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ISPRS Int. J. Geo-Inf., Volume 2, Issue 1 (March 2013), Pages 1-255

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Research

Open AccessArticle A Bottom-Up Approach for Automatically Grouping Sensor Data Layers by their Observed Property
ISPRS Int. J. Geo-Inf. 2013, 2(1), 1-26; doi:10.3390/ijgi2010001
Received: 25 November 2012 / Revised: 5 January 2013 / Accepted: 7 January 2013 / Published: 30 January 2013
Cited by 1 | PDF Full-text (455 KB) | HTML Full-text | XML Full-text
Abstract
The Sensor Web is a growing phenomenon where an increasing number of sensors are collecting data in the physical world, to be made available over the Internet. To help realize the Sensor Web, the Open Geospatial Consortium (OGC) has developed open standards [...] Read more.
The Sensor Web is a growing phenomenon where an increasing number of sensors are collecting data in the physical world, to be made available over the Internet. To help realize the Sensor Web, the Open Geospatial Consortium (OGC) has developed open standards to standardize the communication protocols for sharing sensor data. Spatial Data Infrastructures (SDIs) are systems that have been developed to access, process, and visualize geospatial data from heterogeneous sources, and SDIs can be designed specifically for the Sensor Web. However, there are problems with interoperability associated with a lack of standardized naming, even with data collected using the same open standard. The objective of this research is to automatically group similar sensor data layers. We propose a methodology to automatically group similar sensor data layers based on the phenomenon they measure. Our methodology is based on a unique bottom-up approach that uses text processing, approximate string matching, and semantic string matching of data layers. We use WordNet as a lexical database to compute word pair similarities and derive a set-based dissimilarity function using those scores. Two approaches are taken to group data layers: mapping is defined between all the data layers, and clustering is performed to group similar data layers. We evaluate the results of our methodology. Full article
Open AccessArticle Improving the GIS-DRP Approach by Means of DelineatingRunoff Characteristics with New Discharge Relevant Parameters
ISPRS Int. J. Geo-Inf. 2013, 2(1), 27-49; doi:10.3390/ijgi2010027
Received: 6 December 2012 / Revised: 15 January 2013 / Accepted: 22 January 2013 / Published: 31 January 2013
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Abstract
At present it is common to use geographic information system (GIS) applications to assess runoff generation. One of these GIS-based tools to generate maps of dominant runoff processes is the so called GIS-DRP approach. The tool, which has been developed mainly based [...] Read more.
At present it is common to use geographic information system (GIS) applications to assess runoff generation. One of these GIS-based tools to generate maps of dominant runoff processes is the so called GIS-DRP approach. The tool, which has been developed mainly based on agricultural areas, uses commonly available input data like a digital elevation model (DEM), geological information as well as land use information. The aim of this study is to test, validate and improve this GIS-DRP method for forested and silviculture areas. Hence, soil-hydrologic investigations and several mapping techniques of dominant runoff processes were conducted on 25 test-plots in four forested catchments in Rhineland-Palatinate (Germany) and the Grand Duchy of Luxembourg. By comparing the results of the mapping techniques and those of the test plots, weak points in the original GIS-DRP method were detected. Subsequently, it was possible to enhance the GIS-DRP approach by incorporating new discharge relevant parameters like topsoil sealing, extreme weather events and semipermeability of the substratum. Moreover, the improved GIS-DRP approach can be widely used in different landscapes and for different fields of application. The adapted method can now support foresters and decision makers in forestry planning, answer questions concerning the landscape water balance and peripheral water retention or provide extra information for sustainable forest planning in times of a changing climate. Full article
Open AccessArticle Assessing the Geographic Representativity of Farm Accountancy Data
ISPRS Int. J. Geo-Inf. 2013, 2(1), 50-66; doi:10.3390/ijgi2010050
Received: 6 December 2012 / Revised: 25 January 2013 / Accepted: 28 January 2013 / Published: 6 February 2013
Cited by 2 | PDF Full-text (612 KB) | HTML Full-text | XML Full-text
Abstract
The environment affects agriculture, via soils, weather, etc. and agriculture affects the environment locally at farm level and via its impact on climate change. Locating agriculture within its spatial environment is thus important for farmers and policy makers. Within the EU countries [...] Read more.
