Geomatic Engineering

This paper describes the analysis of L-band radiometric measurement data gathered with the synthetic aperture radiometer HUT-2D during several ground-based and airborne measurement campaigns. The radiometric data are analyzed from the instrument's performance point of view, aiming to verify the theoretical performance of an instrument of this kind and to assess the performance of the HUT-2D radiometer system in particular. The data sets considered for the study consist of measurements of well-known natural targets, such as cosmic background radiation, and measurements of pure water scenes, the brightness temperature of which is possible to model based on in situ measurements. We define four figures of merit, which are applicable for synthetic aperture radiometers. These are radiometric resolution, image bias, pixel-to-pixel random error, and temporal stability. Then, we use the selected data sets to assess these in the case of HUT-2D. The experimental results are discussed and compared to the theoretical values, where applicable. Also, we discuss possibilities to improve the presented performance. The main results of this paper are the consolidated performance parameters of the HUT-2D instrument. We study and discuss the properties of the error components related to the technology in a general level, and study the scalability of the errors as a function of the measured targets. In particular, the stability of the direction-dependent error component is pointed out, and a mitigation guideline is proposed.

Realtime satellite clock corrections are usually estimated using undifferenced phase and range observations from a global network. Because a large number of ambiguity parameters must be estimated, the computation is timeconsuming. Consequently, only a sparse global network of limited number of stations is processed by most IGS Realtime Analysis Centers with an update rate of 5 s. In addition, it is very desirable to build the capability to simultaneously estimate clock corrections for multi-GNSS constellations. Although the estimation can be sped up by epoch-differenced observations that eliminate ambiguities, the derived clocks can contain a satellite-specific bias that diminishes the contribution of range observations. We introduce a computationally efficient approach for realtime clock estimation. Both the epoch-differenced phase and undifferenced range observations are used together to estimate the epoch-differenced satellite clocks and the initial clock bias for each satellite and receiver. The biased clock corrections accumulated from the estimated epoch-differenced clocks are then aligned with the estimated clock biases and provided as the final clock corrections to users. The algorithm is incorporated into the EPOS-RT software developed at GFZ (GeoForschungsZentrum) and experimentally validated with the IGS global network. The comparison with the GFZ rapid products shows that the accuracy of the clock estimation with the new approach is comparable with that of the undifferenced approach, whereas the computation time is reduced to one-tenth. As a result, estimation of high-rate satellite clocks from a large reference network and tracking satellites of multi-GNSS constellations becomes achievable.

Geostatistics is a distinctive methodology within the field of spatial statistics. In the past, it has been linked to particular problems (e.g., spatial interpolation by kriging) and types of spatial data (attributes defined on continuous space). It has been used more by physical than human geographers because of the nature of their types of data. The approach taken by geostatisticians has several features that distinguish it from the methods typically used by human geographers for analyzing spatial variation associated with regional data, and we discuss these. Geostatisticians attach much importance to estimating and modeling the variogram to explore and analyze spatial variation because of the insight it provides. This article identifies the benefits of geostatistics, reviews its uses, and examines some of the recent developments that make it valuable for the analysis of data on areal supports across a wide range of problems.

such as colour illustrations and examples representing several case studies of current hot topics such as security and health, boxes with biographies and information provided by GIS practitioners, the provision and dedication to Internet resources for GIS e-learning (such as ESRI Virtual Campus), and with questions and references provided for further study. The book is organised into 21 chapters in total. It is structured in such a way that the main GIS issues presented are principles, techniques, analysis and management and policy. Each chapter begins with a brief overview and a list of learning objectives. The chapter is supported by questions for further study, and references for further reading, which are provided at the end of each chapter. Chapters 1 and 2 provide an introduction to GIS and its applications. Chapters 3 to 6, Principles, provide information on data representation, nature of data, data georeferencing and uncertainty. Chapters 7 to 11, Techniques, present GIS software, data modelling, geographical databases and distribution of GIS. Chapters 12 to 16, Analysis, cover the issues of cartography, map production, ''geovisualisation'', spatial analysis (queries, measurements, transformation) and spatial modelling. Chapters 17 to 20, Management and policy, introduce practical issues and examples of GIS use within management and policy in business sectors. Finally, an Epilogue (Chapter 21) is provided summarising what is presented in the book and providing information about further reading. Summarising, I found the book clear to read and easy to understand, however, the language used in some parts is unusual, but without being misleading or confusing. Also, the book is focused mainly on ESRI products rather than other related software suites (for example, Intergraph Inc. and Autodesk Inc.), although relevant information is provided. I suggest that this book is suitable both for university level students (of Geography, Civil Engineering, Forestry, for example) and MSc level students. Moreover, professional GIS users, who wish to improve their knowledge of GIS, may also find it valuable for its resources and the presentation of a range of practical issues.

