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Earth Observation Data in Sustainable Urban Science Research
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Earth Observation Data in Sustainable Urban Science Research

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 15416

Special Issue Editors

Dr. Tilottama Ghosh

E-Mail Website
Guest Editor
Earth Observation Group, Payne Institute for Public Policy, Colorado School of Mines, Golden, CO 80401, USA
Interests: nighttime light remote sensing; socio-economic studies; demography; land use and land cover change; urbanization
Special Issues, Collections and Topics in MDPI journals
Dr. Pranab K. Roy Chowdhury

E-Mail
Guest Editor
School of Environment and Forest Sciences, University of Washington, Seattle, WA, USA
Interests: sustainability science; energy geography; spatial analysis and modeling; land use land cover change

Special Issue Information

Dear Colleagues,

The world is undergoing rapid urbanization, and it is now expected that about 68% of the world population will be living in urban areas by the year 2050. Such rapid growth poses several sustainability challenges related to ensuring environmental sustainability, resource management, and well-being of urban inhabitants. However, efforts to address these issues have been severely thwarted by the lack of usable data at urban scales. Given that most projected urban growth is expected in data-starved regions around the world, alternative data sources and methods are urgently needed for research. Earth observation (EO) geospatial datasets offer a much-needed avenue to explore the aforementioned critical questions, which would also necessitate efficient methods to use such datasets to answer sustainable questions. The erstwhile DMSP-OLS nighttime lights data and newer VIIRS DNB data are prime examples of such applications, especially in studying urban population, economy, carbon emission, etc. Similarly, open geospatial datasets have enabled research into urban mobility, urban inequality, location planning, disaster response; and city-level open datasets are being used to monitor urban parameters, while promoting open governance. We invite papers related to the use of EO data to study various aspects of urban sustainability, at the local, regional, and global scale.

Dr. Tilottama Ghosh
Dr. Pranab K. Roy Chowdhury
Guest Editor

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. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly 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 1700 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 sustainability
  • earth observation data
  • urbanization
  • urban inequality
  • urban resource management
  • geospatial data

Published Papers (4 papers)