The environment affects agriculture, via soils, weather, etc. and agriculture affects the environment locally at farm level and via its impact on climate change. Locating agriculture within its spatial environment is thus important for farmers and policy makers. Within the EU countries collect detailed farm data to understand the technical and financial performance of farms; the Farm Accountancy Data Network. However, knowledge of the spatial-environmental context of these farms is reported at gross scale. In this paper, Irish farm accounting data is geo-referenced using address matching to a national address database. An analysis of the geographic distribution of the survey farms, illustrated through a novel 2D ranked pair plot of the coordinates, compared to the national distribution of farms shows a trend in the location of survey farms that leads to a statistical difference in the climatic variables associated with the farm. The farms in the survey have significantly higher accumulated solar radiation values than the national average. As a result, the survey may not be representative spatially of the pattern of environment x farm system. This could have important considerations when using FADN data in modelling climate change impacts on agri-economic performance. Full article
(This article belongs to the Special Issue Geospatial Monitoring and Modelling of Environmental Change)
Open AccessArticle Towards Improving Query Performance of Web Feature Services (WFS) for Disaster Response
ISPRS Int. J. Geo-Inf. 2013, 2(1), 67-81; doi:10.3390/ijgi2010067
Received: 6 January 2013 / Revised: 21 January 2013 / Accepted: 31 January 2013 / Published: 6 February 2013
Cited by 6 | PDF Full-text (757 KB) | HTML Full-text | XML Full-text
Abstract
While OGC’s WFS facilitates disseminating heterogeneous spatial data over the Web and allows feature-level geospatial information sharing and synchronization, performance issues challenge the efficient and effective utilization of WFS for disaster response. Literature shows that obtaining spatial information becomes very slow when [...] Read more.
While OGC’s WFS facilitates disseminating heterogeneous spatial data over the Web and allows feature-level geospatial information sharing and synchronization, performance issues challenge the efficient and effective utilization of WFS for disaster response. Literature shows that obtaining spatial information becomes very slow when querying WFS systems from large geospatial databases over the Internet. Solutions on how to improve the WFS system performance so that spatial data can be delivered to disaster responders within a reasonable amount of time are needed. This paper proposes a parallel approach based on Voronoi diagram indexing and data/task parallelism for improving the query performance of WFS systems for disaster applications. Experimental results show that the parallel approach can significantly improve the response time needed to process the spatial queries from a massive volume of spatial data for disaster response. Full article
Figures

Open AccessArticle Pioneering GML Deployment for NSDI — Case Study of USTIGER/GML
ISPRS Int. J. Geo-Inf. 2013, 2(1), 82-93; doi:10.3390/ijgi2010082
Received: 29 November 2012 / Revised: 14 January 2013 / Accepted: 1 February 2013 / Published: 18 February 2013
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Abstract
The National Spatial Data Infrastructure (NSDI) is defined as the technologies, policies and people necessary to promote sharing of geospatial data throughout all levels of government, the private and non-profit sectors and the academic community. The US Census Bureau is the federal [...] Read more.
The National Spatial Data Infrastructure (NSDI) is defined as the technologies, policies and people necessary to promote sharing of geospatial data throughout all levels of government, the private and non-profit sectors and the academic community. The US Census Bureau is the federal agency lead for administrative units data, one of the seven data themes identified by the NSDI framework. The administrative unit is a unit with administrative responsibilities. These units are organized as nodes/lines/areas feature data. The OpenGIS Geography Markup Language (GML) is the XML grammar to express the geographic features. This study at the US Census Bureau investigates how the general-purpose GML standard could be leveraged and extended to describe the most comprehensive geographic dataset with national coverage in the US. Challenges and problems in dealing with data volume, GML document structure, GML schema design and GML document naming are analyzed, followed by proposed solutions proven for feasibility. Our results show that one key point in making a successful GML deployment for NSDI is to reflect the characteristics of the geographic data through a carefully designed GML schema, structure and organization. The lessons learned may be useful to others transforming NSDI framework data and other large geospatial datasets into GML structures. Full article
Open AccessArticle Quantifying Landscape-Scale Patterns of Temperate Forests over Time by Means of Neutral Simulation Models
ISPRS Int. J. Geo-Inf. 2013, 2(1), 94-109; doi:10.3390/ijgi2010094
Received: 1 January 2013 / Revised: 19 February 2013 / Accepted: 21 February 2013 / Published: 1 March 2013
Cited by 5 | PDF Full-text (547 KB) | HTML Full-text | XML Full-text
Abstract
Several studies attempt to describe changes in the spatial patterns of forests over time, resorting to the comparison of landscape pattern indices (LPI), but new methods for quantifying landscape differences in a statistical context are necessary. In this paper, we quantified and [...] Read more.