Despite the relatively high cost of airborne lidar-derived digital elevation models (DEMs), such products are usually presented without a satisfactory associated estimate of accuracy. For the most part, DEM accuracy estimates are typically provided by comparing lidar heights against a finite sample of check point coordinates from an independent source of higher accuracy, supposing a normal distribution of the derived height differences or errors. This paper proposes a new methodology to assess the vertical accuracy of lidar DEMs using confidence intervals constructed from a finite sample of errors computed at check points. A non-parametric approach has been tested where no particular error distribution is assumed, making the proposed methodology especially applicable to non-normal error distributions of the type usually found in DEMs derived from lidar. The performance of the proposed model was experimentally validated using Monte Carlo simulation on 18 vertical error datasets. Fifteen of these data-sets were computed from original lidar data provided by the International Society for Photogrammetry and Remote Sensing Working Group III/3, using their respective filtered reference data as ground truth. The three remaining data-sets were provided by the Natural Environment Research Council's Airborne Research and Survey Facility lidar system, together with check points acquired using high precision kinematic GPS. The results proved promising, the proposed models reproducing the statistical behaviour of vertical errors of lidar using a favourable number of check points, even in the cases of data-sets with non-normally distributed residuals. This research can therefore be considered as a potentially important step towards improving the quality control of lidar-derived DEMs.

Aerial photography for archaeology has been developing its approaches and techniques over the past 100 years so that it now integrates the results of reconnaissance with extensive interpretative and analytical surveys. This paper introduces the philosophy and approach of the English Heritage (EH) Aerial Survey team, covering aerial reconnaissance and the National Mapping Programme (NMP), as well as the potential developments and opportunities in Europe. In the 1980s there was a debate over the nature of the evidence derived from aerial photographs, especially how to describe archaeological features. As part of NMP a classification and recording system has been devised which meets most of the users' needs, be they national organisations, county archaeologists, commercial contractors or university-based researchers. The maps and records produced by NMP are used to further our understanding of the past human settlement in England, not only at the individual site level, but also in regional or landscape contexts. This paper provides an overview of the current progress of NMP and acts as an entrée for explaining the current research and recording of archaeological landscapes throughout Europe. Recent developments in Britain and Europe have provided the opportunity for a greater priority to be given to aerial survey and accelerating programmes of mapping. In Europe the ending of the cold war has allowed greater access to aerial photographs and the possibility of beginning new reconnaissance, as well as introducing new forms of remote sensing. All these developments have led to a transformation of our understanding of prehistoric, Roman and medieval archaeology.

An adaptive and fast constant false alarm rate (CFAR) algorithm based on automatic censoring (AC) is proposed for target detection in high-resolution synthetic aperture radar (SAR) images. First, an adaptive global threshold is selected to obtain an index matrix which labels whether each pixel of the image is a potential target pixel or not. Second, by using the index matrix, the clutter environment can be determined adaptively to prescreen the clutter pixels in the sliding window used for detecting. The G 0 distribution, which can model multilook SAR images within an extensive range of degree of homogeneity, is adopted as the statistical model of clutter in this paper. With the introduction of AC, the proposed algorithm gains good CFAR detection performance for homogeneous regions, clutter edge, and multitarget situations. Meanwhile, the corresponding fast algorithm greatly reduces the computational load. Finally, target clustering is implemented to obtain more accurate target regions. According to the theoretical performance analysis and the experiment results of typical real SAR images, the proposed algorithm is shown to be of good performance and strong practicability.

In Earth observation programs there is a need of passive low frequency (L-band) measurements to monitor soil moisture and ocean salinity with high spatial resolution 10-20 Km, a radiometric resolution of 1K and a revisit time of 1-3 days [1]. Compared to total power radiometers aperture synthesis interferometric radiometers are technologically attractive because of their reduced mass and hardware requirements. In this field it should be mentioned the one-dimensional (1-D) linear interferometer ESTAR developed by NASA [2] and MIRAS a two-dimensional (2-D) Y-shaped interferometer currently under study by European Space Agency (ESA) [3]. Interferometer radiometers measure the correlation between pairs of nondirective antennas. Each complex correlation is a sample of the "visibility" function which, in the ideal case, is the spatial Fourier transform of the brightness temperature distribution. Since most receiver phase and amplitude errors can be hardware calibrated, Fourier based iterative inversion methods will be useful when antenna errors are small, their radiation voltage patterns are not too different, and mutual coupling is small. In order to minimize on-board hardware requirements-antennas, receivers and correlators-the choice of the interferometer array shape is of great importance since it determines the (u; v) sampling strategy and the minimum number of visibility samples required for a determined aliasing level. In this sense, Y-shaped and triangularshaped arrays with equally spaced antennas are optimal. The main contribution of this paper is a technique that allows us to process the visibility samples over the hexagonal sampling grids given by Y-shaped and triangular-shaped arrays with standard rectangular FFT routines. Since no interpolation processes are involved, the risk of induced artifacts in the recovered brightness temperature over the wide field of view required in Earth observation missions is minimized and signal to noise ratio (SNR) is preserved.

