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Earth Observation in Planning for Sustainable Urban Development

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (28 February 2018) | Viewed by 76515

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Dr. Richard Sliuzas

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Faculty of Geoinformation Science and Earth Observation (ITC), University of Twente, PO Box 217, 7500AE Enschede, The Netherlands
Interests: urbanization; slum mapping and monitoring; urban growth analysis and modelling; disaster risk reduction; urban planning

Special Issue Information

Dear Colleagues

As more than half of the world’s population is now generally considered to be urban, with continued high rates of urbanization expected in many of the world’s least developed economies; and with pressing concerns of climate change, inequality and urban disaster risk reduction around the globe; there can be little doubt that addressing the sustainability of urban development is of critical importance to humanity. Adopting a sustainability perspective to urban development demands an integrated, transdisciplinary, multi-sectoral and dynamic view of the urban and interacting processes that shape urban regions. Earth Observation (EO) is an increasingly important source of data for addressing the numerous severe problems that confront societies and their governance systems in their consideration of sustainability issues.

There is a vast array of users and uses of EO data for urban data acquisition, but which of these are contributing to the search for sustainability in urban development? Furthermore, what are the promising developments in methods and applications in which EO data are used in integrated, systems based approaches that bring together the environmental, social and economic concerns that frame sustainability? This Special Issue invites contributions that describe the development of innovative EO methods and applications related to strengthening planning capabilities for sustainable urban development. Submissions are encouraged to cover a broad range of topics and may relate to one of more of the following spatial scales: global, urban region, city and neighbourhood. Papers with a strong relevance to policy and decision making at these scales and that include innovative combinations of EO and other data are particularly sought.

For example, EO in:

Regional/Urban eco-system assessment and monitoring
- Blue and green infrastructures
- Air quality
- Urban biodiversity assessment
Disaster risk reduction and climate change
- Urban hazard assessment
- Disaster risk assessment
- Urban heat islands
- Urban land subsidence and flood risk
- Quantifying vulnerability with EO data
Monitoring and modelling physical urban development
- Global urban monitoring systems
- Urban land use change
- Urban expansion modelling
- Urban densification
- Compact cities
Use of Unmanned Aerial Vehicles (UAVs)
- Small area mapping
Data extraction and classification methods
- Object based approaches
- Machine learning
- Multi-temporal classification
- Community based methods
Spatial metrics in urban sustainability assessments
Socio-economic applications
- Population estimation
- Land use change
- Quality of life indicators
- Land tenure and cadastral mapping
3D urban data for sustainability
Governance and geo-ethics
- Data ownership
- Privacy considerations
- Geo-ethics and urban EO
- (Geo)Information as a human right

Dr. Richard Sliuzas
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. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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Published Papers (8 papers)

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Research

19 pages, 3777 KiB

Open AccessEditor’s ChoiceArticle

Unveiling 25 Years of Planetary Urbanization with Remote Sensing: Perspectives from the Global Human Settlement Layer

by Michele Melchiorri, Aneta J. Florczyk, Sergio Freire, Marcello Schiavina, Martino Pesaresi and Thomas Kemper

Remote Sens. 2018, 10(5), 768; https://doi.org/10.3390/rs10050768 - 16 May 2018

Cited by 138 | Viewed by 16463

Abstract

In the last few decades the magnitude and impacts of planetary urban transformations have become increasingly evident to scientists and policymakers. The ability to understand these processes remained limited in terms of territorial scope and comparative capacity for a long time: data availability and harmonization were among the main constraints. Contemporary technological assets, such as remote sensing and machine learning, allow for analyzing global changes in the settlement process with unprecedented detail. The Global Human Settlement Layer (GHSL) project set out to produce detailed datasets to analyze and monitor the spatial footprint of human settlements and their population, which are key indicators for the global policy commitments of the 2030 Development Agenda. In the GHSL, Earth Observation plays a key role in the detection of built-up areas from the Landsat imagery upon which population distribution is modelled. The combination of remote sensing imagery and population modelling allows for generating globally consistent and detailed information about the spatial distribution of built-up areas and population. The GHSL data facilitate a multi-temporal analysis of human settlements with global coverage. The results presented in this article focus on the patterns of development of built-up areas, population and settlements. The article reports about the present status of global urbanization (2015) and its evolution since 1990 by applying to the GHSL the Degree of Urbanisation definition of the European Commission Directorate General for Regional and Urban Policy (DG-Regio) and the Statistical Office of the European Communities (EUROSTAT). The analysis portrays urbanization dynamics at a regional level and per country income classes to show disparities and inequalities. This study analyzes how the 6.1 billion urban dwellers are distributed worldwide. Results show the degree of global urbanization (which reached 85% in 2015), the more than 100 countries in which urbanization has increased between 1990 and 2015, and the tens of countries in which urbanization is today above the global average and where urbanization grows the fastest. The paper sheds light on the key role of urban areas for development, on the patterns of urban development across the regions of the world and on the role of a new generation of data to advance urbanization theory and reporting. Full article

