Urban Morphology and Environment Monitoring

A special issue of Geomatics (ISSN 2673-7418).

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 2994

Special Issue Editors


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Guest Editor
1. Center for Geographic Information System, University of the Punjab, Lahore, Pakistan
2. Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
3. Remote Sensing, GIS and Climatic Research Lab, National Center of GIS and Space Applications, University of the Punjab, Lahore, Pakistan
Interests: spatial data science; digital technologies; smart sensing; earth observation; environmental monitoring; landscape ecology; tropical forest ecology; urban ecology; smart cities; urban climate; climate change; vegetation-climate interaction; land-cover land-use change; drought; cropland; air pollution; water quality; cloud computing; machine learning; big data for SDGs
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Guest Editor
College of Management, Shenzhen University, Shenzhen 518060, China
Interests: environmental science; environmental economics; resource economics; social policy; clean energy; urban expansion
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Guest Editor
School of Engineering and Technology, Asian Institute of Technology, Pathum Thani 12120, Thailand
Interests: climate change; environment; location based services; urban development; spatial patterns and processes

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Guest Editor
Department of Landscape Architecture, Faculty of Architecture and Design, Selcuk University, Konya, Turkey
Interests: landscape architecture; urban planning; landscape ecology; urban sustainability; tourism; environmental science; sustainable development; land use planning; landscape design

Special Issue Information

Dear Colleagues,

Unprecedented urban growth is one of the most critical global challenges. Understanding the urban design, urban sprawl, morphological changes and their association with urban climate and environment can provide critical insight into sustainable urban practices.

During the last five decades, the emergence of Geospatial technologies has transformed methods for urban planning and monitoring associated environmental challenges. Satellite remote sensing data-derived land-use change along with modeling of climate and socio-economic drivers of change are playing a vital role in advancing interdisciplinary research.

The huge amount of data currently produced by modern Earth Observation (EO) missions, the availability of high-performance computing platforms and the implementation of artificial intelligence (AI) provide new opportunities to advance our knowledge.

Considering these advances, this Special Issue invites manuscripts that present new developments and methodologies, best practices, and applications to address the issues related to urban morphology and environment, such as the United Nations’ call for “Sustainable Cities and Communities”.

Dr. Sawaid Abbas
Dr. Ghaffar Ali
Prof. Dr. Nitin Kumar Tripathi
Dr. Sertaç Güngör
Guest Editors

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Keywords

  • urban sprawl
  • landscape architecture
  • urban planning
  • landscape ecology
  • urban microclimate
  • densification of urban area
  • urban heat island (UHI)
  • solar energy
  • low carbon societies
  • urban ecology
  • land cover land use
  • urban green spaces
  • urban morphology and publica health
  • monitoring change
  • machine learning
  • biophysical and social data integration
  • sustainable development goals monitoring

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Published Papers (1 paper)

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Research

20 pages, 4100 KiB  
Article
Temporal Autocorrelation of Sentinel-1 SAR Imagery for Detecting Settlement Expansion
by James Kapp and Jaco Kemp
Geomatics 2023, 3(3), 427-446; https://doi.org/10.3390/geomatics3030023 - 21 Aug 2023
Viewed by 1288
Abstract
Urban areas are rapidly expanding globally. The detection of settlement expansion can, however, be challenging due to the rapid rate of expansion, especially for informal settlements. This paper presents a solution in the form of an unsupervised autocorrelation-based approach. Temporal autocorrelation function (ACF) [...] Read more.
Urban areas are rapidly expanding globally. The detection of settlement expansion can, however, be challenging due to the rapid rate of expansion, especially for informal settlements. This paper presents a solution in the form of an unsupervised autocorrelation-based approach. Temporal autocorrelation function (ACF) values derived from hyper-temporal Sentinel-1 imagery were calculated for all time lags using VV backscatter values. Various thresholds were applied to these ACF values in order to create urban change maps. Two different orbital combinations were tested over four informal settlement areas in South Africa. Promising results were achieved in the two of the study areas with mean normalized Matthews Correlation Coefficients (MCCn) of 0.79 and 0.78. A lower performance was obtained in the remaining two areas (mean MCCn of 0.61 and 0.65) due to unfavorable building orientations and low building densities. The first results also indicate that the most stable and optimal ACF-based threshold of 95 was achieved when using images from both relative orbits, thereby incorporating more incidence angles. The results demonstrate the capacity of ACF-based methods for detecting settlement expansion. Practically, this ACF-based method could be used to reduce the time and labor costs of detecting and mapping newly built settlements in developing regions. Full article
(This article belongs to the Special Issue Urban Morphology and Environment Monitoring)
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