Abstract—In Earth observation programs there is a need of passive low frequency (L-band) measurements to monitor soil moisture and ocean salinity with high spatial resolution 10–20 Km, a radiometric resolution of 1K and a revisit time of 1–3 days [1]. Compared to total power radiometers ...

Three kilns and a collection of baked bricks from Italian archaeological sites have been studied for archaeointensity determination using the Thellier method as modified by Coe. All sites are dated based on archaeological information and their ages range from 500 to 800 AD and 1500 to 1700 AD. Rock magnetic studies identify magnetite, Ti- magnetite and hematite as the main magnetic minerals, and magnetic susceptibility versus temperature shows a good thermal stability of the samples. The intensity results have been corrected for anisotropy of the thermoremanent magnetization and cooling rate effects. The new data together with 136 previously published results are used to estimate the variation of the Earth's magnetic field over the past three millennia. The time distribution of the Italian absolute intensity data is irregular with the majority of determinations concentrated during the last four centuries, while older periods are very poorly covered. Most of the data come from vo...

This article proposes a batch-mode algorithm to handle the large databases generated from experimentations using probe vehicles. This algorithm can locate raw Global Positioning System (GPS) positions on a map, but can also be used to correct map-matching errors introduced by real time map-matching algorithms. For each journey, the algorithm globally searches for the closest path to the GPS positions, and so is inspired from the “path to path” algorithm's family. It uses the Multiple Hypothesis Technique (MHT) and relies on an innovative weighting system based on the area between the GPS points and the arcs making up the path. For high performance, the algorithm uses an iterative program and the data is stored in tree form.

The patterns of seasonal variation of surface plant pigment concentration (Chl) in the Newfoundland Region were studied using the remotely sensed data from CZCS (1978CZCS ( -1986 and SeaWiFS radiometers (from September 1997 to October 1999. Sea Surface Temperature (SST) data obtained from AVHRR radiometers and Sea Surface Height (SSH) data obtained from TOPEX/POSEIDON altimeter were used then to interprete the observed patterns in terms of physical factors which influence the growth of phytoplankton . Stable seasonal cycles of both SST and Chl were observed in all parts of the region under study (Labrador Current, Newfoundland Bank, Flemish Pass, frontal zone between Gulf Stream and Labrador Current).

The Spirit rover has traversed a distance of 3.2 km (actual distance traveled instead of odometry) and Opportunity at 1.2 km. We localized the landers in the Gusev Crater and on the Meridiani Planum using two-way Doppler radio positioning technology and cartographic triangulations through landmarks visible in both orbital and ground images. Additional high-resolution orbital images were taken to verify the determined lander positions. Visual odometry and bundleadjustment technologies were applied to overcome wheel slippages, azimuthal angle drift and other navigation errors (as large as 21 percent). We generated timely topographic products including 68 orthophoto maps and 3D Digital Terrain Models, eight horizontal rover traverse maps, vertical traverse profiles up to Sol 214 for Spirit and Sol 62 for Opportunity, and five 3D crater models. A web-based landing-site Geographic Information System (GIS) has been set up at The Ohio State University to update and disseminate the daily localization and topographic information to support tactical and strategic operations of the mission. Also described in this paper are applications of the data for science operations planning, geological traverse survey, survey of windrelated features, and other science applications. The majority of the instruments onboard both rovers are healthy at this moment, and they will continue to explore the two landing sites on the Martian surface. We expect to report further localization and topographic mapping results to be achieved in the rest of the mission period and in post-mission data processing.

The present study focuses on the identification and quantification of land-cover changes occurring in the coastal stretches of the East Godavari delta, Andhra Pradesh, India. The analysis of series of multi-temporal satellite data provides an accurate quantification and therefore a better understanding of the process of land-cover changes during 1990-2005. Land-cover changes were quantified based on normalized difference vegetation index (NDVI) image differencing and a post-classification comparison approach. The change detection results were examined in terms of the proportion of land-cover classes and change trajectories with particular emphasis on coastal aquaculture development within the study area. The study shows that the total area under aquacultural ponds increased from 2985 ha in 1990 to 7067 ha in 2005. The major changes in the study area occurred during 1990-1994, when 2873 ha of agricultural land and 762 ha of degraded mangroves were converted into aquacultural ponds. The prediction of landcover distribution in 2010 on the basis of a Markov chain shows a continuing upward trend of the aquaculture area (8267 ha) with less impact on the mangrove area. The analysis predicts that the agricultural land area will continue to decrease from 50 122 to 46 978 ha during 2005-2010.

