ISPRS International Journal of Geo-Information

697 KiB

Open AccessCorrection

Correction: Brodzik, M.J., et al. EASE-Grid 2.0: Incremental but Significant Improvements for Earth-Gridded Data Sets. ISPRS International Journal of Geo-Information 2012, 1, 32–45

by Mary J. Brodzik, Brendan Billingsley, Terry Haran, Bruce Raup and Matthew H. Savoie

ISPRS Int. J. Geo-Inf. 2014, 3(3), 1154-1156; https://doi.org/10.3390/ijgi3031154 - 24 Sep 2014

Cited by 59 | Viewed by 12859

Abstract

We wish to make the following corrections to this paper [1]: [...] Full article

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3185 KiB

Open AccessArticle

The Relationship between an Invasive Shrub and Soil Moisture: Seasonal Interactions and Spatially Covarying Relations

by Yuhong He

ISPRS Int. J. Geo-Inf. 2014, 3(3), 1139-1153; https://doi.org/10.3390/ijgi3031139 - 19 Sep 2014

Cited by 3 | Viewed by 7237

Abstract

Recent studies indicate that positive relationships between invasive plants and soil can contribute to further plant invasions. However, it remains unclear whether these relations remain unchanged throughout the growing season. In this study, spatial sequences of field observations along a transect were used [...] Read more.

Recent studies indicate that positive relationships between invasive plants and soil can contribute to further plant invasions. However, it remains unclear whether these relations remain unchanged throughout the growing season. In this study, spatial sequences of field observations along a transect were used to reveal seasonal interactions and spatially covarying relations between one common invasive shrub (Tartarian Honeysuckle, Lonicera tatarica) and soil moisture in a tall grassland habitat. Statistical analysis over the transect shows that the contrast between soil moisture in shrub and herbaceous patches vary with season and precipitation. Overall, a negatively covarying relationship between shrub and soil moisture (i.e., drier surface soils at shrub microsites) exists during the very early growing period (e.g., May), while in summer a positively covarying phenomenon (i.e., wetter soils under shrubs) is usually evident, but could be weakened or vanish during long precipitation-free periods. If there is sufficient rainfall, surface soil moisture and leaf area index (LAI) often spatially covary with significant spatial oscillations at an invariant scale (which is governed by the shrub spatial pattern and is about 8 m), but their phase relation in space varies with season, consistent with the seasonal variability of the co-varying phenomena between shrub invasion and soil water content. The findings are important for establishing a more complete picture of how shrub invasion affects soil moisture. Full article

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Map indicating the study area, the study transect (the yellow line), the large shrub patches in yellow circle, and field photos taken from an herbaceous macrosite and a shrub macrosite at four observation times. Full article ">Figure 2
Spatial sequences of leaf area index (LAI) and soil volumetric water content (VWC, top 12 cm) along the transect at four observation times (a,b) LAI and VWC on 10–11 May, (c,d) LAI and VWC on 21–22 June, (e,f) LAI and VWC on 15–17 July, and (g,h) LAI and VWC on 20–21 August 2010). Black solid (unfilled) diamond represents shrub (herbaceous) microsite. Full article ">Figure 3
(a) Average LAI of all shrub patches (black bars) and of all herbaceous patches (grey bars) along the transect at four observation times (from left to right: 10–11 May, 21–22 June, 15–17 July, and 20–21 August 2010). The error bars indicate 95% confidence intervals; (b) Similar to (a), but for soil volumetric water content (VWC, top 12 cm); (c) Daily precipitation amounts at this site during May–August 2010; Triangles in panel (c) indicate the four observation times in panels (a) and (b). Full article ">Figure 4
Wavelet power spectrum of LAI and soil volumetric water content (VWC, top 12 cm) spatial sequences obtained at four observation times ((a,b) wavelet power spectrum of LAI and VWC on 10–11 May, (c,d) wavelet power spectrum of LAI and VWC on 21–22 June, (e,f) wavelet power spectrum of LAI and VWC on 15–17 July and (g,h) wavelet power spectrum of LAI and VWC on 20–21 August 2010). The wavelet power (colored shading) is normalized by 1/σ2 (σ2 represent the variance of each spatial sequence). The vertical axis represents the spatial scale of variation (i.e., the Fourier period), while the horizontal axis is the transect. The thick black contour indicates the 95% significance level. The thin dash line indicates the cone of influence, i.e., the wavelet analysis results outside the cone are subject to edge effects. Full article ">Figure 5
Wavelet coherence between spatial sequences of LAI and soil moisture at four observation times ((a) wavelet coherence on 10–11 May, (b) wavelet coherence on 21–22 June, (c) wavelet coherence on 15–17 July, and (d) wavelet coherence on 20–21 August 2010). The colored shading represents the wavelet squared coherence. The thick black line represents the 95% significance level. The vectors (only plotted for the squared coherence greater than 0.5 for clarity) denote the phase relationship between the sequences (pointing right is for in-phase relation; left: Anti-phase; up: LAI lags soil moisture 90°; down: LAI leads soil moisture by 90°). The dash line indicates the cone of edge effects. Full article ">

