Special Issue "Optical Remote Sensing Applications 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: 31 July 2021.

Special Issue Editors

Dr. Saeid Homayouni
E-Mail Website
Guest Editor
Centre Eau Terre Environnement, Institut National de la Recherche Scientifique (INRS), Quebec City, Canada
Interests: geomatics, remote sensing, and the analysis of optical and synthetic aperture radar Earth observations through artificial intelligence and machine learning approaches for urban and agro-environmental applications
Dr. Ying Zhang
E-Mail Website
Guest Editor
Canada Centre for Mapping and Earth Observation, Natural Resources Canada, Ottawa, Canada
Interests: optical remote sensing technology development and EO-based spatial analysis for applications related to urban and mining development

Special Issue Information

Dear Colleagues,

Urban areas have been the center of human settlement and civilization. They also play essential roles in various aspects of human life, including economic, political, cultural, and educational activities. On the other hand, these areas are physically and geographically complex systems and phenomena due to the presence and integration of various elements such as residential, industrial, infrastructure, road networks, green spaces, and water bodies. As such, these phenomena are the study subject of experts and researchers in different fields, from social to physical sciences and engineering. In particular, the physical characteristics of an urban area are essential for various applications in geography, sustainable development, urban planning, and civil engineering. Geospatial information, with different levels of details at local and regional scales, can provide a valuable source of information to reach the ultimate objectives of urban studies.

Remote sensing technology and techniques are among the most effective observation and analysis tools for provision of the geospatial information about urban land complexes. From the beginning of the remote sensing era, aerial photography has provided unprecedented views of the urban area. In addition, Earth observation (EO) systems, such as Landsat satellites, have acquired unique and valuable spatial, spectral, and temporal information of surfaces of the planet, including urban areas. This collection of EOs has progressively continued and been improved by new operational spaceborne, airborne, and drone imagery, as well as optical, lidar, thermal, and radar data sources. In addition, technology revolutions related to open data and informatics resources, big data, and cloud computing platforms bring both opportunities and challenges for the user and the academic community in urban studies.

The main objective of this Special Issue (SI) of the Remote Sensing journal is to promote recent thematic research and development applications and state-of-the-art outcomes and results based on optical Earth observations. For this SI, we invite researchers with different expertise and interest to consider this opportunity and submit their papers on both applications and methodologies on “Optical Remote Sensing for Urban Area.”

Dr. Saeid Homayouni
Dr. Ying Zhang
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 papers will be 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 2400 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

  • Spatiotemporal analysis of urban land
  • Urban natural/manmade hazards
  • Land-cover mapping
  • Land-cover land-use changes (LCLUC) and modeling
  • Urban feature detection and extraction
  • Urbanization impacts and sustainable development
  • Change detection
  • Green space monitoring
  • 3D mapping and modeling from remote sensing data
  • Big data
  • Data mining
  • Image processing
  • Machine and deep learning
  • Object-based image analysis

Published Papers (7 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Inconsistency among Landsat Sensors in Land Surface Mapping: A Comprehensive Investigation Based on Simulation
Remote Sens. 2021, 13(7), 1383; https://doi.org/10.3390/rs13071383 - 03 Apr 2021
Viewed by 338
Abstract
Comprehensive investigations on the between-sensor comparability among Landsat sensors have been relatively limited compared with the increasing use of multi-temporal Landsat records in time series analyses. More seriously, the sensor-related difference has not always been considered in applications. Accordingly, comparisons were conducted among [...] Read more.
Comprehensive investigations on the between-sensor comparability among Landsat sensors have been relatively limited compared with the increasing use of multi-temporal Landsat records in time series analyses. More seriously, the sensor-related difference has not always been considered in applications. Accordingly, comparisons were conducted among all Landsat sensors available currently, including Multispectral Scanner (MSS), Thematic Mappers (TM), Enhanced Thematic Mappers (ETM+), and Operational Land Imager (OLI)) in land cover mapping, based on a collection of synthesized, multispectral data. Compared to TM, OLI showed obvious between-sensor differences in channel reflectance, especially over the near infrared (NIR) and shortwave infrared (SWIR) channels, and presented positive bias in vegetation spectral indices. OLI did not always outperform TM and ETM+ in classification, which related to the methods used. Furthermore, the channels over SWIR of TM and its successors contributed largely to enhancement of inter-class separability and to improvement of classification. Currently, the inclusion of MSS data is confronted with significant challenges regarding the consistency of surface mapping. Considering the inconsistency among the Landsat sensors, it is applicable to generate a consistent time series of spectral indices through proper transformation models. Meanwhile, it suggests the generation of specific class(es) based on interest instead of including all classes simultaneously. Full article
(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)
Show Figures