(This article belongs to the Special Issue Earth Observation in Planning for Sustainable Urban Development)

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23 pages, 22699 KiB

Open AccessArticle

A Combined Approach to Classifying Land Surface Cover of Urban Domestic Gardens Using Citizen Science Data and High Resolution Image Analysis

by Fraser Baker, Claire L. Smith and Gina Cavan

Remote Sens. 2018, 10(4), 537; https://doi.org/10.3390/rs10040537 - 31 Mar 2018

Cited by 31 | Viewed by 7925

Abstract

Domestic gardens are an important component of cities, contributing significantly to urban green infrastructure (GI) and its associated ecosystem services. However, domestic gardens are incredibly heterogeneous which presents challenges for quantifying their GI contribution and associated benefits for sustainable urban development. This study applies an innovative methodology that combines citizen science data with high resolution image analysis to create a garden dataset in the case study city of Manchester, UK. An online Citizen Science Survey (CSS) collected estimates of proportional coverage for 10 garden land surface types from 1031 city residents. High resolution image analysis was conducted to validate the CSS estimates, and to classify 7 land surface cover categories for all garden parcels in the city. Validation of the CSS land surface estimations revealed a mean accuracy of 76.63% (s = 15.24%), demonstrating that citizens are able to provide valid estimates of garden surface coverage proportions. An Object Based Image Analysis (OBIA) classification achieved an estimated overall accuracy of 82%, with further processing required to classify shadow objects. CSS land surface estimations were then extrapolated across the entire classification through calculation of within image class proportions, to provide the proportional coverage of 10 garden land surface types (buildings, hard impervious surfaces, hard pervious surfaces, bare soil, trees, shrubs, mown grass, rough grass, cultivated land, water) within every garden parcel in the city. The final dataset provides a better understanding of the composition of GI in domestic gardens and how this varies across the city. An average garden in Manchester has 50.23% GI, including trees (16.54%), mown grass (14.46%), shrubs (9.19%), cultivated land (7.62%), rough grass (1.97%) and water (0.45%). At the city scale, Manchester has 49.0% GI, and around one fifth (20.94%) of this GI is contained within domestic gardens. This is useful evidence to inform local urban development policies. Full article

(This article belongs to the Special Issue Earth Observation in Planning for Sustainable Urban Development)

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17 pages, 11579 KiB

Open AccessArticle

Coupling Uncertainties with Accuracy Assessment in Object-Based Slum Detections, Case Study: Jakarta, Indonesia

by Jati Pratomo, Monika Kuffer, Javier Martinez and Divyani Kohli

Remote Sens. 2017, 9(11), 1164; https://doi.org/10.3390/rs9111164 - 13 Nov 2017

Cited by 49 | Viewed by 8662

Abstract

Object-Based Image Analysis (OBIA) has been successfully used to map slums. In general, the occurrence of uncertainties in producing geographic data is inevitable. However, most studies concentrated solely on assessing the classification accuracy and neglecting the inherent uncertainties. Our research analyses the impact of uncertainties in measuring the accuracy of OBIA-based slum detection. We selected Jakarta as our case study area because of a national policy of slum eradication, which is causing rapid changes in slum areas. Our research comprises of four parts: slum conceptualization, ruleset development, implementation, and accuracy and uncertainty measurements. Existential and extensional uncertainty arise when producing reference data. The comparison of a manual expert delineations of slums with OBIA slum classification results into four combinations: True Positive, False Positive, True Negative and False Negative. However, the higher the True Positive (which lead to a better accuracy), the lower the certainty of the results. This demonstrates the impact of extensional uncertainties. Our study also demonstrates the role of non-observable indicators (i.e., land tenure), to assist slum detection, particularly in areas where uncertainties exist. In conclusion, uncertainties are increasing when aiming to achieve a higher classification accuracy by matching manual delineation and OBIA classification. Full article