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Open AccessArticle

A Concept for Uncertainty-Aware Analysis of Land Cover Change Using Geovisual Analytics

by Christoph Kinkeldey

ISPRS Int. J. Geo-Inf. 2014, 3(3), 1122-1138; https://doi.org/10.3390/ijgi3031122 - 19 Sep 2014

Cited by 6 | Viewed by 6665

Abstract

Analysis of land cover change is one of the major challenges in the remote sensing and GIS domain, especially when multi-temporal or multi-sensor analyses are conducted. One of the reasons is that errors and inaccuracies from multiple datasets (for instance caused by sensor [...] Read more.

Analysis of land cover change is one of the major challenges in the remote sensing and GIS domain, especially when multi-temporal or multi-sensor analyses are conducted. One of the reasons is that errors and inaccuracies from multiple datasets (for instance caused by sensor bias or spatial misregistration) accumulate and can lead to a high amount of erroneous change. A promising approach to counter this challenge is to quantify and visualize uncertainty, i.e., to deal with imperfection instead of ignoring it. Currently, in GIS the incorporation of uncertainty into change analysis is not easily possible. We present a concept for uncertainty-aware change analysis using a geovisual analytics (GVA) approach. It is based on two main elements: first, closer integration of change detection and analysis steps; and second, visual communication of uncertainty during analysis. Potential benefits include better-informed change analysis, support for choosing change detection parameters and reduction of erroneous change by filtering. In a case study with a change scenario in an area near Hamburg, Germany, we demonstrate how erroneous change can be filtered out using uncertainty. For this, we implemented a software prototype according to the concept presented. We discuss the potential and limitations of the concept and provide recommendations for future work. Full article

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Open AccessEditorial

Introduction to the Special Issue: Coastal GIS

by Timothy Nyerges

ISPRS Int. J. Geo-Inf. 2014, 3(3), 1118-1121; https://doi.org/10.3390/ijgi3031118 - 16 Sep 2014

Viewed by 4906

Abstract

This special issue of the ISPRS International Journal of Geographic Information about “Coastal GIS” is motivated by many circumstances. More than one-half of the world’s human population lives in coastal areas (within 200 kilometers of coast) as of 2000 [1]. The trend toward [...] Read more.

This special issue of the ISPRS International Journal of Geographic Information about “Coastal GIS” is motivated by many circumstances. More than one-half of the world’s human population lives in coastal areas (within 200 kilometers of coast) as of 2000 [1]. The trend toward coastal habitation is expected to continue in the US with the total being 75 percent by 2025, meaning that coastal human–environment interactions will likely increase and intensify [2]. Geographic information systems (GIS) are being developed and used by technical specialists, stakeholder publics, and executive/policy decision makers for improving our understanding and management of coastal areas, separately and together as more organizations focus on improving the sustainability and resilience of coastal systems. Coastal systems—defined as the area of land closely connected to the sea, including barrier islands, wetlands, mudflats, beaches, estuaries, cities, towns, recreational areas, and maritime facilities, the continental seas and shelves, and the overlying atmosphere—are subject to complex and dynamic interactions among natural and human-driven processes. Coastal systems are crucial to regional and national economies, hosting valued human-built infrastructure and providing ecosystem services that sustain human well-being. This special issue of IJGI about coastal GIS presents a collection of nine papers that address many of the issues mentioned above. [...] Full article

(This article belongs to the Special Issue Coastal GIS)