Background: Automated adverse outcome surveillance tools and methods have potential utility in quality improvement and medical product surveillance activities. Their use for assessing hospital performance on the basis of patient outcomes has received little attention. We compared risk-adjusted sequential probability ratio testing (RA-SPRT) implemented in an automated tool to Massachusetts public reports of 30-day mortality after isolated coronary artery bypass graft surgery. Methods: A total of 23,020 isolated adult coronary artery bypass surgery admissions performed in Massachusetts hospitals between January 1, 2002 and September 30, 2007 were retrospectively re-evaluated. The RA-SPRT method was implemented within an automated surveillance tool to identify hospital outliers in yearly increments. We used an overall type I error rate of 0.05, an overall type II error rate of 0.10, and a threshold that signaled if the odds of dying 30-days after surgery was at least twice than expected. Annual hospital outlier status, based on the statereported classification, was considered the gold standard. An event was defined as at least one occurrence of a higher-than-expected hospital mortality rate during a given year. Results: We examined a total of 83 hospital-year observations. The RA-SPRT method alerted 6 events among three hospitals for 30-day mortality compared with 5 events among two hospitals using the state public reports, yielding a sensitivity of 100% (5/5) and specificity of 98.8% (79/80).

The advent of high-end miniature technology, together with the increasing availability of large scale digital geographic data products, has created a demand for techniques and methodologies that assist in the automated generation of maps specifically tailored to mobile GIS applications. This paper concerns itself with the problem of automatic generation of schematic maps. Schematic maps are diagrammatic representations based on linear abstractions of networks. In the context of mobile mapping they are seen as being a particularly useful means of displaying transportation networks. This paper describes an algorithm that automates the production of schematic maps. The algorithm makes use of the simulated annealing optimisation technique. An implementation of the algorithm is also presented, together with experimental results.

This paper presents the results of the application of the Ground Penetrating Radar (GPR) method, or Georadar, in outlining a zone of contamination due to solid residues at the waste burial site of Rio Claro in the state of São Paulo, SE Brazil. A total of eight GPR profiles with 50-and 100-MHz antennae were surveyed. Six profiles were located within the landfill site and the remaining two were outside. The main objective of the GPR survey was to evaluate the side extension of contamination. A Vertical Electric Sounding (VES) survey was performed at four points within the site in order to investigate the vertical extent of the contamination plume and to define the bottom of the landfill. Two additional VESs were done outside the landfill with the purpose of determining the top of the ground water table and the geoelectric stratigraphy of the background. From the interpretation of the GPR profiles, it was possible to locate the top of the contamination plume and to infer that it was migrating laterally beyond the limits of the waste disposal site. This was observed along the profile situated close to the highway SP-127, which was about 20 m from the limit of the site. The signature of the contaminant appears as a discontinuous reflector that is believed to be a shallow ground water table. The discontinuity is marked by a shadow zone, which is characteristic of conductive contaminant residues. The contamination did not move far enough to reach a sugar cane plantation located at approximately 100 m from the border of the site. In the regions free from contamination, the ground water table was mapped at approximately 10 m of depth, and it was characterized by a strong and continuous reflector. The radar signal penetrated deep enough and enabled the identification of a second reflector at approximately 14 m deep, interpreted as the contact between the Rio Claro and the Corumbataí formations. The contact is marked by the presence of gravel characterized by ferruginous concretes, which cause the strong amplitude reflection in the GPR profile. Within the landfill site, the quantitative interpretation of the VES results showed the contamination zone. The base of the landfill varies between 11 and 15 m deep. Outside the landfill site, the VES results showed no indication of contamination and allowed the determination of the top of the ground water table and the contact between the Rio Claro and the Corumbataí formations. The results of GPR and VES showed a good agreement and the integrated interpretations were supported by local geology and information from several boreholes, about 17 m depth, on average. The bottom of the landfill reaches a maximum of 14.5 m depth. D