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Research

16 pages, 12874 KiB  
Article
Improving Urban Land Cover Classification with Combined Use of Sentinel-2 and Sentinel-1 Imagery
by Bin Hu, Yongyang Xu, Xiao Huang, Qimin Cheng, Qing Ding, Linze Bai and Yan Li
ISPRS Int. J. Geo-Inf. 2021, 10(8), 533; https://doi.org/10.3390/ijgi10080533 - 09 Aug 2021
Cited by 36 | Viewed by 3673
Abstract
Accurate land cover mapping is important for urban planning and management. Remote sensing data have been widely applied for urban land cover mapping. However, obtaining land cover classification via optical remote sensing data alone is difficult due to spectral confusion. To reduce the [...] Read more.
Accurate land cover mapping is important for urban planning and management. Remote sensing data have been widely applied for urban land cover mapping. However, obtaining land cover classification via optical remote sensing data alone is difficult due to spectral confusion. To reduce the confusion between dark impervious surface and water, the Sentinel-1A Synthetic Aperture Rader (SAR) data are synergistically combined with the Sentinel-2B Multispectral Instrument (MSI) data. The novel support vector machine with composite kernels (SVM-CK) approach, which can exploit the spatial information, is proposed to process the combination of Sentinel-2B MSI and Sentinel-1A SAR data. The classification based on the fusion of Sentinel-2B and Sentinel-1A data yields an overall accuracy (OA) of 92.12% with a kappa coefficient (KA) of 0.89, superior to the classification results using Sentinel-2B MSI imagery and Sentinel-1A SAR imagery separately. The results indicate that the inclusion of Sentinel-1A SAR data to Sentinel-2B MSI data can improve the classification performance by reducing the confusion between built-up area and water. This study shows that the land cover classification can be improved by fusing Sentinel-2B and Sentinel-1A imagery. Full article
(This article belongs to the Special Issue Earth Observation Data in Sustainable Urban Science Research)
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12 pages, 2723 KiB  
Article
Big Data Supported the Identification of Urban Land Efficiency in Eurasia by Indicator SDG 11.3.1
by Chaopeng Li, Guoyin Cai and Mingyi Du
ISPRS Int. J. Geo-Inf. 2021, 10(2), 64; https://doi.org/10.3390/ijgi10020064 - 02 Feb 2021
Cited by 15 | Viewed by 2562
Abstract
Indicator 11.3.1 of the UN Sustainable Development Goals (SDG 11.3.1) was designed to test land-use efficiency, which was defined as the ratio of the land consumption rate (LCR) to the population growth rate (PGR), namely, LCRPGR. This study calculates the PGRs, LCRs, and [...] Read more.
Indicator 11.3.1 of the UN Sustainable Development Goals (SDG 11.3.1) was designed to test land-use efficiency, which was defined as the ratio of the land consumption rate (LCR) to the population growth rate (PGR), namely, LCRPGR. This study calculates the PGRs, LCRs, and LCRPGRs for 333 cities from 1990–2000 and 391 cities from 2000–2015 in four geographical divisions in Eurasia according to the method given by UN metadata. The results indicate that Europe and Japan have the lowest PGR and LCR, indicating that this region’s level of urbanization is the highest. South and Central Asia have the lowest values of LCRPGR, indicating relatively lower urban land supply during the measurement periods. Compared with the mean LCRPGR in a region, the average values from SDG 11.3.1 by different types of cities in a region can have more guiding significance for urban sustainable development. While paying attention to the urban land-use efficiency of mega and extra-large cities, more attention should be paid to the coordination relationship between urban land supply and population growth in large, medium, and small cities. Additionally, the method from UN metadata works well for most urban expansion cities but is not suitable for cities with small changes in urban populations. Full article
(This article belongs to the Special Issue Earth Observation Data in Sustainable Urban Science Research)
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19 pages, 3233 KiB  
Article
Examining Land Use/Land Cover Change and the Summertime Surface Urban Heat Island Effect in Fast-Growing Greater Hefei, China: Implications for Sustainable Land Development
by Ying-ying Li, Yu Liu, Manjula Ranagalage, Hao Zhang and Rui Zhou
ISPRS Int. J. Geo-Inf. 2020, 9(10), 568; https://doi.org/10.3390/ijgi9100568 - 29 Sep 2020
Cited by 17 | Viewed by 2776
Abstract
In this study, a retrospective analysis of the relationship between the land use/land cover (LULC) change and associated surface urban heat island (SUHI) effect in fast-growing Greater Hefei between 1995 and 2016 was performed. Our results reveal the heterogeneous patterns of LULC change. [...] Read more.
In this study, a retrospective analysis of the relationship between the land use/land cover (LULC) change and associated surface urban heat island (SUHI) effect in fast-growing Greater Hefei between 1995 and 2016 was performed. Our results reveal the heterogeneous patterns of LULC change. The concentric buffer-based urban–rural gradient analysis reveals that most of the newly emerging developed land occurred within downtown Hefei. In contrast, in three suburban municipality/county jurisdictions, the overall area change in the non-developed land was much lower, but the net increase in developed land is remarkable. Meanwhile, the spatiotemporal patterns of SUHI are in good agreement with that of the developed land, as evidenced by the notable increase in SUHI intensity (SUHII) levels and SUHI spatial extent (SUHISE) in response to the rapid urban expansion, particularly along transportation corridors. In addition, partial least square regression (PLSR) models indicate that the buffer-based predictors/independent variables are significantly related to the responses (SUHII and SUHISE), explaining approximately 61.3% of the variance in the SUHII and 79.8% of the variance in the SUHISE, respectively. Furthermore, the relative strength of the independent variables in determining the relationship was quantitatively examined. The findings of this study provide clear evidence for decision making for sustainable land development and mitigation of the SUHI effect. Full article
(This article belongs to the Special Issue Earth Observation Data in Sustainable Urban Science Research)
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21 pages, 13706 KiB  
Article
Mapping and Monitoring Urban Environment through Sentinel-1 SAR Data: A Case Study in the Veneto Region (Italy)
by Andrea Semenzato, Salvatore Eugenio Pappalardo, Daniele Codato, Umberto Trivelloni, Silvano De Zorzi, Sabrina Ferrari, Massimo De Marchi and Matteo Massironi
ISPRS Int. J. Geo-Inf. 2020, 9(6), 375; https://doi.org/10.3390/ijgi9060375 - 07 Jun 2020
Cited by 21 | Viewed by 5651
Abstract
Focusing on a sustainable and strategic urban development, local governments and public administrations, such as the Veneto Region in Italy, are increasingly addressing their urban and territorial planning to meet national and European policies, along with the principles and goals of the 2030 [...] Read more.
Focusing on a sustainable and strategic urban development, local governments and public administrations, such as the Veneto Region in Italy, are increasingly addressing their urban and territorial planning to meet national and European policies, along with the principles and goals of the 2030 Agenda for the Sustainable Development. In this regard, we aim at testing a methodology based on a semi-automatic approach able to extract the spatial extent of urban areas, referred to as “urban footprint”, from satellite data. In particular, we exploited Sentinel-1 radar imagery through multitemporal analysis of interferometric coherence as well as supervised and non-supervised classification algorithms. Lastly, we compared the results with the land cover map of the Veneto Region for accuracy assessments. Once properly processed and classified, the radar images resulted in high accuracy values, with an overall accuracy ranging between 85% and 90% and percentages of urban footprint differing by less than 1%–2% with respect to the values extracted from the reference land cover map. These results provide not only a reliable and useful support for strategic urban planning and monitoring, but also potentially identify a solid organizational dataflow process to prepare geographic indicators that will help answering the needs of the 2030 Agenda (in particular the goal 11 “Sustainable Cities and Communities”). Full article
(This article belongs to the Special Issue Earth Observation Data in Sustainable Urban Science Research)
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