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Open AccessArticle

The Potential of Urban Agriculture in Montréal: A Quantitative Assessment

by Daniel Haberman, Laura Gillies, Aryeh Canter, Valentine Rinner, Laetitia Pancrazi and Federico Martellozzo

ISPRS Int. J. Geo-Inf. 2014, 3(3), 1101-1117; https://doi.org/10.3390/ijgi3031101 - 10 Sep 2014

Cited by 58 | Viewed by 19513

Abstract

Growing food in urban areas could solve a multitude of social and environmental problems. These potential benefits have resulted in an increased demand for urban agriculture (UA), though quantitative data is lacking on the feasibility of conversion to large-scale practices. This study uses [...] Read more.

Growing food in urban areas could solve a multitude of social and environmental problems. These potential benefits have resulted in an increased demand for urban agriculture (UA), though quantitative data is lacking on the feasibility of conversion to large-scale practices. This study uses multiple land use scenarios to determine different spaces that could be allocated to vegetable production in Montréal, including residential gardens, industrial rooftops and vacant space. Considering a range of both soil-bound and hydroponic yields, the ability of these scenarios to render Montréal self-sufficient in terms of vegetable production is assessed. The results show that the island could easily satisfy its vegetable demand if hydroponics are implemented on industrial rooftops, though these operations are generally costly. Using only vacant space, however, also has the potential to meet the city’s demand and requires lower operating costs. A performance index was developed to evaluate the potential of each borough to meet its own vegetable demand while still maintaining an elevated population density. Most boroughs outside of the downtown core are able to satisfy their vegetable demand efficiently due to their land use composition, though results vary greatly depending on the farming methods used, indicating the importance of farm management. Full article

(This article belongs to the Special Issue Geographic Information Science and Remote Sensing Techniques for Sustainable Urban and Regional Planning)

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Open AccessArticle

Spatial Representation of Coastal Risk: A Fuzzy Approach to Deal with Uncertainty

by Amaneh Jadidi, Mir Abolfazl Mostafavi, Yvan Bédard and Kyarash Shahriari

ISPRS Int. J. Geo-Inf. 2014, 3(3), 1077-1100; https://doi.org/10.3390/ijgi3031077 - 26 Aug 2014

Cited by 17 | Viewed by 8592

Abstract

Spatial information for coastal risk assessment is inherently uncertain. This uncertainty may be due to different spatial and temporal components of geospatial data and to their semantics. The spatial uncertainty can be expressed either quantitatively or qualitatively. Spatial uncertainty in coastal risk assessment [...] Read more.

Spatial information for coastal risk assessment is inherently uncertain. This uncertainty may be due to different spatial and temporal components of geospatial data and to their semantics. The spatial uncertainty can be expressed either quantitatively or qualitatively. Spatial uncertainty in coastal risk assessment itself arises from poor spatial representation of risk zones. Indeed, coastal risk is inherently a dynamic, complex, scale-dependent, and vague, phenomenon in concept. In addition, representing the associated zones with polygons having well-defined boundaries does not provide a realistic method for efficient and accurate representing of the risk. This paper proposes a conceptual framework, based on fuzzy set theory, to deal with the problems of ill-defined risk zone boundaries and the inherent uncertainty issues. To do so, the nature and level of uncertainty, as well as the way to model it are characterized. Then, a fuzzy representation method is developed where the membership functions are derived based on expert-knowledge. The proposed approach is then applied in the Perce region (Eastern Quebec, Canada) and results are presented and discussed. Full article

(This article belongs to the Special Issue Coastal GIS)

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927 KiB

Open AccessReview

Where 2.0 Australia’s Environment? Crowdsourcing, Volunteered Geographic Information, and Citizens Acting as Sensors for Environmental Sustainability

by Alister Clark

ISPRS Int. J. Geo-Inf. 2014, 3(3), 1058-1076; https://doi.org/10.3390/ijgi3031058 - 14 Aug 2014

Cited by 9 | Viewed by 7812

Abstract

Crowdsourcing, volunteered geographic information (VGI) and citizens acting as sensors are currently being used in Australia via GeoWeb 2.0 applications for environmental sustainability purposes. This paper situates the origins of these practices, phenomena and concepts within the intersection of Web 2.0 and emerging [...] Read more.

