Effects of Pansharpening on Vegetation Indices

isprsISPRS International Journal of Geo-Information, 2014, 3(2), 507-522

By Brian Johnson

“This study evaluated the effects of image pansharpening on Vegetation Indices (VIs), and found that pansharpening was able to downscale single-date and multi-temporal Landsat 8 VI data without introducing significant distortions in VI values. Four fast pansharpening methods—Fast Intensity-Hue-Saturation (FIHS), Brovey Transform (BT), Additive Wavelet Transform (AWT), and Smoothing Filter-based Intensity Modulation (SFIM)—and two VIs—Normalized Difference Vegetation Index (NDVI) and Simple Ratio (SR)—were tested. The NDVI and SR formulas were both found to cause some spatial information loss in the pansharpened multispectral (MS) bands, and this spatial information loss from VI transformations was not specific to Landsat 8 imagery (it will occur for any type of imagery).

Color infrared Landsat 8 images acquired on 29 May 2013 (a) and 5 November 2013 (b). Red, Green, and Blue colors correspond to Band 5, Band 4, and Band 3. The yellow rectangle shows the location of the inset maps in Section 5.3.

Color infrared Landsat 8 images acquired on 29 May 2013 (a) and 5 November 2013 (b). Red, Green, and Blue colors correspond to Band 5, Band 4, and Band 3. The yellow rectangle shows the location of the inset maps in Section 5.3.

“BT, SFIM, and other similar pansharpening methods that inject spatial information from the panchromatic (Pan) band by multiplication, lose all of the injected spatial information after the VI calculations. FIHS, AWT, and other similar pansharpening methods that inject spatial information by addition, lose some spatial information from the Pan band after VI calculations as well. Nevertheless, for all of the single- and multi-date VI images, the FIHS and AWT pansharpened images were more similar to the higher resolution reference data than the unsharpened VI images were, indicating that pansharpening was effective in downscaling the VI data. FIHS best enhanced the spectral and spatial information of the single-date and multi-date VI images, followed by AWT, and neither significantly over- or under-estimated VI values. ”

A Hierarchical Approach to Optimizing Bus Stop Distribution in Large and Fast Developing Cities

isprsISPRS International Journal of Geo-Information, 2014, 3(2), 554-564

By Zhengdong Huang and Xuejun Liu

“Public transit plays a key role in shaping the transportation structure of large and fast growing cities. To cope with high population and employment density, such cities usually resort to multi-modal transit services, such as rail, BRT and bus. These modes are strategically connected to form an effective transit network. Among the transit modes, bus stops need to be properly deployed to maintain an acceptable walking accessibility. This paper presents a hierarchical process for optimizing bus stop locations in the context of fast growing multi-modal transit services.

Distribution of existing and optimized bus stops.

Distribution of existing and optimized bus stops.

“Three types of bus stops are identified hierarchically, which includes connection stops, key stops and ordinary stops. Connection stops are generated manually to connect with other transit facilities. Key stops and ordinary stops are optimized with coverage models that are respectively weighted by network centrality measure and potential demand. A case study in a Chinese city suggests the hierarchical approach may generate more effective stop distribution. ”

OGC Seeks Comment on Charter for New Urban Planning Domain Working Group

OGC_Logo_Border_Blue_3DAn OGC Urban Planning Domain Working Group (SWG) is being chartered to define the role for OGC standards and related activities within the Urban Planning Discipline and to provide an open forum for the discussion and presentation of interoperability requirements, use cases, pilots, and implementations of OGC standards in this domain. Initiators of the new DWG seek comments from the public on the draft new charter. The comment period closes on  6 August 2014.

A “Smart City” invests in human and social capital, physical infrastructure and information communications technology (ICT) infrastructure to sustain quality of life in the urban environment. Technologies and trends such as Augmented Reality (AR), Smart Cities, Smart Grids, Sensor Webs, the Internet of Things (IoT), LBS (Location Based Services), Facilities Management, navigation (indoor and outdoor) and “Big Data” Analytics all can play important roles in informing urban planners. Also, these technologies are permanent and rapidly evolving elements of life in modern cities, which makes them subjects for urban studies and urban planning.

