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Application of Photogrammetry and Remote Sensing in Urban Areas

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

Deadline for manuscript submissions: closed (26 April 2024) | Viewed by 6299

Special Issue Editors


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Guest Editor
L@bISEN, Equipe LSL, Yncrea Ouest, 20 Rue Cuirasse Bretagne, 29200 Brest, France
Interests: face recognition; image processing; optics; pattern recognition; remote sensing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
AE 1210 CEDETE, University Orléans, 45067 Orléans, France
Interests: GIS; spatial analysis; geosimulation; geomodelling; remote sensing; machine learning

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Guest Editor
LSL Team, L@bisen, ISEN Nantes, Yncréa Ouest, 33 Quater Avenue du Champ de Manœuvre, 44 470 Carquefou, France
Interests: remote sensing; machine learning; optics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Inhabited by more than 55% of the Earth’s population, urban areas urgently need to address three main challenges:  traffic mitigation, eco-system preservation and the prevention of global extreme natural events. Both for public health and for the achievement of Sustainable Development Goals, facing these challenges has become a necessity.

Remote sensing and photogrammetry techniques are of great interest for urban area analysis. Multispectral, hyperspectral, synthetic aperture radar (SAR), LiDAR, and photogrammetry data—either used alone or in combination—have shown their efficiency for urban studies. They are performed at a local scale (when using unmanned aerial vehicles), at a regional scale (when using airborne sensors) or at a global scale (when using satellites). Up-to-date machine learning and data mining advances offer promising results for extracting meaningful information from these big data.

This Special Issue aims to document the most recent advances in the two techniques of photogrammetry and remote sensing for urban environment studies.

Topics of interest include, but are not limited to:

  • Comprehensive data constitution for socio-economic analysis and urban planning (urban region function recognition, building height assessment through photogrammetry, etc.);
  • Addressing traffic issues and smart human mobility (bus management, delivery truck congestion mitigation, etc.);
  • Mitigation of global extreme natural events impact (what-if scenarios, damage mapping and hurricane damage area prediction, river and sea floods, earthquakes, etc.);
  • Urban ecology (urban heat islands, vegetation biomass monitoring, urban wildlife watching, etc.);
  • Urban growth impact on landscapes (urban sprawl, deforestation, ground slope destabilization, etc.);
  • Urban pollution monitoring at local, regional and global scale (urban air quality assessment, water quality assessment, soil contamination detection, anthropic activities mapping, etc.);
  • Urban photogrammetry and remote sensing data processing and exploitation (data regularization, data mining, machine learning, etc.);
  • Data fusion of urban heterogeneous data arising from photogrammetry, remote sensing, Internet of Things or any other method;
  • Urban digital twins.

Dr. Marwa Elbouz
Dr. Gaëtan Palka
Dr. Catherine Baskiotis
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

  • remote sensing
  • photogrammetry
  • urban heterogeneous data fusion
  • urban planning
  • urban traffic mitigation
  • urban digital twins
  • urban pollution monitoring
  • global extreme event
  • machine learning
  • data mining

Published Papers (3 papers)