Crowdsourcing, volunteered geographic information (VGI) and citizens acting as sensors are currently being used in Australia via GeoWeb 2.0 applications for environmental sustainability purposes. This paper situates the origins of these practices, phenomena and concepts within the intersection of Web 2.0 and emerging online and mobile spatial technologies, herein called the GeoWeb 2.0. The significance of these origins is akin to a revolution in the way information is created, curated and distributed, attributed with transformative social impacts. Applications for environmental sustainability have the potential to be similarly transformative or disruptive. However, Web 2.0 is not described or conceptualised consistently within the literature. Australian examples implementing the GeoWeb 2.0 for environmental sustainability are diverse, but the reasons for this are difficult to ascertain. There is little published by the creators of such applications on their decisions, and Australian research is nascent, occurring across a variety of disciplinary approaches. While a substantial research literature emanates from North America and Europe, its transferability to Australia requires careful assessment. This paper contributes to this assessment by providing a review of relevant literature in the context of Australian examples for environmental sustainability. Full article

(This article belongs to the Special Issue Geoweb 2.0)

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7590 KiB

Open AccessArticle

Geographical Variation of Incidence of Chronic Obstructive Pulmonary Disease in Manitoba, Canada

by Mahmoud Torabi and Katie Galloway

ISPRS Int. J. Geo-Inf. 2014, 3(3), 1039-1057; https://doi.org/10.3390/ijgi3031039 - 29 Jul 2014

Cited by 2 | Viewed by 5938

Abstract

We aimed to study the geographic variation in the incidence of COPD. We used health survey data (weighted to the population level) to identify 56,944 cases of COPD in Manitoba, Canada from 2001 to 2010. We used five cluster detection procedures, circular spatial [...] Read more.

We aimed to study the geographic variation in the incidence of COPD. We used health survey data (weighted to the population level) to identify 56,944 cases of COPD in Manitoba, Canada from 2001 to 2010. We used five cluster detection procedures, circular spatial scan statistic (CSS), flexible spatial scan statistic (FSS), Bayesian disease mapping (BYM), maximum likelihood estimation (MLE), and local indicator of spatial association (LISA). Our results showed that there are some regions in southern Manitoba that are potential clusters of COPD cases. The FSS method identified more regions than the CSS and LISA methods and the BYM and MLE methods identified similar regions as potential clusters. Most of the regions identified by the MLE and BYM methods were also identified by the FSS method and most of the regions identified by the CSS method were also identified by most of the other methods. The CSS, FSS and LISA methods identify potential clusters but are not able to control for confounders at the same time. However, the BYM and MLE methods can simultaneously identify potential clusters and control for possible confounders. Overall, we recommend using the BYM and MLE methods for cluster detection in areas with similar population and structure of regions as those in Manitoba. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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The order of most likely clusters of COPD for the CSS, FSS, and LISA (based on the p-value) methods, and the special effects of the regional COPD risks for the BYM and MLE methods; in the case of cluster A. Major urban centre (Winnipeg region) is incorporated as an inset. (a) CSS; (b) FSS; (c) BYM; (d) MLE; (e) LISA. Full article ">Figure 2
The order of most likely clusters of COPD for the CSS and FSS methods, and the special effects of the regional COPD risks for the BYM and MLE methods; in the case of cluster B. Major urban centre (Winnipeg region) is incorporated as an inset. (a) CSS; (b) FSS; (c) BYM; (d) MLE. Full article ">Figure 3
The order of most likely clusters of COPD for the CSS and FSS methods, and the special effects of the regional COPD risks for the BYM and MLE methods; in the case of cluster C. Major urban centre (Winnipeg region) is incorporated as an inset. (a) CSS; (b) FSS; (c) BYM; (d) MLE. Full article ">Figure 4
The order of most likely clusters of COPD for the CSS and FSS methods, and the special effects of the regional COPD risks for the BYM and MLE methods; in the case of cluster D. Major urban centre (Winnipeg region) is incorporated as an inset. (a) CSS; (b) FSS; (c) BYM; (d) MLE. Full article ">

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Open AccessCommunication

Holistics 3.0 for Health

by David John Lary, Steven Woolf, Fazlay Faruque and James P. LePage

ISPRS Int. J. Geo-Inf. 2014, 3(3), 1023-1038; https://doi.org/10.3390/ijgi3031023 - 24 Jul 2014

Cited by 11 | Viewed by 6710

Abstract

Human health is part of an interdependent multifaceted system. More than ever, we have increasingly large amounts of data on the body, both spatial and non-spatial, its systems, disease and our social and physical environment. These data have a geospatial component. An exciting [...] Read more.

