Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Browser

► Journal Browser

Remote Sensing for Urban Morphology

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (30 July 2019) | Viewed by 24159

Share This Special Issue

Special Issue Editor

Prof. Dr. Victor Mesev

E-Mail Website
Guest Editor

Department of Geography, Florida State University, Tallahassee, FL 32306, USA
Interests: image classification; urban morphology; dasymetric mapping; population
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing is critical for measuring the rate of growth of cities around the world. Concerns regarding overcrowding, traffic congestion, lack of green space, and air and water pollution affect the quality of human life. Urban morphology is central to these concerns, because the condition and configuration of buildings and roads produce the social form and economic consumption of urban areas.

This Special Issue focusses on research regarding the techniques and applications of remote sensing for measuring urban morphology. The focus is on the data from high spatial resolution sensors, LiDAR, and links with GIS that measure the tangible physical structures that form urban morphologies. Also, research on how, over time, these measurements determine the spatial extent and rate of urban change, which is critical to understanding how cities develop around the world, including urban sprawl, sustainable- or eco-cities, and unregulated developments.

Prof. Victor Mesev
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

High spatial resolution urban mapping
LiDAR, GIS and 3D urban morphology
Urban population and social forms
Urban sprawl
Eco-cities
Unplanned and informal settlements

Published Papers (6 papers)

Download All Papers

Editorial

Jump to: Research

2 pages, 148 KiB

Open AccessEditorial

Editorial for Special Issue: “Remote Sensing for Urban Morphology”

by Victor Mesev

Remote Sens. 2019, 11(24), 2986; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11242986 - 12 Dec 2019

Viewed by 1523

Abstract

Remote sensing remains critical for measuring the rate of growth of cities around the world, particularly the rapidly expanding cities in economically developing countries [...] Full article

(This article belongs to the Special Issue Remote Sensing for Urban Morphology)

Research

Jump to: Editorial

30 pages, 72739 KiB

Open AccessFeature PaperArticle

Multisensor Characterization of Urban Morphology and Network Structure

by Christopher Small

Remote Sens. 2019, 11(18), 2162; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11182162 - 17 Sep 2019

Cited by 17 | Viewed by 3292

Abstract

The combination of decameter resolution Sentinel 2 and hectometer resolution VIIRS offers the potential to quantify urban morphology at scales spanning the range from individual objects to global scale settlement networks. Multi-season spectral characteristics of built environments provide an independent complement to night light brightness compared for 12 urban systems. High fractions of spectrally stable impervious surface combined with persistent deep shadow between buildings are compared to road network density and outdoor lighting inferred from night light. These comparisons show better spatial agreement and more detailed representation of a wide range of built environments than possible using Landsat and DMSP-OLS. However, they also show that no single low luminance brightness threshold provides optimal spatial correlation to built extent derived from Sentinel in different urban systems. A 4-threshold comparison of 6 regional night light networks shows consistent spatial scaling, spanning 3 to 5 orders of magnitude in size and number with rank-size slopes consistently near −1. This scaling suggests a dynamic balance among the processes of nucleation, growth and interconnection. Rank-shape distributions based on √Area/Perimeter of network components scale similarly to rank-size distributions at higher brightness thresholds, but show both progressive then abrupt increases in fractal dimension of the largest, most interconnected network components at lower thresholds. Full article

(This article belongs to the Special Issue Remote Sensing for Urban Morphology)

► Show Figures

Graphical abstract

20 pages, 7408 KiB

Open AccessArticle

Long Integral Time Continuous Panorama Scanning Imaging Based on Bilateral Control with Image Motion Compensation

by Dapeng Tian, Yutang Wang, Zhongshi Wang, Fuchao Wang and Huijun Gao

Remote Sens. 2019, 11(16), 1924; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11161924 - 17 Aug 2019

Cited by 8 | Viewed by 2644

Abstract

Urban remote sensing with moving carriers enables comprehensive monitoring of an urban area. High spatial resolution and wide covering are always required to improve the performance and efficiency of remote sensing. Continuous scanning imaging is a feasible solution. However, imaging motion degrades the performance of a remote sensing system. Rotating motion leads to the loss of key urban morphology information of a panorama imaging. Image translation results in blurry images. For high spatial resolution and high efficiency imaging with low illumination condition, such as imaging at dusk, long-focus lens and long integral time must be further utilized, which makes the problem more severe. In this paper, a novel image motion compensation method is proposed to compensate for image rotation and image translation simultaneously. A quantitative description of image motion, including both image rotation and image translation, is first developed based on the principle of geometrical optics and then analyzed in detail through numerical simulations. Furthermore, a comprehensive image rotation compensation method is developed based on four-channel bilateral control with sliding mode controller, at the same time image translation compensation is performed according to the quantitative relationship of the motion of the scan mirror and image translation compensator. The experimental results show that the proposed method provides effective compensation for image rotation and image translation. This enables acquisition of high spatial resolution urban panoramic images. Full article

(This article belongs to the Special Issue Remote Sensing for Urban Morphology)