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Research

21 pages, 4327 KiB  
Article
Research on the Extraction Method Comparison and Spatial-Temporal Pattern Evolution for the Built-Up Area of Hefei Based on Multi-Source Data Fusion
by Jianwei Huang, Chaoqun Chu, Lu Wang, Zhaofu Wu, Chunju Zhang, Jun Geng, Yongchao Zhu and Min Yu
Remote Sens. 2023, 15(23), 5617; https://doi.org/10.3390/rs15235617 - 04 Dec 2023
Viewed by 2350
Abstract
With the development of urban built-up areas, accurately extracting the urban built-up area and spatiotemporal pattern evolution trends could be valuable for understanding urban sprawl and human activities. Considering the coarse spatial resolution of nighttime light (NTL) data and the inaccurate regional boundary [...] Read more.
With the development of urban built-up areas, accurately extracting the urban built-up area and spatiotemporal pattern evolution trends could be valuable for understanding urban sprawl and human activities. Considering the coarse spatial resolution of nighttime light (NTL) data and the inaccurate regional boundary reflection on point of interest (POI) data, land surface temperature (LST) data were introduced. A composite index method (LJ–POI–LST) was proposed based on the positive relationship for extracting the boundary and reflecting the spatial-temporal evolution of urban built-up areas involving the NTL, POIs, and LST data from 1993 to 2018 in this paper. This paper yielded the following results: (1) There was a spatial-temporal pattern evolution from north-east to south-west with a primary quadrant orientation of IV, V, and VI in the Hefei urban area from 1993–2018. The medium-speed expansion rate, with an average value of 14.3 km2/a, was much faster than the population growth rate. The elasticity expansion coefficient of urbanization of 1.93 indicated the incongruous growth rate between the urban area and population, leading to an incoordinate and unreasonable development trend in Hefei City. (2) The detailed extraction accuracy for urban and rural junctions, urban forest parks, and other error-prone areas was improved, and the landscape connectivity and fragmentation were optimized according to the LJ–POI–LST composite index based on a high-resolution remote sensing validation image in the internal spatial structure. (3) Compared to the conventional NTL data and the LJ–POI index, the LJ–POI–LST composite index method displayed an extraction accuracy greater than 85%, with a similar statistical and landscape pattern index result. This paper provides a suitable method for the positive relationship among these LST, NTL, and POI data for accurately extracting the boundary and reflecting the spatial-temporal evolution of urban built-up areas by the fusion data. Full article
(This article belongs to the Special Issue Application of Photogrammetry and Remote Sensing in Urban Areas)
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19 pages, 13062 KiB  
Article
Automatic Detection and Dynamic Analysis of Urban Heat Islands Based on Landsat Images
by Ni Na, Dandan Xu, Wen Fang, Yihan Pu, Yanqing Liu and Haobin Wang
Remote Sens. 2023, 15(16), 4006; https://doi.org/10.3390/rs15164006 - 12 Aug 2023
Viewed by 1344
Abstract
Given rapid global urban development, increases to impervious surfaces, urban population growth, building construction, and energy consumption result in the urban heat island (UHI) phenomenon. However, the spatial extent of UHIs is not clearly mapped in many UHI studies based on a remote [...] Read more.
Given rapid global urban development, increases to impervious surfaces, urban population growth, building construction, and energy consumption result in the urban heat island (UHI) phenomenon. However, the spatial extent of UHIs is not clearly mapped in many UHI studies based on a remote sensing approach. Therefore, we developed a method to extract the spatial extent of the UHI during the period from 2000 to 2021 in Nanjing, China, and explored the impact of urban two- and three-dimensional expansion on UHI spatial extent and UHI intensity. After cropland effects (i.e., bare soil) were eliminated, our proposed method combines the Getis-Ord-Gi* and the standard deviation of the normalized difference vegetation index (NDVI STD) to extract the UHI area from Landsat 5 and Landsat 8 images using land surface temperature (LST) spatial autocorrelation characteristics and the seasonal variation of vegetation. Our results show the following: (1) Bare farmland has a large influence on the extraction results of UHI—combined with the seasonal variation characteristics of NDVI STD, the impact of bare soil on UHI extraction was highly reduced, strongly improving the accuracy of UHI extraction. (2) The dynamics of the UHI area are consistent with the changes in the built-up area in Nanjing at both spatial and temporal scales, but with the increase of the urban green ratio, the UHI area of mature urban areas trends to decrease due to the cooling effect of green space. (3) The accumulation of population and GDP promote the vertical expansion of urban buildings. When the two-dimensional expansion of the city reaches saturation, the UHI intensity is primarily affected by three-dimensional urban expansion. Full article
(This article belongs to the Special Issue Application of Photogrammetry and Remote Sensing in Urban Areas)
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19 pages, 110495 KiB  
Article
Monitoring Geological Risk Areas in the City of São Paulo Based on Multi-Temporal High-Resolution 3D Models
by Amanda Mendes de Sousa, Camila Duelis Viana, Guilherme Pereira Bento Garcia and Carlos Henrique Grohmann
Remote Sens. 2023, 15(12), 3028; https://doi.org/10.3390/rs15123028 - 09 Jun 2023
Cited by 2 | Viewed by 1517
Abstract
This paper presents a multi-temporal comparison of high-resolution 3D digital models from two urban areas susceptible to landslides in three time periods. The study areas belong to the São Paulo landslide risk mapping database and are named “CEU Paz” (CP) and “Parque Santa [...] Read more.
This paper presents a multi-temporal comparison of high-resolution 3D digital models from two urban areas susceptible to landslides in three time periods. The study areas belong to the São Paulo landslide risk mapping database and are named “CEU Paz” (CP) and “Parque Santa Madalena I” (PSM). For each area, a lidar digital surface model (DSM) (2017) and two structure-from-motion multi-view stereo DSMs (2019 and 2022) built from drone imagery were combined using raster algebra to generate three digital surface models of differences (DoDs). The DoDs were able to highlight changes in vegetation cover and buildings, which are important characteristics for evaluating geological risks in an urban context. Still, they were unable to highlight changes in the ground surface. The results demonstrate that the method greatly supports monitoring, allowing for greater detail and ease of detecting large-scale changes. Even with promising results, this technique should be understood as one more tool for mapping risk areas without replacing fieldwork. Full article
(This article belongs to the Special Issue Application of Photogrammetry and Remote Sensing in Urban Areas)
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