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Remote Sensing
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Urban Planning Supported by Remote Sensing Technology II
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Urban Planning Supported by Remote Sensing Technology II

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

Deadline for manuscript submissions: 31 July 2024 | Viewed by 5041

Special Issue Editors

Dr. Christiane Weber

E-Mail Website
Guest Editor
DR CNRS, TETIS Research Unit, AgroParisTech, CIRAD, CNRS, Irstea, Maison de la Télédétection, 500 rue Jean-François Breton, 34000 Montpellier, France
Interests: urban environment; urban multifunctionality; remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Jingxia Wang

E-Mail Website
Guest Editor
1. Department of Urban Studies and Planning, The University of Sheffield, Western Bank, Sheffield S10 2TN, UK
2. Institute of Geography, Ruhr University Bochum, 44801 Bochum, Germany
Interests: landscape and urban ecology; land resources management; landscape planning and management; social-ecological systems research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This is the second Special Issue concerning the contributions of Urban Planning Supported by Remote Sensing Technology.

Remote sensing associated with urban NTIC innovations has strongly changed urban planning practices and tools.

Imagery has reinforced the importance of representation and location identification, largely supported by GIS capacities and development.

From the late 1970s to date, tremendous imagery enhancing has led to changes in practices, tools and norms. From inventory to global comparison products, imagery has pushed towards usable and homogeneous products that are valuable at various scales (EU or global products). If remote sensing products are variably disseminated in urban and planning offices, their impact is not negligeable.

Actual challenges regarding climate change and biodiversity conservation favor the importance of images in various evaluation directives and plans. Vegetation, water, sealed surfaces, and soils are resources that could be monitored regularly with the help of RS imagery. As such, products might be introduced in urban heat surface or local climatic zone identification, nature-based solutions design, urban ecological infrastructures, or urban health projects management. RS is a strong asset for urban complexity management.

Multispectral, superspectral, and hyperspectral sensors have diversified observation capacities and offered a large panel of applications: from cartography to prospective modelling promoting urban elements monitoring at various scales, from regional to local, and introducing imagery in urban planning practices and citizen applications.

Actual trends turn to integrated developments mixing massive information capacities, modelling, visualization capacities and collaborative assessments. Citizen sciences emerge, stressing the crucial role of spatial technologies for a large part of the population in their daily lives, and, consequently, the role of these spatial technologies for planning developments.

Spatial imagery development has promoted the use and the benefit of RS products in planning technologies for sustainable cities development and crises management. However, some difficulties might compel the introduction of RS products in planning rules, laws or territorial directives. As such, it might also be interesting to identify bottlenecks and practical problems that halt these potential disseminations.

Numerous applications can illustrate the interest of imagery in urban planning practices, and several tools or applications can be described in various contexts. This Special Issue aims to be an opportunity to share experiences, at various scales (urban project to metropolitan planning issue), and to confront both contextual positions, methodological choices and developments, and results for various countries or regions.

Suggested themes and article types for submissions:

  • Artificial and sealed surfaces monitoring;
  • Urban disaster management;
  • Subsidence monitoring;
  • Biodiversity monitoring;
  • Urban vegetation monitoring;
  • HUI and SHUI determination and monitoring;
  • Urban ecological infrastructure;
  • Nature-based solution;
  • Citizen sciences;
  • Sensors capacities and future development;
  • Enhanced methodologies, such as deep learning, spectral fusion, time-series analysis;
  • Data mining;
  • Data analyses;
  • Urban indicators.

Dr. Christiane Weber
Dr. Jingxia Wang
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

  • urban challenges
  • urban monitoring
  • urban imagery
  • urban practices and tools
  • urban spatial technologies
  • news urban sensors issues

Published Papers (3 papers)