Human health is part of an interdependent multifaceted system. More than ever, we have increasingly large amounts of data on the body, both spatial and non-spatial, its systems, disease and our social and physical environment. These data have a geospatial component. An exciting new era is dawning where we are simultaneously collecting multiple datasets to describe many aspects of health, wellness, human activity, environment and disease. Valuable insights from these datasets can be extracted using massively multivariate computational techniques, such as machine learning, coupled with geospatial techniques. These computational tools help us to understand the topology of the data and provide insights for scientific discovery, decision support and policy formulation. This paper outlines a holistic paradigm called Holistics 3.0 for analyzing health data with a set of examples. Holistics 3.0 combines multiple big datasets set in their geospatial context describing as many areas of a problem as possible with machine learning and causality, to both learn from the data and to construct tools for data-driven decisions. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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A schematic illustrating the key components of Holistics 3.0: (1) multiple geospatial datasets describing many aspects of a problem holistically; (2) use of machine learning to build empirical decision support tools; and (3) augmentation by inferences about causality. Taken together, this paradigm is called Holistics 3.0. Full article ">Figure 2
A map showing the 8329 PM2.5 measurement site locations from 55 countries (red squares) that were used over the period 1997–present. The greatest density of sites is in North America, Europe and Asia. However, there are also southern hemisphere sites in South America, South Africa, Australia and New Zealand. The background color scale shows the global topography and bathymetry. Full article ">Figure 3
The monthly average of our prototype machine learning PM2.5 product (µg/m3) for August 2001. The average of the observations at a given site is overlaid as color-filled circles when observations were available for at least a third of the days. Notice the good agreement between the PM2.5 product and the observations. Furthermore, as would be expected, in summer, the eastern U.S. has much higher PM2.5 concentration than the western U.S. (a) Alaska highlighting common fire areas associated with elevated PM2.5; (b,c) the good agreement between our product and the observations; (d) the elevated PM2.5 with the heavily agricultural Central Valley in California, the highly populated Los Angeles Metro Area, the Sonoran Desert, one of the most active dust source regions in the U.S., the Four Corners Power Plants, some of the largest coal-fired generating stations in the U.S., and the Great Salt Lake Desert. Full article ">Figure 4
Two examples of the scatter diagrams for fully non-linear, non-parametric, multi-variate estimates of life expectancy: (a) 10,339 variables in the American Community Survey (U.S. Census Bureau) with a bivariate p-value for life expectancy of less than 0.05; (b) seven variables in the American Community Survey (U.S. Census Bureau) with a bivariate p-value for life expectancy of less than 10−240. Blue circles depict the training data. Red squares depict randomly selected, totally independent validation data not used in the training. The green line is the ideal 1:1 line for a perfect fit. Full article ">

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Open AccessArticle

Field Spectroscopy Metadata System Based on ISO and OGC Standards

by Marcos Jiménez, Magdalena González, Alberto Amaro and Alix Fernández-Renau

ISPRS Int. J. Geo-Inf. 2014, 3(3), 1003-1022; https://doi.org/10.3390/ijgi3031003 - 21 Jul 2014

Cited by 16 | Viewed by 7447

Abstract

Field spectroscopy has undergone a remarkable growth over the past two decades in terms of use and application to different scientific disciplines. This work presents an important step forward to improve the interoperability for the spectral library interchange in the field spectroscopy scientific [...] Read more.

