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Remote Sensing of Urban Form

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

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 11336

Special Issue Editor


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Guest Editor
Ruhr-Universitat Bochum, Bochum, Germany
Interests: urban ecology; urban metabolism; urban planning; ecosystem services; socio-ecological systems; remote sensing

Special Issue Information

Dear Colleagues,

Remote sensing is widely used to analyze urban form. In an increasingly urbanized world, a better understanding of urban form can greatly support the development and evaluation of regional and national policies and the understanding of the environmental impact of urban development, thus facilitating the preparation and implementation of urban and regional planning.

Urban form is key to advancing towards sustainable urban transformations. A better understanding of urban form can contribute to solving pressing global problems of climate adaptation, ecological deterioration, and social equity that are present in current patterns of local and global urban development. To advance, we need conceptually sounded, detailed, and accurate representations of the spatial complexity, drivers, and patterns of urban form emerging from different spatiotemporal conditions.

In this Special Issue, we will collect a set of contributions on remote sensing approaches to analyze urban form by means of remotely sensed data and image processing, emphasizing quantitative and empirical measures and focusing on one or more of the following aspects:

  • Approaches coupled with wider spectral ranges;
  • Effective multitemporal approaches to understand the variations of urban form and its transitions over time. Change-detection analyses conducted in only two discrete time periods are restricting the ability to monitor and analyze urban form over extended time periods, describing patterns only partially and mostly concealing the processes driving them. We encourage the use of multitemporal remote sensing techniques to explore the temporal dimension of urban form with sufficient observations to monitor intermediate structural changes and their fluctuations;
  • Implications of spectral, radiometric, and temporal resolution to describe and compare urban forms. These contributions might inspire the development of sensors better suited to capture the spatial and spectral complexity and the heterogeneity of urban forms;
  • Remote sensing approaches of the third dimension (3D) of urban form, state-of-the-art methods for 3D modeling of  urban form, novel indicators/landscape metrics of 3D urban form;
  • Remote sensing approaches going beyond the binary classification of urban land uses describing urban form as a continuous, rather than a categorical, phenomenon, multi-label and proportion-cover classification systems, adaptation of pre-established databases, consequences of dichotomic classification systems for urban form analysis;
  • Remote sensing approaches that effectively tackle the interplay between human-constructed materials (technomass), soil–plant continuum, and water. This in the context where the soil–plant continuum and water are often overlooked as material components of urban form. Biomass–technomass modelling, approaches addressing multi-layer classifications.

Dr. Luis Inostroza
Guest Editor

Manuscript Submission Information

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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

  • Urban form
  • Multitemporal assessment
  • Soil–plant continuum
  • Continuous urbanization
  • 3D urban form
  • Radiometric indexes
  • Spatial heterogeneity

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Published Papers (4 papers)