Several studies attempt to describe changes in the spatial patterns of forests over time, resorting to the comparison of landscape pattern indices (LPI), but new methods for quantifying landscape differences in a statistical context are necessary. In this paper, we quantified and assessed the statistical significance of the forests pattern changes, which have occurred since the end of WWII in Central Italy (Isernia). To do this; based on the proportion of forest cover (pi) and contagion (H) of three land cover maps (1954–1981–2006); we generated 100 forest maps with predictable results through the midpoint displacement algorithm. Then, for both observed and simulated maps, we computed a set of LPI (number of patches, cohesion, largest forest patch index and area weighted mean shape index) and we derived their empirical distributions; finally, we compared the empirical distributions using the non-parametric Kruskal-Wallis test. Our results show significant changes in the spatial pattern of forests and underline the process of natural forest re-growth, which, in the area, is constrained by “remnants” of traditional activities. The adopted approach could be extended to a large ensemble of landscapes and spatial scales and could become a standard procedure when comparing patterns in time. Full article
(This article belongs to the Special Issue Geospatial Monitoring and Modelling of Environmental Change)
Open AccessArticle Potential Impact of Climate Changes on the Inundation Risk Levels in a Dam Break Scenario
ISPRS Int. J. Geo-Inf. 2013, 2(1), 110-134; doi:10.3390/ijgi2010110
Received: 10 December 2012 / Revised: 6 February 2013 / Accepted: 15 February 2013 / Published: 4 March 2013
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Abstract
The overall objective of the study is to generate information for an enhanced land use planning with respect to flood hazards. The study assesses the potential impact of climate change by simulating a dam break scenario in a high intensity rainfall event [...] Read more.
The overall objective of the study is to generate information for an enhanced land use planning with respect to flood hazards. The study assesses the potential impact of climate change by simulating a dam break scenario in a high intensity rainfall event and evaluates the vulnerability risk in the downstream region by integrating ArcGIS and Hydrologic Engineering Centers River Analysis System (HEC-RAS) technologies. In the past century, the evidence of climate changes are observed in terms of increase in high intensity rainfall events. These events are of high concern, as increased inflow rates may increase the probability of a dam failure, leading to higher magnitude flooding events involving multiple consequences. The 100 year historical rainfall data for the central Mississippi region reveals an increased trend in the intensity of rainfall rates after the 1970s. With more than 10% of high hazard dams in the central region, the damage can be far accumulative. The study determines occurrence of the high intensity rainfall event in the past 100 years for central Mississippi and simulates a Ross Barnett Reservoir dam break scenario and evaluates the vulnerability risks due to inundation in the immediate downstream region, which happens to be the State Capital. The results indicate that the inundation due to a Ross Barnett Reservoir failure under high intensity rainfall event is comparable to a catastrophic flood event experienced by the region in 1979, which almost equals a 200-year flood magnitude. The results indicate that the extent and depth of flood waters poses a significant destructive threat to the state capital, inundating various infrastructural and transportation networks. Full article
(This article belongs to the Special Issue Space-Based Technologies for Disaster Risk Management)
Open AccessArticle Spatial Search Techniques for Mobile 3D Queries in Sensor Web Environments
ISPRS Int. J. Geo-Inf. 2013, 2(1), 135-154; doi:10.3390/ijgi2010135
Received: 26 December 2012 / Revised: 25 February 2013 / Accepted: 26 February 2013 / Published: 8 March 2013
Cited by 1 | PDF Full-text (1454 KB) | HTML Full-text | XML Full-text
Abstract
Developing mobile geo-information systems for sensor web applications involves technologies that can access linked geographical and semantically related Internet information. Additionally, in tomorrow’s Web 4.0 world, it is envisioned that trillions of inexpensive micro-sensors placed throughout the environment will also become available [...] Read more.