Field spectroscopy has undergone a remarkable growth over the past two decades in terms of use and application to different scientific disciplines. This work presents an important step forward to improve the interoperability for the spectral library interchange in the field spectroscopy scientific community, by establishing an XML-based metadata system using published International Organization for Standardization (ISO) standards and Open Geospatial Consortium (OGC) specifications. The proposed methodology is structured using three different XML files: each spectral library file acquired during a field campaign is accompanied by an XML file encoded according to the ISO 19156 standard, which carries the information related to the material or surface measured and the sampling procedure applied; the spectral libraries acquired on the same date share an XML file encoded according to the ISO 19115 standard, to represent dataset-level metadata; finally, all of the spectral libraries for the entire field campaign are referenced to an XML file encoded according to the Sensor Model Language (SensorML) specification, for information related to the field spectrometer characteristics and status. This structure ensures that the ISO 19156 files are not very large and avoids the repetition of many common metadata elements required to describe the dataset and sensor description. Full article

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891 KiB

Open AccessArticle

A Conceptual List of Indicators for Urban Planning and Management Based on Earth Observation

by Nektarios Chrysoulakis, Christian Feigenwinter, Dimitrios Triantakonstantis, Igor Penyevskiy, Abraham Tal, Eberhard Parlow, Guy Fleishman, Sebnem Düzgün, Thomas Esch and Mattia Marconcini

ISPRS Int. J. Geo-Inf. 2014, 3(3), 980-1002; https://doi.org/10.3390/ijgi3030980 - 21 Jul 2014

Cited by 37 | Viewed by 13571

Abstract

Sustainable development is a key component in urban studies. Earth Observation (EO) can play a valuable role in sustainable urban development and planning, since it represents a powerful data source with the potential to provide a number of relevant urban sustainability indicators. To [...] Read more.

Sustainable development is a key component in urban studies. Earth Observation (EO) can play a valuable role in sustainable urban development and planning, since it represents a powerful data source with the potential to provide a number of relevant urban sustainability indicators. To this end, in this paper we propose a conceptual list of EO-based indicators capable of supporting urban planning and management. Three cities with different typologies, namely Basel, Switzerland; Tel Aviv, Israel; and Tyumen, Russia were selected as case studies. The EO-based indicators are defined to effectively record the physical properties of the urban environment in a diverse range of environmental sectors such as energy efficiency, air pollution and public health, water, transportation and vulnerability to hazards. The results assess the potential of EO to support the development of a set of urban environmental indicators towards sustainable urban planning and management. Full article

(This article belongs to the Special Issue Geographic Information Science and Remote Sensing Techniques for Sustainable Urban and Regional Planning)

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Open AccessArticle

A Geospatial Approach for Prioritizing Wind Farm Development in Northeast Nebraska, USA

by Adam Miller and Ruopu Li

ISPRS Int. J. Geo-Inf. 2014, 3(3), 968-979; https://doi.org/10.3390/ijgi3030968 - 17 Jul 2014

Cited by 54 | Viewed by 10550

Abstract

Being cleaner and climate friendly, wind energy has been increasingly utilized to meet the ever-growing global energy demands. In the State of Nebraska, USA, a wide gap exists between wind resource and actual energy production, and it is imperative to expand the wind [...] Read more.

Being cleaner and climate friendly, wind energy has been increasingly utilized to meet the ever-growing global energy demands. In the State of Nebraska, USA, a wide gap exists between wind resource and actual energy production, and it is imperative to expand the wind energy development. Because of the formidable costs associated with wind energy development, the locations for new wind turbines need to be carefully selected to provide the greatest benefit for a given investment. Geographic Information Systems (GIS) have been widely used to identify the suitable wind farm locations. In this study, a GIS-based multi-criteria approach was developed to identify the areas that are best suited to wind energy development in Northeast Nebraska, USA. Seven criteria were adopted in this method, including distance to roads, closeness to transmission lines, population density, wind potential, land use, distance to cities, slope and exclusionary areas. The suitability of wind farm development was modeled by a weighted overlay of geospatial layers corresponding to these criteria. The results indicate that the model is capable of identifying locations highly suited for wind farm development. The approach could help identify suitable wind farm locations in other areas with a similar geographic background. Full article

(This article belongs to the Special Issue GIS for Renewable Energy)

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2760 KiB

Open AccessArticle

Determination of Suitable Areas for the Generation of Wind Energy in Germany: Potential Areas of the Present and Future

by Valerie Blankenhorn and Bernd Resch

ISPRS Int. J. Geo-Inf. 2014, 3(3), 942-967; https://doi.org/10.3390/ijgi3030942 - 16 Jul 2014

Cited by 13 | Viewed by 12257

Abstract

Shortly after the Fukushima Daiichi nuclear disaster in 2011, the Federal Government of Germany decided to change the structure of the country’s energy supply system by ending nuclear energy conversion and strongly promoting the development of renewable energies. In order to politically set [...] Read more.