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Research

12 pages, 8368 KiB  
Article
The Potential of 3-D Building Height Data to Characterize Socioeconomic Activities: A Case Study from 38 Cities in China
by Guojiang Yu, Zixuan Xie, Xuecao Li, Yixuan Wang, Jianxi Huang and Xiaochuang Yao
Remote Sens. 2022, 14(9), 2087; https://doi.org/10.3390/rs14092087 - 27 Apr 2022
Cited by 7 | Viewed by 2563
Abstract
Urban forms are closely related to the urban environment, providing great potential to analyze human socioeconomic activities. However, limited studies have investigated the impacts of three-dimensional (3-D) urban forms on socioeconomic activities across cities. In this paper, we explored the relationship between urban [...] Read more.
Urban forms are closely related to the urban environment, providing great potential to analyze human socioeconomic activities. However, limited studies have investigated the impacts of three-dimensional (3-D) urban forms on socioeconomic activities across cities. In this paper, we explored the relationship between urban form and socioeconomic activities using 3-D building height data from 38 cities in China. First, we aggregated the building footprint data and calculated three building indicators at the grid scale, based on which the spatial patterns of building height and road density were analyzed. Then, we examined the capacities of two-dimensional (2D)/3D urban forms in characterizing socioeconomic activities using satellite-derived nighttime light (NTL) data. Finally, we analyzed the relationship between road density distributions and building heights across 38 cities in China. Our results suggest that the building height information can improve the correlation between urban form and NTL. Different patterns of road distribution were revealed according to the distribution of road density change from the building hotspots, showing the capacity of 3-D building height data in helping characterize socioeconomic activities. Our study indicates that the 3-D building height information is of great potential to support a variety of studies in urban domains, such as population distribution and carbon emissions, with significantly improved capacities. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Form)
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24 pages, 10737 KiB  
Article
Multitemporal Change Detection Analysis in an Urbanized Environment Based upon Sentinel-1 Data
by Lars Gruenhagen and Carsten Juergens
Remote Sens. 2022, 14(4), 1043; https://doi.org/10.3390/rs14041043 - 21 Feb 2022
Cited by 9 | Viewed by 3252
Abstract
The German Ruhr area is a highly condensed urban area that experienced a tremendous structural change over recent decades with the replacement of the coal and steel industries by other sectors. Consequently, a lot of major land cover changes happened. To retrospectively quantify [...] Read more.
The German Ruhr area is a highly condensed urban area that experienced a tremendous structural change over recent decades with the replacement of the coal and steel industries by other sectors. Consequently, a lot of major land cover changes happened. To retrospectively quantify such land cover changes, this study analysed synthetic aperture radar images of the Sentinel-1 satellites by applying the Google Earth Engine. Three satellite images are analysed by the multitemporal difference-adjusted dispersion threshold approach to capture land cover changes such as demolished buildings and new buildings by applying a threshold. This approach uses synthetic aperture radar data that are rarely considered in previously existing land cover change services. Urbanization or urban sprawl leads to changes in the urban form globally. These can be caused, for example, by migration or regionally by structural change, etc., such as in the study area presented here. The results are validated with reference data sets, which are publicly available nationally (e.g., house contour lines, normalized digital terrain model, digital orthophotos) or which are publicly available globally like the Global Urban Footprint and the World Settlement Footprint. Based on this, land cover changes could be identified for 21 locations within the study area of the city of Bochum. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Form)
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18 pages, 8748 KiB  
Article
Detecting High-Rise Buildings from Sentinel-2 Data Based on Deep Learning Method
by Liwei Li, Jinming Zhu, Gang Cheng and Bing Zhang
Remote Sens. 2021, 13(20), 4073; https://doi.org/10.3390/rs13204073 - 12 Oct 2021
Cited by 4 | Viewed by 2113
Abstract
High-rise buildings (HRBs) as a modern and visually distinctive land use play an important role in urbanization. Large-scale monitoring of HRBs is valuable in urban planning and environmental protection and so on. Due to the complex 3D structure and seasonal dynamic image features [...] Read more.
High-rise buildings (HRBs) as a modern and visually distinctive land use play an important role in urbanization. Large-scale monitoring of HRBs is valuable in urban planning and environmental protection and so on. Due to the complex 3D structure and seasonal dynamic image features of HRBs, it is still challenging to monitor large-scale HRBs in a routine way. This paper extends our previous work on the use of the Fully Convolutional Networks (FCN) model to extract HRBs from Sentinel-2 data by studying the influence of seasonal and spatial factors on the performance of the FCN model. 16 Sentinel-2 subset images covering four diverse regions in four seasons were selected for training and validation. Our results indicate the performance of the FCN-based method at the extraction of HRBs from Sentinel-2 data fluctuates among seasons and regions. The seasonal change of accuracy is larger than that of the regional change. If an optimal season can be chosen to get a yearly best result, F1 score of detected HRBs can reach above 0.75 for all regions with most errors located on the boundary of HRBs. FCN model can be trained on seasonally and regionally combined samples to achieve similar or even better overall accuracy than that of the model trained on an optimal combination of season and region. Uncertainties exist on the boundary of detected results and may be relieved by revising the definition of HRBs in a more rigorous way. On the whole, the FCN based method can be largely effective at the extraction of HRBs from Sentinel-2 data in regions with a large diversity in culture, latitude, and landscape. Our results support the possibility to build a powerful FCN model on a larger size of training samples for operational monitoring HRBs at the regional level or even on a country scale. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Form)
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19 pages, 4185 KiB  
Article
Integrating Backdating and Transfer Learning in an Object-Based Framework for High Resolution Image Classification and Change Analysis
by Yuguo Qian, Weiqi Zhou, Wenjuan Yu, Lijian Han, Weifeng Li and Wenhui Zhao
Remote Sens. 2020, 12(24), 4094; https://doi.org/10.3390/rs12244094 - 15 Dec 2020
Cited by 11 | Viewed by 2334
Abstract
Classification and change analysis based on high spatial resolution imagery are highly desirable for urban landscapes. However, methods with both high accuracy and efficiency are lacking. Here, we present a novel approach that integrates backdating and transfer learning under an object-based framework. Backdating [...] Read more.
Classification and change analysis based on high spatial resolution imagery are highly desirable for urban landscapes. However, methods with both high accuracy and efficiency are lacking. Here, we present a novel approach that integrates backdating and transfer learning under an object-based framework. Backdating is used to optimize the target area to be classified, and transfer learning is used to select training samples for classification. We further compare the new approach with that of using backdating or transfer learning alone. We found: (1) The integrated new approach had higher overall accuracy for both classifications (85.33%) and change analysis (88.67%), which were 2.0% and 4.0% higher than that of backdating, and 9.3% and 9.0% higher than that of transfer learning, respectively. (2) Compared to approaches using backdating alone, the use of transfer learning in the new approach allows automatic sample selection for supervised classification, and thereby greatly improves the efficiency of classification, and also reduces the subjectiveness of sample selection. (3) Compared to approaches using transfer learning alone, the use of backdating in the new approach allows the classification focusing on the changed areas, only 16.4% of the entire study area, and therefore greatly improves the efficiency and largely avoid the false change. In addition, the use of a reference map for classification can improve accuracy. This new approach would be particularly useful for large area classification and change analysis. Full article
(This article belongs to the Special Issue Remote Sensing of Urban Form)
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