Developing mobile geo-information systems for sensor web applications involves technologies that can access linked geographical and semantically related Internet information. Additionally, in tomorrow’s Web 4.0 world, it is envisioned that trillions of inexpensive micro-sensors placed throughout the environment will also become available for discovery based on their unique geo-referenced IP address. Exploring these enormous volumes of disparate heterogeneous data on today’s location and orientation aware smartphones requires context-aware smart applications and services that can deal with “information overload”. 3DQ (Three Dimensional Query) is our novel mobile spatial interaction (MSI) prototype that acts as a next-generation base for human interaction within such geospatial sensor web environments/urban landscapes. It filters information using “Hidden Query Removal” functionality that intelligently refines the search space by calculating the geometry of a three dimensional visibility shape (Vista space) at a user’s current location. This 3D shape then becomes the query “window” in a spatial database for retrieving information on only those objects visible within a user’s actual 3D field-of-view. 3DQ reduces information overload and serves to heighten situation awareness on constrained commercial off-the-shelf devices by providing visibility space searching as a mobile web service. The effects of variations in mobile spatial search techniques in terms of query speed vs. accuracy are evaluated and presented in this paper. Full article
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Open AccessArticle A New Algorithm for Identifying Possible Epidemic Sources with Application to the German Escherichia coli Outbreak
ISPRS Int. J. Geo-Inf. 2013, 2(1), 155-200; doi:10.3390/ijgi2010155
Received: 21 December 2012 / Revised: 19 February 2013 / Accepted: 19 February 2013 / Published: 11 March 2013
Cited by 5 | PDF Full-text (3069 KB) | HTML Full-text | XML Full-text
Abstract
In this paper we describe a recently developed algorithm called Topological Weighted Centroid (TWC). TWC takes locations of an event of interest and analyzes the possible associated dynamics using the ideas of free energy and entropy. This novel mathematical tool has been [...] Read more.
In this paper we describe a recently developed algorithm called Topological Weighted Centroid (TWC). TWC takes locations of an event of interest and analyzes the possible associated dynamics using the ideas of free energy and entropy. This novel mathematical tool has been applied to a real world example, the epidemic outbreak caused by Escherichia coli that occurred in Germany in 2011, to point out the real source of the outbreak. Other four examples of application to other epidemic spreads are described: Chikungunya fever of 2007 in Italy; Foot and mouth disease of 1967 in England; Cholera of 1854 in London; and the Russian influenza of 1889–1890 in Sweden. Comparisons have been made with other already published algorithms: Rossmo Algorithm, NES, LVM, Mexican Prob. The TWC results are significantly superior in comparison with other algorithms according to four independent indexes: distance from the peak, sensitivity, specificity and searching area. They are consistent with the idea that the spread of infectious disease is not random but follows a progression based on inherent, but as yet undiscovered, mathematical laws. The TWC method could provide an additional powerful tool for the investigation of the early stages of an epidemic and novel simulation methods for understanding the process through which a disease is spread. Full article
(This article belongs to the Special Issue Spatial Analysis and Data Mining)
Open AccessArticle Pygrass: An Object Oriented Python Application Programming Interface (API) for Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS)
ISPRS Int. J. Geo-Inf. 2013, 2(1), 201-219; doi:10.3390/ijgi2010201
Received: 1 January 2013 / Revised: 21 January 2013 / Accepted: 21 February 2013 / Published: 11 March 2013
Cited by 5 | PDF Full-text (1269 KB) | HTML Full-text | XML Full-text
Abstract
PyGRASS is an object-oriented Python Application Programming Interface (API) for Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS), a powerful open source GIS widely used in academia, commercial settings and governmental agencies. We present the architecture of the PyGRASS library, [...] Read more.