(This article belongs to the Special Issue Earth Observation in Planning for Sustainable Urban Development)

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18 pages, 3988 KiB

Open AccessArticle

Detection of Informal Settlements from VHR Images Using Convolutional Neural Networks

by Nicholus Mboga, Claudio Persello, John Ray Bergado and Alfred Stein

Remote Sens. 2017, 9(11), 1106; https://doi.org/10.3390/rs9111106 - 30 Oct 2017

Cited by 117 | Viewed by 10196

Abstract

Information about the location and extent of informal settlements is necessary to guide decision making and resource allocation for their upgrading. Very high resolution (VHR) satellite images can provide this useful information, however, different urban settlement types are hard to be automatically discriminated and extracted from VHR imagery, because of their abstract semantic class definition. State-of-the-art classification techniques rely on hand-engineering spatial-contextual features to improve the classification results of pixel-based methods. In this paper, we propose to use convolutional neural networks (CNNs) for learning discriminative spatial features, and perform automatic detection of informal settlements. The experimental analysis is carried out on a QuickBird image acquired over Dar es Salaam, Tanzania. The proposed technique is compared against support vector machines (SVMs) using texture features extracted from grey level co-occurrence matrix (GLCM) and local binary patterns (LBP), which result in accuracies of 86.65% and 90.48%, respectively. CNN leads to better classification, resulting in an overall accuracy of 91.71%. A sensitivity analysis shows that deeper networks result in higher accuracies when large training sets are used. The study concludes that training CNN in an end-to-end fashion can automatically learn spatial features from the data that are capable of discriminating complex urban land use classes. Full article

(This article belongs to the Special Issue Earth Observation in Planning for Sustainable Urban Development)

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20 pages, 3716 KiB

Open AccessArticle

Scale- and Region-Dependence in Landscape-PM_2.5 Correlation: Implications for Urban Planning

by Huihui Feng, Bin Zou and Yumeng Tang

Remote Sens. 2017, 9(9), 918; https://doi.org/10.3390/rs9090918 - 2 Sep 2017

Cited by 80 | Viewed by 7164

Abstract

Under rapid urbanization, many cities in China suffer from serious fine particulate matter (PM_2.5) pollution. As the emission sources or adsorption sinks, land use and the corresponding landscape pattern unavoidably affect the concentration. However, the correlation varies with different regions and scales, leaving a significant gap for urban planning. This study clarifies the correlation with the aid of in situ and satellite-based spatial datasets over six urban agglomerations in China. Two coverage and four landscape indices are adopted to represent land use and landscape pattern. Specifically, the coverage indices include the area ratios of forest (F_PLAND) and built-up areas (C_PLAND). The landscape indices refer to the perimeter-area fractal dimension index (PAFRAC), interspersion and juxtaposition index (IJI), aggregation index (AI), Shannon’s diversity index (SHDI). Then, the correlation between PM_2.5 concentration with the selected indices are evaluated from supporting the potential urban planning. Results show that the correlations are weak with the in situ PM_2.5 concentration, which are significant with the regional value. It means that land use coverage and landscape pattern affect PM_2.5 at a relatively large scale. Furthermore, regional PM_2.5 concentration negatively correlate to F_PLAND and positively to C_PLAND (significance at p < 0.05), indicating that forest helps to improve air quality, while built-up areas worsen the pollution. Finally, the heterogeneous landscape presents positive correlation to the regional PM_2.5 concentration in most regions, except for the urban agglomeration with highly-developed urban (i.e., the Jing-Jin-Ji and Chengdu-Chongqing urban agglomerations). It suggests that centralized urbanization would be helpful for PM_2.5 pollution controlling by reducing the emission sources in most regions. Based on the results, the potential urban planning is proposed for controlling PM_2.5 pollution for each urban agglomeration. Full article

(This article belongs to the Special Issue Earth Observation in Planning for Sustainable Urban Development)

