Special Issue "Urban Planning Supported by Remote Sensing Technology"

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

Deadline for manuscript submissions: 30 June 2022 | Viewed by 3022

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 Issue Information

Dear Colleagues,

Remote sensing associated with urban NTIC innovations have 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 70s until now, tremendous imagery enhancing has lead 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 population in daily life, 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 illustrated the interest of imagery in urban planning practices, and several tools or applications can be described in various contexts. This Special Issue might be the 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: like 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 2500 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

Article
Ha Long—Cam Pha Cities Evolution Analysis Utilizing Remote Sensing Data
Remote Sens. 2022, 14(5), 1241; https://doi.org/10.3390/rs14051241 - 03 Mar 2022
Viewed by 651
Abstract
Socio-economic development has promoted the modification of land cover patterns in the coastal area of Ha Long, Cam Pha cities since the 1990s. The urban growth, together with intensive coal mining activities, has improved the life quality of residents. However, it has also [...] Read more.
Socio-economic development has promoted the modification of land cover patterns in the coastal area of Ha Long, Cam Pha cities since the 1990s. The urban growth, together with intensive coal mining activities, has improved the life quality of residents. However, it has also caused many environmental problems in this region. Change detection techniques based on post-classification comparison were applied for monitoring the spatial and temporal evolution of land covers. The confusion matrix for 2001 and 2019 showed high overall accuracy (97.99%, 94.95%) and Kappa coefficient (0.97, 0.92), respectively. Statistics from classified images have revealed that man-made features increased by about 15.32%, while natural features, mangrove jungles, and water bodies decreased 10.64%, 1.96%, 2.72%, respectively, and urban evolution presents various dynamics, soft in the first period (1991–2001), but stronger in the second period (2001–2019) with different characteristics. The study also expresses the constraint of topographic and geologic resources, which have prevented the urban development in this coastal area. Such obtained results are very important for understanding interactions and relations between natural and human phenomena and they may help authorities by providing indicators and maps able to highlight necessary actions for sustainable development. Full article
(This article belongs to the Special Issue Urban Planning Supported by Remote Sensing Technology)
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Article
Spatiotemporal Patterns and Driving Force of Urbanization and Its Impact on Urban Ecology
Remote Sens. 2022, 14(5), 1160; https://doi.org/10.3390/rs14051160 - 26 Feb 2022
Viewed by 533
Abstract
Urbanization inevitably poses a threat to urban ecology by altering its external structure and internal attributes. Nighttime light (NTL) has become increasingly extensive and practical, offering a special perspective on the world in revealing urbanization. In this study, we applied the Normalized Impervious [...] Read more.
Urbanization inevitably poses a threat to urban ecology by altering its external structure and internal attributes. Nighttime light (NTL) has become increasingly extensive and practical, offering a special perspective on the world in revealing urbanization. In this study, we applied the Normalized Impervious Surface Index (NISI) constructed by NTL and MODIS NDVI to examine the urbanization process in the Yangtze River Delta (YRD). Geographical detectors combined with factors involving human and natural influences were utilized to investigate the drive mechanism. Urban ecology stress was evaluated based on changes in urban morphological patterns and fractional vegetation cover (FVC). The results showed that the NISI can largely overcome the obstacle of directly coupling NTL data in performing urbanization and has efficient applicability in the long-term pixel scale. Built-up areas in the YRD increased by 2.83 times during the past two decades, from 2053.5 to 7872.5 km2. Urbanization intensity has saturated the city center and is spilling over into the suburbs, which show a “cold to hot” spatial clustering distribution. Economic factors are the primary forces driving urbanization, and road network density is becoming essential as factor that reflects urban infrastructure. Urban geometry pattern changes in fractal dimension (FD) and compactness revealed the ecological stress from changing urban external structure, and internal ecological stress was clear from the negative effect on 63.4% FVC. This impact gradually increased in urban expanded area and synchronously decreased when urbanization saturated the core area. An analysis of ecological stress caused by urbanization from changing physical structure and social attributes can provide evidence for urban management and coordinated development. Full article
(This article belongs to the Special Issue Urban Planning Supported by Remote Sensing Technology)
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Article
Monitoring and Forecasting of Urban Expansion Using Machine Learning-Based Techniques and Remotely Sensed Data: A Case Study of Gharbia Governorate, Egypt
Remote Sens. 2021, 13(22), 4498; https://doi.org/10.3390/rs13224498 - 09 Nov 2021
Viewed by 1379
Abstract
Rapid population growth is the main driver of the accelerating urban sprawl into agricultural lands in Egypt. This is particularly obvious in governorates where there is no desert backyard (e.g., Gharbia) for urban expansion. This work presents an overview of machine learning-based and [...] Read more.
Rapid population growth is the main driver of the accelerating urban sprawl into agricultural lands in Egypt. This is particularly obvious in governorates where there is no desert backyard (e.g., Gharbia) for urban expansion. This work presents an overview of machine learning-based and state-of-the-art remote sensing products and methodologies to address the issue of random urban expansion, which negatively impacts environmental sustainability. The study aims (1) to investigate the land-use/land-cover (LULC) changes over the past 27 years, and to simulate the future LULC dynamics over Gharbia; and (2) to produce an Urbanization Risk Map in order for the decision-makers to be informed of the districts with priority for sustainable planning. Time-series Landsat images were utilized to analyze the historical LULC change between 1991 and 2018, and to predict the LULC change by 2033 and 2048 based on a logistic regression–Markov chain model. The results show that there is a rapid urbanization trend corresponding to a diminution of the agricultural land. The agricultural sector represented 91.2% of the total land area in 1991, which was reduced to 83.7% in 2018. The built-up area exhibited a similar (but reversed) pattern. The results further reveal that the observed LULC dynamics will continue in a like manner in the future, confirming a remarkable urban sprawl over the agricultural land from 2018 to 2048. The cultivated land changes have a strong negative correlation with the built-up cover changes (the R2 were 0.73 in 1991–2003, and 0.99 in 2003–2018, respectively). Based on the Fuzzy TOPSIS technique, Mahalla Kubra and Tanta are the districts which were most susceptible to the undesirable environmental and socioeconomic impacts of the persistent urbanization. Such an unplanned loss of the fertile agricultural lands of the Nile Delta could negatively influence the production of premium agricultural crops for the local market and export. This study is substantial for the understanding of future trends of LULC changes, and for the proposal of alternative policies to reduce urban sprawl on fertile agricultural lands. Full article
(This article belongs to the Special Issue Urban Planning Supported by Remote Sensing Technology)
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