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Special Issue "Societal and Economic Benefits of Earth Observation Technologies"

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

Deadline for manuscript submissions: closed (31 August 2017)

Special Issue Editors

Guest Editor
Prof. Dr. Qihao Weng

Center for Urban and Environmental Change, Department of Geography, Geology, and Anthropology, Indiana State University, Terre Haute, IN 47809, USA
Website1 | Website2 | E-Mail
Phone: +1 812 237 2255
Fax: +1 812 237 8029
Interests: remote sensing; GIS; land use and land cover change; urban environment and ecosystem
Guest Editor
Prof. Dr. Guangxing Wang

Department of Geography Southern Illinois University at Carbondale, USA
Website | E-Mail
Phone: 618-453-6017
Interests: Forest inventory; forest carbon modeling; land use/cover change; soil erosion; uncertainty analysis
Guest Editor
Dr. George Xian

SAIC, USGS Center for Earth Resources Observation and Science, Sioux Falls, SD 57198, USA
E-Mail
Interests: remote sensing of land cover and urban; surface thermal properties; regional climate change
Guest Editor
Prof. Hua Liu

Department of Political Science & Geography, Old Dominion University, Norfolk, VA 23529, USA
Website | E-Mail
Phone: 757-683-3846
Interests: remote sensing; GIS; urban environmental change; climate change and sea level rise; public health; and flooding assessment and monitoring

Special Issue Information

Dear Colleagues,

Driven by the needs of societal and economic development, geospatial technology has become one of the three top emerging technologies in the 21st century. The frontiers of geospatial technology, earth observation and remote sensing techniques are receiving increased interest from the academia, governments, industries, among others. China is a rapidly evolving nation within space and earth observation technologies. Since the 1980s, great progress has been made in optical, microwave, and hyperspectral remote sensing. Over the past 30 years, the Chinese government has paid great attention to the development of forestry and greatly increased forest cover in the country. However, rapid economic growth and urbanization have resulted in dramatic changes in land use and land cover throughout China. Especially, urbanization has not only increased in built-up areas but also greatly speeded up the decrease of the agricultural land resources. The increase of human activities and the increasing development of the economy have led to degradation of ecosystems, environmental deterioration, decrease of lakes and wetlands, and the degradation of air and the amount and quality of water, which has made China’s sustainable development face a great challenge. At the same time, during the past few decades the complexity of global change and its interaction with human activities have also posed significant challenges to the scientific community. Thus, in recent years, earth observation and remote sensing-based geospatial technologies have been widely applied to monitoring, assessing and analyzing China’s and global ecosystems, environments and resources, and to provide potential solutions to enhance the understanding of global change.

This is a Special Issue of Remote Sensing (MDPI) in conjunction with the Fourth International Workshop on Earth Observation and Remote Sensing Applications (EORSA 2016), to be held in Guangzhou, China, on July 4–6, 2016 (www.eorsa2016.org).

In this Special Issue, we will explore societal and economic benefits of Earth Observation (EO) technologies, the current state of EO and remote sensing technologies to understand the Earth’s surface properties, patterns, processes, ecosystems, and environments at various spatial and temporal scales, and to provide potential solutions to enhance the understanding of global change and sustainable development. The virtues and importance of remote sensing imagery and data from various ground, aircraft, and satellite platforms will be assessed. Moreover, we wish to explore how improved sensors and analytical techniques can be employed to better characterize and quantify land surface forms, patterns, and processes and to assess, monitor, and model natural and human systems. The topics may include but are not limited to the following:

  • Global change, carbon cycling, and energy systems;
  • Remote sensing of urban areas, urbanization, and sustainability;
  • Remote sensing of coastal areas and oceans;
  • Remote sensing of wetlands and water resources;
  • Croplands, rangelands, agricultural systems, and soil studies;
  • Remote sensing of forests and ecosystems;
  • Remote sensing of atmosphere and air quality;
  • Human environmental and infectious diseases;
  • Land use, land cover, and regional environmental changes; and
  • New data and sensors, algorithms and techniques for detection, interpretation, characterization, and modeling of the Earth surface features.

Authors are requested to check and follow specific Instructions to Authors: https://dl.dropboxusercontent.com/u/165068305/Remote_Sensing-Additional_Instructions.pdf.