In all of those technology domains, open standards can facilitate the development, publication, discovery, assessment, analysis, portrayal and use of information.The OGC Urban Planning Domain Working Group intends to discover requirements for open geospatial standards in information systems involved in the planning, design, use, maintenance and governance of publicly accessible spaces. Requirements presented and discussed in OGC Domain Working Groups are typically addressed in existing or yet-to-be chartered OGC Standards Working Groups and in the OGC’s collaborative activities with other standards development organizations.

The OGC® is an international consortium of more than 475 companies, government agencies, research organizations, and universities participating in a consensus process to develop publicly available geospatial standards. OGC standards support interoperable solutions that “geo-enable” the Web, wireless and location-based services, and mainstream IT. Visit the OGC website at http://www.opengeospatial.org/.

[Source: OGC press release]

A Comparison of Advanced Regression Algorithms for Quantifying Urban Land Cover

rs-remotesensing-logoRemote Sensing, Volume 6, Issue 7, Published Online 07 July 2014

Akpona Okujeni, Sebastian van der Linden, Benjamin Jakimow, Andreas Rabe, Jochem Verrelst, and Patrick Hostert

“Quantitative methods for mapping sub-pixel land cover fractions are gaining increasing attention, particularly with regard to upcoming hyperspectral satellite missions. We evaluated five advanced regression algorithms combined with synthetically mixed training data for quantifying urban land cover from HyMap data at 3.6 and 9 m spatial resolution. Methods included support vector regression (SVR), kernel ridge regression (KRR), artificial neural networks (NN), random forest regression (RFR) and partial least squares regression (PLSR). Our experiments demonstrate that both kernel methods SVR and KRR yield high accuracies for mapping complex urban surface types, i.e., rooftops, pavements, grass- and tree-covered areas. SVR and KRR models proved to be stable with regard to the spatial and spectral differences between both images and effectively utilized the higher complexity of the synthetic training mixtures for improving estimates for coarser resolution data. Observed deficiencies mainly relate to known problems arising from spectral similarities or shadowing. The remaining regressors either revealed erratic (NN) or limited (RFR and PLSR) performances when comprehensively mapping urban land cover.

Generation of binary and ternary synthetic mixtures.

Generation of binary and ternary synthetic mixtures.

“Our findings suggest that the combination of kernel-based regression methods, such as SVR and KRR, with synthetically mixed training data is well suited for quantifying urban land cover from imaging spectrometer data at multiple scales.”

Socio-environmental Drivers and Suicide in Australia: Bayesian Spatial Analysis

BMC Public HealthBMC Public Health 2014, 14:681, Published 4 July 2014

By Xin Qi, Wenbiao Hu, Kerrie Mengersen, and Shilu Tong

Background
The impact of socio-environmental factors on suicide has been examined in many studies. Few of them, however, have explored these associations from a spatial perspective, especially in assessing the association between meteorological factors and suicide. This study examined the association of meteorological and socio-demographic factors with suicide across small areas over different time periods.

Methods
Suicide, population and socio-demographic data (e.g., population of Aboriginal and Torres Strait Islanders (ATSI), and unemployment rate (UNE)) at the Local Government Area (LGA) level were obtained from the Australian Bureau of Statistics for the period of 1986 to 2005. Information on meteorological factors (rainfall, temperature and humidity) was supplied by Australian Bureau of Meteorology (BoM). A Bayesian Conditional Autoregressive (CAR) Model was applied to explore the association of socio-demographic and meteorological factors with suicide across LGAs.

Suicide rates across LGAs and unincorporated SLAs, 1996-2000.

Suicide rates across LGAs and unincorporated SLAs, 1996-2000.

Results
In Model I (socio-demographic factors), proportion of ATSI and UNE were positively associated with suicide from 1996 to 2000 (Relative Risk (RR)ATSI = 1.0107, 95% Credible Interval (CI): 1.0062-1.0151; RRUNE = 1.0187, 95% CI: 1.0060-1.0315), and from 2001 to 2005 (RRATSI = 1.0126, 95% CI: 1.0076-1.0176; RRUNE = 1.0198, 95% CI: 1.0041-1.0354). Socio-Economic Index for Area (SEIFA) and IND, however, had negative associations with suicide between 1986 and 1990 (RRSEIFA = 0.9983, 95% CI: 0.9971-0.9995; RRATSI = 0.9914, 95% CI: 0.9848-0.9980). Model II (meteorological factors): a 1[degree sign]C higher yearly mean temperature across LGAs increased the suicide rate by an average by 2.27% (95% CI: 0.73%, 3.82%) in 1996-2000, and 3.24% (95% CI: 1.26%, 5.21%) in 2001-2005. The associations between socio-demographic factors and suicide in Model III (socio-demographic and meteorological factors) were similar to those in Model I; but, there is no substantive association between climate and suicide in Model III.