Figure 1

Open AccessArticle
Shadow Detection and Compensation from Remote Sensing Images under Complex Urban Conditions
Remote Sens. 2021, 13(4), 699; https://doi.org/10.3390/rs13040699 - 14 Feb 2021
Viewed by 650
Abstract
Due to the block of high-rise objects and the influence of the sun’s altitude and azimuth, shadows are inevitably formed in remote sensing images particularly in urban areas, which causes missing information in the shadow region. In this paper, we propose a new [...] Read more.
Due to the block of high-rise objects and the influence of the sun’s altitude and azimuth, shadows are inevitably formed in remote sensing images particularly in urban areas, which causes missing information in the shadow region. In this paper, we propose a new method for shadow detection and compensation through objected-based strategy. For shadow detection, the shadow was highlighted by an improved shadow index (ISI) combined color space with an NIR band, then ISI was reconstructed by the objects acquired from the mean-shift algorithm to weaken noise interference and improve integrity. Finally, threshold segmentation was applied to obtain the shadow mask. For shadow compensation, the objects from segmentation were treated as a minimum processing unit. The adjacent objects are likely to have the same ambient light intensity, based on which we put forward a shadow compensation method which always compensates shadow objects with their adjacent non-shadow objects. Furthermore, we presented a dynamic penumbra compensation method (DPCM) to define the penumbra scope and accurately remove the penumbra. Finally, the proposed methods were compared with the stated-of-art shadow indexes, shadow compensation method and penumbra compensation methods. The experiments show that the proposed method can accurately detect shadow from urban high-resolution remote sensing images with a complex background and can effectively compensate the information in the shadow region. Full article
(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)
Show Figures

Figure 1

Open AccessArticle
Identification and Portrait of Urban Functional Zones Based on Multisource Heterogeneous Data and Ensemble Learning
Remote Sens. 2021, 13(3), 373; https://doi.org/10.3390/rs13030373 - 21 Jan 2021
Viewed by 555
Abstract
Urban functional zones are important space carriers for urban economic and social function. The accurate and rapid identification of urban functional zones is of great significance to urban planning and resource allocation. However, the factors considered in the existing functional zone identification methods [...] Read more.
Urban functional zones are important space carriers for urban economic and social function. The accurate and rapid identification of urban functional zones is of great significance to urban planning and resource allocation. However, the factors considered in the existing functional zone identification methods are not comprehensive enough, and the recognition of functional zones stops at their categories. This paper proposes a framework that combines multisource heterogeneous data to identify the categories of functional zones and draw the portraits of functional zones. The framework comprehensively describes the features of functional zones from four aspects: building-level metrics, landscape metrics, semantic metrics, and human activity metrics, and uses a combination of ensemble learning and active learning to balance the identification accuracy of functional zones and the labeling cost during large-scale generalization. Furthermore, sentiment analysis, word cloud analysis, and land cover proportion maps are added to the portraits of typical functional zones to make the image of functional zones vivid. The experiment carried out within the Fifth Ring Road, Haidian District, Beijing, shows that the overall accuracy of the method reached 82.37% and the portraits of the four typical functional zones are clear. The method in this paper has good repeatability and generalization, which is helpful to carry out quantitative and objective research on urban functional zones. Full article
(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)
Show Figures