Prof. Qihao Weng
Prof. Guangxing Wang
Dr. George Xian
Prof. Hua Liu
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 monthly 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 1600 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.

Published Papers (6 papers)

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Research

Open AccessArticle Monitoring Urban Clusters Expansion in the Middle Reaches of the Yangtze River, China, Using Time-Series Nighttime Light Images
Remote Sens. 2017, 9(10), 1007; doi:10.3390/rs9101007
Received: 7 August 2017 / Revised: 14 September 2017 / Accepted: 22 September 2017 / Published: 28 September 2017
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Abstract
The urban clusters in the Middle Reaches of the Yangtze River (MRYR) in China include the Chang-Zhu-Tan urban agglomeration, the Wuhan metropolitan area, and the Poyang Lake urban agglomeration. While previous studies of urban expansion in China focused mainly on the coastal regions,
[...] Read more.
The urban clusters in the Middle Reaches of the Yangtze River (MRYR) in China include the Chang-Zhu-Tan urban agglomeration, the Wuhan metropolitan area, and the Poyang Lake urban agglomeration. While previous studies of urban expansion in China focused mainly on the coastal regions, this study aimed to investigate urban expansion patterns and factors in the MRYR, which are crucial for urban development in Central China. A neighborhood statistics analysis (NSA) method and a local-optimized threshold method were used to detect urban changes during 1992–2011 from the time-series Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) nighttime light (NTL) images. The evolution of urban expansion intensity and landscape metrics were analyzed at multiple spatial scales, including the whole region, urban agglomeration, and city scales. Finally, the expanded STochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model was built to explore the factors that controlled NTL intensity. The results revealed that urban areas extracted from the NTL data were consistent with those extracted from the Landsat Thematic Mapper data, with an overall accuracy of 81.74% and a Kappa of 0.40. A relatively slow urbanization pace was observed from 1992 to 2002 in the MRYR region, which then accelerated in the period of 2002 to 2007 and then slowed down between 2007 and 2011. Additionally, urban expansion exhibited a radial pattern. The results further indicated that major factors controlling NTL intensity were gross domestic product, followed by total investment in fixed assets, tertiary industry, urban construction area, non-agricultural population, and industrial output in the city clusters. The study provides important insights for further studies on the urbanization processes in the MRYR region. Full article
(This article belongs to the Special Issue Societal and Economic Benefits of Earth Observation Technologies)
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Open AccessArticle GDP Spatialization and Economic Differences in South China Based on NPP-VIIRS Nighttime Light Imagery
Remote Sens. 2017, 9(7), 673; doi:10.3390/rs9070673
Received: 27 April 2017 / Revised: 27 June 2017 / Accepted: 29 June 2017 / Published: 1 July 2017
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Abstract
Accurate data on gross domestic product (GDP) at pixel level are needed to understand the dynamics of regional economies. GDP spatialization is the basis of quantitative analysis on economic diversities of different administrative divisions and areas with different natural or humanistic attributes. Data
[...] Read more.
Accurate data on gross domestic product (GDP) at pixel level are needed to understand the dynamics of regional economies. GDP spatialization is the basis of quantitative analysis on economic diversities of different administrative divisions and areas with different natural or humanistic attributes. Data from the Visible Infrared Imaging Radiometer Suite (VIIRS), carried by the Suomi National Polar-orbiting Partnership (NPP) satellite, are capable of estimating GDP, but few studies have been conducted for mapping GDP at pixel level and further pattern analysis of economic differences in different regions using the VIIRS data. This paper produced a pixel-level (500 m × 500 m) GDP map for South China in 2014 and quantitatively analyzed economic differences among diverse geomorphological types. Based on a regression analysis, the total nighttime light (TNL) of corrected VIIRS data were found to exhibit R2 values of 0.8935 and 0.9243 for prefecture GDP and county GDP, respectively. This demonstrated that TNL showed a more significant capability in reflecting economic status (R2 > 0.88) than other nighttime light indices (R2 < 0.52), and showed quadratic polynomial relationships with GDP rather than simple linear correlations at both prefecture and county levels. The corrected NPP-VIIRS data showed a better fit than the original data, and the estimation at the county level was better than at the prefecture level. The pixel-level GDP map indicated that: (a) economic development in coastal areas was higher than that in inland areas; (b) low altitude plains were the most developed areas, followed by low altitude platforms and low altitude hills; and (c) economic development in middle altitude areas, and low altitude hills and mountains remained to be strengthened. Full article