Conclusions
Proportion of Aboriginal and Torres Strait Islanders, unemployment and temperature appeared to be statistically associated with of suicide incidence across LGAs among all selected variables, especially in recent years. The results indicated that socio-demographic factors played more important roles than meteorological factors in the spatial pattern of suicide incidence.”

The Gravity of Pollination: Integrating At-site Features into Spatial Analysis of Contemporary Pollen Movement

meMolecular Ecology, Accepted July 2014

By Michelle F. DiLeo, Jenna C. Siu, Matthew K. Rhodes, Adriana López-Villalobos, Angela Redwine, Kelly Ksiazek, and Rodney J. Dyer

“Pollen-mediated gene flow is a major driver of spatial genetic structure in plant populations. Both individual plant characteristics and site-specific features of the landscape can modify the perceived attractiveness of plants to their pollinators and thus play an important role in shaping spatial genetic variation. Most studies of landscape-level genetic connectivity in plants have focused on the effects of inter-individual distance using spatial and increasingly ecological separation; yet have not incorporated individual plant characteristics or other at-site ecological variables. Using spatially explicit simulations, we first tested the extent to which the inclusion of at-site variables influencing local pollination success improved the statistical characterization of genetic connectivity based upon examination of pollen pool genetic structure. The addition of at-site characteristics provided better models than those that only considered inter-individual spatial distance (e.g., IBD). Models parameterized using conditional genetic covariance (e.g., Population Graphs) also outperformed those assuming panmixia. In a natural population of Cornus florida L. (Cornaceae), we showed that the addition of at-site characteristics (clumping of primary canopy opening above each maternal tree and maternal tree floral output) provided significantly better models describing gene flow than models including only between-site spatial (IBD) and ecological (Isolation By Resistance) variables. Overall, our results show that including inter-individual and local ecological variation greatly aids in characterizing landscape-level measures of contemporary gene flow.”

The OGC adopts Open Modelling Interface (OpenMI) Standard

OGC_Logo_Border_Blue_3DThe Open Geospatial Consortium (OGC®) membership has approved the Open Modelling Interface Standard Version 2 (OpenMI) as an OGC standard. This standard defines a means by which independently developed computer models of environmental processes, or indeed any processes, can exchange data as they run and hence facilitates the modelling of interacting processes.

The original driver for the OpenMI was the European Water Framework Directive and the requirement for an integrated approach to water management. It was foreseen that implementing the Directive would be very challenging and that there would be a need to provide help, in the form of decision support systems (DSS), to environmental managers. As Earth systems are complex and interrelated, these DSS would need to bring together many models in order to better understand and predict the environmental impacts of events and policies. To make it feasible to link together models of different processes from different suppliers and hence simulate process interaction, the European Commission therefore co-funded the research and development of a generic model interface, the outcome of which is the OpenMI.

Roger Moore, chairman of the OpenMI Association, said, “The OpenMI Association sees huge opportunities ahead for many stakeholder groups if the linking of models of different processes as they run can be made simple and reliable. Our immediate goal is to facilitate the integrated modelling needed to understand Earth system processes and hence help scientists, policy makers and managers find sustainable solutions to environmental challenges. By publishing the OpenMI as an adopted OGC standard, we seek to make the OpenMI standard available and accessible to the worldwide modelling community.”

Environmental modelling is not the only application of integrated modelling. For example, a possible shorter term application will simply be in enabling developers to convert their existing large, and often unmanageable applications, into sets of linkable components. This could change the modelling market from one for complete systems into one for components and services. It could make it much easier for products to be brought to market, widen participation and dramatically drive up the rate of innovation.

The standard can be viewed and downloaded at http://www.opengeospatial.org/standards/openmi. More information is available on the OpenMI website at www.openmi.org.

“Progress toward a sustainable future depends on our improved understanding of Earth systems and our collective ability to act from the local to global levels,” said Mark Reichardt, President and CEO of the OGC. “This partnership with OpenMI enables our organizations to work more closely to assure that open standards-based modelling capabilities can be seamlessly and rapidly integrated into processing environments.”

The OpenMI Association is an entirely open not-for-profit international group of organizations and people dedicated to taking the OpenMI (Open Modelling Interface) forward into the future. Its primary objectives are to develop, maintain and promote the OpenMI and integrated modelling. Learn more about the OpenMI Association at http://www.openmi.org.

The OGC is an international consortium of more than 475 companies, government agencies, research organizations, and universities participating in a consensus process to develop publicly available geospatial standards. OGC standards support interoperable solutions that “geo-enable” the Web, wireless and location-based services, sensors and mainstream IT. OGC standards empower technology developers to make geospatial information and services accessible and useful with any application that needs to be geospatially enabled. Visit the OGC website at http://www.opengeospatial.org/contact.

[Source: OGC press release]

Web GIS-Based Public Health Surveillance Systems: A Systematic Review

isprsISPRS International Journal of Geo-Information, 2014, 3(2), 481-506

By Hui Luan and Jane Law

“Web Geographic Information System (Web GIS) has been extensively and successfully exploited in various arenas. However, to date, the application of this technology in public health surveillance has yet to be systematically explored in the Web 2.0 era. We reviewed existing Web GIS-based Public Health Surveillance Systems (WGPHSSs) and assessed them based on 20 indicators adapted from previous studies. The indicators comprehensively cover various aspects of WGPHSS development, including metadata, data, cartography, data analysis, and technical aspects. Our literature search identified 58 relevant journal articles and 27 eligible WGPHSSs. Analyses of results revealed that WGPHSSs were frequently used for infectious-disease surveillance, and that geographical and performance inequalities existed in their development. The latest Web and Web GIS technologies have been used in developing WGPHSSs; however, significant deficiencies in data analysis, system compatibility, maintenance, and accessibility exist. A balance between public health surveillance and privacy concerns has yet to be struck. Use of news and social media as well as Web-user searching records as data sources, participatory public health surveillance, collaborations among health sectors at different spatial levels and among various disciplines, adaption or reuse of existing WGPHSSs, and adoption of geomashup and open-source development models were identified as the directions for advancing WGPHSSs.”

OGC Seeks Comments on Charter for Agriculture Domain Working Group

OGC_Logo_Border_Blue_3DAn OGC Agriculture Domain Working Group (SWG) is being chartered as an open forum for the discussion and presentation of interoperability requirements, use cases, pilots, and implementations of OGC standards in the Agriculture domain. Initiators of the new SWG seek comments from the public on the draft charter. The comment period closes on 2014-07-31. Comments should be submitted to charter-requests@opengeospatial.org.

Few human activities are more tied to location, geography, and the geospatial landscape than agriculture. Farming businesses, food supply chains, and public agricultural policies are increasingly tied as well to quantitative data about crops, soils, water, weather, markets, energy, and biotechnology. Agriculture now touches many aspects of the work that OGC is doing to promote interoperability of geospatial data and geographic analysis. Data users include growers, consultants, landowners, suppliers, and foodstuff processors, as well as regulators at all levels of government.They have common needs to exchange data on the extent and utilization of farmland, soil and crop characteristics, water availability, environmental impacts, etc. The complexity in global food supply chains is leading to societal needs for tracking and tracing of products for purposes of food safety, tax collections, and customs operations efficiency. All of these activities require information standards that support market and regulatory transparency.

The rapid evolution of information technology in agriculture is being driven above all by historic challenges to traditional agricultural practice now posed by climate change, increasing population, shortage of water and arable land, pollution, and changing diet. As agriculture moves into an era of large-scale geospatial information exchange, it seems timely for an OGC Domain Working Group to support development, implementation and use of open interface and encoding standards and best practices that maximize interoperability and address these challenges.

The OGC® is an international consortium of more than 475 companies, government agencies, research organizations, and universities participating in a consensus process to develop publicly available geospatial standards. OGC standards support interoperable solutions that “geo-enable” the Web, wireless and location-based services, and mainstream IT. Visit the OGC website at http://www.opengeospatial.org/.

[Source: OGC press release]