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Urban Remote Sensing and Sustainable Development
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Urban Remote Sensing and Sustainable Development

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 May 2019) | Viewed by 36724

Special Issue Editor

Prof. Dr. John Trinder

E-Mail Website
Guest Editor
Surveying and Geospatial Engineering, School of Civil and Environmental Engineering, The University of New South Wales (UNSW), Sydney, NSW 2052, Australia
Interests: photogrammetry and remote sensing; geospatial information systems; SAR remote sensing; feature extraction from images; sustainable development; ecosystem services
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the announcement of the UN Sustainable Development Goals (SDGs) of the 2030 Agenda for Sustainable Development in 2016, all nations of the UN, over the next fifteen years, will mobilize efforts to end all forms of poverty, fight inequalities, and tackle climate change, while ensuring that no one is left behind. Since urban areas currently comprise more than 54% of the world’s populations and that figure is rising, the sustainability of urban areas should be one of the key goals for study. Indeed, SDG11 refers to ‘Sustainable Cities and Communities’. There is a range of satellite remotely sensing data available for studying aspects of urban sustainability, including from high and medium resolution satellites, as well as data derived from a range of piloted airborne and RPAS (Remote Piloted Airborne Systems) sensors. These sensors may including imaging and ranging systems, such as LiDAR and SAR (Synthetic Aperture Radar).

There are many significant unknowns in the consideration of sustainable cities and communities. Some relevant questions are: Is an urban area sustainable? what is a sustainable city or how can an urban area become sustainable? how is sustainability assessed? what levels of green and open space versus developed building spaces are appropriate for a sustainable urban environment? Remote sensing technologies should be able to be address some of these questions.

Key Issues: While not limiting the topics covered by submitted papers, suggested applications of remote sensing for urban sustainability could cover:

  • How to assess the sustainability of urban environments
  • What sustainability Indicators (SI) can be developed for assessing the sustainability of urban areas
  • How can policy makers be guided on developing sustainable cities, as well as on monitoring and assessing the sustainability of urban areas
  • How to develop and monitor sustainable industry which contributes to employment in cities
  • Monitoring and managing the effects of climate change on urban environments
  • Measurement of air quality and studies of air pollution
  • Contributions towards sustainable energy consumption
  • Sustainable infrastructure development for urban areas
  • Regional spatial data infrastructure development for decision making in urban areas

I encourage all researchers with interests in the applications of remote sensing for urban sustainability, to consider contributing to this special issue.

Prof. John Trinder
Guest Editor

Manuscript Submission Information

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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. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

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Published Papers (5 papers)

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Research

20 pages, 5717 KiB  
Article
DEM Generation from Fixed-Wing UAV Imaging and LiDAR-Derived Ground Control Points for Flood Estimations
by Jairo R. Escobar Villanueva, Luis Iglesias Martínez and Jhonny I. Pérez Montiel
Sensors 2019, 19(14), 3205; https://doi.org/10.3390/s19143205 - 20 Jul 2019
Cited by 63 | Viewed by 10821
Abstract
Geospatial products, such as digital elevation models (DEMs), are important topographic tools for tackling local flood studies. This study investigates the contribution of LiDAR elevation data in DEM generation based on fixed-wing unmanned aerial vehicle (UAV) imaging for flood applications. More specifically, it [...] Read more.
Geospatial products, such as digital elevation models (DEMs), are important topographic tools for tackling local flood studies. This study investigates the contribution of LiDAR elevation data in DEM generation based on fixed-wing unmanned aerial vehicle (UAV) imaging for flood applications. More specifically, it assesses the accuracy of UAV-derived DEMs using the proposed LiDAR-derived control point (LCP) method in a Structure-from-Motion photogrammetry processing. Also, the flood estimates (volume and area) of the UAV terrain products are compared with a LiDAR-based reference. The applied LCP-georeferencing method achieves an accuracy comparable with other studies. In addition, it has the advantage of using semi-automatic terrain data classification and is readily applicable in flood studies. Lastly, it proves the complementarity between LiDAR and UAV photogrammetry at the local level. Full article
(This article belongs to the Special Issue Urban Remote Sensing and Sustainable Development)
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Figure 1