(This article belongs to the Special Issue Societal and Economic Benefits of Earth Observation Technologies)
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Open AccessArticle A Hybrid Approach for Three-Dimensional Building Reconstruction in Indianapolis from LiDAR Data
Remote Sens. 2017, 9(4), 310; doi:10.3390/rs9040310
Received: 24 December 2016 / Revised: 14 March 2017 / Accepted: 20 March 2017 / Published: 26 March 2017
Cited by 1 | PDF Full-text (10381 KB) | HTML Full-text | XML Full-text
Abstract
3D building models with prototypical roofs are more valuable in many applications than 2D building footprints. This research proposes a hybrid approach, combining the data- and model-driven approaches for generating LoD2-level building models by using medium resolution (0.91 m) LiDAR nDSM, the 2D
[...] Read more.
3D building models with prototypical roofs are more valuable in many applications than 2D building footprints. This research proposes a hybrid approach, combining the data- and model-driven approaches for generating LoD2-level building models by using medium resolution (0.91 m) LiDAR nDSM, the 2D building footprint and the high resolution orthophoto for the City of Indianapolis, USA. The main objective is to develop a GIS-based procedure for automatic reconstruction of complex building roof structures in a large area with high accuracy, but without requiring high-density point data clouds and computationally-intensive algorithms. A multi-stage strategy, which combined step-edge detection, roof model selection and ridge detection techniques, was adopted to extract key features and to obtain prior knowledge for 3D building reconstruction. The entire roof can be reconstructed successfully by assembling basic models after their shapes were reconstructed. This research finally created a 3D city model at the Level of Detail 2 (LoD2) according to the CityGML standard for the downtown area of Indianapolis (included 519 buildings).The reconstruction achieved 90.6% completeness and 96% correctness for seven tested buildings whose roofs were mixed by different shapes of structures. Moreover, 86.3% of completeness and 90.9% of correctness were achieved for 38 commercial buildings with complex roof structures in the downtown area, which indicated that the proposed method had the ability for large-area building reconstruction. The major contribution of this paper lies in designing an efficient method to reconstruct complex buildings, such as those with irregular footprints and roof structures with flat, shed and tiled sub-structures mixed together. It overcomes the limitation that building reconstruction using coarse resolution LiDAR nDSM cannot be based on precise horizontal ridge locations, by adopting a novel ridge detection method. Full article
(This article belongs to the Special Issue Societal and Economic Benefits of Earth Observation Technologies)
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Open AccessArticle Is Spatial Resolution Critical in Urbanization Velocity Analysis? Investigations in the Pearl River Delta
Remote Sens. 2017, 9(1), 80; doi:10.3390/rs9010080
Received: 21 October 2016 / Revised: 2 January 2017 / Accepted: 9 January 2017 / Published: 17 January 2017
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Abstract
Grid-based urbanization velocity analysis of remote sensing imagery is used to measure urban growth rates. However, it remains unclear how critical the spatial resolution of the imagery is to such grid-based approaches. This research therefore investigated how urbanization velocity estimates respond to different
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Grid-based urbanization velocity analysis of remote sensing imagery is used to measure urban growth rates. However, it remains unclear how critical the spatial resolution of the imagery is to such grid-based approaches. This research therefore investigated how urbanization velocity estimates respond to different spatial resolutions, as determined by the grid sizes used. Landsat satellite images of the Pearl River Delta (PRD) in China from the years 2000, 2005, 2010 and 2015 were hierarchically aggregated using different grid sizes. Statistical analyses of urbanization velocity derived using different spatial resolutions (or grid sizes) were used to investigate the relationships between socio-economic indicators and the velocity of urbanization for 27 large cities in PRD. The results revealed that those cities with above-average urbanization velocities remain unaffected by the spatial resolution (or grid-size), and the relationships between urbanization velocities and socio-economic indicators are independent of spatial resolution (or grid sizes) used. Moreover, empirical variogram models, the local variance model, and the geographical variance model all indicated that coarse resolution version (480 m) of Landsat images based on aggregated pixel yielded more appropriate results than the original fine resolution version (30 m), when identifying the characteristics of spatial autocorrelation and spatial structure variability of urbanization patterns and processes. The results conclude that the most appropriate spatial resolution for investigations into urbanization velocities is not always the highest resolution. The resulting patterns of urbanization velocities at different spatial resolutions can be used as a basis for studying the spatial heterogeneity of other datasets with variable spatial resolutions, especially for evaluating the capability of a multi-resolution dataset in reflecting spatial structure and spatial autocorrelation features in an urban environment. Full article