PyGRASS is an object-oriented Python Application Programming Interface (API) for Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS), a powerful open source GIS widely used in academia, commercial settings and governmental agencies. We present the architecture of the PyGRASS library, covering interfaces to GRASS modules, vector and raster data, with a focus on the new capabilities that it provides to GRASS users and developers. Our design concept of the module interface allows the direct linking of inputs and outputs of GRASS modules to create process chains, including compatibility checks, process control and error handling. The module interface was designed to be easily extended to work with remote processing services (Web Processing Service (WPS), Web Service Definition Language (WSDL)/Simple Object Access Protocol (SOAP)). The new object-oriented Python programming API introduces an abstract layer that opens the possibility to use and access transparently the efficient raster and vector functions of GRASS that are implemented in C. The design goal was to provide an easy to use, but powerful, Python interface for users and developers who are not familiar with the programming language C and with the GRASS C-API. We demonstrate the capabilities, scalability and performance of PyGRASS with several dedicated tests and benchmarks. We compare and discuss the results of the benchmarks with dedicated C implementations. Full article
(This article belongs to the Special Issue Geospatial Monitoring and Modelling of Environmental Change)
Open AccessArticle Mapping Urban Tree Species Using Very High Resolution Satellite Imagery: Comparing Pixel-Based and Object-Based Approaches
ISPRS Int. J. Geo-Inf. 2013, 2(1), 220-236; doi:10.3390/ijgi2010220
Received: 1 January 2013 / Revised: 25 February 2013 / Accepted: 26 February 2013 / Published: 13 March 2013
Cited by 6 | PDF Full-text (1596 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
We assessed the potential of multi-spectral GeoEye imagery for biodiversity assessment in an urban context in Bangalore, India. Twenty one grids of 150 by 150 m were randomly located in the city center and all tree species within these grids mapped in [...] Read more.
We assessed the potential of multi-spectral GeoEye imagery for biodiversity assessment in an urban context in Bangalore, India. Twenty one grids of 150 by 150 m were randomly located in the city center and all tree species within these grids mapped in the field. The six most common species, collectively representing 43% of the total trees sampled, were selected for mapping using pixel-based and object-based approaches. All pairs of species were separable based on spectral reflectance values in at least one band, with Peltophorum pterocarpum being most distinct from other species. Object-based approaches were consistently superior to pixel-based methods, which were particularly low in accuracy for tree species with small canopy sizes, such as Cocos nucifera and Roystonea regia. There was a strong and significant correlation between the number of trees determined on the ground and from object-based classification. Overall, object-based approaches appear capable of discriminating the six most common species in a challenging urban environment, with substantial heterogeneity of tree canopy sizes. Full article
(This article belongs to the Special Issue Geospatial Monitoring and Modelling of Environmental Change)
Open AccessArticle Exploratory Spatial Data Analysis of Congenital Malformations (CM) in Israel, 2000–2006
ISPRS Int. J. Geo-Inf. 2013, 2(1), 237-255; doi:10.3390/ijgi2010237
Received: 10 January 2013 / Revised: 11 March 2013 / Accepted: 13 March 2013 / Published: 19 March 2013
Cited by 4 | PDF Full-text (834 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Congenital Malformations (CM) impose a heavy burden on families and society. Identification of spatial patterns of CM is useful for understanding the epidemiology of this public health issue. In Israel, about 1,000,000 births and 25,000 CM cases at 37 groups were geocoded [...] Read more.
Congenital Malformations (CM) impose a heavy burden on families and society. Identification of spatial patterns of CM is useful for understanding the epidemiology of this public health issue. In Israel, about 1,000,000 births and 25,000 CM cases at 37 groups were geocoded during 2000–2006. These were geo-analyzed using global-Moran’s-I statistics. Eight groups demonstrated geospatial heterogeneity and were further analyzed at both the census tract (Local Indicator of Spatial Association (LISA) and hot spot analyses) and street levels (spatial scan statistics with two population threshold sizes). The positional definition of results is further discussed in relevance to possible exposure to teratogenic sources in the region. Limitations of data and methods used are presented as well. Full article

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