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20 pages, 11541 KiB

Open AccessArticle

Comparative Assessment of Two Vegetation Fractional Cover Estimating Methods and Their Impacts on Modeling Urban Latent Heat Flux Using Landsat Imagery

by Kai Liu, Hongbo Su and Xueke Li

Remote Sens. 2017, 9(5), 455; https://doi.org/10.3390/rs9050455 - 8 May 2017

Cited by 17 | Viewed by 6905

Abstract

Quantifying vegetation fractional cover (VFC) and assessing its role in heat fluxes modeling using medium resolution remotely sensed data has received less attention than it deserves in heterogeneous urban regions. This study examined two approaches (Normalized Difference Vegetation Index (NDVI)-derived and Multiple Endmember Spectral Mixture Analysis (MESMA)-derived methods) that are commonly used to map VFC based on Landsat imagery, in modeling surface heat fluxes in urban landscape. For this purpose, two different heat flux models, Two-source energy balance (TSEB) model and Pixel Component Arranging and Comparing Algorithm (PCACA) model, were adopted for model evaluation and analysis. A comparative analysis of the NDVI-derived and MESMA-derived VFCs showed that the latter achieved more accurate estimates in complex urban regions. When the two sources of VFCs were used as inputs to both TSEB and PCACA models, MESMA-derived urban VFC produced more accurate urban heat fluxes (Bowen ratio and latent heat flux) relative to NDVI-derived urban VFC. Moreover, our study demonstrated that Landsat imagery-retrieved VFC exhibited greater uncertainty in obtaining urban heat fluxes for the TSEB model than for the PCACA model. Full article

(This article belongs to the Special Issue Earth Observation in Planning for Sustainable Urban Development)

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16 pages, 6155 KiB

Open AccessArticle

InSAR Time-Series Analysis of Land Subsidence under Different Land Use Types in the Eastern Beijing Plain, China

by Chaofan Zhou, Huili Gong, Beibei Chen, Jiwei Li, Mingliang Gao, Feng Zhu, Wenfeng Chen and Yue Liang

Remote Sens. 2017, 9(4), 380; https://doi.org/10.3390/rs9040380 - 19 Apr 2017

Cited by 62 | Viewed by 8946

Abstract

In the Beijing plain, the long-term groundwater overexploitation, exploitation, and the utilization of superficial urban space have led to land subsidence. In this study, the spatial–temporal analysis of land subsidence in Beijing was assessed by using the small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) technique based on 47 TerraSAR-X SAR images from 2010 to 2015. Distinct variations of the land subsidence were found in the study regions. The maximum annual land subsidence rate was 146 mm/year from 2011 to 2015. The comparison between the SBAS InSAR results and the ground leveling measurements showed that the InSAR land subsidence results achieved a precision of 2 mm. In 2013, the maximum displacement reached 132 and 138 mm/year in the Laiguangying and DongbalizhuangDajiaoting area. Our analysis showed that the serious land subsidence mainly occurred in the following land use types: water area and wetland, paddy field, upland soils, vegetable land, and peasant-inhabited land. Our results could provide a useful reference for groundwater exploitation and urban planning. Full article

(This article belongs to the Special Issue Earth Observation in Planning for Sustainable Urban Development)

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23 pages, 3374 KiB

Open AccessArticle

The Use of Landscape Metrics and Transfer Learning to Explore Urban Villages in China

by Hui Liu, Xin Huang, Dawei Wen and Jiayi Li

Remote Sens. 2017, 9(4), 365; https://doi.org/10.3390/rs9040365 - 13 Apr 2017

Cited by 30 | Viewed by 8319

Abstract

Urban villages (UVs), the main settlements of rural migrant workers and low-income groups in metropolitan areas of China, have become of major concern to city managers and researchers due to the rapid urbanization in recent years. A clear understanding of their evolution and spatial relationships with the city is of great importance to policy formulation, implementation and assessment. In this paper, we propose a new framework based on landscape metrics and transfer learning for the long-term monitoring and analysis of UVs, and we apply it to Shenzhen and Wuhan, two metropolitan cities of China, with high-resolution satellite images acquired from 2003–2012 and 2009–2015, respectively. In the framework, landscape metrics are used for identifying the UVs and quantifying their evolution patterns on the basis of a city-UV-building hierarchical landscape model. Transfer learning is also introduced to use the samples and features across the spatial and temporal domains, which reduces the time and labor cost, as well as improves the mapping accuracies by 3–10%. The results show that the total area of UVs has decreased by less than

6 %

in Shenzhen and more than

45 %

in Wuhan. Moreover, we observe significant spatial correlations in the development of UVs in Shenzhen. By contrast, no strong spatial correlations are found in Wuhan’s UVs, indicating that their development is largely independent of the spatial location. The results reveal two typical strategies, i.e., demolition and renovation, towards the redevelopment of UVs in China. Full article

(This article belongs to the Special Issue Earth Observation in Planning for Sustainable Urban Development)

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