Graphical abstract

Open AccessArticle
Monitoring Spatiotemporal Changes of Impervious Surfaces in Beijing City Using Random Forest Algorithm and Textural Features
Remote Sens. 2021, 13(1), 153; https://doi.org/10.3390/rs13010153 - 05 Jan 2021
Viewed by 614
Abstract
As the capital city of China, Beijing has experienced unprecedented economic and population growth and dramatic impervious surface changes during the last few decades. An application of the classification method combining the spectral and textural features based on Random Forest was conducted to [...] Read more.
As the capital city of China, Beijing has experienced unprecedented economic and population growth and dramatic impervious surface changes during the last few decades. An application of the classification method combining the spectral and textural features based on Random Forest was conducted to monitor the spatial and temporal changes of Beijing’s impervious surfaces. This classification strategy achieved excellent performance in the impervious surface extraction in complex urban areas, as the Kappa coefficient reached 0.850. Based on this strategy, the impervious surfaces inside Beijing’s sixth ring road in 1997, 2002, 2007, 2013, and 2017 were extracted. As the development of Beijing has a special regional feature, the changes of impervious surfaces within the sixth ring road were assessed. The findings are as follows: (1) the textural features can significantly improve the classification accuracy of land cover in urban areas, especially for the impervious surface with high albedo. (2) Impervious surfaces within the sixth ring road expanded dramatically from 1997 to 2017, had three expanding periods: 1997–2002, 2002–2007, and 2013–2017, and only shrank in 2007–2013. There are different possible major driving factors for each period. (3) The region between the fifth and sixth ring roads in Beijing underwent the most significant changes in the two decades. (4) The inner three regions are relatively highly urbanized areas compared to the outer two regions. Urbanization processes in the interior regions tend to be completed compared to the exterior regions. Full article
(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)
Show Figures

Graphical abstract

Open AccessArticle
A Novel Urban Composition Index Based on Water-Impervious Surface-Pervious Surface (W-I-P) Model for Urban Compositions Mapping Using Landsat Imagery
Remote Sens. 2021, 13(1), 3; https://doi.org/10.3390/rs13010003 - 22 Dec 2020
Viewed by 441
Abstract
Monitoring urban compositions spatially and temporally is a crucial issue for urban planning and management. Nowadays, remote sensing techniques have been widely applied for urban compositions extraction. Compared with other remote sensing techniques, spectral indices have significant advantages due to their parameter-free and [...] Read more.
Monitoring urban compositions spatially and temporally is a crucial issue for urban planning and management. Nowadays, remote sensing techniques have been widely applied for urban compositions extraction. Compared with other remote sensing techniques, spectral indices have significant advantages due to their parameter-free and easy implementation. However, existing indices cannot extract different urban compositions well, and some of them can only extract one composition with less attention to other urban compositions. In this study, based on the water- impervious surface-pervious surface (W-I-P) model, a novel urban composition index (UCI) was developed by analyzing the robust features from the global spectral samples. Additionally, a semi-empirical threshold of UCI was proposed to extract different urban compositions (water, impervious surface area and pervious surface area). Four cities of China were selected as study areas, Landsat-8 images and Google Earth images were used for quantitative analysis. Correlation analysis, separability analysis, and accuracy assessment were conducted among UCI and five other existed indices (single and multiple composition indices) at the urban and global scales. Results indicated that UCI had a stronger correlation with the ISA proportion and a higher separability between each urban composition. UCI also achieved the highest overall accuracy and Kappa coefficient in urban compositions extraction. The suggested semi-empirical threshold was also testified to be reliable and can be a reference for practical application. There is convincing evidence that UCI is a simple, efficient, and reliable index for urban compositions extraction. Full article
(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)
Show Figures

Graphical abstract

Open AccessArticle
Detection of Undocumented Building Constructions from Official Geodata Using a Convolutional Neural Network
Remote Sens. 2020, 12(21), 3537; https://doi.org/10.3390/rs12213537 - 28 Oct 2020
Viewed by 597
Abstract
Undocumented building constructions are buildings or stories that were built years ago, but are missing in the official digital cadastral maps (DFK). The detection of undocumented building constructions is essential to urban planning and monitoring. The state of Bavaria, Germany, uses two semi-automatic [...] Read more.
Undocumented building constructions are buildings or stories that were built years ago, but are missing in the official digital cadastral maps (DFK). The detection of undocumented building constructions is essential to urban planning and monitoring. The state of Bavaria, Germany, uses two semi-automatic detection methods for this task that suffer from a high false alarm rate. To solve this problem, we propose a novel framework to detect undocumented building constructions using a Convolutional Neural Network (CNN) and official geodata, including high resolution optical data and the Normalized Digital Surface Model (nDSM). More specifically, an undocumented building pixel is labeled as “building” by the CNN but does not overlap with a building polygon of the DFK. The class of old or new undocumented building can be further separated when a Temporal Digital Surface Model (tDSM) is introduced in the stage of decision fusion. In a further step, undocumented story construction is detected as the pixels that are “building” in both DFK and predicted results from CNN, but shows a height deviation from the tDSM. By doing so, we have produced a seamless map of undocumented building constructions for one-quarter of the state of Bavaria, Germany at a spatial resolution of 0.4 m, which has proved that our framework is robust to detect undocumented building constructions at large-scale. Considering that the official geodata exploited in this research is advantageous because of its high quality and large coverage, a transferability analysis experiment is also designed in our research to investigate the sampling strategies for building detection at large-scale. Our results indicate that building detection results in unseen areas at large-scale can be improved when training samples are collected from different districts. In an area where training samples are available, local training sampless collection and training can save much time and effort. Full article
(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)
Show Figures

Graphical abstract

Open AccessArticle
The Influence of CLBP Window Size on Urban Vegetation Type Classification Using High Spatial Resolution Satellite Images
Remote Sens. 2020, 12(20), 3393; https://doi.org/10.3390/rs12203393 - 16 Oct 2020
Viewed by 379
Abstract
Urban vegetation can regulate ecological balance, reduce the influence of urban heat islands, and improve human beings’ mental state. Accordingly, classification of urban vegetation types plays a significant role in urban vegetation research. This paper presents various window sizes of completed local binary [...] Read more.
Urban vegetation can regulate ecological balance, reduce the influence of urban heat islands, and improve human beings’ mental state. Accordingly, classification of urban vegetation types plays a significant role in urban vegetation research. This paper presents various window sizes of completed local binary pattern (CLBP) texture features classifying urban vegetation based on high spatial-resolution WorldView-2 images in areas of Shanghai (China) and Lianyungang (Jiangsu province, China). To demonstrate the stability and universality of different CLBP window textures, two study areas were selected. Using spectral information alone and spectral information combined with texture information, imagery is classified using random forest (RF) method based on vegetation type, showing that use of spectral information with CLBP window textures can achieve 7.28% greater accuracy than use of only spectral information for urban vegetation type classification, with accuracy greater for single vegetation types than for mixed ones. Optimal window sizes of CLBP textures for grass, shrub, arbor, shrub-grass, arbor-grass, and arbor-shrub-grass are 3 × 3, 3 × 3, 11 × 11, 9 × 9, 9 × 9, 7 × 7 for urban vegetation type classification. Furthermore, optimal CLBP window size is determined by the roughness of vegetation texture. Full article
(This article belongs to the Special Issue Optical Remote Sensing Applications in Urban Areas)
Show Figures

Graphical abstract

Back to TopTop