Shortly after the Fukushima Daiichi nuclear disaster in 2011, the Federal Government of Germany decided to change the structure of the country’s energy supply system by ending nuclear energy conversion and strongly promoting the development of renewable energies. In order to politically set the course for sustainable energy supply in this time of transition, it is important to analyze the factors influencing the future development of renewable energies. This work contributes to this purpose in the field of onshore wind electricity generation by displaying the temporal development of areas suitable for wind energy use. The availability of such areas is crucial to the extension of sites for wind energy plants. In our approach, the current potential area is determined by excluding areas unsuitable for this kind of electricity generation. For the determination of potential areas of the future, assumptions are made based on the expansion of settlement and traffic areas, and the occupation of protection areas. According to various scenarios, a decline of potential areas between 3% and 8% between 2011 and 2030 is indicated. Full article

(This article belongs to the Special Issue GIS for Renewable Energy)

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391 KiB

Open AccessArticle

Design of a GIS-Based Web Application for Simulating Biofuel Feedstock Yields

by Olga Prilepova, Quinn Hart, Justin Merz, Nathan Parker, Varaprasad Bandaru and Bryan Jenkins

ISPRS Int. J. Geo-Inf. 2014, 3(3), 929-941; https://doi.org/10.3390/ijgi3030929 - 16 Jul 2014

Cited by 7 | Viewed by 7884

Abstract

Short rotation woody crops (SRWC), such as hybrid poplar, have the potential to serve as a valuable feedstock for cellulosic biofuels. Spatial estimates of biomass yields under different management regimes are required for assisting stakeholders in making better management decisions and to establish [...] Read more.

Short rotation woody crops (SRWC), such as hybrid poplar, have the potential to serve as a valuable feedstock for cellulosic biofuels. Spatial estimates of biomass yields under different management regimes are required for assisting stakeholders in making better management decisions and to establish viable woody cropping systems for biofuel production. To support stakeholders in their management decisions, we have developed a GIS-based web interface using a modified 3PG model for spatially predicting poplar biomass yields under different management and climate conditions in the U.S. Pacific Northwest region. The application is implemented with standard HTML5 components, allowing its use in a modern browser and dynamically adjusting to the client screen size and device. In addition, cloud storage of the results makes them accessible on any Internet-enabled device. The web interface appears simple, but is powerful in parameter manipulation and in visualizing and sharing the results. Overall, this application comprises dynamic features that enable users to run SRWC crop growth simulations based on GIS information and contributes significantly to choosing appropriate feedstock growing locations, anticipating the desired physiological properties of the feedstock and incorporating the management and policy analysis needed for growing hybrid poplar plantations. Full article

(This article belongs to the Special Issue GIS for Renewable Energy)

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1848 KiB

Open AccessArticle

Vertical Measurements in Oblique Aerial Imagery

by Massimiliano Molinari, Stefano Medda and Samir Villani

ISPRS Int. J. Geo-Inf. 2014, 3(3), 914-928; https://doi.org/10.3390/ijgi3030914 - 14 Jul 2014

Cited by 7 | Viewed by 7889

Abstract

This article first introduces oblique aerial imagery, then describes how vertical distances can be measured once the pixel distances of the original pictures are known. The calculations require that, not only all camera settings be known, but also that one relies on the [...] Read more.

This article first introduces oblique aerial imagery, then describes how vertical distances can be measured once the pixel distances of the original pictures are known. The calculations require that, not only all camera settings be known, but also that one relies on the availability of detailed digital terrain and digital surface models (DSM and DTM), in order to provide the necessary ground level for calculating vertical distances. The algorithm is finally implemented in an online viewer. Full article

(This article belongs to the Special Issue Geographic Information Science and Remote Sensing Techniques for Sustainable Urban and Regional Planning)

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3116 KiB

Open AccessArticle

Dasymetric Mapping and Spatial Modeling of Mosquito Vector Exposure, Chesapeake, Virginia, USA

by Haley Cleckner and Thomas R. Allen

ISPRS Int. J. Geo-Inf. 2014, 3(3), 891-913; https://doi.org/10.3390/ijgi3030891 - 14 Jul 2014

Cited by 10 | Viewed by 11009

Abstract

Complex biophysical, social, and human behavioral factors influence population vulnerability to vector-borne diseases. Spatially and temporally dynamic environmental and anthropogenic patterns require sophisticated mapping and modeling techniques. While many studies use environmental variables to predict risk, human population vulnerability has been a challenge [...] Read more.