Figure 1
<p>Map of the selected study area and location of control points for DEM generation. Hydrologic sub-basin (red dashed polygons) and numbers (bold type) were delineated and coded from local flood simulations performed by Nardini and Miguez [<a href="#B44-sensors-19-03205" class="html-bibr">44</a>].</p> Full article ">Figure 2
<p>General outline of the methodology.</p> Full article ">Figure 3
<p>General methodology for ground control point (GCP) and LiDAR-derived control point (LCP) determination (method applied); (<b>a</b>) Leica TCR 403 total station used for GCP; (<b>b</b>) LiDAR DEM altimetric reference (displayed in shaded relief) with LCPs placed on study area.</p> Full article ">Figure 4
<p>Checkpoint locations for DEM accuracy assessment.</p> Full article ">Figure 5
<p>Box plots for comparison of <span class="html-italic">ΔZ</span> error for each model (No. of checkpoints = 104).</p> Full article ">Figure 6
<p>Histogram of the <span class="html-italic">ΔZ</span> values (n = 587) for each model. Superimposed on the histogram are the expected normal distribution curves with mean and RMSE estimated from all the data (red). The Shapiro-Wilk test results are shown (if <span class="html-italic">p</span>-value ≥ 0.05, <span class="html-italic">ΔZ</span> are normally distributed).</p> Full article ">Figure 7
<p>Accuracy (absolute and relative) of UAV-derived models, and comparison with LiDAR (no. of checkpoints = 104). Relative accuracy ratios are shown in brackets. USGS/ASPRS accuracy standards (<b>left</b>), as well as expected accuracy (<b>right</b>) are shown as horizontal dashed lines.</p> Full article ">Figure 8
<p>Flood extent to the corresponding DEM in 704 and 603 sub-basins.</p> Full article ">Figure 9
<p>This is a figure, Similarity of flood progression curves compared to LiDAR at 704 (<b>a</b>) and 603 (<b>b</b>) sub-basins. Bray-Curtis index is shown in brackets. Volume is given in 1k cubic meters (m<sup>3</sup>).</p> Full article ">
24 pages, 10993 KiB  
Article
Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme
by Guoyin Cai, Huiqun Ren, Liuzhong Yang, Ning Zhang, Mingyi Du and Changshan Wu
Sensors 2019, 19(14), 3120; https://doi.org/10.3390/s19143120 - 15 Jul 2019
Cited by 41 | Viewed by 7223
Abstract
Urban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper [...] Read more.
Urban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper (TM) data, cannot uncover detailed LULC information. Further, very high resolution (VHR) satellite imagery, such as IKONOS and QuickBird data, can only be applied to a small area, largely due to the data unavailability and high computation cost. As a result, little research has been conducted to extract detailed urban LULC information for a large urban area. This study, therefore, developed a three-layer classification scheme for deriving detailedurban LULC information by integrating newly launched Chinese GF-1 (medium resolution) and GF-2 (very high resolution) satellite imagery and synthetically incorporating geometry, texture, and spectral information through multi-resolution image segmentation and object-based image classification (OBIA). Homogeneous urban LULC types such as water bodies or large areas of vegetation could be derived from GF-1 imagery with 16 m and 8 m spatial resolutions, while heterogeneous urban LULC types such as industrial buildings, residential buildings, and roads could be extracted from GF-2 imagery with 3.2 m and 0.8 m spatial resolutions. The multi-resolution segmentation method and a random forest algorithm were employed to perform image segmentation and object-based image classification, respectively. An analysis of the results suggests an overall accuracy of 0.89 and 0.87 were achieved for the second and third level urban LULC classification maps, respectively. Therefore, the three-layer classification scheme has the potential to derive high accuracy urban LULC information through integrating medium and high-resolution remote sensing imagery. Full article
(This article belongs to the Special Issue Urban Remote Sensing and Sustainable Development)
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Figure 1