(This article belongs to the Special Issue Societal and Economic Benefits of Earth Observation Technologies)
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Open AccessArticle Deformation Monitoring and Analysis of the Geological Environment of Pudong International Airport with Persistent Scatterer SAR Interferometry
Remote Sens. 2016, 8(12), 1021; doi:10.3390/rs8121021
Received: 13 October 2016 / Revised: 28 November 2016 / Accepted: 8 December 2016 / Published: 14 December 2016
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Abstract
Many coastal cities have undertaken reclamation projects to satisfy the land demands of rapid urbanization. However, the foundations of reclaimed land are susceptible to settlement and can have undesirable environmental impacts that could adversely affect these dense, populated areas. In the case of
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Many coastal cities have undertaken reclamation projects to satisfy the land demands of rapid urbanization. However, the foundations of reclaimed land are susceptible to settlement and can have undesirable environmental impacts that could adversely affect these dense, populated areas. In the case of international airports built on reclaimed areas especially, regional-scale deformation must be monitored to ensure operational security for public safety. Persistent Scatterer SAR Interferometry (PSI) technology has proven to be an effective tool to detect ground deformation in urban areas. However, it is still a challenge to apply PSI to effectively monitor settlement at airports built on newly developed coastal reclamation areas because of the scarcity of identifiable targets. Moreover, additional issues arise as the complicated deformation patterns associated with the underlying geological conditions make it difficult to interpret InSAR-derived results. In this study, a time-series analysis of a high-resolution TerraSAR-X satellite image stack acquired from September 2011 to October 2012 was performed by employing a modified PSI technique to retrieve the mean deformation velocity and time series of surface deformation at Pudong International Airport. Qualitative evaluation of spatial distribution and temporal evolution of deformation was conducted by joint analyses of deformation measurements and local geological data. Detailed analysis of various driving forces for deformation patterns confirmed that the results of deformation monitoring obtained by PSI are reliable and consistent with that of local geological surveys. Since the factors responsible for the subsidence within the airport are still at play, ongoing and routine deformation monitoring is warranted. Full article
(This article belongs to the Special Issue Societal and Economic Benefits of Earth Observation Technologies)
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Open AccessArticle A Method for Exploring the Link between Urban Area Expansion over Time and the Opportunity for Crime in Saudi Arabia
Remote Sens. 2016, 8(10), 863; doi:10.3390/rs8100863
Received: 11 July 2016 / Revised: 4 October 2016 / Accepted: 14 October 2016 / Published: 19 October 2016
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Abstract
Urban area expansion is one of the most critical types of worldwide change, and most urban areas are experiencing increased growth in population and infrastructure development. Urban change leads to many changes in the daily activities of people living within an affected area.
[...] Read more.
Urban area expansion is one of the most critical types of worldwide change, and most urban areas are experiencing increased growth in population and infrastructure development. Urban change leads to many changes in the daily activities of people living within an affected area. Many studies have suggested that urbanization and crime are related. However, they focused particularly on land uses, types of land use, and urban forms, such as the physical features of neighbourhoods, roads, shopping centres, and bus stations. Understanding the correlation between urban area expansion and crime is very important for criminologists and urban planning decision-makers. In this study, we have used satellite images to measure urban expansion over a 10-year period and tested the correlations between these expansions and the number of criminal activities within these specific areas. The results show that there is a measurable relationship between urban expansion and criminal activities. Our findings support the crime opportunity theory as one possibility, which suggests that population density and crime are conceptually related. We found the correlations are stronger where there has been greater urban growth. Many other factors that may affect crime rate are not included in this paper, such as information on the spatial details of the population, city planning, economic considerations, the distance from the city centre, neighbourhood quality, and police numbers. However, this study will be of particular interest to those who aim to use remote sensing to study patterns of crime. Full article
(This article belongs to the Special Issue Societal and Economic Benefits of Earth Observation Technologies)
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