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Research

25 pages, 12449 KiB  
Article
Municipal and Urban Renewal Development Index System: A Data-Driven Digital Analysis Framework
by Xi Wang, Xuecao Li, Tinghai Wu, Shenjing He, Yuxin Zhang, Xianyao Ling, Bin Chen, Lanchun Bian, Xiaodong Shi, Ruoxi Zhang, Jie Wang, Li Zheng, Jun Li and Peng Gong
Remote Sens. 2024, 16(3), 456; https://doi.org/10.3390/rs16030456 - 24 Jan 2024
Viewed by 1383
Abstract
Urban renewal planning and development are vital for enhancing the living quality of city residents. However, such improvement activities are often expensive, time-consuming, and in need of standardization. The convergence of remote sensing technologies, social big data, and artificial intelligence solutions has created [...] Read more.
Urban renewal planning and development are vital for enhancing the living quality of city residents. However, such improvement activities are often expensive, time-consuming, and in need of standardization. The convergence of remote sensing technologies, social big data, and artificial intelligence solutions has created unprecedented opportunities for comprehensive digital planning and analysis in urban renewal development and management. However, fast interdisciplinary development imposes some challenges because the data collected and the solutions built are defined piece by piece and require further fusion and integration of knowledge, evaluation standards, systematic analyses, and new methodologies. To address these challenges, we propose a municipal and urban renewal development index (MUDI) system with data modeling and mathematical analysis models. The MUDI system is applied and studied in three circumstances: (1) at regional level, 337 cities are selected in China to demonstrate the MUDI system’s comparable analysis capabilities on a large scale across cities; (2) at city level, 285 residential communities are selected in Xiamen to demonstrate the use of remote sensing data as key MUDIs for a temporal urban land change analysis; and (3) at the level of residential neighborhoods’ urban renewal practices, Xiamen’s Yingping District is selected to demonstrate the MUDI system’s project management capabilities. We find that the MUDI system is highly effective in municipal and urban data model building through the abstraction and summation of grid-based satellite and social big data. Secondly, the MUDI system enables comprehension of the high dimensionality and complexity of multisource datasets for municipal and urban renewal development. Thirdly, the system is applied to enable the use of the newly developed UMAP algorithm, a model based on Riemannian geometry and algebraic topology, and the carrying out of a principal component analysis for the key dimensions and an index correlation analysis. Fourthly, various artificial intelligence-driven algorithms can be developed for urban renewal analyses based on the MUDIs. The MUDI system is a new and effective method for urban renewal planning and management that can be flexibly extended and applied to various cities and urban districts. Full article
(This article belongs to the Special Issue Urban Planning Supported by Remote Sensing Technology II)
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27 pages, 11541 KiB  
Article
Construction and Optimisation of Ecological Networks in High-Density Central Urban Areas: The Case of Fuzhou City, China
by Jianwei Geng, Kunyong Yu, Menglian Sun, Zhen Xie, Ruxian Huang, Yihan Wang, Qiuyue Zhao and Jian Liu
Remote Sens. 2023, 15(24), 5666; https://doi.org/10.3390/rs15245666 - 07 Dec 2023
Cited by 1 | Viewed by 855
Abstract
Constructing and optimising ecological networks in high-density cities plays an important role in mitigating urban ecological problems. Our study uses comprehensive evaluation methods such as Morphological Spatial Pattern Analysis (MSPA), the Remote Sensing Ecological Index (RSEI), and Connectivity to identify ecological source areas [...] Read more.
Constructing and optimising ecological networks in high-density cities plays an important role in mitigating urban ecological problems. Our study uses comprehensive evaluation methods such as Morphological Spatial Pattern Analysis (MSPA), the Remote Sensing Ecological Index (RSEI), and Connectivity to identify ecological source areas in Fuzhou City, and constructs and optimises the network using the Minimum Cumulative Resistance (MCR) model, current theory, and other methods. Meanwhile, the changes in urban landscape connectivity under different development scenarios were explored. The results show that the following: (1) the identification of ecological source sites based on the integrated approach is better than the single MSPA method; (2) the ecological network of Fuzhou City consists of 44 ecological source sites and 92 corridors; and (3) among the various development modes, transforming the top 30% of the bare land patches in Fuzhou City into green spaces improves the overall connectivity of the ecological network the most. The results can provide auxiliary decision-making for ecological construction in high-density cities. Full article
(This article belongs to the Special Issue Urban Planning Supported by Remote Sensing Technology II)
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23 pages, 35553 KiB  
Article
Capturing Small-Scale Surface Temperature Variation across Diverse Urban Land Uses with a Small Unmanned Aerial Vehicle
by Junaid Ahmad and Jessica A. Eisma
Remote Sens. 2023, 15(8), 2042; https://doi.org/10.3390/rs15082042 - 12 Apr 2023
Cited by 2 | Viewed by 2302
Abstract
Urbanization increases the urban land surface temperature (LST), challenging society and the environment. This study measured the LST of diverse land uses (LU) in Dallas–Fort Worth (DFW) using a high-resolution (8 cm) thermal infrared sensor onboard a small, unmanned aerial vehicle (UAV). LUs [...] Read more.
Urbanization increases the urban land surface temperature (LST), challenging society and the environment. This study measured the LST of diverse land uses (LU) in Dallas–Fort Worth (DFW) using a high-resolution (8 cm) thermal infrared sensor onboard a small, unmanned aerial vehicle (UAV). LUs included park (PA), industrial (IA), residential low-cost (RLC), and residential high-cost (RHC) areas. LST was collected by the UAV at different times on eight nonconsecutive days. UAV-collected LST was compared with that from Landsat 8-9 and in situ measurements. RHC reported the highest mean LST, and PA showed the lowest mean LST. Dark-colored asphalt shingle roofs in RHC had the highest mean LST range at 35.67 °C. Lower LST was measured in shaded areas and under thick green cover, whereas areas with thin green cover occasionally reported higher LST than pavements. The micro-urban heat island (MUHI) was calculated between LUs and within land cover types (roof, pavement, green, and water). The MUHI varied from 4.83 °C to 15.85 °C between LUs and 0.2 °C to 23.5 °C within LUs for the less than 1 km2 study area. While the UAV thermal sensor and Landsat demonstrated a similar trend of LST variation, the UAV sensor reported more intense MUHI. An average percent bias (PBIAS) of 5.1% was calculated between the UAV sensor and in situ measurements. This study helps inform the urban design process by demonstrating how land use decisions impact LST locally and provides valuable insight for studies concerned with fine-scale urban LST variability. Full article
(This article belongs to the Special Issue Urban Planning Supported by Remote Sensing Technology II)
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