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Spatio-Temporal Analysis of Urbanization Using GIS and Remote Sensing

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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 2024 | Viewed by 8087

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Special Issue Editor

Dr. Tomaž Podobnikar

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Faculty of Civil and Geodetic Engineering, University of Ljubljana, Jamova 2, SI-1000 Ljubljana, Slovenia
Interests: spatial analysis; geomorphometry; DEM; DTM; GIS; remote sensing; geovisual analytics; spatial data quality; image processing; spatial generalization; spatial data integration; spatial statistics; (palaeo)environmental analysis; landscape archaeology; natural hazard
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Dear Colleagues,

Digitized old maps from different periods of the last centuries, aerial photogrammetric products for the past three-quarters of a century, periodically captured satellite imagery from different sensors starting globally 50 years ago, constantly updated spatial datasets and maps, outcomes of predictions, our accumulated knowledge, etc., all have the potential to be used or re-used for analysis with different spatio-temporal analyses for a wide range of urbanization purposes. An alternative scenario is a near real-time spatio-temporal analysis of data concerning currently running events.

The concept of GIS, which is based mainly on vector and raster data layers, and attributes is well recognized, where the associated tools and services support the implementation of the spatial analysis with visualizations and animations. This well-established system is also commonly used by the temporal component which appears as an additional, special unidirectional akin dimension related to the 2D or 3D physical space. In this case, the sequence of themes sampled to the time slices can be captured in the individual layers, e.g., as a multi-annual dataset of the built-up areas. Another common option is to assign different attributes to the single layer, e.g., number of inhabitants in settlements (as point features), recorded in a multi-column table for each year. There are numerous other options, for example, to map a difference from the previous state, outline features moving over the geographic space on a single layer, e.g., to present vehicle positions in the city over time, or even employ a space-time cube structure with its extensive analytical capabilities, etc.

This Special Issue aims to focus on two components that influence the result of the typical urbanization research, firstly different kinds of data sources (e.g., generated with remote sensing) and secondly their interaction with the relevant spatial analysis in GIS. In addition to the two, there is a third, temporal dimension, which integratively implements the conceptual complexity. The questions that can arise are, how to efficiently harmonize data from different sources which may comprise historical maps, aerial photographs, satellite imagery from various passive and active sensors with different resolutions, surveying data, etc., into a spatio-temporal form? Related to the above, how to optimally archive data for future spatio-temporal analysis? In addition, what alternative analyses of changes through space-time should be developed, which may involve various disciplines, such as spatial statistics, geography, geomorphometry, environmental, economic social and human sciences, health, etc., to examine urbanization patterns, transport modalities, city growth, the impact of climate change on urban areas, and similar?

The Special Issue aims to examine a complex temporal component of the geospatial approaches to the data that come from various sensors and data sources, where the continuous nature of the time needs to be quantized into discrete forms, which can be further analyzed with the remote sensing and GIS tools.

We suggest themes that focus on optimal solutions between types of spatial data sources and Spatio-temporal analysis in RS and especially in GIS to get an optimal result. We propose to focus the authors’ solutions primarily on the improved data sets, new methods, and overall innovative solutions, where the applications are primarily presented in the form of the use cases.

We prefer original research articles, as well as encourage the following article types: perspective and technical note.

Dr. Tomaž Podobnikar
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. 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

time series
spatio-temporal data
spatio-spatial analysis
spatio-temporal statistics
spatio-temporal concept in GIS
spatio-temporal visual data exploration
spatio-temporal urbanization patterns
movement analysis
GIS
remote sensing
geospatial

Published Papers (5 papers)

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Research

25 pages, 14684 KiB

Open AccessArticle

Exploring the Relationship between Urban Vibrancy and Built Environment Using Multi-Source Data: Case Study in Munich