Figure 1
<p>The study area (standard false color composition from GF-1 satellite imagery).</p> Full article ">Figure 2
<p>Land use land cover samples collection and field investigation, (<b>a</b>) collected training and testing samples; (<b>b</b>) field work route and investigation sites.</p> Full article ">Figure 3
<p>Flowchart of image pre-processing.</p> Full article ">Figure 4
<p>Flowchart of information combination from GF-1 and GF-2 multi-resolution satellite imagery.</p> Full article ">Figure 5
<p>Flowchart for extracting water bodies.</p> Full article ">Figure 6
<p>(<b>a</b>) a coal ash site and (<b>b</b>) its appearance in the image.</p> Full article ">Figure 7
<p>Flowchart for extracting vegetation and barren lands.</p> Full article ">Figure 8
<p>Flowchart for extracting farm lands, roads and squares, and buildings.</p> Full article ">Figure 9
<p>Urban LULC map of Changchun city: (<b>a</b>) the third layer, and (<b>b</b>) the second layer.</p> Full article ">Figure 10
<p>Part of the zoomed view of the LULC layer 3 map (left) and the corresponding GF-2 standard false color composition image (right) in the central urban region of Changchun City.</p> Full article ">
19 pages, 12176 KiB  
Article
Empowering Citizens through Perceptual Sensing of Urban Environmental and Health Data Following a Participative Citizen Science Approach
by Manuel Ottaviano, María Eugenia Beltrán-Jaunsarás, José Gabriel Teriús-Padrón, Rebeca I. García-Betances, Sergio González-Martínez, Gloria Cea, Cecilia Vera, María Fernanda Cabrera-Umpiérrez and María Teresa Arredondo Waldmeyer
Sensors 2019, 19(13), 2940; https://doi.org/10.3390/s19132940 - 3 Jul 2019
Cited by 25 | Viewed by 6836
Abstract
The growth of the urban population together with a high concentration of air pollution have important health impacts on citizens who are exposed to them, causing serious risks of the development and evolution of different chronic diseases. This paper presents the design and [...] Read more.
The growth of the urban population together with a high concentration of air pollution have important health impacts on citizens who are exposed to them, causing serious risks of the development and evolution of different chronic diseases. This paper presents the design and development of a novel participatory citizen science-based application and data ecosystem model. These developments are imperative and scientifically designed to gather and process perceptual sensing of urban, environmental, and health data. This data acquisition approach allows citizens to gather and generate environment- and health-related data through mobile devices. The sum of all citizens’ data will continuously enrich and increase the volumes of data coming from the city sensors and sources across geographical locations. These scientifically generated data, coupled with data from the city sensors and sources, will enable specialized predictive analytic solutions to empower citizens with urban, environmental, and health recommendations, while enabling new data-driven policies. Although it is difficult for citizens to relate their personal behaviour to large-scale problems such as climate change, pollution, or public health, the developed ecosystem provides the necessary tools to enable a greener and healthier lifestyle, improve quality of life, and contribute towards a more sustainable local environment. Full article
(This article belongs to the Special Issue Urban Remote Sensing and Sustainable Development)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Four-step methodological approach.</p> Full article ">Figure 2
<p>PulsAir mobile application mockup interface designs: (<b>a</b>) Home Menu, first version; (<b>b</b>) Home Menu, second version; (<b>c</b>) My City Module; (<b>d</b>) Leaderboard.</p> Full article ">Figure 3
<p>PulsAir mobile application interface screenshots: (<b>a</b>) Home Menu; (<b>b</b>) “Me” module; (<b>c</b>) “My City” module; (<b>d</b>) “My Points” section; (<b>e</b>) Level up message; (<b>f</b>) Leaderboard.</p> Full article ">Figure 4
<p>PulsAir mobile application workflow. CVD—cardiovascular disease.</p> Full article ">Figure 5
<p>Big data-oriented ecosystem. GUI—graphical user interface.</p> Full article ">
14 pages, 5276 KiB  
Article
Evaluation of the Equity of Urban Park Green Space Based on Population Data Spatialization: A Case Study of a Central Area of Wuhan, China
by Chuandong Tan, Yuhan Tang and Xuefei Wu
Sensors 2019, 19(13), 2929; https://doi.org/10.3390/s19132929 - 2 Jul 2019
Cited by 35 | Viewed by 6423
Abstract
To measure the equity of urban park green space, spatial matching between service supply and user group demand should be taken into consideration. However, if the demographic data, with the administrative division as the basic unit, are directly applied to characterize the spatial [...] Read more.
To measure the equity of urban park green space, spatial matching between service supply and user group demand should be taken into consideration. However, if the demographic data, with the administrative division as the basic unit, are directly applied to characterize the spatial distribution of a user group, it may introduce inevitable deviation into the evaluation results due to the low-resolution nature and modifiable areal unit problem of such data. Taking the central area of Wuhan as an example, the population data spatialization method based on land use modeling was used to build a geographically weighted regression (GWR) model of land cover type and demographic data, and the spatial distribution of the population of the 150 m grid was obtained by inversion. Then, the equity of park green space in Wuhan central city was evaluated by population spatial data and network accessibility. The results showed that (1) the range of park green space in the central urban area of Wuhan was within a walking distance of 15 min, accounting for 25.8% of the total study area and covering 54.2% of the population in the study area; (2) the equity of park green space in Hongshan District was the worst; (3) and the use of population spatial data can measure equity on a more precise scale. Full article
(This article belongs to the Special Issue Urban Remote Sensing and Sustainable Development)
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Figure 1