Complex biophysical, social, and human behavioral factors influence population vulnerability to vector-borne diseases. Spatially and temporally dynamic environmental and anthropogenic patterns require sophisticated mapping and modeling techniques. While many studies use environmental variables to predict risk, human population vulnerability has been a challenge to incorporate into spatial risk models. This study demonstrates and applies dasymetric mapping techniques to map spatial patterns of vulnerable human populations and characterize potential exposure to mosquito vectors of West Nile Virus across Chesapeake, Virginia. Mosquito vector abundance is quantified and combined with a population vulnerability index to evaluate exposure of human populations to mosquitoes. Spatial modeling is shown to capture the intersection of environmental factors that produce spatial hotspots in mosquito vector abundance, which in turn poses differential risks over time to humans. Such approaches can help design overall mosquito pest management and identify high-risk areas in advance of extreme weather. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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1324 KiB

Open AccessArticle

Rapid Prototyping — A Tool for Presenting 3-Dimensional Digital Models Produced by Terrestrial Laser Scanning

by Juho-Pekka Virtanen, Hannu Hyyppä, Matti Kurkela, Matti Vaaja, Petteri Alho and Juha Hyyppä

ISPRS Int. J. Geo-Inf. 2014, 3(3), 871-890; https://doi.org/10.3390/ijgi3030871 - 4 Jul 2014

Cited by 11 | Viewed by 10371

Abstract

Rapid prototyping has received considerable interest with the introduction of affordable rapid prototyping machines. These machines can be used to manufacture physical models from three-dimensional digital mesh models. In this paper, we compare the results obtained with a new, affordable, rapid prototyping machine, [...] Read more.

Rapid prototyping has received considerable interest with the introduction of affordable rapid prototyping machines. These machines can be used to manufacture physical models from three-dimensional digital mesh models. In this paper, we compare the results obtained with a new, affordable, rapid prototyping machine, and a traditional professional machine. Two separate data sets are used for this, both of which were acquired using terrestrial laser scanning. Both of the machines were able to produce complex and highly detailed geometries in plastic material from models based on terrestrial laser scanning. The dimensional accuracies and detail levels of the machines were comparable, and the physical artifacts caused by the fused deposition modeling (FDM) technique used in the rapid prototyping machines could be found in both models. The accuracy of terrestrial laser scanning exceeded the requirements for manufacturing physical models of large statues and building segments at a 1:40 scale. Full article

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Open AccessEditorial

GIS and Public Health

by Stefania Bertazzon

ISPRS Int. J. Geo-Inf. 2014, 3(3), 868-870; https://doi.org/10.3390/ijgi3030868 - 30 Jun 2014

Cited by 4 | Viewed by 6807

Abstract

This Special Issue on GIS and public health is the result of a highly selective process, which saw the participation of some 20 expert peer-reviewers and led to the acceptance of one half of the high-quality submissions received over the past year. Many [...] Read more.

This Special Issue on GIS and public health is the result of a highly selective process, which saw the participation of some 20 expert peer-reviewers and led to the acceptance of one half of the high-quality submissions received over the past year. Many threads link these papers to each other and, indeed, to our original call for papers, but the element that most clearly emerges from these works is the inextricable connection between public health and the environment. Indeed, GIS analysis of public health simply cannot disregard the geospatial dimension of environmental resources and risks. What consistently emerges from these analyses is that current geospatial research can only scratch the surface of the complex interactions of spatial resources, risks, and public health. In today’s world, or at least in the developed world, researchers and practitioners can count on virtually endless data, on inexpensive computational power, and on seamless connectivity. In this research environment, these papers point to the need for improved analytical tools, covering concepts, representation, modeling and reliability. These works are important contributions that help us to identify what advances in geospatial analysis can better address the complex interactions of public health with our physical and cultural environment, and bridge research and practice, so that geospatial analyses can inform public health policy making. [...] Full article

(This article belongs to the Special Issue GIS in Public Health)

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ISPRS Int. J. Geo-Inf., Volume 3, Issue 3 (September 2014) – 18 articles , Pages 868-1156

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