by Chao Gao, Shasha Li, Maopeng Sun, Xiyang Zhao and Dewen Liu

Remote Sens. 2024, 16(6), 1107; https://doi.org/10.3390/rs16061107 - 21 Mar 2024

Cited by 1 | Viewed by 700

Abstract

Urbanization has profoundly reshaped the patterns and forms of modern urban landscapes. Understanding how urban transportation and mobility are affected by spatial planning is vital. Urban vibrancy, as a crucial metric for monitoring urban development, contributes to data-driven planning and sustainable growth. However, empirical studies on the relationship between urban vibrancy and the built environment in European cities remain limited, lacking consensus on the contribution of the built environment. This study employs Munich as a case study, utilizing night-time light, housing prices, social media, points of interest (POIs), and NDVI data to measure various aspects of urban vibrancy while constructing a comprehensive assessment framework. Firstly, the spatial distribution patterns and spatial correlation of various types of urban vibrancy are revealed. Concurrently, based on the 5Ds built environment indicator system, the multi-dimensional influence on urban vibrancy is investigated. Subsequently, the Geodetector model explores the heterogeneity between built environment indicators and comprehensive vibrancy along with its economic, social, cultural, and environmental dimensions, elucidating their influence mechanism. The results show the following: (1) The comprehensive vibrancy in Munich exhibits a pronounced uneven distribution, with a higher vibrancy in central and western areas and lower vibrancy in northern and western areas. High-vibrancy areas are concentrated along major roads and metro lines located in commercial and educational centers. (2) Among multiple models, the geographically weighted regression (GWR) model demonstrates the highest explanatory efficacy on the relationship between the built environment and vibrancy. (3) Economic, social, and comprehensive vibrancy are significantly influenced by the built environment, with substantial positive effects from the POI density, building density, and road intersection density, while mixed land use shows little impact. (4) Interactions among built environment factors significantly impact comprehensive vibrancy, with synergistic interactions among the population density, building density, and POI density generating positive effects. These findings provide valuable insights for optimizing the resource allocation and functional layout in Munich, emphasizing the complex spatiotemporal relationship between the built environment and urban vibrancy while offering crucial guidance for planning. Full article

(This article belongs to the Special Issue Spatio-Temporal Analysis of Urbanization Using GIS and Remote Sensing)

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20 pages, 10547 KiB

Open AccessArticle

Spatiotemporal Features of the Surface Urban Heat Island of Bacău City (Romania) during the Warm Season and Local Trends of LST Imposed by Land Use Changes during the Last 20 Years

by Lucian Sfîcă, Alexandru-Constantin Corocăescu, Claudiu-Ștefănel Crețu, Vlad-Alexandru Amihăesei and Pavel Ichim

Remote Sens. 2023, 15(13), 3385; https://doi.org/10.3390/rs15133385 - 3 Jul 2023

Cited by 1 | Viewed by 1099

Abstract

Using MODIS and Landsat LST images, the present paper advances a series of results on the characteristics of the surface heat island (SUHI) of Bacău City (Romania) during the warm season (April to September) for a period of 20 years (2001–2020). At the same time, given their higher temporal resolution and their availability for both day and night, MODIS LST was used to understand the spatial features of the SUHI in relation to land use. In this way, a total of 946 MODIS Terra and 483 Landsat satellite images were used to outline the main LST characteristics of the days with clear sky in this middle-sized city in northeast Romania. In order to analyze MODIS LST changes in relation to land use changes in the period 2001–2018, we used the standardized CORINE Land Cover datasets. With the help of the Rodionov test, we were able to determine the geometry and intensity of the SUHI. During the day, the spatial extension of the SUHI reaches its maximum level and is delimited by the isotherm of 31.0 °C, which is 1.5–2.0 °C warmer than the neighboring non-urban areas. During the night, the SUHI has a more regulated spatial extension around the central area of the city, delimited by the 15.5 °C isotherm with LST values that are 1.0–1.5 °C warmer than the surrounding non-urban areas. Additionally, from a methodological point of view, we highlight that resampled MODIS and Landsat images at a spatial resolution of 500 m can be used with confidence to understand the detailed spatial features of the SUHI. The results of this study could help the elaboration of future policies meant to mitigate the effects of urbanization on the SUHI in an era of increasing air temperatures during summer. Full article

(This article belongs to the Special Issue Spatio-Temporal Analysis of Urbanization Using GIS and Remote Sensing)

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24 pages, 9912 KiB

Open AccessArticle

Spatiotemporal Characteristics of the Mud Receiving Area Were Retrieved by InSAR and Interpolation

by Bo Hu and Zhongya Qiao

Remote Sens. 2023, 15(2), 351; https://doi.org/10.3390/rs15020351 - 6 Jan 2023

Viewed by 1457

Abstract

The mud receiving area is an important sand storage area for dredging sea sand reclamation and sand-dumping in the waterway. The sediment accumulation area generated in the process of sand dumping and sand storage has an impact on the surrounding transportation facilities and the normal use of the entire sand storage area. From 6 August 2021 to 9 May 2022, The Sentinel-1A 24-view SLC data covering the sludge area were used to monitor the safety around the seawall road by InSAR technology. Synthetic aperture radar differential interferometry (Differential InSAR, D-InSAR) technology can obtain surface micro deformation information through single-time differential interference processing, mainly used for sudden surface deformation. D-InSAR technology detected five accumulation areas with a thickness of more than 10 cm near the seawall road, earth embankment, and cofferdam, and TS-InSAR (Time series InSAR) technology was used to retrieve the deformation of the surrounding road. The road settlement is a slight settlement distributed between ±5 mm/a. This paper uses the leveling results combined with variance analysis to verify the fusion of different TS-InSAR methods while considering the area of data loss due to causes such as loss of coherence. This paper also considers the common ground continuity and uses the adjacent interpolation and bilinear interpolation algorithm to improve knowledge of the study area seawall road and the surrounding soil embankment deformation data of the road. Compared with the leveling data, the difference between the missing data and the leveling data after interpolation is stable at about 1–7 mm, which increases the risk level of part of the road which needs to be maintained. It provides a reference method to make up for the missing data caused by ground incoherence. Full article

(This article belongs to the Special Issue Spatio-Temporal Analysis of Urbanization Using GIS and Remote Sensing)

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19 pages, 4508 KiB

Open AccessArticle

Ratio of Land Consumption Rate to Population Growth Rate in the Major Metropolitan Areas of Romania

by Iulian-Horia Holobâcă, József Benedek, Cosmina-Daniela Ursu, Mircea Alexe and Kinga Temerdek-Ivan

Remote Sens. 2022, 14(23), 6016; https://doi.org/10.3390/rs14236016 - 27 Nov 2022

Cited by 4 | Viewed by 1821

Abstract

In 2015, the 2030 Agenda for Sustainable Development was adopted by all United Nations Member States and includes a set of 17 Sustainable Development Goals. The indicator, “Ratio of land consumption rate to population growth rate” (indicator 11.3.1) was proposed for the monitoring of urban development. The present study proposes the analysis of the built-up space evolution in relation to the demographic growth in the main metropolitan areas of Romania using the 11.3.1 indicator. Land consumption rate and population growth rate (LCRPGR) is used to assess the sustainability of urban growth, which takes into account both the change in the built-up area and in the population. LCRPGR is calculated as the ratio of land consumption rate (LCR) and the population growth rate (PGR). The analysis was conducted at the metropolitan area level for the 2006–2009, 2009–2015 and 2015–2020 periods. LCR and PGR proved to be very useful indicators for the monitoring of the intensity of built-up changes in the eight metropolitan areas both in time and in space and are useful for the local and central administrations, in both the context of achieving the sustainable development targets and goals and in conducting urban design and planning. Full article

(This article belongs to the Special Issue Spatio-Temporal Analysis of Urbanization Using GIS and Remote Sensing)

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19 pages, 9698 KiB

Open AccessArticle

GMR-Net: Road-Extraction Network Based on Fusion of Local and Global Information

by Zixuan Zhang, Xuan Sun and Yuxi Liu

Remote Sens. 2022, 14(21), 5476; https://doi.org/10.3390/rs14215476 - 31 Oct 2022

Cited by 7 | Viewed by 1453

Abstract

Road extraction from high-resolution remote-sensing images has high application values in various fields. However, such work is susceptible to the influence of the surrounding environment due to the diverse slenderness and complex connectivity of roads, leading to false judgment and omission during extraction. To solve this problem, a road-extraction network, the global attention multi-path dilated convolution gated refinement Network (GMR-Net), is proposed. The GMR-Net is facilitated by both local and global information. A residual module with an attention mechanism is first designed to obtain global and other aggregate information for each location’s features. Then, a multi-path dilated convolution (MDC) approach is used to extract road features at different scales, i.e., to achieve multi-scale road feature extraction. Finally, gated refinement units (GR) are proposed to filter out ambiguous features for the gradual refinement of details. Multiple road-extraction methods are compared in this study using the Deep-Globe and Massachusetts datasets. Experiments on these two datasets demonstrate that the proposed method achieves F1-scores of 87.38 and 85.70%, respectively, outperforming other approaches on segmentation accuracy and generalization ability. Full article

(This article belongs to the Special Issue Spatio-Temporal Analysis of Urbanization Using GIS and Remote Sensing)

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