► Show Figures

Figure 1

14 pages, 4630 KiB

Open AccessArticle

Dasymetric Mapping Using UAV High Resolution 3D Data within Urban Areas

by Carla Rebelo, António Manuel Rodrigues and José António Tenedório

Remote Sens. 2019, 11(14), 1716; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11141716 - 19 Jul 2019

Cited by 7 | Viewed by 3853

Abstract

Multi-temporal analysis of census small-area microdata is hampered by the fact that census tract shapes do not often coincide between census exercises. Dasymetric mapping techniques provide a workaround that is nonetheless highly dependent on the quality of ancillary data. The objectives of this work are to: (1) Compare the use of three spatial techniques for the estimation of population according to census tracts: Areal interpolation and dasymetric mapping using control data—building block area (2D) and volume (3D); (2) demonstrate the potential of unmanned aerial vehicle (UAV) technology for the acquisition of control data; (3) perform a sensitivity analysis using Monte Carlo simulations showing the effect of changes in building block volume (3D information) in population estimates. The control data were extracted by a (semi)-automatic solution—3DEBP (3D extraction building parameters) developed using free open source software (FOSS) tools. The results highlight the relevance of 3D for the dasymetric mapping exercise, especially if the variations in height between building blocks are significant. Using low-cost UAV backed systems with a FOSS-only computing framework also proved to be a competent solution with a large scope of potential applications. Full article

(This article belongs to the Special Issue Remote Sensing for Urban Morphology)

► Show Figures

Graphical abstract

21 pages, 2539 KiB

Open AccessArticle

A Generic Classification Scheme for Urban Structure Types

by Arthur Lehner and Thomas Blaschke

Remote Sens. 2019, 11(2), 173; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11020173 - 17 Jan 2019

Cited by 31 | Viewed by 6971

Abstract

This paper presents a proposal for a generic urban structure type (UST) scheme. Initially developed in the context of urban ecology, the UST approach is increasingly popular in the remote sensing community. However, there is no consistent and standardized UST framework. Until now, the terms land use and certain USTs are often used and described synonymously, or components of structure and use are intermingled. We suggest a generic nomenclature and a respective UST scheme that can be applied worldwide by stakeholders of different disciplines. Based on the insights of a rigorous literature analysis, we formulate a generic structural- and object-based typology, allowing for the generation of hierarchically and terminologically consistent USTs. The developed terminology exclusively focuses on morphology, urban structures and the general exterior appearance of buildings. It builds on the delimitation of spatial objects at several scales and leaves out all social aspects and land use aspects of an urban area. These underlying objects or urban artefacts and their structure- and object-related features, such as texture, patterns, shape, etc. are the core of the hierarchically structured UST scheme. Finally, the authors present a generic framework for the implementation of a remote sensing-based UST classification along with the requirements regarding sensors, data and data types. Full article

(This article belongs to the Special Issue Remote Sensing for Urban Morphology)

► Show Figures

Graphical abstract

24 pages, 7708 KiB

Open AccessArticle

A Comprehensive Evaluation of Approaches for Built-Up Area Extraction from Landsat OLI Images Using Massive Samples

by Tao Zhang and Hong Tang

Remote Sens. 2019, 11(1), 2; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11010002 - 20 Dec 2018

Cited by 20 | Viewed by 4955

Abstract

Detailed information about built-up areas is valuable for mapping complex urban environments. Although a large number of classification algorithms for such areas have been developed, they are rarely tested from the perspective of feature engineering and feature learning. Therefore, we launched a unique investigation to provide a full test of the Operational Land Imager (OLI) imagery for 15-m resolution built-up area classification in 2015, in Beijing, China. Training a classifier requires many sample points, and we proposed a method based on the European Space Agency’s (ESA) 38-m global built-up area data of 2014, OpenStreetMap, and MOD13Q1-NDVI to achieve the rapid and automatic generation of a large number of sample points. Our aim was to examine the influence of a single pixel and image patch under traditional feature engineering and modern feature learning strategies. In feature engineering, we consider spectra, shape, and texture as the input features, and support vector machine (SVM), random forest (RF), and AdaBoost as the classification algorithms. In feature learning, the convolutional neural network (CNN) is used as the classification algorithm. In total, 26 built-up land cover maps were produced. The experimental results show the following: (1) The approaches based on feature learning are generally better than those based on feature engineering in terms of classification accuracy, and the performance of ensemble classifiers (e.g., RF) are comparable to that of CNN. Two-dimensional CNN and the 7-neighborhood RF have the highest classification accuracies at nearly 91%; (2) Overall, the classification effect and accuracy based on image patches are better than those based on single pixels. The features that can highlight the information of the target category (e.g., PanTex (texture-derived built-up presence index) and enhanced morphological building index (EMBI)) can help improve classification accuracy. The code and experimental results are available at https://github.com/zhangtao151820/CompareMethod. Full article

(This article belongs to the Special Issue Remote Sensing for Urban Morphology)

► Show Figures

Graphical abstract

Journal Menu

Journal Browser

Remote Sensing for Urban Morphology

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (6 papers)

Editorial

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI