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Special Issue "Urban Remote Sensing"

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

Deadline for manuscript submissions: closed (15 March 2011)

Special Issue Information

Dear Colleagues,

With over half the planets human population estimated to live in urban agglomerations, urban systems pose unique Remote Sensing challenges to monitoring green-space and vegetation status, urban sprawl and population growth, traffic and pollution characterization, urban heat island effects, impermeability mapping, disaster response and civil defense.

With recent advances in the resolution of remote sensing systems, in-situ measurement networks, GPS and live video feeds, semi/automated geo-object based image analysis (GEOBIA) and web-based geospatial data infrastructures, new holistic opportunities exist for modeling the physical, environmental and socioeconomic variables governing and emerging from complex urban settings. However, much remains to be accomplished. In response to these needs, this Special Issue seeks to highlight leading edge Urban Remote Sensing research that represents critical advances in the algorithms, methodologies and applications related to the analysis and visualization of urbanscapes with geospatial technologies.

Submissions in a broad range of Urban Remote Sensing related research and applications are encouraged. Topics may include, but are not limited to:

  • 3D urban modeling from satellite, airborne and terrestrial sensors
  • Data processing methods and algorithms
  • Disasters, monitoring and change analysis
  • Multi-disciplinary case studies
  • Multiscale sensors and systems: specific ‘urbanscape’ challenges
  • Multispectral/SAR sensor data integration
  • Radar and LiDAR applications
  • Scale issues related to urbanscapes
  • The Urban Heat Island Effect and thermal sensing
  • Urban applications of high-resolution optical sensors
  • Urban data synthesis and analysis
  • Visualization issues and Virtual Reality applications

Prof. Dr. Thomas Blaschke
Dr. Geoffrey J. Hay
Guest Editors

Published Papers (16 papers)

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Research

Jump to: Review, Other

Open AccessArticle Monitoring Urban Tree Cover Using Object-Based Image Analysis and Public Domain Remotely Sensed Data
Remote Sens. 2011, 3(10), 2243-2262; doi:10.3390/rs3102243
Received: 11 August 2011 / Revised: 14 October 2011 / Accepted: 14 October 2011 / Published: 21 October 2011
Cited by 21 | PDF Full-text (1458 KB) | HTML Full-text | XML Full-text
Abstract
Urban forest ecosystems provide a range of social and ecological services, but due to the heterogeneity of these canopies their spatial extent is difficult to quantify and monitor. Traditional per-pixel classification methods have been used to map urban canopies, however, such techniques [...] Read more.
Urban forest ecosystems provide a range of social and ecological services, but due to the heterogeneity of these canopies their spatial extent is difficult to quantify and monitor. Traditional per-pixel classification methods have been used to map urban canopies, however, such techniques are not generally appropriate for assessing these highly variable landscapes. Landsat imagery has historically been used for per-pixel driven land use/land cover (LULC) classifications, but the spatial resolution limits our ability to map small urban features. In such cases, hyperspatial resolution imagery such as aerial or satellite imagery with a resolution of 1 meter or below is preferred. Object-based image analysis (OBIA) allows for use of additional variables such as texture, shape, context, and other cognitive information provided by the image analyst to segment and classify image features, and thus, improve classifications. As part of this research we created LULC classifications for a pilot study area in Seattle, WA, USA, using OBIA techniques and freely available public aerial photography. We analyzed the differences in accuracies which can be achieved with OBIA using multispectral and true-color imagery. We also compared our results to a satellite based OBIA LULC and discussed the implications of per-pixel driven vs. OBIA-driven field sampling campaigns. We demonstrated that the OBIA approach can generate good and repeatable LULC classifications suitable for tree cover assessment in urban areas. Another important finding is that spectral content appeared to be more important than spatial detail of hyperspatial data when it comes to an OBIA-driven LULC. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
Open AccessArticle Optimizing Spatial Resolution of Imagery for Urban Form Detection—The Cases of France and Vietnam
Remote Sens. 2011, 3(10), 2128-2147; doi:10.3390/rs3102128
Received: 1 August 2011 / Revised: 2 September 2011 / Accepted: 5 September 2011 / Published: 26 September 2011
Cited by 8 | PDF Full-text (1644 KB) | HTML Full-text | XML Full-text
Abstract
The multitude of satellite data products available offers a large choice for urban studies. Urban space is known for its high heterogeneity in structure, shape and materials. To approach this heterogeneity, finding the optimal spatial resolution (OSR) is needed for urban form [...] Read more.
The multitude of satellite data products available offers a large choice for urban studies. Urban space is known for its high heterogeneity in structure, shape and materials. To approach this heterogeneity, finding the optimal spatial resolution (OSR) is needed for urban form detection from remote sensing imagery. By applying the local variance method to our datasets (pan-sharpened images), we can identify OSR at two levels of observation: individual urban elements and urban districts in two agglomerations in West Europe (Strasbourg, France) and in Southeast Asia (Da Nang, Vietnam). The OSR corresponds to the minimal variance of largest number of spectral bands. We carry out three categories of interval values of spatial resolutions for identifying OSR: from 0.8 m to 3 m for isolated objects, from 6 m to 8 m for vegetation area and equal or higher than 20 m for urban district. At the urban district level, according to spatial patterns, form, size and material of elements, we propose the range of OSR between 30 m and 40 m for detecting administrative districts, new residential districts and residential discontinuous districts. The detection of industrial districts refers to a coarser OSR from 50 m to 60 m. The residential continuous dense districts effectively need a finer OSR of between 20 m and 30 m for their optimal identification. We also use fractal dimensions to identify the threshold of homogeneity/heterogeneity of urban structure at urban district level. It seems therefore that our approaches are robust and transferable to different urban contexts. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
Open AccessArticle Segment-Based Land Cover Mapping of a Suburban Area—Comparison of High-Resolution Remotely Sensed Datasets Using Classification Trees and Test Field Points
Remote Sens. 2011, 3(8), 1777-1804; doi:10.3390/rs3081777
Received: 14 June 2011 / Revised: 11 July 2011 / Accepted: 5 August 2011 / Published: 19 August 2011
Cited by 13 | PDF Full-text (2405 KB) | HTML Full-text | XML Full-text
Abstract
In order to better understand and exploit the rich information content of new remotely sensed datasets, there is a need for comparative land cover classification studies. In this study, the automatic classification of a suburban area was investigated by using (1) digital [...] Read more.
In order to better understand and exploit the rich information content of new remotely sensed datasets, there is a need for comparative land cover classification studies. In this study, the automatic classification of a suburban area was investigated by using (1) digital aerial image data; (2) digital aerial image data and laser scanner data; (3) a high-resolution optical QuickBird satellite image; (4) high-resolution airborne synthetic aperture radar (SAR) data; and (5) SAR data and laser scanner data. A segment-based approach was applied. The classification rules for distinguishing buildings, trees, vegetated ground, and non-vegetated ground were created automatically by using permanent test field points in a training area and the classification tree method. The accuracy of the results was evaluated by using test field points in validation areas. The highest overall accuracies were obtained when laser scanner data were used to separate high and low objects: 97% in Test 2, and 82% in Test 5. The overall accuracies in the other tests were 74% (Test 1), 67% (Test 3), and 68% (Test 4). An important contributing factor for the lower accuracy in Tests 3 and 4 was the lower spatial resolution of the datasets. The classification tree method and test field points provided a feasible and automated means of comparing the classifications. The approach is well suited for rapid analyses of new datasets to predict their quality and potential for land cover classification. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
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Open AccessArticle An Object-Based Classification Approach for Mapping Migrant Housing in the Mega-Urban Area of the Pearl River Delta (China)
Remote Sens. 2011, 3(8), 1710-1723; doi:10.3390/rs3081710
Received: 20 June 2011 / Revised: 19 July 2011 / Accepted: 8 August 2011 / Published: 16 August 2011
Cited by 6 | PDF Full-text (969 KB) | HTML Full-text | XML Full-text
Abstract
Urban areas develop on formal and informal levels. Informal development is often highly dynamic, leading to a lag of spatial information about urban structure types. In this work, an object-based remote sensing approach will be presented to map the migrant housing urban [...] Read more.
Urban areas develop on formal and informal levels. Informal development is often highly dynamic, leading to a lag of spatial information about urban structure types. In this work, an object-based remote sensing approach will be presented to map the migrant housing urban structure type in the Pearl River Delta, China. SPOT5 data were utilized for the classification (auxiliary data, particularly up-to-date cadastral data, were not available). A hierarchically structured classification process was used to create (spectral) independence from single satellite scenes and to arrive at a transferrable classification process. Using the presented classification approach, an overall classification accuracy of migrant housing of 68.0% is attained. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
Open AccessArticle Extracting Buildings from True Color Stereo Aerial Images Using a Decision Making Strategy
Remote Sens. 2011, 3(8), 1553-1567; doi:10.3390/rs3081553
Received: 20 May 2011 / Revised: 11 July 2011 / Accepted: 11 July 2011 / Published: 25 July 2011
Cited by 6 | PDF Full-text (610 KB) | HTML Full-text | XML Full-text
Abstract
The automatic extraction of buildings from true color stereo aerial imagery in a dense built-up area is the main focus of this paper. Our approach strategy aimed at reducing the complexity of the image content by means of a three-step procedure [...] Read more.
The automatic extraction of buildings from true color stereo aerial imagery in a dense built-up area is the main focus of this paper. Our approach strategy aimed at reducing the complexity of the image content by means of a three-step procedure combining reliable geospatial image analysis techniques. Even if it is a rudimentary first step towards a more general approach, the method presented proved useful in urban sprawl studies for rapid map production in flat area by retrieving indispensable information on buildings from scanned historic aerial photography. After the preliminary creation of a photogrammetric model to manage Digital Surface Model and orthophotos, five intermediate mask-layers data (Elevation, Slope, Vegetation, Shadow, Canny, Shadow, Edges) were processed through the combined use of remote sensing image processing and GIS software environments. Lastly, a rectangular building block model without roof structures (Level of Detail, LoD1) was automatically generated. System performance was evaluated with objective criteria, showing good results in a complex urban area featuring various types of building objects. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
Open AccessArticle Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong
Remote Sens. 2011, 3(7), 1535-1552; doi:10.3390/rs3071535
Received: 21 May 2011 / Revised: 28 May 2011 / Accepted: 4 July 2011 / Published: 13 July 2011
Cited by 50 | PDF Full-text (790 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, the effect of urban heat island is analyzed using the Landsat TM data and ASTER data in 2005 as a case study in Hong Kong. Two algorithms were applied to retrieve the land surface temperature (LST) distribution from the [...] Read more.
In this paper, the effect of urban heat island is analyzed using the Landsat TM data and ASTER data in 2005 as a case study in Hong Kong. Two algorithms were applied to retrieve the land surface temperature (LST) distribution from the Landsat TM and ASTER data. The spatial pattern of LST in the study area is retrieved to characterize their local effects on urban heat island. In addition, the correlation between LST and the normalized difference vegetation index (NDVI), the normalized difference build-up index (NDBI) is analyzed to explore the impacts of the green land and the build-up land on the urban heat island by calculating the ecological evaluation index of sub-urban areas. The results indicate that the effect of urban heat island in Hong Kong is mainly located in three sub-urban areas, namely, Kowloon Island, the northern Hong Kong Island and Hong Kong International Airport. The correlation between LST and NDVI, NDBI also indicates that the negative correlation of LST and NDVI suggests that the green land can weaken the effect on urban heat island, while the positive correlation between LST and NDBI means that the built-up land can strengthen the effect of urban heat island in our case study. Although satellite data (e.g., Landsat TM and ASTER thermal bands data) can be applied to examine the distribution of urban heat islands in places such as Hong Kong, the method still needs to be refined with in situ measurements of LST in future studies. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
Open AccessArticle Remote Sensing-Based Characterization of Settlement Structures for Assessing Local Potential of District Heat
Remote Sens. 2011, 3(7), 1447-1471; doi:10.3390/rs3071447
Received: 12 May 2011 / Revised: 28 June 2011 / Accepted: 30 June 2011 / Published: 8 July 2011
Cited by 15 | PDF Full-text (962 KB) | HTML Full-text | XML Full-text
Abstract
In Europe, heating of houses and commercial areas is one of the major contributors to greenhouse gas emissions. When considering the drastic impact of an increasing emission of greenhouse gases as well as the finiteness of fossil resources, the usage of efficient [...] Read more.
In Europe, heating of houses and commercial areas is one of the major contributors to greenhouse gas emissions. When considering the drastic impact of an increasing emission of greenhouse gases as well as the finiteness of fossil resources, the usage of efficient and renewable energy generation technologies has to be increased. In this context, small-scale heating networks are an important technical component, which enable the efficient and sustainable usage of various heat generation technologies. This paper investigates how the potential of district heating for different settlement structures can be assessed. In particular, we analyze in which way remote sensing and GIS data can assist the planning of optimized heat allocation systems. In order to identify the best suited locations, a spatial model is defined to assess the potential for small district heating networks. Within the spatial model, the local heat demand and the economic costs of the necessary heat allocation infrastructure are compared. Therefore, a first and major step is the detailed characterization of the settlement structure by means of remote sensing data. The method is developed on the basis of a test area in the town of Oberhaching in the South of Germany. The results are validated through detailed in situ data sets and demonstrate that the model facilitates both the calculation of the required input parameters and an accurate assessment of the district heating potential. The described method can be transferred to other investigation areas with a larger spatial extent. The study underlines the range of applications for remote sensing-based analyses with respect to energy-related planning issues. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
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Open AccessArticle Geospatial Technologies to Improve Urban Energy Efficiency
Remote Sens. 2011, 3(7), 1380-1405; doi:10.3390/rs3071380
Received: 1 May 2011 / Revised: 23 June 2011 / Accepted: 27 June 2011 / Published: 5 July 2011
Cited by 13 | PDF Full-text (1068 KB) | HTML Full-text | XML Full-text
Abstract
The HEAT (Home Energy Assessment Technologies) pilot project is a FREE Geoweb mapping service, designed to empower the urban energy efficiency movement by allowing residents to visualize the amount and location of waste heat leaving their homes and communities as easily as [...] Read more.
The HEAT (Home Energy Assessment Technologies) pilot project is a FREE Geoweb mapping service, designed to empower the urban energy efficiency movement by allowing residents to visualize the amount and location of waste heat leaving their homes and communities as easily as clicking on their house in Google Maps. HEAT incorporates Geospatial solutions for residential waste heat monitoring using Geographic Object-Based Image Analysis (GEOBIA) and Canadian built Thermal Airborne Broadband Imager technology (TABI-320) to provide users with timely, in-depth, easy to use, location-specific waste-heat information; as well as opportunities to save their money and reduce their green-house-gas emissions. We first report on the HEAT Phase I pilot project which evaluates 368 residences in the Brentwood community of Calgary, Alberta, Canada, and describe the development and implementation of interactive waste heat maps, energy use models, a Hot Spot tool able to view the 6+ hottest locations on each home and a new HEAT Score for inter-city waste heat comparisons. We then describe current challenges, lessons learned and new solutions as we begin Phase II and scale from 368 to 300,000+ homes with the newly developed TABI-1800. Specifically, we introduce a new object-based mosaicing strategy, an adaptation of Emissivity Modulation to correct for emissivity differences, a new Thermal Urban Road Normalization (TURN) technique to correct for scene-wide microclimatic variation. We also describe a new Carbon Score and opportunities to update city cadastral errors with automatically defined thermal house objects. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
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Open AccessArticle Toronto’s Urban Heat Island—Exploring the Relationship between Land Use and Surface Temperature
Remote Sens. 2011, 3(6), 1251-1265; doi:10.3390/rs3061251
Received: 15 April 2011 / Revised: 8 June 2011 / Accepted: 14 June 2011 / Published: 21 June 2011
Cited by 30 | PDF Full-text (947 KB) | HTML Full-text | XML Full-text
Abstract
The urban heat island effect is linked to the built environment and threatens human health during extreme heat events. In this study, we analyzed whether characteristic land uses within an urban area are associated with higher or lower surface temperatures, and whether [...] Read more.
The urban heat island effect is linked to the built environment and threatens human health during extreme heat events. In this study, we analyzed whether characteristic land uses within an urban area are associated with higher or lower surface temperatures, and whether concentrations of “hot” land uses exacerbate this relationship. Zonal statistics on a thermal remote sensing image for the City of Toronto revealed statistically significant differences between high average temperatures for commercial and resource/industrial land use (29.1 °C), and low average temperatures for parks and recreational land (25.1 °C) and water bodies (23.1 °C). Furthermore, higher concentrations of either of these land uses were associated with more extreme surface temperatures. We also present selected neighborhoods to illustrate these results. The paper concludes by recommending that municipal planners and decision-makers formulate policies and regulations that are specific to the problematic land uses, in order to mitigate extreme heat. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
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Open AccessArticle Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data
Remote Sens. 2011, 3(6), 1188-1210; doi:10.3390/rs3061188
Received: 24 March 2011 / Revised: 5 May 2011 / Accepted: 1 June 2011 / Published: 14 June 2011
Cited by 26 | PDF Full-text (1652 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers [...] Read more.
In this paper, two main approaches for automatic building detection and localization using high spatial resolution imagery and LiDAR data are compared and evaluated: thresholding-based and object-based classification. The thresholding-based approach is founded on the establishment of two threshold values: one refers to the minimum height to be considered as building, defined using the LiDAR data, and the other refers to the presence of vegetation, which is defined according to the spectral response. The other approach follows the standard scheme of object-based image classification: segmentation, feature extraction and selection, and classification, here performed using decision trees. In addition, the effect of the inclusion in the building detection process of contextual relations with the shadows is evaluated. Quality assessment is performed at two different levels: area and object. Area-level evaluates the building delineation performance, whereas object-level assesses the accuracy in the spatial location of individual buildings. The results obtained show a high efficiency of the evaluated methods for building detection techniques, in particular the thresholding-based approach, when the parameters are properly adjusted and adapted to the type of urban landscape considered. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
Open AccessArticle Development of a New Ground Truth Database for Global Urban Area Mapping from a Gazetteer
Remote Sens. 2011, 3(6), 1177-1187; doi:10.3390/rs3061177
Received: 13 April 2011 / Revised: 21 May 2011 / Accepted: 31 May 2011 / Published: 3 June 2011
Cited by 6 | PDF Full-text (2492 KB) | HTML Full-text | XML Full-text
Abstract
We developed a ground truth database for urban areas from the Global Rural-Urban Mapping Project (GRUMP) Settlement Points gazetteer of populated place names by visually interpreting 3,734 urban points on satellite images, thus acquiring 2,144 urban and 1,388 non-urban data points. Our [...] Read more.
We developed a ground truth database for urban areas from the Global Rural-Urban Mapping Project (GRUMP) Settlement Points gazetteer of populated place names by visually interpreting 3,734 urban points on satellite images, thus acquiring 2,144 urban and 1,388 non-urban data points. Our database contained many more urban data points than the existing databases, which had only 0 to 11 ground truth data points. We used our database in combination with the Degree Confluence Project database to assess the accuracy of eight satellite-derived urban area maps, among which the MODIS Terra + Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid was the most accurate (84% overall accuracy; kappa coefficient, 0.63). Moreover, the most recently published maps were not necessarily the most accurate. We compared the accuracy assessment results of our database with those of another database and found that ours detected more errors of commission but included less chance agreement. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
Open AccessArticle Mapping Green Spaces in Bishkek—How Reliable can Spatial Analysis Be?
Remote Sens. 2011, 3(6), 1088-1103; doi:10.3390/rs3061088
Received: 19 April 2011 / Revised: 16 May 2011 / Accepted: 17 May 2011 / Published: 30 May 2011
Cited by 7 | PDF Full-text (2225 KB) | HTML Full-text | XML Full-text
Abstract
Within urban areas, green spaces play a critically important role in the quality of life. They have remarkable impact on the local microclimate and the regional climate of the city. Quantifying the ‘greenness’ of urban areas allows comparing urban areas at several [...] Read more.
Within urban areas, green spaces play a critically important role in the quality of life. They have remarkable impact on the local microclimate and the regional climate of the city. Quantifying the ‘greenness’ of urban areas allows comparing urban areas at several levels, as well as monitoring the evolution of green spaces in urban areas, thus serving as a tool for urban and developmental planning. Different categories of vegetation have different impacts on recreation potential and microclimate, as well as on the individual perception of green spaces. However, when quantifying the ‘greenness’ of urban areas the reliability of the underlying information is important in order to qualify analysis results. The reliability of geo-information derived from remote sensing data is usually assessed by ground truth validation or by comparison with other reference data. When applying methods of object based image analysis (OBIA) and fuzzy classification, the degrees of fuzzy membership per object in general describe to what degree an object fits (prototypical) class descriptions. Thus, analyzing the fuzzy membership degrees can contribute to the estimation of reliability and stability of classification results, even when no reference data are available. This paper presents an object based method using fuzzy class assignments to outline and classify three different classes of vegetation from GeoEye imagery. The classification result, its reliability and stability are evaluated using the reference-free parameters Best Classification Result and Classification Stability as introduced by Benz et al. in 2004 and implemented in the software package eCognition (www.ecognition.com). To demonstrate the application potentials of results a scenario for quantifying urban ‘greenness’ is presented. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
Open AccessArticle Satellite-Observed Urbanization Characters in Shanghai, China: Aerosols, Urban Heat Island Effect, and Land–Atmosphere Interactions
Remote Sens. 2011, 3(1), 83-99; doi:10.3390/rs3010083
Received: 16 November 2010 / Revised: 20 December 2010 / Accepted: 4 January 2011 / Published: 7 January 2011
Cited by 17 | PDF Full-text (1135 KB) | HTML Full-text | XML Full-text
Abstract
Urbanization reflects how human-activities affect natural climate system. Accurately assessing the urban system by comparing it with the nearby rural regions helps to identify the impacts of urbanization. This work uses the recent satellite observed aerosol, skin temperature, land cover, albedo, cloud [...] Read more.
Urbanization reflects how human-activities affect natural climate system. Accurately assessing the urban system by comparing it with the nearby rural regions helps to identify the impacts of urbanization. This work uses the recent satellite observed aerosol, skin temperature, land cover, albedo, cloud fraction and water vapor measurements to reveal how the city of Shanghai, one of the biggest, dense urban areas in East Asia, affects land surface and atmosphere conditions. In addition, the National Aeronautics and Space Administration (NASA) ground observations from AErosol RObotic NETwork (AERONET) is also used to reveal diurnal, seasonal, and interannual variations of the heavy aerosol load over Shanghai region. Furthermore, Shanghai reduces surface albedo, total column water vapor, cloud fraction and increases land skin temperature than rural region. These observations prove that Shanghai significantly modifies local and regional land surface physical properties as well as physical processes, which lead to the urban heat island effect (UHI). Full article
(This article belongs to the Special Issue Urban Remote Sensing)

Review

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Open AccessReview Can the Future EnMAP Mission Contribute to Urban Applications? A Literature Survey
Remote Sens. 2011, 3(9), 1817-1846; doi:10.3390/rs3091817
Received: 17 June 2011 / Revised: 12 August 2011 / Accepted: 15 August 2011 / Published: 25 August 2011
Cited by 11 | PDF Full-text (321 KB) | HTML Full-text | XML Full-text
Abstract
With urban populations and their footprints growing globally, the need to assess the dynamics of the urban environment increases. Remote sensing is one approach that can analyze these developments quantitatively with respect to spatially and temporally large scale changes. With the 2015 [...] Read more.
With urban populations and their footprints growing globally, the need to assess the dynamics of the urban environment increases. Remote sensing is one approach that can analyze these developments quantitatively with respect to spatially and temporally large scale changes. With the 2015 launch of the spaceborne EnMAP mission, a new hyperspectral sensor with high signal-to-noise ratio at medium spatial resolution, and a 21 day global revisit capability will become available. This paper presents the results of a literature survey on existing applications and image analysis techniques in the context of urban remote sensing in order to identify and outline potential contributions of the future EnMAP mission. Regarding urban applications, four frequently addressed topics have been identified: urban development and planning, urban growth assessment, risk and vulnerability assessment and urban climate. The requirements of four application fields and associated image processing techniques used to retrieve desired parameters and create geo-information products have been reviewed. As a result, we identified promising research directions enabling the use of EnMAP for urban studies. First and foremost, research is required to analyze the spectral information content of an EnMAP pixel used to support material-based land cover mapping approaches. This information can subsequently be used to improve urban indicators, such as imperviousness. Second, we identified the global monitoring of urban areas as a promising field of investigation taking advantage of EnMAP’s spatial coverage and revisit capability. However, owing to the limitations of EnMAPs spatial resolution for urban applications, research should also focus on hyperspectral resolution enhancement to enable retrieving material information on sub-pixel level. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
Open AccessReview Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems—An Overview
Remote Sens. 2011, 3(8), 1743-1776; doi:10.3390/rs3081743
Received: 24 June 2011 / Revised: 5 August 2011 / Accepted: 10 August 2011 / Published: 19 August 2011
Cited by 33 | PDF Full-text (3111 KB) | HTML Full-text | XML Full-text
Abstract
Cities are complex systems composed of numerous interacting components that evolve over multiple spatio-temporal scales. Consequently, no single data source is sufficient to satisfy the information needs required to map, monitor, model, and ultimately understand and manage our interaction within such urban [...] Read more.
Cities are complex systems composed of numerous interacting components that evolve over multiple spatio-temporal scales. Consequently, no single data source is sufficient to satisfy the information needs required to map, monitor, model, and ultimately understand and manage our interaction within such urban systems. Remote sensing technology provides a key data source for mapping such environments, but is not sufficient for fully understanding them. In this article we provide a condensed urban perspective of critical geospatial technologies and techniques: (i) Remote Sensing; (ii) Geographic Information Systems; (iii) object-based image analysis; and (iv) sensor webs, and recommend a holistic integration of these technologies within the language of open geospatial consortium (OGC) standards in-order to more fully understand urban systems. We then discuss the potential of this integration and conclude that this extends the monitoring and mapping options beyond “hard infrastructure” by addressing “humans as sensors”, mobility and human-environment interactions, and future improvements to quality of life and of social infrastructures. Full article
(This article belongs to the Special Issue Urban Remote Sensing)

Other

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Open AccessLetter Estimating Urban Heat Island Effects on the Temperature Series of Uccle (Brussels, Belgium) Using Remote Sensing Data and a Land Surface Scheme
Remote Sens. 2010, 2(12), 2773-2784; doi:10.3390/rs2122773
Received: 12 October 2010 / Revised: 26 October 2010 / Accepted: 7 December 2010 / Published: 10 December 2010
Cited by 11 | PDF Full-text (706 KB) | HTML Full-text | XML Full-text
Abstract
In this letter, the urban heat island effects on the temperature time series of Uccle (Brussels, Belgium) during the summers months 1960–1999 was estimated using both ground-based weather stations and remote sensing imagery, combined with a numerical land surface scheme including state-of-the-art [...] Read more.
In this letter, the urban heat island effects on the temperature time series of Uccle (Brussels, Belgium) during the summers months 1960–1999 was estimated using both ground-based weather stations and remote sensing imagery, combined with a numerical land surface scheme including state-of-the-art urban parameterization, the Town Energy Balance Scheme. Analysis of urban warming based on remote sensing method reveals that the urban bias on minimum temperature is rising at a higher rate, 2.5 times (2.85 ground-based observed) more, than on maximum temperature, with a linear trend of 0.15 °C (0.19 °C ground-based observed) and 0.06 °C (0.06 °C ground-based observed) per decade respectively. The results based on remote sensing imagery are compatible with estimates of urban warming based on weather stations. Therefore, the technique presented in this work is a useful tool in estimating the urban heat island contamination in long time series, countering the drawbacks of a ground-observational approach. Full article
(This article belongs to the Special Issue Urban Remote Sensing)
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