Figure 1
<p>The location and administrative boundary of the study area.</p> Full article ">Figure 2
<p>The street-level population density of the central area of Wuhan City in 2015.</p> Full article ">Figure 3
<p>Urban park green space (<b>a</b>) and road network (<b>b</b>) in the central area of Wuhan City.</p> Full article ">Figure 4
<p>Land cover in the central area of Wuhan City.</p> Full article ">Figure 5
<p>Comparison of Residual Moran’s I index for traditional least squares method (OLS) (<b>a</b>) and global weighted regression (GWR) (<b>b</b>) models.</p> Full article ">Figure 6
<p>Spatialized population density map of Wuhan’s central area in 2015.</p> Full article ">Figure 7
<p>Service areas for urban park green space according to the network analysis methods.</p> Full article ">Figure 8
<p>Low-accessibility population spatial distribution.</p> Full article ">Figure 9
<p>Low-accessibility population at the street scale.</p> Full article ">
12 pages, 5482 KiB  
Article
Semantic Segmentation of Building Roof in Dense Urban Environment with Deep Convolutional Neural Network: A Case Study Using GF2 VHR Imagery in China
by Yuchu Qin, Yunchao Wu, Bin Li, Shuai Gao, Miao Liu and Yulin Zhan
Sensors 2019, 19(5), 1164; https://doi.org/10.3390/s19051164 - 7 Mar 2019
Cited by 30 | Viewed by 4615
Abstract
This paper presents a novel approach for semantic segmentation of building roofs in dense urban environments with a Deep Convolution Neural Network (DCNN) using Chinese Very High Resolution (VHR) satellite (i.e., GF2) imagery. To provide an operational end-to-end approach for accurately mapping build [...] Read more.
This paper presents a novel approach for semantic segmentation of building roofs in dense urban environments with a Deep Convolution Neural Network (DCNN) using Chinese Very High Resolution (VHR) satellite (i.e., GF2) imagery. To provide an operational end-to-end approach for accurately mapping build roofs with feature extraction and image segmentation, a fully convolutional DCNN with both convolutional and deconvolutional layers is designed to perform building roof segmentation. We selected typical cities with dense and diverse urban environments in different metropolitan regions of China as study areas, and sample images were collected over cities. High performance GPU-mounted workstations are employed to perform the model training and optimization. With the building roof samples collected over different cities, the predictive model with convolution layers is developed for building roof segmentation. The validation shows that the overall accuracy (OA) and the mean Intersection Over Union (mIOU) of DCNN-based semantic segmentation results are 94.67% and 0.85, respectively, and the CRF-refined segmentation results achieved OA of 94.69% and mIOU of 0.83. The results suggest that the proposed approach is a promising solution for building roof mapping with VHR images over large areas in dense urban environments with different building patterns. With the operational acquisition of GF2 VHR imagery, it is expected to develop an automated pipeline of operational built-up area monitoring, and the timely update of building roof map could be applied in urban management and assessment of human settlement-related sustainable development goals over large areas. Full article
(This article belongs to the Special Issue Urban Remote Sensing and Sustainable Development)
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Figure 1

Figure 1
<p>Sample images captured by GF2 PMS sensors at 27 August 2016: the three images cover the same area around the National Stadium at Beijing, where the upper left is a panchromatic image with resolution of 1.0 m, the upper right is the true color composite of a multi-spectral image with resolution of 4.0 m, and the bottom one is the pan-sharped image of the panchromatic and multi-spectral images.</p> Full article ">Figure 2
<p>Architecture of the designed DCNN for building roof segmentation: Conv-N (N = 1–14) and Deconv-N (N = 1–5) denote the convolutional, deconvolutional fiters, respectively; Pooling-N (N = 1–5) are max pooling layers; Scale Fusion-N (N = 1–3) denotes the per-pixel addition layer for different features; FS, NF are filter size and number of filters.</p> Full article ">Figure 3
<p>The change of loss value in model training stage: <span class="html-italic">X</span> axis and <span class="html-italic">Y</span> axis denote the training steps and the training loss, respectively; the upper figure shows the process with training step of 1–1000. The bottom figure shows the entire training process; the color of the bottom figure denotes the density of steps, while the dashed lines are training bounds with loss values of 0.075, 0.005, 0.015, and the blue line is the moving average of training losses with window size of 1000.</p> Full article ">Figure 4
<p>Visual comparison of the building roof segmentation results in different urban environment: for each row, images from left to right are true color composite GF2 PMS imagery (512 Pixel × 512 Pixel), manual delineation of building roof, DCNN segmentation results and CRF-refined building roof segmentation, respectively; for the images with segmentation results, green mask is True Positive (TP, building roof pixel was correctly classified), blue mask is False Negative (FN, building roof pixel was classified as non-building roof) and red mask is False Positive (FP, non-building roof pixel was classified as building roof).</p> Full article ">
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