The increasing availability of open geospatial data, such as building footprint vector data and LiDAR (Light Detection and Ranging) point clouds, has provided opportunities to generate large-scale 3D city models at low cost. However, using unclassified point clouds with building footprints to estimate building heights may yield erroneous results due to potential errors and anomalies in both datasets and their integration. Some of the points within footprints often reflect irrelevant objects other than roofs, leading to biases in height estimation, and few studies have developed systematic methods to filter them out. In this paper, a LiDAR point classification methodology is proposed that extracts only rooftop points for building height estimation. The LiDAR points are characterized by point, footprint, and neighborhood-based features and classified by the Random Forest (RF) algorithm into four classes-rooftop, wall, ground, and high outlier. The percentage of correctly classified points among 15,577 sample points in Columbus, Ohio, amounts to 96.5%. Conducting this classification separately for different building types (commercial, residential, skyscraper, and small constructions) does not significantly change the overall accuracy. The footprint-based features contribute most to predicting the classes correctly. Height validation results based on a sample of 498 buildings show that (1) using average or median heights with classified points provides the best estimates, minimizing the disparities between computed heights and ground truth and (2) the RF method yields outcomes much closer to ground truth than earlier classification approaches. Some outcomes are visualized in 3D format using Google Earth 3D Imagery and ArcScene.

The goal of this study is to propose a new classification of African ecosystems based on an 8-year analysis of Normalized Difference Vegetation Index (NDVI) data sets from SPOT/VEGETATION. We develop two methods of classification. The first method is obtained from a k-nearest neighbour (k-NN) classifier, which represents a simple machine learning algorithm in pattern recognition. The second method is hybrid in that it combines k-NN clustering, hierarchical principles and the Fast Fourier Transform (FFT). The nomenclature of the two classifications relies on three levels of vegetation structural categories based on the Land Cover Classification System (LCCS). The two main outcomes are: (i) The delineation of the spatial distribution of ecosystems into five bioclimatic ecoregions at the African continental scale; (ii) Two ecosystem maps were made sequentially: an initial map with 92 ecosystems from the k-NN, plus a deduced hybrid classification with 73 classes, which better reflects the bio-geographical patterns. The inclusion of bioclimatic information and successive k-NN clustering elements helps to enhance the discrimination of ecosystems. Adopting this hybrid approach makes the ecosystem identification and labelling more flexible and more accurate in comparison to straightforward methods of classification. The validation of the hybrid classification, conducted by crossing-comparisons with validated continental maps, displayed a mapping accuracy of 54% to 61%.

We present a quantitative procedure to evaluate the intrinsic noise level (INL) of the noise cross-correlation function (NCF). The method is applied to realistic NCFs derived from the continuous data recorded by the seismic arrays in Taiwan and Korea. The obtained temporal evolution of NCF noise level follows fairly the prediction of the theoretical formulation, confirming the feasibility of the method. We then apply the obtained INL to the assessment of data quality and the source characteristics of ambient noise. We show that the INL-based signal-to-noise ratio provides an exact measure for the true noise level within the NCF and better resolving power for the NCF quality, and such measurement can be implemented to any time windows of the NCFs to evaluate the quality of overtones or coda waves. Moreover, since NCF amplitudes are influenced by both the population and excitation strengths of noises, while INL is primarily sensitive to the overall source population, with information from both measurements, we may better constrain the source characteristics of seismic ambient noises.

Current broadband sensors are not capable of separating the initial stages of forest damage. The current investigation evaluates the potential of hyperspectral data for detecting the initial stages of forest damage at the canopy level in the Norway spruce (Picea abies (L.) Karst) forests of Czech Republic. Hyperspectral canopy reflectance imagery and foliar samples were acquired contemporaneously for 23 study sites in August 1998. The sites were selected along an air pollution gradient to represent the full range of damage conditions in even-aged spruce forests. The changes in canopy and foliar reflectance, chemistry and pigments associated with forest damage were established. The potential of a large number of spectral indices to identify initial forest damage was determined. Canopy hyperspectral data were able to separate healthy from initially damaged canopies, and therefore provided an improved capability for assessment of forest physiology as compared to broadband systems. The 673-724 nm region exhibited maximum sensitivity to initial damage. The nine spectral indices having the highest potential as indicators of the initial damage included: three simple band ratios, two derivative indices, two modelled red-edge parameters and two normalized bands. The sensitivity of these indices to damage was explained primarily by their relationship to foliar structural chemical compounds, which differed significantly by damage class.

Land cover mapping and carbon pools estimates in the Southwestern Brazilian Amazon basin. Cartographie de l'occupation du sol et estimation des flux de carbone dans la région du sud ouest du bassin amazonien brésilien. MORAES Jener (1), SEYLER Frederique (2), CERRI Carlos (2), VOLKOFF Boris (3)

This paper examines the potential of mobile phones to be used as front-end sensors for photogrammetric procedures and applications. For this purpose, two mobile phone cameras (Sony Ericsson K750i and Nokia N93) were calibrated over an indoor 3D testfield, using a self-calibrating bundle adjustment. Geometric accuracy tests were carried out in order to evaluate their metric performances and to compare the results with respect to two off-the-shelf digital still video cameras (Sony DSC W100 and Sony DSC F828). The geometric accuracy evaluation comprised an absolute accuracy test, JPEG test and temporal stability test. The radiometric capabilities of all cameras (except that the DSC W100 was replaced with a DSC T100 camera) were also evaluated and compared by carrying out modulation transfer function (MTF) analysis, image noise analysis and an operating range test. Substantial systematic errors were diagnosed in some systems. However, with proper calibration it is believed that these devices can be used for many photogrammetric tasks.

The current work presents the testing of a modeling strategy that has been recently developed to simulate the gross and net carbon fluxes of Mediterranean forest ecosystems. The strategy is based on the use of a NDVI-driven parametric model, C-Fix, and of a biogeochemical model, BIOME-BGC, whose outputs are combined to simulate the behavior of forest ecosystems at different development stages. The performances of the modeling strategy are evaluated in three Italian study sites (San Rossore, Lecceto and Pianosa), where carbon fluxes are being measured through the eddy correlation technique. These sites are characterized by variable Mediterranean climates and are covered by different types of forest vegetation (pine wood, Holm oak forest and Macchia, respectively). The results of the tests indicate that the modeling strategy is generally capable of reproducing monthly GPP and NEE patterns in all three study sites. The highest accuracy is obtained in the most mature, homogenous pine wood of San Rossore, while the worst results are found in the Lecceto forest, where there are the most heterogeneous terrain, soil and vegetation conditions. The main error sources are identified in the inaccurate definition of the model inputs, particularly those regulating the site water budgets, which exert a strong control on forest productivity during the Mediterranean summer dry season. In general, the incorporation of NDVI-derived fAPAR estimates corrects for most of these errors and renders the forest flux simulations more stable and accurate.

We present measurements of Joule heat production in a fluid gallium vortex permeated by a uniform transverse magnetic field. We find that the Joule heat production increases as the square of the imposed field intensity for weak and moderately strong magnetic fields and magnetic Reynolds numbers up to about one. For stronger magnetic fields, Lorentz forces destroy the 2-D structure of the vortex and the Joule heat production becomes nearly independent of the intensity of the imposed magnetic field. We derive scaling laws relating fluid velocity in the vortex, imposed magnetic field intensity and Joule heat production, for both low and high magnetic Reynolds number regimes. Application of these laws to magnetic induction in the Earth's fluid core indicates that Joule heat production by this mechanism is large enough to limit the intensity of magnetic fields within the core.

Accurate computation of heat flux is a primary motivation for convection calculations, yet heat fluxes have generally been calculated to substantially less than optimal accuracy, given the accompanying temperature and velocity fields. We analyse this problem in the context of the finite element method, using bilinear shape functions to interpolate the nodal values of velocity and temperature throughout the medium. Our method, however, is general and also applies to finite difference and higher order finite elements. Consider (dimensionless) vertical heat flux qz = wT -dT/dz, with w vertical velocity and T temperature. The first term is quadratic within (bilinear) elements and continuous at nodes, while the second term is constant within elements and discontinuous at nodes. Usual practice is to evaluate qz at element centres. We instead assign to each element the volume average of qz over that element. Element values are then projected to nodes using a modification of a technique used for pressure smoothing (Hughes et al.) Our approach reduces the calculated depth variation in horizontallyaveraged flux by more than a factor of 10 and shows more rapid convergence of Nusselt number Nu as a function of grid size. It also eliminates the artificial diffusion of heat out of supposed insulating side walls.

We all do it: we all communicate about space and we do it with ease as one of our most basic natural abilities. Even animals do it -for example, bees as they dance to communicate distance, direction and the quality of a food source. Humans utilize a variety of senses and channels for communicating about space, among them the use of spoken and written language, gestures, sketches, or the gentle physical force when taking a person by the arm or leading in a dance. In addition, we have learned to interpret the language of experts communicating about space -such as in maps (cartography), posted signs (semiotics) or the affordance of designed spaces (architecture).

Environmental modelling languages provide the possibility to construct models in two or three spatial dimensions. These models can be static models, without a time component, or dynamic models. Dynamic models are simulations run forward in time, where the state of the model at time t is defined as a function of its state in a period or time step preceding t. Since inputs and parameters of environmental models are associated with errors, environmental modelling languages need to provide techniques to calculate how these errors propagate to the output(s) of the model. Since these techniques are not yet available, the paper describes concepts for extending an environmental-modelling language with functionality for error-propagation modelling. The approach models errors in inputs and parameters as stochastic variables, while the error in the model outputs is approximated with a Monte Carlo simulation. A modelling language is proposed which combines standard functions in a structured script (program) for building environmental models, and calculation of error propagation in these models. A prototype implementation of the language is used in three example models to illustrate the concepts.

A quick search of "exurbia" on the internet yields various definitions: 'the region outside the suburbs of a city, consisting of residential areas (exurbs) that are occupied predominantly by rich commuters (exurbanites) (www. wordreference.com/English); and the cryptically self-referential 'a typically exurban area ' (www.bartleby.com). Nighttime satellite imagery provided by the Defense Meteorological Satellite Program's Operational Linescan System (DMSP OLS) shows expansive areas of low light surrounding all major metropolitan areas. These areas would be characterized as pure vegetation by 30 meter resolution Landsat imagery yet they contain large numbers of people who have significant social, economic, and ecological impacts (e.g. traffic congestion and problems associated with the urban-wildlands interface). Questions explored in this paper are: 1) How big are 'exurban' areas in the United States?; 2) What cities have relatively large exurban areas and what cities have relatively small exurban areas?; How many people live in exurban areas?; 4) What are the costs and benefits of exurban areas and who pays for and/or receives them?; and finally, 5)Who lives in exurban areas? Conventional wisdom suggests that these 'exurbanites' are rich commuters who choose to live in natural settings beyond the city and suburbia; however, astronomic increases in real estate prices in places like Boston, New York, San Francisco, and Los Angeles suggest that many 'exurbanites' may be middle income teachers, police officers, and nurses trying to find affordable real estate. Our initial results suggest that 37% of the U.S. population lives in exurban areas that account for 14% of the land area. Purely urban areas account for 1.7% of the land area and house 55% of the population; and, rural areas (84% of the land area) contain only 8% of the population.

Multitemporal satellite synthetic aperture radar (SAR) images are a useful source of information for geophysicists to monitor changing regions. In this paper, a new approach is proposed to extract from multitemporal SAR images two kinds of information: temporal changes (flooded areas, coastline erosion, etc.) and stable spatial features (roads, rivers, etc.). The novelty of the proposed approach is to detect simultaneously these two kinds of discontinuities. In a first step, the contrast and the heterogeneity information is extracted by a "multitemporal" application of the ratio of local means and by new three-dimensional texture parameters based on the log-cumulants. In a second step, the resulting attributes that measure the time variability or the presence of spatial features are merged. An interactive fuzzy fusion approach is proposed to provide end-users with a simple and easily understandable tool for tuning the change-detection results. The performances of the proposed attributes and fusion technique are presented on a set of seven multitemporal SAR images acquired by the European Remote Sensing (ERS-1) satellite.

Body integrity identity disorder (BIID), or xenomelia, is a failure to integrate a fully functional limb into a coherent body schema. It manifests as the desire for amputation of the particular limb below an individually stable 'demarcation line.' Here we show, in five individuals with xenomelia, defective temporal order judgments to two tactile stimuli, one proximal, the other distal of the demarcation line. Spatio-temporal integration, known to be mediated by the parietal lobes, was biased towards the undesired body part, apparently capturing the individual's attention in a pathologically exaggerated way. This finding supports the view of xenomelia as a parietal lobe syndrome.

In this paper, the effect of grey level quantization on the ability of co-occurrence probability statistics to classify natural textures is studied. Generally, as a function of increasing grey levels, many of the statistics demonstrate a decrease in classification ability while a few maintain constant classification accuracy. None of the individual statistics show increasing classification accuracy throughout all grey levels. Correlation analysis is used to rationalize a preferred subset of statistics. The preferred statistics set (contrast, correlation, and ...

We propose a novel consensus notion, called "partial consensus", for distributed GM-PHD (Gaussian mixture probability hypothesis density) fusion based on a peer-to-peer (P2P) sensor network, in which only highly-weighted posterior Gaussian components (GCs) are disseminated in the P2P communication for fusion while the insignificant GCs are not involved. The partial consensus does not only enjoy high efficiency in both network communication and local fusion computation, but also significantly reduces the affect of potential false data (clutter) to the filter, leading to increased signal-to-noise ratio at local sensors. Two "conservative" mixture reduction schemes are advocated for fusing the shared GCs in a fully distributed manner. One is given by pairwise averaging GCs between sensors based on Hungarian assignment and the other is merging close GCs based a new GM merging scheme. The proposed approaches have a close connection to the conservative fusion approaches known as covariance union and arithmetic mean density. In parallel, average consensus is sought on the cardinality distribution (namely the GM weight sum) among sensors. Simulations for tracking either a single target or multiple targets that simultaneously appear are presented based on a sensor network where each sensor operates a GM-PHD filter, in order to compare our approaches with the benchmark generalized covariance intersection approach. The results demonstrate that the partial, arithmetic average, consensus outperforms the complete, geometric average, consensus.

Background: Patient-centered medicine is developing alongside the concepts of personalized medicine and tailored therapeutics. The main objective of patient-centered medicine is to improve health outcomes of individual patients in everyday clinical practice, taking into account the patient's objectives, preferences, values as well as the available economic resources. Discussion: Patient-centered medicine implies a paradigm shift in the relationship between doctors and patients, but also requires the development of patient-oriented research. Patient-oriented research should not be based on the evaluation of medical interventions in the average patient, but on the identification of the best intervention for every individual patient, the study of heterogeneity and the assignment of greater value to observations and exceptions. The development of information-based technologies can help to close the gap between clinical research and clinical practice, a fundamental step for any advance in this field.

In order to correct the altimeter range for tropospheric humidity, a microwave radiometer is added to altimetry missions. Over open ocean, the combination altimeter/radiometer is satisfactory. This is not the case in coastal zones, where the signal coming from the surrounding land surfaces contaminates the radiometer measurement and makes the humidity retrieval method unsuitable. In this paper, a radiometer simulator is built. This simulator is used to evaluate the current methods to retrieve the wet tropospheric correction in transition areas. The purpose of this paper is to analyze and compare the performances of these methods. After examining simple correction methods (extension of the open sea wet tropospheric correction, use of the meteorological model value), we evaluated the feasibility and performances of two methods which propose to take into account the land surface effect in the brightness temperature estimation. The latter was found to give significantly better results.

Background: Computer Aided Diagnosis (CAD), which can automate the detection process for ocular diseases, has attracted extensive attention from clinicians and researchers alike. It not only alleviates the burden on the clinicians by providing objective opinion with valuable insights, but also offers early detection and easy access for patients. Method: We review ocular CAD methodologies for various data types. For each data type, we investigate the databases and the algorithms to detect different ocular diseases. Their advantages and shortcomings are analyzed and discussed. Result: We have studied three types of data (i.e., clinical, genetic and imaging) that have been commonly used in existing methods for CAD. The recent developments in methods used in CAD of ocular diseases (such as Diabetic Retinopathy, Glaucoma, Age-related Macular Degeneration and Pathological Myopia) are investigated and summarized comprehensively. Conclusion: While CAD for ocular diseases has shown considerable progress over the past years, the clinical importance of fully automatic CAD systems which are able to embed clinical knowledge and integrate heterogeneous data sources still show great potential for future breakthrough.

A polarimetric coherent electromagnetic scattering model for short branching vegetation is developed in this paper. With the realistic structures that reasonably describe the relative positions of the particles, this model is able to consider the coherent effect due to the phase difference between the scattered fields from different particles, and account for the second-order, near-field interaction between particles, to which the relative positions and orientation of the particles are essential. The model validation with measurements is also presented, and excellent agreement is obtained. The polarimetric radar backscatter measurements for soybean plants using truck-mounted scatterometers were conducted at L-band and C-band under different soil-moisture conditions. Through an extensive ground truth, the important plant and rough surface parameters such as the soil moisture and surface roughness, vegetation dielectric constant, and geometry of the soybean plants, were characterized for model verification. It is found that the second-order near-field scattering is significant at C-band for fully grown soybeans due to the high vegetation particle density, and at L-band, the contribution from the second-order near field is negligible. The coherence effect is shown to be important at L-band and to a much lesser extent at C-band. This model is then used to demonstrate its ability for estimating the physical parameters of a soybean field, including soil moisture from a polarimetric set of AIRSAR images.

We consider the tensor-based spectral-spatial feature extraction problem for hyperspectral image classification. First, a tensor framework based on circular convolution is proposed. Based on this framework, we extend the traditional principal component analysis (PCA) to its tensorial version tensor PCA (TPCA), which is applied to the spectral-spatial features of hyperspectral image data. The experiments show that the classification accuracy obtained using TPCA features is significantly higher than the accuracies obtained by its rivals.

 Lahore, Gujranwala, and Okara are the most polluted city based on PM2.5  Jhang, Multan, and Vehari are the most polluted cities based on AOD  Aerosols, nighttime lights, population, cropland, and fire show same spatial patterns  Pakistan's entire population is exposed to long-term PMx (x = 1, 2.5, & 10)  Pakistan's air quality is mainly affected by local anthropogenic sources Highlights (for review) 2