, the following:

  • Urban environment classification and change analysis from multi-source data;
  • Application of new sensors (e.g., UAV, SAR, and LiDAR) in urban environment analysis;
  • Urban growth and urbanization modeling;
  • New innovative algorithms (e.g., machining learning) in modeling urban environments;
  • Human–environment interactions in urban environments;
  • Sustainable urban development;
  • Urban air, water, heat, population, and energy consumption, and their dynamics.

Dr. Wenliang Li
Dr. Xiaoma Li
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

  • geospatial analysis
  • remote sensing
  • big data
  • city science

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 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

Jump to: Other

26 pages, 7532 KiB  
Article
Forecasting Urban Sprawl Dynamics in Islamabad: A Neural Network Approach
by Saddam Sarwar, Hafiz Usman Ahmed Khan, Falin Wu, Sarah Hasan, Muhammad Zohaib, Mahzabin Abbasi and Tianyang Hu
Remote Sens. 2025, 17(3), 492; https://doi.org/10.3390/rs17030492 - 31 Jan 2025
Viewed by 344
Abstract
In the past two decades, Islamabad has experienced significant urbanization. As a result of inadequate urban planning and spatial distribution, it has significantly influenced land use–land cover (LULC) changes and green areas. To assess these changes, there is an increasing need for reliable [...] Read more.
In the past two decades, Islamabad has experienced significant urbanization. As a result of inadequate urban planning and spatial distribution, it has significantly influenced land use–land cover (LULC) changes and green areas. To assess these changes, there is an increasing need for reliable and appropriate information about urbanization. Landsat imagery is categorized into four thematic classes using a supervised classification method called the support vector machine (SVM): built-up, bareland, vegetation, and water. The results of the change detection of post-classification show that the city region increased from 6.37% (58.09 km2) in 2000 to 28.18% (256.49 km2) in 2020, while vegetation decreased from 46.97% (428.28 km2) to 34.77% (316.53 km2) and bareland decreased from 45.45% (414.37 km2) to 35.87% (326.49 km2). Utilizing a land change modeler (LCM), forecasts of the future conditions in 2025, 2030, and 2035 are predicted. The artificial neural network (ANN) model embedded in IDRISI software 18.0v based on a well-defined backpropagation (BP) algorithm was used to simulate future urban sprawl considering the historical pattern for 2015–2020. Selected landscape morphological measures were used to quantify and analyze changes in spatial structure patterns. According to the data, the urban area grew at a pace of 4.84% between 2015 and 2020 and will grow at a rate of 1.47% between 2020 and 2035. This growth in the metropolitan area will encroach further into vegetation and bareland. If the existing patterns of change persist over the next ten years, a drop in the mean Euclidian Nearest Neighbor Distance (ENN) of vegetation patches is anticipated (from 104.57 m to 101.46 m over 2020–2035), indicating an accelerated transformation of the landscape. Future urban prediction modeling revealed that there would be a huge increase of 49% in urban areas until the year 2035 compared to the year 2000. The results show that in rapidly urbanizing areas, there is an urgent need to enhance land use laws and policies to ensure the sustainability of the ecosystem, urban development, and the preservation of natural resources. Full article
(This article belongs to the Special Issue Remote Sensing and Geospatial Analysis in Urban Environments in the Big Data Era)
►▼ Show Figures

Figure 1

Other

Jump to: Research

13 pages, 23549 KiB  
Technical Note
Opposing Impacts of Greenspace Fragmentation on Land Surface Temperature in Urban and Surrounding Rural Areas: A Case Study in Changsha, China
by Weiye Wang, Xiaoma Li, Chuchu Li and Dexin Gan
Remote Sens. 2024, 16(19), 3609; https://doi.org/10.3390/rs16193609 - 27 Sep 2024
Cited by 1 | Viewed by 838
Abstract
Managing the amount of greenspace (i.e., increasing or decreasing greenspace coverage) and optimizing greenspace configuration (i.e., increasing or decreasing greenspace fragmentation) are cost-effective approaches to cooling the environment. The spatial variations in their impacts on the thermal environment, as well as their relative [...] Read more.
Managing the amount of greenspace (i.e., increasing or decreasing greenspace coverage) and optimizing greenspace configuration (i.e., increasing or decreasing greenspace fragmentation) are cost-effective approaches to cooling the environment. The spatial variations in their impacts on the thermal environment, as well as their relative importance, are of great importance for greenspace planning and management but are far from thoroughly understood. Taking Changsha, China as an example, this study investigated the spatial variations of the impacts of greenspace amount (measured as a percent of greenspace) and greenspace fragmentation (measured by edge density of greenspace) on the Landsat-derived land surface temperature (LST) using geographically weighted regression (GWR), and also uncovered the spatial pattern of their relative importance. The results indicated that: (1) Greenspace amount showed significantly negative relationships with LST for 91.73% of the study area. (2) Both significantly positive and negative relationships were obtained between greenspace fragmentation and LST, covering 14.90% and 13.99% of the study area, respectively. (3) The negative relationship between greenspace fragmentation and LST is mainly located in the urban areas, while the positive relationship appeared in the rural areas. (4) Greenspace amount made a larger contribution to regulating LST than greenspace fragmentation in 93.31% of the study area, but the latter had stronger roles in about 6.69% of the study area, mainly in the city center. These findings suggest that spatially varied greenspace planning and management strategies should be adopted to improve the thermal environment. Full article
(This article belongs to the Special Issue Remote Sensing and Geospatial Analysis in Urban Environments in the Big Data Era)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop