Suitability of Spatial Interpolation Techniques in Varying Aquifer Systems of a Basaltic Terrain for Monitoring Groundwater Availability

NASA Astrophysics Data System (ADS)

Katpatal, Y. B.; Paranjpe, S. V.; Kadu, M. S.

2017-12-01

Geological formations act as aquifer systems and variability in the hydrological properties of aquifers have control over groundwater occurrence and dynamics. To understand the groundwater availability in any terrain, spatial interpolation techniques are widely used. It has been observed that, with varying hydrogeological conditions, even in a geologically homogenous set up, there are large variations in observed groundwater levels. Hence, the accuracy of groundwater estimation depends on the use of appropriate interpretation techniques. The study area of the present study is Venna Basin of Maharashtra State, India which is a basaltic terrain with four different types of basaltic layers laid down horizontally; weathered vesicular basalt, weathered and fractured basalt, highly weathered unclassified basalt and hard massive basalt. The groundwater levels vary with topography as different types of basalts are present at varying depths. The local stratigraphic profiles were generated at different types of basaltic terrains. The present study aims to interpolate the groundwater levels within the basin and to check the co-relation between the estimated and the observed values. The groundwater levels for 125 observation wells situated in these different basaltic terrains for 20 years (1995 - 2015) have been used in the study. The interpolation was carried out in Geographical Information System (GIS) using ordinary kriging and Inverse Distance Weight (IDW) method. A comparative analysis of the interpolated values of groundwater levels is carried out for validating the recorded groundwater level dataset. The results were co-related to various types of basaltic terrains present in basin forming the aquifer systems. Mean Error (ME) and Mean Square Errors (MSE) have been computed and compared. It was observed that within the interpolated values, a good correlation does not exist between the two interpolation methods used. The study concludes that in crystalline basaltic terrain, interpolation methods must be verified with the changes in the geological profiles.

Programming an Artificial Neural Network Tool for Spatial Interpolation in GIS - A Case Study for Indoor Radio Wave Propagation of WLAN.

PubMed

Sen, Alper; Gümüsay, M Umit; Kavas, Aktül; Bulucu, Umut

2008-09-25

Wireless communication networks offer subscribers the possibilities of free mobility and access to information anywhere at any time. Therefore, electromagnetic coverage calculations are important for wireless mobile communication systems, especially in Wireless Local Area Networks (WLANs). Before any propagation computation is performed, modeling of indoor radio wave propagation needs accurate geographical information in order to avoid the interruption of data transmissions. Geographic Information Systems (GIS) and spatial interpolation techniques are very efficient for performing indoor radio wave propagation modeling. This paper describes the spatial interpolation of electromagnetic field measurements using a feed-forward back-propagation neural network programmed as a tool in GIS. The accuracy of Artificial Neural Networks (ANN) and geostatistical Kriging were compared by adjusting procedures. The feedforward back-propagation ANN provides adequate accuracy for spatial interpolation, but the predictions of Kriging interpolation are more accurate than the selected ANN. The proposed GIS ensures indoor radio wave propagation model and electromagnetic coverage, the number, position and transmitter power of access points and electromagnetic radiation level. Pollution analysis in a given propagation environment was done and it was demonstrated that WLAN (2.4 GHz) electromagnetic coverage does not lead to any electromagnetic pollution due to the low power levels used. Example interpolated electromagnetic field values for WLAN system in a building of Yildiz Technical University, Turkey, were generated using the selected network architectures to illustrate the results with an ANN.

Programming an Artificial Neural Network Tool for Spatial Interpolation in GIS - A Case Study for Indoor Radio Wave Propagation of WLAN

PubMed Central

Şen, Alper; Gümüşay, M. Ümit; Kavas, Aktül; Bulucu, Umut

2008-01-01

Wireless communication networks offer subscribers the possibilities of free mobility and access to information anywhere at any time. Therefore, electromagnetic coverage calculations are important for wireless mobile communication systems, especially in Wireless Local Area Networks (WLANs). Before any propagation computation is performed, modeling of indoor radio wave propagation needs accurate geographical information in order to avoid the interruption of data transmissions. Geographic Information Systems (GIS) and spatial interpolation techniques are very efficient for performing indoor radio wave propagation modeling. This paper describes the spatial interpolation of electromagnetic field measurements using a feed-forward back-propagation neural network programmed as a tool in GIS. The accuracy of Artificial Neural Networks (ANN) and geostatistical Kriging were compared by adjusting procedures. The feedforward back-propagation ANN provides adequate accuracy for spatial interpolation, but the predictions of Kriging interpolation are more accurate than the selected ANN. The proposed GIS ensures indoor radio wave propagation model and electromagnetic coverage, the number, position and transmitter power of access points and electromagnetic radiation level. Pollution analysis in a given propagation environment was done and it was demonstrated that WLAN (2.4 GHz) electromagnetic coverage does not lead to any electromagnetic pollution due to the low power levels used. Example interpolated electromagnetic field values for WLAN system in a building of Yildiz Technical University, Turkey, were generated using the selected network architectures to illustrate the results with an ANN. PMID:27873854

Spatial interpolation of forest conditions using co-conditional geostatistical simulation

Treesearch

H. Todd Mowrer

2000-01-01

In recent work the author used the geostatistical Monte Carlo technique of sequential Gaussian simulation (s.G.s.) to investigate uncertainty in a GIS analysis of potential old-growth forest areas. The current study compares this earlier technique to that of co-conditional simulation, wherein the spatial cross-correlations between variables are included. As in the...

Summary on Several Key Techniques in 3D Geological Modeling

PubMed Central

2014-01-01

Several key techniques in 3D geological modeling including planar mesh generation, spatial interpolation, and surface intersection are summarized in this paper. Note that these techniques are generic and widely used in various applications but play a key role in 3D geological modeling. There are two essential procedures in 3D geological modeling: the first is the simulation of geological interfaces using geometric surfaces and the second is the building of geological objects by means of various geometric computations such as the intersection of surfaces. Discrete geometric surfaces that represent geological interfaces can be generated by creating planar meshes first and then spatially interpolating; those surfaces intersect and then form volumes that represent three-dimensional geological objects such as rock bodies. In this paper, the most commonly used algorithms of the key techniques in 3D geological modeling are summarized. PMID:24772029

Prediction of soil attributes through interpolators in a deglaciated environment with complex landforms

NASA Astrophysics Data System (ADS)

Schünemann, Adriano Luis; Inácio Fernandes Filho, Elpídio; Rocha Francelino, Marcio; Rodrigues Santos, Gérson; Thomazini, Andre; Batista Pereira, Antônio; Gonçalves Reynaud Schaefer, Carlos Ernesto

2017-04-01

The knowledge of environmental variables values, in non-sampled sites from a minimum data set can be accessed through interpolation technique. Kriging and the classifier Random Forest algorithm are examples of predictors with this aim. The objective of this work was to compare methods of soil attributes spatialization in a recent deglaciated environment with complex landforms. Prediction of the selected soil attributes (potassium, calcium and magnesium) from ice-free areas were tested by using morphometric covariables, and geostatistical models without these covariables. For this, 106 soil samples were collected at 0-10 cm depth in Keller Peninsula, King George Island, Maritime Antarctica. Soil chemical analysis was performed by the gravimetric method, determining values of potassium, calcium and magnesium for each sampled point. Digital terrain models (DTMs) were obtained by using Terrestrial Laser Scanner. DTMs were generated from a cloud of points with spatial resolutions of 1, 5, 10, 20 and 30 m. Hence, 40 morphometric covariates were generated. Simple Kriging was performed using the R package software. The same data set coupled with morphometric covariates, was used to predict values of the studied attributes in non-sampled sites through Random Forest interpolator. Little differences were observed on the DTMs generated by Simple kriging and Random Forest interpolators. Also, DTMs with better spatial resolution did not improved the quality of soil attributes prediction. Results revealed that Simple Kriging can be used as interpolator when morphometric covariates are not available, with little impact regarding quality. It is necessary to go further in soil chemical attributes prediction techniques, especially in periglacial areas with complex landforms.

Application of validation data for assessing spatial interpolation methods for 8-h ozone or other sparsely monitored constituents.

PubMed

Joseph, John; Sharif, Hatim O; Sunil, Thankam; Alamgir, Hasanat

2013-07-01

The adverse health effects of high concentrations of ground-level ozone are well-known, but estimating exposure is difficult due to the sparseness of urban monitoring networks. This sparseness discourages the reservation of a portion of the monitoring stations for validation of interpolation techniques precisely when the risk of overfitting is greatest. In this study, we test a variety of simple spatial interpolation techniques for 8-h ozone with thousands of randomly selected subsets of data from two urban areas with monitoring stations sufficiently numerous to allow for true validation. Results indicate that ordinary kriging with only the range parameter calibrated in an exponential variogram is the generally superior method, and yields reliable confidence intervals. Sparse data sets may contain sufficient information for calibration of the range parameter even if the Moran I p-value is close to unity. R script is made available to apply the methodology to other sparsely monitored constituents. Copyright © 2013 Elsevier Ltd. All rights reserved.

Ground and satellite based assessment of meteorological droughts: The Coello river basin case study

NASA Astrophysics Data System (ADS)

Cruz-Roa, A. F.; Olaya-Marín, E. J.; Barrios, M. I.

2017-10-01

The spatial distribution of droughts is a key factor for designing water management policies at basin scale in arid and semi-arid regions. Ground hydro-meteorological data in neo-tropical areas are scarce; therefore, the merging of ground and satellite datasets is a promissory approach for improving our understanding of water distribution. This paper compares three monthly rainfall interpolation methods for drought evaluation. The ordinary kriging technique based on ground data, and cokriging with elevation as auxiliary variable were compared against cokriging using the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA). Twenty rain gauge stations and the 3B42V7 version of the TMPA research dataset were considered. Comparisons were made over the Coello river basin (Colombia) at 3″ spatial resolution covering a period of eight years (1998-2005). The best spatial rainfall estimation was found for cokriging using ground data and elevation. The spatial support of TMPA dataset is very coarse for a merged interpolation with ground data, this spatial scales discrepancy highlight the need to consider scaling rules in the interpolation process.

Super-resolution mapping using multi-viewing CHRIS/PROBA data

NASA Astrophysics Data System (ADS)

Dwivedi, Manish; Kumar, Vinay

2016-04-01

High-spatial resolution Remote Sensing (RS) data provides detailed information which ensures high-definition visual image analysis of earth surface features. These data sets also support improved information extraction capabilities at a fine scale. In order to improve the spatial resolution of coarser resolution RS data, the Super Resolution Reconstruction (SRR) technique has become widely acknowledged which focused on multi-angular image sequences. In this study multi-angle CHRIS/PROBA data of Kutch area is used for SR image reconstruction to enhance the spatial resolution from 18 m to 6m in the hope to obtain a better land cover classification. Various SR approaches like Projection onto Convex Sets (POCS), Robust, Iterative Back Projection (IBP), Non-Uniform Interpolation and Structure-Adaptive Normalized Convolution (SANC) chosen for this study. Subjective assessment through visual interpretation shows substantial improvement in land cover details. Quantitative measures including peak signal to noise ratio and structural similarity are used for the evaluation of the image quality. It was observed that SANC SR technique using Vandewalle algorithm for the low resolution image registration outperformed the other techniques. After that SVM based classifier is used for the classification of SRR and data resampled to 6m spatial resolution using bi-cubic interpolation. A comparative analysis is carried out between classified data of bicubic interpolated and SR derived images of CHRIS/PROBA and SR derived classified data have shown a significant improvement of 10-12% in the overall accuracy. The results demonstrated that SR methods is able to improve spatial detail of multi-angle images as well as the classification accuracy.

Remote sensing of evapotranspiration using automated calibration: Development and testing in the state of Florida

NASA Astrophysics Data System (ADS)

Evans, Aaron H.

Thermal remote sensing is a powerful tool for measuring the spatial variability of evapotranspiration due to the cooling effect of vaporization. The residual method is a popular technique which calculates evapotranspiration by subtracting sensible heat from available energy. Estimating sensible heat requires aerodynamic surface temperature which is difficult to retrieve accurately. Methods such as SEBAL/METRIC correct for this problem by calibrating the relationship between sensible heat and retrieved surface temperature. Disadvantage of these calibrations are 1) user must manually identify extremely dry and wet pixels in image 2) each calibration is only applicable over limited spatial extent. Producing larger maps is operationally limited due to time required to manually calibrate multiple spatial extents over multiple days. This dissertation develops techniques which automatically detect dry and wet pixels. LANDSAT imagery is used because it resolves dry pixels. Calibrations using 1) only dry pixels and 2) including wet pixels are developed. Snapshots of retrieved evaporative fraction and actual evapotranspiration are compared to eddy covariance measurements for five study areas in Florida: 1) Big Cypress 2) Disney Wilderness 3) Everglades 4) near Gainesville, FL. 5) Kennedy Space Center. The sensitivity of evaporative fraction to temperature, available energy, roughness length and wind speed is tested. A technique for temporally interpolating evapotranspiration by fusing LANDSAT and MODIS is developed and tested. The automated algorithm is successful at detecting wet and dry pixels (if they exist). Including wet pixels in calibration and assuming constant atmospheric conductance significantly improved results for all but Big Cypress and Gainesville. Evaporative fraction is not very sensitive to instantaneous available energy but it is sensitive to temperature when wet pixels are included because temperature is required for estimating wet pixel evapotranspiration. Data fusion techniques only slightly outperformed linear interpolation. Eddy covariance comparison and temporal interpolation produced acceptable bias error for most cases suggesting automated calibration and interpolation could be used to predict monthly or annual ET. Maps demonstrating spatial patterns of evapotranspiration at field scale were successfully produced, but only for limited spatial extents. A framework has been established for producing larger maps by creating a mosaic of smaller individual maps.

Precipitation interpolation in mountainous areas

NASA Astrophysics Data System (ADS)

Kolberg, Sjur

2015-04-01

Different precipitation interpolation techniques as well as external drift covariates are tested and compared in a 26000 km2 mountainous area in Norway, using daily data from 60 stations. The main method of assessment is cross-validation. Annual precipitation in the area varies from below 500 mm to more than 2000 mm. The data were corrected for wind-driven undercatch according to operational standards. While temporal evaluation produce seemingly acceptable at-station correlation values (on average around 0.6), the average daily spatial correlation is less than 0.1. Penalising also bias, Nash-Sutcliffe R2 values are negative for spatial correspondence, and around 0.15 for temporal. Despite largely violated assumptions, plain Kriging produces better results than simple inverse distance weighting. More surprisingly, the presumably 'worst-case' benchmark of no interpolation at all, simply averaging all 60 stations for each day, actually outperformed the standard interpolation techniques. For logistic reasons, high altitudes are under-represented in the gauge network. The possible effect of this was investigated by a) fitting a precipitation lapse rate as an external drift, and b) applying a linear model of orographic enhancement (Smith and Barstad, 2004). These techniques improved the results only marginally. The gauge density in the region is one for each 433 km2; higher than the overall density of the Norwegian national network. Admittedly the cross-validation technique reduces the gauge density, still the results suggest that we are far from able to provide hydrological models with adequate data for the main driving force.

Ambient Ozone Exposure in Czech Forests: A GIS-Based Approach to Spatial Distribution Assessment

PubMed Central

Hůnová, I.; Horálek, J.; Schreiberová, M.; Zapletal, M.

2012-01-01

Ambient ozone (O3) is an important phytotoxic pollutant, and detailed knowledge of its spatial distribution is becoming increasingly important. The aim of the paper is to compare different spatial interpolation techniques and to recommend the best approach for producing a reliable map for O3 with respect to its phytotoxic potential. For evaluation we used real-time ambient O3 concentrations measured by UV absorbance from 24 Czech rural sites in the 2007 and 2008 vegetation seasons. We considered eleven approaches for spatial interpolation used for the development of maps for mean vegetation season O3 concentrations and the AOT40F exposure index for forests. The uncertainty of maps was assessed by cross-validation analysis. The root mean square error (RMSE) of the map was used as a criterion. Our results indicate that the optimal interpolation approach is linear regression of O3 data and altitude with subsequent interpolation of its residuals by ordinary kriging. The relative uncertainty of the map of O3 mean for the vegetation season is less than 10%, using the optimal method as for both explored years, and this is a very acceptable value. In the case of AOT40F, however, the relative uncertainty of the map is notably worse, reaching nearly 20% in both examined years. PMID:22566757

GRID3D-v2: An updated version of the GRID2D/3D computer program for generating grid systems in complex-shaped three-dimensional spatial domains

NASA Technical Reports Server (NTRS)

Steinthorsson, E.; Shih, T. I-P.; Roelke, R. J.

1991-01-01

In order to generate good quality systems for complicated three-dimensional spatial domains, the grid-generation method used must be able to exert rather precise controls over grid-point distributions. Several techniques are presented that enhance control of grid-point distribution for a class of algebraic grid-generation methods known as the two-, four-, and six-boundary methods. These techniques include variable stretching functions from bilinear interpolation, interpolating functions based on tension splines, and normalized K-factors. The techniques developed in this study were incorporated into a new version of GRID3D called GRID3D-v2. The usefulness of GRID3D-v2 was demonstrated by using it to generate a three-dimensional grid system in the coolent passage of a radial turbine blade with serpentine channels and pin fins.

Interpolating Non-Parametric Distributions of Hourly Rainfall Intensities Using Random Mixing

NASA Astrophysics Data System (ADS)

Mosthaf, Tobias; Bárdossy, András; Hörning, Sebastian

2015-04-01

The correct spatial interpolation of hourly rainfall intensity distributions is of great importance for stochastical rainfall models. Poorly interpolated distributions may lead to over- or underestimation of rainfall and consequently to wrong estimates of following applications, like hydrological or hydraulic models. By analyzing the spatial relation of empirical rainfall distribution functions, a persistent order of the quantile values over a wide range of non-exceedance probabilities is observed. As the order remains similar, the interpolation weights of quantile values for one certain non-exceedance probability can be applied to the other probabilities. This assumption enables the use of kernel smoothed distribution functions for interpolation purposes. Comparing the order of hourly quantile values over different gauges with the order of their daily quantile values for equal probabilities, results in high correlations. The hourly quantile values also show high correlations with elevation. The incorporation of these two covariates into the interpolation is therefore tested. As only positive interpolation weights for the quantile values assure a monotonically increasing distribution function, the use of geostatistical methods like kriging is problematic. Employing kriging with external drift to incorporate secondary information is not applicable. Nonetheless, it would be fruitful to make use of covariates. To overcome this shortcoming, a new random mixing approach of spatial random fields is applied. Within the mixing process hourly quantile values are considered as equality constraints and correlations with elevation values are included as relationship constraints. To profit from the dependence of daily quantile values, distribution functions of daily gauges are used to set up lower equal and greater equal constraints at their locations. In this way the denser daily gauge network can be included in the interpolation of the hourly distribution functions. The applicability of this new interpolation procedure will be shown for around 250 hourly rainfall gauges in the German federal state of Baden-Württemberg. The performance of the random mixing technique within the interpolation is compared to applicable kriging methods. Additionally, the interpolation of kernel smoothed distribution functions is compared with the interpolation of fitted parametric distributions.

Development of Spatial Scaling Technique of Forest Health Sample Point Information

NASA Astrophysics Data System (ADS)

Lee, J. H.; Ryu, J. E.; Chung, H. I.; Choi, Y. Y.; Jeon, S. W.; Kim, S. H.

2018-04-01

Forests provide many goods, Ecosystem services, and resources to humans such as recreation air purification and water protection functions. In rececnt years, there has been an increase in the factors that threaten the health of forests such as global warming due to climate change, environmental pollution, and the increase in interest in forests, and efforts are being made in various countries for forest management. Thus, existing forest ecosystem survey method is a monitoring method of sampling points, and it is difficult to utilize forests for forest management because Korea is surveying only a small part of the forest area occupying 63.7 % of the country (Ministry of Land Infrastructure and Transport Korea, 2016). Therefore, in order to manage large forests, a method of interpolating and spatializing data is needed. In this study, The 1st Korea Forest Health Management biodiversity Shannon;s index data (National Institute of Forests Science, 2015) were used for spatial interpolation. Two widely used methods of interpolation, Kriging method and IDW(Inverse Distance Weighted) method were used to interpolate the biodiversity index. Vegetation indices SAVI, NDVI, LAI and SR were used. As a result, Kriging method was the most accurate method.

Kriging with Unknown Variance Components for Regional Ionospheric Reconstruction.

PubMed

Huang, Ling; Zhang, Hongping; Xu, Peiliang; Geng, Jianghui; Wang, Cheng; Liu, Jingnan

2017-02-27

Ionospheric delay effect is a critical issue that limits the accuracy of precise Global Navigation Satellite System (GNSS) positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where variations in the ionosphere are larger. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on Total Electron Content (TEC) semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospheric delays. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC) and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The statistics of results indicate that the daily ionospheric variations during the experimental period characterized by the proposed approach have good agreement with the other methods, ranging from 10 to 80 TEC Unit (TECU, 1 TECU = 1 × 10 16 electrons/m²) with an overall mean of 28.2 TECU. The proposed method can produce more appropriate estimations whose general TEC level is as smooth as the ordinary Kriging but with a smaller standard deviation around 3 TECU than others. The residual results show that the interpolation precision of the new proposed method is better than the ordinary Kriging and polynomial interpolation by about 1.2 TECU and 0.7 TECU, respectively. The root mean squared error of the proposed new Kriging with variance components is within 1.5 TECU and is smaller than those from other methods under comparison by about 1 TECU. When compared with ionospheric grid points, the mean squared error of the proposed method is within 6 TECU and smaller than Kriging, indicating that the proposed method can produce more accurate ionospheric delays and better estimation accuracy over China regional area.

Kriging with Unknown Variance Components for Regional Ionospheric Reconstruction

PubMed Central

Huang, Ling; Zhang, Hongping; Xu, Peiliang; Geng, Jianghui; Wang, Cheng; Liu, Jingnan

2017-01-01

Ionospheric delay effect is a critical issue that limits the accuracy of precise Global Navigation Satellite System (GNSS) positioning and navigation for single-frequency users, especially in mid- and low-latitude regions where variations in the ionosphere are larger. Kriging spatial interpolation techniques have been recently introduced to model the spatial correlation and variability of ionosphere, which intrinsically assume that the ionosphere field is stochastically stationary but does not take the random observational errors into account. In this paper, by treating the spatial statistical information on ionosphere as prior knowledge and based on Total Electron Content (TEC) semivariogram analysis, we use Kriging techniques to spatially interpolate TEC values. By assuming that the stochastic models of both the ionospheric signals and measurement errors are only known up to some unknown factors, we propose a new Kriging spatial interpolation method with unknown variance components for both the signals of ionosphere and TEC measurements. Variance component estimation has been integrated with Kriging to reconstruct regional ionospheric delays. The method has been applied to data from the Crustal Movement Observation Network of China (CMONOC) and compared with the ordinary Kriging and polynomial interpolations with spherical cap harmonic functions, polynomial functions and low-degree spherical harmonic functions. The statistics of results indicate that the daily ionospheric variations during the experimental period characterized by the proposed approach have good agreement with the other methods, ranging from 10 to 80 TEC Unit (TECU, 1 TECU = 1 × 1016 electrons/m2) with an overall mean of 28.2 TECU. The proposed method can produce more appropriate estimations whose general TEC level is as smooth as the ordinary Kriging but with a smaller standard deviation around 3 TECU than others. The residual results show that the interpolation precision of the new proposed method is better than the ordinary Kriging and polynomial interpolation by about 1.2 TECU and 0.7 TECU, respectively. The root mean squared error of the proposed new Kriging with variance components is within 1.5 TECU and is smaller than those from other methods under comparison by about 1 TECU. When compared with ionospheric grid points, the mean squared error of the proposed method is within 6 TECU and smaller than Kriging, indicating that the proposed method can produce more accurate ionospheric delays and better estimation accuracy over China regional area. PMID:28264424

Projection correlation based view interpolation for cone beam CT: primary fluence restoration in scatter measurement with a moving beam stop array.

PubMed

Yan, Hao; Mou, Xuanqin; Tang, Shaojie; Xu, Qiong; Zankl, Maria

2010-11-07

Scatter correction is an open problem in x-ray cone beam (CB) CT. The measurement of scatter intensity with a moving beam stop array (BSA) is a promising technique that offers a low patient dose and accurate scatter measurement. However, when restoring the blocked primary fluence behind the BSA, spatial interpolation cannot well restore the high-frequency part, causing streaks in the reconstructed image. To address this problem, we deduce a projection correlation (PC) to utilize the redundancy (over-determined information) in neighbouring CB views. PC indicates that the main high-frequency information is contained in neighbouring angular projections, instead of the current projection itself, which provides a guiding principle that applies to high-frequency information restoration. On this basis, we present the projection correlation based view interpolation (PC-VI) algorithm; that it outperforms the use of only spatial interpolation is validated. The PC-VI based moving BSA method is developed. In this method, PC-VI is employed instead of spatial interpolation, and new moving modes are designed, which greatly improve the performance of the moving BSA method in terms of reliability and practicability. Evaluation is made on a high-resolution voxel-based human phantom realistically including the entire procedure of scatter measurement with a moving BSA, which is simulated by analytical ray-tracing plus Monte Carlo simulation with EGSnrc. With the proposed method, we get visually artefact-free images approaching the ideal correction. Compared with the spatial interpolation based method, the relative mean square error is reduced by a factor of 6.05-15.94 for different slices. PC-VI does well in CB redundancy mining; therefore, it has further potential in CBCT studies.

Spatial interpolation of pesticide drift from hand-held knapsack sprayers used in potato production

NASA Astrophysics Data System (ADS)

Garcia-Santos, Glenda; Pleschberger, Martin; Scheiber, Michael; Pilz, Jürgen

2017-04-01

Tropical mountainous regions in developing countries are often neglected in research and policy but represent key areas to be considered if sustainable agricultural and rural development is to be promoted. One example is the lack of information of pesticide drift soil deposition, which can support pesticide risk assessment for soil, surface water, bystanders and off-target plants and fauna. This is considered a serious gap, given the evidence of pesticide-related poisoning in those regions. Empirical data of drift deposition of a pesticide surrogate, Uranine tracer, were obtained within one of the highest potato producing regions in Colombia. Based on the empirical data, different spatial interpolation techniques i.e. Thiessen, inverse distance squared weighting, co-kriging, pair-copulas and drift curves depending on distance and wind speed were tested and optimized. Results of the best performing spatial interpolation methods, suitable curves to assess mean relative drift and implications on risk assessment studies will be presented.

Conflict Prediction Through Geo-Spatial Interpolation of Radicalization in Syrian Social Media

DTIC Science & Technology

2015-09-24

TRAC-M-TM-15-031 September 2015 Conflict Prediction Through Geo-Spatial Interpolation of Radicalization in Syrian Social Media ...Spatial Interpolation of Radicalization in Syrian Social Media Authors MAJ Adam Haupt Dr. Camber Warren...Spatial Interpolation of Radicalization in Syrian Social 1RAC Project Code 060114 Media 6. AUTHOR(S) MAJ Haupt, Dr. Warren 7. PERFORMING OR

Error Estimation in an Optimal Interpolation Scheme for High Spatial and Temporal Resolution SST Analyses

NASA Technical Reports Server (NTRS)

Rigney, Matt; Jedlovec, Gary; LaFontaine, Frank; Shafer, Jaclyn

2010-01-01

Heat and moisture exchange between ocean surface and atmosphere plays an integral role in short-term, regional NWP. Current SST products lack both spatial and temporal resolution to accurately capture small-scale features that affect heat and moisture flux. NASA satellite is used to produce high spatial and temporal resolution SST analysis using an OI technique.

Ordinary kriging vs inverse distance weighting: spatial interpolation of the sessile community of Madagascar reef, Gulf of Mexico.

PubMed

Zarco-Perello, Salvador; Simões, Nuno

2017-01-01

Information about the distribution and abundance of the habitat-forming sessile organisms in marine ecosystems is of great importance for conservation and natural resource managers. Spatial interpolation methodologies can be useful to generate this information from in situ sampling points, especially in circumstances where remote sensing methodologies cannot be applied due to small-scale spatial variability of the natural communities and low light penetration in the water column. Interpolation methods are widely used in environmental sciences; however, published studies using these methodologies in coral reef science are scarce. We compared the accuracy of the two most commonly used interpolation methods in all disciplines, inverse distance weighting (IDW) and ordinary kriging (OK), to predict the distribution and abundance of hard corals, octocorals, macroalgae, sponges and zoantharians and identify hotspots of these habitat-forming organisms using data sampled at three different spatial scales (5, 10 and 20 m) in Madagascar reef, Gulf of Mexico. The deeper sandy environments of the leeward and windward regions of Madagascar reef were dominated by macroalgae and seconded by octocorals. However, the shallow rocky environments of the reef crest had the highest richness of habitat-forming groups of organisms; here, we registered high abundances of octocorals and macroalgae, with sponges, Millepora alcicornis and zoantharians dominating in some patches, creating high levels of habitat heterogeneity. IDW and OK generated similar maps of distribution for all the taxa; however, cross-validation tests showed that IDW outperformed OK in the prediction of their abundances. When the sampling distance was at 20 m, both interpolation techniques performed poorly, but as the sampling was done at shorter distances prediction accuracies increased, especially for IDW. OK had higher mean prediction errors and failed to correctly interpolate the highest abundance values measured in situ , except for macroalgae, whereas IDW had lower mean prediction errors and high correlations between predicted and measured values in all cases when sampling was every 5 m. The accurate spatial interpolations created using IDW allowed us to see the spatial variability of each taxa at a biological and spatial resolution that remote sensing would not have been able to produce. Our study sets the basis for further research projects and conservation management in Madagascar reef and encourages similar studies in the region and other parts of the world where remote sensing technologies are not suitable for use.

Ordinary kriging vs inverse distance weighting: spatial interpolation of the sessile community of Madagascar reef, Gulf of Mexico

PubMed Central

Simões, Nuno

2017-01-01

Information about the distribution and abundance of the habitat-forming sessile organisms in marine ecosystems is of great importance for conservation and natural resource managers. Spatial interpolation methodologies can be useful to generate this information from in situ sampling points, especially in circumstances where remote sensing methodologies cannot be applied due to small-scale spatial variability of the natural communities and low light penetration in the water column. Interpolation methods are widely used in environmental sciences; however, published studies using these methodologies in coral reef science are scarce. We compared the accuracy of the two most commonly used interpolation methods in all disciplines, inverse distance weighting (IDW) and ordinary kriging (OK), to predict the distribution and abundance of hard corals, octocorals, macroalgae, sponges and zoantharians and identify hotspots of these habitat-forming organisms using data sampled at three different spatial scales (5, 10 and 20 m) in Madagascar reef, Gulf of Mexico. The deeper sandy environments of the leeward and windward regions of Madagascar reef were dominated by macroalgae and seconded by octocorals. However, the shallow rocky environments of the reef crest had the highest richness of habitat-forming groups of organisms; here, we registered high abundances of octocorals and macroalgae, with sponges, Millepora alcicornis and zoantharians dominating in some patches, creating high levels of habitat heterogeneity. IDW and OK generated similar maps of distribution for all the taxa; however, cross-validation tests showed that IDW outperformed OK in the prediction of their abundances. When the sampling distance was at 20 m, both interpolation techniques performed poorly, but as the sampling was done at shorter distances prediction accuracies increased, especially for IDW. OK had higher mean prediction errors and failed to correctly interpolate the highest abundance values measured in situ, except for macroalgae, whereas IDW had lower mean prediction errors and high correlations between predicted and measured values in all cases when sampling was every 5 m. The accurate spatial interpolations created using IDW allowed us to see the spatial variability of each taxa at a biological and spatial resolution that remote sensing would not have been able to produce. Our study sets the basis for further research projects and conservation management in Madagascar reef and encourages similar studies in the region and other parts of the world where remote sensing technologies are not suitable for use. PMID:29204321

Validating the Kinematic Wave Approach for Rapid Soil Erosion Assessment and Improved BMP Site Selection to Enhance Training Land Sustainability

DTIC Science & Technology

2014-02-01

installation based on a Euclidean distance allocation and assigned that installation’s threshold values. The second approach used a thin - plate spline ...installation critical nLS+ thresholds involved spatial interpolation. A thin - plate spline radial basis functions (RBF) was selected as the...the interpolation of installation results using a thin - plate spline radial basis function technique. 6.5 OBJECTIVE #5: DEVELOP AND

Local-scale spatial modelling for interpolating climatic temperature variables to predict agricultural plant suitability

NASA Astrophysics Data System (ADS)

Webb, Mathew A.; Hall, Andrew; Kidd, Darren; Minansy, Budiman

2016-05-01

Assessment of local spatial climatic variability is important in the planning of planting locations for horticultural crops. This study investigated three regression-based calibration methods (i.e. traditional versus two optimized methods) to relate short-term 12-month data series from 170 temperature loggers and 4 weather station sites with data series from nearby long-term Australian Bureau of Meteorology climate stations. The techniques trialled to interpolate climatic temperature variables, such as frost risk, growing degree days (GDDs) and chill hours, were regression kriging (RK), regression trees (RTs) and random forests (RFs). All three calibration methods produced accurate results, with the RK-based calibration method delivering the most accurate validation measures: coefficients of determination ( R 2) of 0.92, 0.97 and 0.95 and root-mean-square errors of 1.30, 0.80 and 1.31 °C, for daily minimum, daily maximum and hourly temperatures, respectively. Compared with the traditional method of calibration using direct linear regression between short-term and long-term stations, the RK-based calibration method improved R 2 and reduced root-mean-square error (RMSE) by at least 5 % and 0.47 °C for daily minimum temperature, 1 % and 0.23 °C for daily maximum temperature and 3 % and 0.33 °C for hourly temperature. Spatial modelling indicated insignificant differences between the interpolation methods, with the RK technique tending to be the slightly better method due to the high degree of spatial autocorrelation between logger sites.

Spatial and spectral interpolation of ground-motion intensity measure observations

USGS Publications Warehouse

Worden, Charles; Thompson, Eric M.; Baker, Jack W.; Bradley, Brendon A.; Luco, Nicolas; Wilson, David

2018-01-01

Following a significant earthquake, ground‐motion observations are available for a limited set of locations and intensity measures (IMs). Typically, however, it is desirable to know the ground motions for additional IMs and at locations where observations are unavailable. Various interpolation methods are available, but because IMs or their logarithms are normally distributed, spatially correlated, and correlated with each other at a given location, it is possible to apply the conditional multivariate normal (MVN) distribution to the problem of estimating unobserved IMs. In this article, we review the MVN and its application to general estimation problems, and then apply the MVN to the specific problem of ground‐motion IM interpolation. In particular, we present (1) a formulation of the MVN for the simultaneous interpolation of IMs across space and IM type (most commonly, spectral response at different oscillator periods) and (2) the inclusion of uncertain observation data in the MVN formulation. These techniques, in combination with modern empirical ground‐motion models and correlation functions, provide a flexible framework for estimating a variety of IMs at arbitrary locations.

Modeling the Spatial and Temporal Variation of Monthly and Seasonal Precipitation on the Nevada Test Site and Vicinity, 1960-2006

USGS Publications Warehouse

Blainey, Joan B.; Webb, Robert H.; Magirl, Christopher S.

2007-01-01

The Nevada Test Site (NTS), located in the climatic transition zone between the Mojave and Great Basin Deserts, has a network of precipitation gages that is unusually dense for this region. This network measures monthly and seasonal variation in a landscape with diverse topography. Precipitation data from 125 climate stations on or near the NTS were used to spatially interpolate precipitation for each month during the period of 1960 through 2006 at high spatial resolution (30 m). The data were collected at climate stations using manual and/or automated techniques. The spatial interpolation method, applied to monthly accumulations of precipitation, is based on a distance-weighted multivariate regression between the amount of precipitation and the station location and elevation. This report summarizes the temporal and spatial characteristics of the available precipitation records for the period 1960 to 2006, examines the temporal and spatial variability of precipitation during the period of record, and discusses some extremes in seasonal precipitation on the NTS.

Fast inverse distance weighting-based spatiotemporal interpolation: a web-based application of interpolating daily fine particulate matter PM2:5 in the contiguous U.S. using parallel programming and k-d tree.

PubMed

Li, Lixin; Losser, Travis; Yorke, Charles; Piltner, Reinhard

2014-09-03

Epidemiological studies have identified associations between mortality and changes in concentration of particulate matter. These studies have highlighted the public concerns about health effects of particulate air pollution. Modeling fine particulate matter PM2.5 exposure risk and monitoring day-to-day changes in PM2.5 concentration is a critical step for understanding the pollution problem and embarking on the necessary remedy. This research designs, implements and compares two inverse distance weighting (IDW)-based spatiotemporal interpolation methods, in order to assess the trend of daily PM2.5 concentration for the contiguous United States over the year of 2009, at both the census block group level and county level. Traditionally, when handling spatiotemporal interpolation, researchers tend to treat space and time separately and reduce the spatiotemporal interpolation problems to a sequence of snapshots of spatial interpolations. In this paper, PM2.5 data interpolation is conducted in the continuous space-time domain by integrating space and time simultaneously, using the so-called extension approach. Time values are calculated with the help of a factor under the assumption that spatial and temporal dimensions are equally important when interpolating a continuous changing phenomenon in the space-time domain. Various IDW-based spatiotemporal interpolation methods with different parameter configurations are evaluated by cross-validation. In addition, this study explores computational issues (computer processing speed) faced during implementation of spatiotemporal interpolation for huge data sets. Parallel programming techniques and an advanced data structure, named k-d tree, are adapted in this paper to address the computational challenges. Significant computational improvement has been achieved. Finally, a web-based spatiotemporal IDW-based interpolation application is designed and implemented where users can visualize and animate spatiotemporal interpolation results.

Fast Inverse Distance Weighting-Based Spatiotemporal Interpolation: A Web-Based Application of Interpolating Daily Fine Particulate Matter PM2.5 in the Contiguous U.S. Using Parallel Programming and k-d Tree

PubMed Central

Li, Lixin; Losser, Travis; Yorke, Charles; Piltner, Reinhard

2014-01-01

Epidemiological studies have identified associations between mortality and changes in concentration of particulate matter. These studies have highlighted the public concerns about health effects of particulate air pollution. Modeling fine particulate matter PM2.5 exposure risk and monitoring day-to-day changes in PM2.5 concentration is a critical step for understanding the pollution problem and embarking on the necessary remedy. This research designs, implements and compares two inverse distance weighting (IDW)-based spatiotemporal interpolation methods, in order to assess the trend of daily PM2.5 concentration for the contiguous United States over the year of 2009, at both the census block group level and county level. Traditionally, when handling spatiotemporal interpolation, researchers tend to treat space and time separately and reduce the spatiotemporal interpolation problems to a sequence of snapshots of spatial interpolations. In this paper, PM2.5 data interpolation is conducted in the continuous space-time domain by integrating space and time simultaneously, using the so-called extension approach. Time values are calculated with the help of a factor under the assumption that spatial and temporal dimensions are equally important when interpolating a continuous changing phenomenon in the space-time domain. Various IDW-based spatiotemporal interpolation methods with different parameter configurations are evaluated by cross-validation. In addition, this study explores computational issues (computer processing speed) faced during implementation of spatiotemporal interpolation for huge data sets. Parallel programming techniques and an advanced data structure, named k-d tree, are adapted in this paper to address the computational challenges. Significant computational improvement has been achieved. Finally, a web-based spatiotemporal IDW-based interpolation application is designed and implemented where users can visualize and animate spatiotemporal interpolation results. PMID:25192146

Spatial interpolation of hourly precipitation and dew point temperature for the identification of precipitation phase and hydrologic response in a mountainous catchment

NASA Astrophysics Data System (ADS)

Garen, D. C.; Kahl, A.; Marks, D. G.; Winstral, A. H.

2012-12-01

In mountainous catchments, it is well known that meteorological inputs, such as precipitation, air temperature, humidity, etc. vary greatly with elevation, spatial location, and time. Understanding and monitoring catchment inputs is necessary in characterizing and predicting hydrologic response to these inputs. This is true all of the time, but it is the most dramatically critical during large storms, when the input to the stream system due to rain and snowmelt creates the potential for flooding. Besides such crisis events, however, proper estimation of catchment inputs and their spatial distribution is also needed in more prosaic but no less important water and related resource management activities. The first objective of this study is to apply a geostatistical spatial interpolation technique (elevationally detrended kriging) to precipitation and dew point temperature on an hourly basis and explore its characteristics, accuracy, and other issues. The second objective is to use these spatial fields to determine precipitation phase (rain or snow) during a large, dynamic winter storm. The catchment studied is the data-rich Reynolds Creek Experimental Watershed near Boise, Idaho. As part of this analysis, precipitation-elevation lapse rates are examined for spatial and temporal consistency. A clear dependence of lapse rate on precipitation amount exists. Certain stations, however, are outliers from these relationships, showing that significant local effects can be present and raising the question of whether such stations should be used for spatial interpolation. Experiments with selecting subsets of stations demonstrate the importance of elevation range and spatial placement on the interpolated fields. Hourly spatial fields of precipitation and dew point temperature are used to distinguish precipitation phase during a large rain-on-snow storm in December 2005. This application demonstrates the feasibility of producing hourly spatial fields and the importance of doing so to support an accurate determination of precipitation phase for assessing catchment hydrologic response to the storm.

Population at risk: using areal interpolation and Twitter messages to create population models for burglaries and robberies

PubMed Central

2018-01-01

ABSTRACT Population at risk of crime varies due to the characteristics of a population as well as the crime generator and attractor places where crime is located. This establishes different crime opportunities for different crimes. However, there are very few efforts of modeling structures that derive spatiotemporal population models to allow accurate assessment of population exposure to crime. This study develops population models to depict the spatial distribution of people who have a heightened crime risk for burglaries and robberies. The data used in the study include: Census data as source data for the existing population, Twitter geo-located data, and locations of schools as ancillary data to redistribute the source data more accurately in the space, and finally gridded population and crime data to evaluate the derived population models. To create the models, a density-weighted areal interpolation technique was used that disaggregates the source data in smaller spatial units considering the spatial distribution of the ancillary data. The models were evaluated with validation data that assess the interpolation error and spatial statistics that examine their relationship with the crime types. Our approach derived population models of a finer resolution that can assist in more precise spatial crime analyses and also provide accurate information about crime rates to the public. PMID:29887766

Geographic patterns and dynamics of Alaskan climate interpolated from a sparse station record

USGS Publications Warehouse

Fleming, Michael D.; Chapin, F. Stuart; Cramer, W.; Hufford, Gary L.; Serreze, Mark C.

2000-01-01

Data from a sparse network of climate stations in Alaska were interpolated to provide 1-km resolution maps of mean monthly temperature and precipitation-variables that are required at high spatial resolution for input into regional models of ecological processes and resource management. The interpolation model is based on thin-plate smoothing splines, which uses the spatial data along with a digital elevation model to incorporate local topography. The model provides maps that are consistent with regional climatology and with patterns recognized by experienced weather forecasters. The broad patterns of Alaskan climate are well represented and include latitudinal and altitudinal trends in temperature and precipitation and gradients in continentality. Variations within these broad patterns reflect both the weakening and reduction in frequency of low-pressure centres in their eastward movement across southern Alaska during the summer, and the shift of the storm tracks into central and northern Alaska in late summer. Not surprisingly, apparent artifacts of the interpolated climate occur primarily in regions with few or no stations. The interpolation model did not accurately represent low-level winter temperature inversions that occur within large valleys and basins. Along with well-recognized climate patterns, the model captures local topographic effects that would not be depicted using standard interpolation techniques. This suggests that similar procedures could be used to generate high-resolution maps for other high-latitude regions with a sparse density of data.

Depth-time interpolation of feature trends extracted from mobile microelectrode data with kernel functions.

PubMed

Wong, Stephen; Hargreaves, Eric L; Baltuch, Gordon H; Jaggi, Jurg L; Danish, Shabbar F

2012-01-01

Microelectrode recording (MER) is necessary for precision localization of target structures such as the subthalamic nucleus during deep brain stimulation (DBS) surgery. Attempts to automate this process have produced quantitative temporal trends (feature activity vs. time) extracted from mobile MER data. Our goal was to evaluate computational methods of generating spatial profiles (feature activity vs. depth) from temporal trends that would decouple automated MER localization from the clinical procedure and enhance functional localization in DBS surgery. We evaluated two methods of interpolation (standard vs. kernel) that generated spatial profiles from temporal trends. We compared interpolated spatial profiles to true spatial profiles that were calculated with depth windows, using correlation coefficient analysis. Excellent approximation of true spatial profiles is achieved by interpolation. Kernel-interpolated spatial profiles produced superior correlation coefficient values at optimal kernel widths (r = 0.932-0.940) compared to standard interpolation (r = 0.891). The choice of kernel function and kernel width resulted in trade-offs in smoothing and resolution. Interpolation of feature activity to create spatial profiles from temporal trends is accurate and can standardize and facilitate MER functional localization of subcortical structures. The methods are computationally efficient, enhancing localization without imposing additional constraints on the MER clinical procedure during DBS surgery. Copyright © 2012 S. Karger AG, Basel.

Spatial uncertainty analysis: Propagation of interpolation errors in spatially distributed models

USGS Publications Warehouse

Phillips, D.L.; Marks, D.G.

1996-01-01

In simulation modelling, it is desirable to quantify model uncertainties and provide not only point estimates for output variables but confidence intervals as well. Spatially distributed physical and ecological process models are becoming widely used, with runs being made over a grid of points that represent the landscape. This requires input values at each grid point, which often have to be interpolated from irregularly scattered measurement sites, e.g., weather stations. Interpolation introduces spatially varying errors which propagate through the model We extended established uncertainty analysis methods to a spatial domain for quantifying spatial patterns of input variable interpolation errors and how they propagate through a model to affect the uncertainty of the model output. We applied this to a model of potential evapotranspiration (PET) as a demonstration. We modelled PET for three time periods in 1990 as a function of temperature, humidity, and wind on a 10-km grid across the U.S. portion of the Columbia River Basin. Temperature, humidity, and wind speed were interpolated using kriging from 700- 1000 supporting data points. Kriging standard deviations (SD) were used to quantify the spatially varying interpolation uncertainties. For each of 5693 grid points, 100 Monte Carlo simulations were done, using the kriged values of temperature, humidity, and wind, plus random error terms determined by the kriging SDs and the correlations of interpolation errors among the three variables. For the spring season example, kriging SDs averaged 2.6??C for temperature, 8.7% for relative humidity, and 0.38 m s-1 for wind. The resultant PET estimates had coefficients of variation (CVs) ranging from 14% to 27% for the 10-km grid cells. Maps of PET means and CVs showed the spatial patterns of PET with a measure of its uncertainty due to interpolation of the input variables. This methodology should be applicable to a variety of spatially distributed models using interpolated inputs.

Image interpolation and denoising for division of focal plane sensors using Gaussian processes.

PubMed

Gilboa, Elad; Cunningham, John P; Nehorai, Arye; Gruev, Viktor

2014-06-16

Image interpolation and denoising are important techniques in image processing. These methods are inherent to digital image acquisition as most digital cameras are composed of a 2D grid of heterogeneous imaging sensors. Current polarization imaging employ four different pixelated polarization filters, commonly referred to as division of focal plane polarization sensors. The sensors capture only partial information of the true scene, leading to a loss of spatial resolution as well as inaccuracy of the captured polarization information. Interpolation is a standard technique to recover the missing information and increase the accuracy of the captured polarization information. Here we focus specifically on Gaussian process regression as a way to perform a statistical image interpolation, where estimates of sensor noise are used to improve the accuracy of the estimated pixel information. We further exploit the inherent grid structure of this data to create a fast exact algorithm that operates in ����(N(3/2)) (vs. the naive ���� (N³)), thus making the Gaussian process method computationally tractable for image data. This modeling advance and the enabling computational advance combine to produce significant improvements over previously published interpolation methods for polarimeters, which is most pronounced in cases of low signal-to-noise ratio (SNR). We provide the comprehensive mathematical model as well as experimental results of the GP interpolation performance for division of focal plane polarimeter.

Comparison of the common spatial interpolation methods used to analyze potentially toxic elements surrounding mining regions.

PubMed

Ding, Qian; Wang, Yong; Zhuang, Dafang

2018-04-15

The appropriate spatial interpolation methods must be selected to analyze the spatial distributions of Potentially Toxic Elements (PTEs), which is a precondition for evaluating PTE pollution. The accuracy and effect of different spatial interpolation methods, which include inverse distance weighting interpolation (IDW) (power = 1, 2, 3), radial basis function interpolation (RBF) (basis function: thin-plate spline (TPS), spline with tension (ST), completely regularized spline (CRS), multiquadric (MQ) and inverse multiquadric (IMQ)) and ordinary kriging interpolation (OK) (semivariogram model: spherical, exponential, gaussian and linear), were compared using 166 unevenly distributed soil PTE samples (As, Pb, Cu and Zn) in the Suxian District, Chenzhou City, Hunan Province as the study subject. The reasons for the accuracy differences of the interpolation methods and the uncertainties of the interpolation results are discussed, then several suggestions for improving the interpolation accuracy are proposed, and the direction of pollution control is determined. The results of this study are as follows: (i) RBF-ST and OK (exponential) are the optimal interpolation methods for As and Cu, and the optimal interpolation method for Pb and Zn is RBF-IMQ. (ii) The interpolation uncertainty is positively correlated with the PTE concentration, and higher uncertainties are primarily distributed around mines, which is related to the strong spatial variability of PTE concentrations caused by human interference. (iii) The interpolation accuracy can be improved by increasing the sample size around the mines, introducing auxiliary variables in the case of incomplete sampling and adopting the partition prediction method. (iv) It is necessary to strengthen the prevention and control of As and Pb pollution, particularly in the central and northern areas. The results of this study can provide an effective reference for the optimization of interpolation methods and parameters for unevenly distributed soil PTE data in mining areas. Copyright © 2018 Elsevier Ltd. All rights reserved.

DEFINITION OF MULTIVARIATE GEOCHEMICAL ASSOCIATIONS WITH POLYMETALLIC MINERAL OCCURRENCES USING A SPATIALLY DEPENDENT CLUSTERING TECHNIQUE AND RASTERIZED STREAM SEDIMENT DATA - AN ALASKAN EXAMPLE.

USGS Publications Warehouse

Jenson, Susan K.; Trautwein, C.M.

1984-01-01

The application of an unsupervised, spatially dependent clustering technique (AMOEBA) to interpolated raster arrays of stream sediment data has been found to provide useful multivariate geochemical associations for modeling regional polymetallic resource potential. The technique is based on three assumptions regarding the compositional and spatial relationships of stream sediment data and their regional significance. These assumptions are: (1) compositionally separable classes exist and can be statistically distinguished; (2) the classification of multivariate data should minimize the pair probability of misclustering to establish useful compositional associations; and (3) a compositionally defined class represented by three or more contiguous cells within an array is a more important descriptor of a terrane than a class represented by spatial outliers.

Spatial interpolation of monthly mean air temperature data for Latvia

NASA Astrophysics Data System (ADS)

Aniskevich, Svetlana

2016-04-01

Temperature data with high spatial resolution are essential for appropriate and qualitative local characteristics analysis. Nowadays the surface observation station network in Latvia consists of 22 stations recording daily air temperature, thus in order to analyze very specific and local features in the spatial distribution of temperature values in the whole Latvia, a high quality spatial interpolation method is required. Until now inverse distance weighted interpolation was used for the interpolation of air temperature data at the meteorological and climatological service of the Latvian Environment, Geology and Meteorology Centre, and no additional topographical information was taken into account. This method made it almost impossible to reasonably assess the actual temperature gradient and distribution between the observation points. During this project a new interpolation method was applied and tested, considering auxiliary explanatory parameters. In order to spatially interpolate monthly mean temperature values, kriging with external drift was used over a grid of 1 km resolution, which contains parameters such as 5 km mean elevation, continentality, distance from the Gulf of Riga and the Baltic Sea, biggest lakes and rivers, population density. As the most appropriate of these parameters, based on a complex situation analysis, mean elevation and continentality was chosen. In order to validate interpolation results, several statistical indicators of the differences between predicted values and the values actually observed were used. Overall, the introduced model visually and statistically outperforms the previous interpolation method and provides a meteorologically reasonable result, taking into account factors that influence the spatial distribution of the monthly mean temperature.

Interpolation methods and the accuracy of lattice-Boltzmann mesh refinement

DOE PAGES

Guzik, Stephen M.; Weisgraber, Todd H.; Colella, Phillip; ...

2013-12-10

A lattice-Boltzmann model to solve the equivalent of the Navier-Stokes equations on adap- tively refined grids is presented. A method for transferring information across interfaces between different grid resolutions was developed following established techniques for finite- volume representations. This new approach relies on a space-time interpolation and solving constrained least-squares problems to ensure conservation. The effectiveness of this method at maintaining the second order accuracy of lattice-Boltzmann is demonstrated through a series of benchmark simulations and detailed mesh refinement studies. These results exhibit smaller solution errors and improved convergence when compared with similar approaches relying only on spatial interpolation. Examplesmore » highlighting the mesh adaptivity of this method are also provided.« less

Kriging in the Shadows: Geostatistical Interpolation for Remote Sensing

NASA Technical Reports Server (NTRS)

Rossi, Richard E.; Dungan, Jennifer L.; Beck, Louisa R.

1994-01-01

It is often useful to estimate obscured or missing remotely sensed data. Traditional interpolation methods, such as nearest-neighbor or bilinear resampling, do not take full advantage of the spatial information in the image. An alternative method, a geostatistical technique known as indicator kriging, is described and demonstrated using a Landsat Thematic Mapper image in southern Chiapas, Mexico. The image was first classified into pasture and nonpasture land cover. For each pixel that was obscured by cloud or cloud shadow, the probability that it was pasture was assigned by the algorithm. An exponential omnidirectional variogram model was used to characterize the spatial continuity of the image for use in the kriging algorithm. Assuming a cutoff probability level of 50%, the error was shown to be 17% with no obvious spatial bias but with some tendency to categorize nonpasture as pasture (overestimation). While this is a promising result, the method's practical application in other missing data problems for remotely sensed images will depend on the amount and spatial pattern of the unobscured pixels and missing pixels and the success of the spatial continuity model used.

Selection of a Geostatistical Method to Interpolate Soil Properties of the State Crop Testing Fields using Attributes of a Digital Terrain Model

NASA Astrophysics Data System (ADS)

Sahabiev, I. A.; Ryazanov, S. S.; Kolcova, T. G.; Grigoryan, B. R.

2018-03-01

The three most common techniques to interpolate soil properties at a field scale—ordinary kriging (OK), regression kriging with multiple linear regression drift model (RK + MLR), and regression kriging with principal component regression drift model (RK + PCR)—were examined. The results of the performed study were compiled into an algorithm of choosing the most appropriate soil mapping technique. Relief attributes were used as the auxiliary variables. When spatial dependence of a target variable was strong, the OK method showed more accurate interpolation results, and the inclusion of the auxiliary data resulted in an insignificant improvement in prediction accuracy. According to the algorithm, the RK + PCR method effectively eliminates multicollinearity of explanatory variables. However, if the number of predictors is less than ten, the probability of multicollinearity is reduced, and application of the PCR becomes irrational. In that case, the multiple linear regression should be used instead.

Estimating changes in urban land and urban population using refined areal interpolation techniques

NASA Astrophysics Data System (ADS)

Zoraghein, Hamidreza; Leyk, Stefan

2018-05-01

The analysis of changes in urban land and population is important because the majority of future population growth will take place in urban areas. U.S. Census historically classifies urban land using population density and various land-use criteria. This study analyzes the reliability of census-defined urban lands for delineating the spatial distribution of urban population and estimating its changes over time. To overcome the problem of incompatible enumeration units between censuses, regular areal interpolation methods including Areal Weighting (AW) and Target Density Weighting (TDW), with and without spatial refinement, are implemented. The goal in this study is to estimate urban population in Massachusetts in 1990 and 2000 (source zones), within tract boundaries of the 2010 census (target zones), respectively, to create a consistent time series of comparable urban population estimates from 1990 to 2010. Spatial refinement is done using ancillary variables such as census-defined urban areas, the National Land Cover Database (NLCD) and the Global Human Settlement Layer (GHSL) as well as different combinations of them. The study results suggest that census-defined urban areas alone are not necessarily the most meaningful delineation of urban land. Instead, it appears that alternative combinations of the above-mentioned ancillary variables can better depict the spatial distribution of urban land, and thus make it possible to reduce the estimation error in transferring the urban population from source zones to target zones when running spatially-refined temporal areal interpolation.

Changing Urbania: Estimating Changes in Urban Land and Urban Population Using Refined Areal Interpolation Techniques

NASA Astrophysics Data System (ADS)

Zoraghein, H.; Leyk, S.; Balk, D.

2017-12-01

The analysis of changes in urban land and population is important because the majority of future population growth will take place in urban areas. The U.S. Census historically classifies urban land using population density and various land-use criteria. This study analyzes the reliability of census-defined urban lands for delineating the spatial distribution of urban population and estimating its changes over time. To overcome the problem of incompatible enumeration units between censuses, regular areal interpolation methods including Areal Weighting (AW) and Target Density Weighting (TDW), with and without spatial refinement, are implemented. The goal in this study is to estimate urban population in Massachusetts in 1990 and 2000 (source zones), within tract boundaries of the 2010 census (target zones), respectively, to create a consistent time series of comparable urban population estimates from 1990 to 2010. Spatial refinement is done using ancillary variables such as census-defined urban areas, the National Land Cover Database (NLCD) and the Global Human Settlement Layer (GHSL) as well as different combinations of them. The study results suggest that census-defined urban areas alone are not necessarily the most meaningful delineation of urban land. Instead it appears that alternative combinations of the above-mentioned ancillary variables can better depict the spatial distribution of urban land, and thus make it possible to reduce the estimation error in transferring the urban population from source zones to target zones when running spatially-refined temporal areal interpolation.

Airborne laser scanning for forest health status assessment and radiative transfer modelling

NASA Astrophysics Data System (ADS)

Novotny, Jan; Zemek, Frantisek; Pikl, Miroslav; Janoutova, Ruzena

2013-04-01

Structural parameters of forest stands/ecosystems are an important complementary source of information to spectral signatures obtained from airborne imaging spectroscopy when quantitative assessment of forest stands are in the focus, such as estimation of forest biomass, biochemical properties (e.g. chlorophyll /water content), etc. The parameterization of radiative transfer (RT) models used in latter case requires three-dimensional spatial distribution of green foliage and woody biomass. Airborne LiDAR data acquired over forest sites bears these kinds of 3D information. The main objective of the study was to compare the results from several approaches to interpolation of digital elevation model (DEM) and digital surface model (DSM). We worked with airborne LiDAR data with different density (TopEye Mk II 1,064nm instrument, 1-5 points/m2) acquired over the Norway spruce forests situated in the Beskydy Mountains, the Czech Republic. Three different interpolation algorithms with increasing complexity were tested: i/Nearest neighbour approach implemented in the BCAL software package (Idaho Univ.); ii/Averaging and linear interpolation techniques used in the OPALS software (Vienna Univ. of Technology); iii/Active contour technique implemented in the TreeVis software (Univ. of Freiburg). We defined two spatial resolutions for the resulting coupled raster DEMs and DSMs outputs: 0.4 m and 1 m, calculated by each algorithm. The grids correspond to the same spatial resolutions of hyperspectral imagery data for which the DEMs were used in a/geometrical correction and b/building a complex tree models for radiative transfer modelling. We applied two types of analyses when comparing between results from the different interpolations/raster resolution: 1/calculated DEM or DSM between themselves; 2/comparison with field data: DEM with measurements from referential GPS, DSM - field tree alometric measurements, where tree height was calculated as DSM-DEM. The results of the analyses show that: 1/averaging techniques tend to underestimate the tree height and the generated surface does not follow the first LiDAR echoes both for 1 m and 0.4 m pixel size; 2/we did not find any significant difference between tree heights calculated by nearest neighbour algorithm and the active contour technique for 1 m pixel output but the difference increased with finer resolution (0.4 m); 3/the accuracy of the DEMs calculated by tested algorithms is similar.

GRID2D/3D: A computer program for generating grid systems in complex-shaped two- and three-dimensional spatial domains. Part 2: User's manual and program listing

NASA Technical Reports Server (NTRS)

Bailey, R. T.; Shih, T. I.-P.; Nguyen, H. L.; Roelke, R. J.

1990-01-01

An efficient computer program, called GRID2D/3D, was developed to generate single and composite grid systems within geometrically complex two- and three-dimensional (2- and 3-D) spatial domains that can deform with time. GRID2D/3D generates single grid systems by using algebraic grid generation methods based on transfinite interpolation in which the distribution of grid points within the spatial domain is controlled by stretching functions. All single grid systems generated by GRID2D/3D can have grid lines that are continuous and differentiable everywhere up to the second-order. Also, grid lines can intersect boundaries of the spatial domain orthogonally. GRID2D/3D generates composite grid systems by patching together two or more single grid systems. The patching can be discontinuous or continuous. For continuous composite grid systems, the grid lines are continuous and differentiable everywhere up to the second-order except at interfaces where different single grid systems meet. At interfaces where different single grid systems meet, the grid lines are only differentiable up to the first-order. For 2-D spatial domains, the boundary curves are described by using either cubic or tension spline interpolation. For 3-D spatial domains, the boundary surfaces are described by using either linear Coon's interpolation, bi-hyperbolic spline interpolation, or a new technique referred to as 3-D bi-directional Hermite interpolation. Since grid systems generated by algebraic methods can have grid lines that overlap one another, GRID2D/3D contains a graphics package for evaluating the grid systems generated. With the graphics package, the user can generate grid systems in an interactive manner with the grid generation part of GRID2D/3D. GRID2D/3D is written in FORTRAN 77 and can be run on any IBM PC, XT, or AT compatible computer. In order to use GRID2D/3D on workstations or mainframe computers, some minor modifications must be made in the graphics part of the program; no modifications are needed in the grid generation part of the program. The theory and method used in GRID2D/3D is described.

GRID2D/3D: A computer program for generating grid systems in complex-shaped two- and three-dimensional spatial domains. Part 1: Theory and method

NASA Technical Reports Server (NTRS)

Shih, T. I.-P.; Bailey, R. T.; Nguyen, H. L.; Roelke, R. J.

1990-01-01

An efficient computer program, called GRID2D/3D was developed to generate single and composite grid systems within geometrically complex two- and three-dimensional (2- and 3-D) spatial domains that can deform with time. GRID2D/3D generates single grid systems by using algebraic grid generation methods based on transfinite interpolation in which the distribution of grid points within the spatial domain is controlled by stretching functions. All single grid systems generated by GRID2D/3D can have grid lines that are continuous and differentiable everywhere up to the second-order. Also, grid lines can intersect boundaries of the spatial domain orthogonally. GRID2D/3D generates composite grid systems by patching together two or more single grid systems. The patching can be discontinuous or continuous. For continuous composite grid systems, the grid lines are continuous and differentiable everywhere up to the second-order except at interfaces where different single grid systems meet. At interfaces where different single grid systems meet, the grid lines are only differentiable up to the first-order. For 2-D spatial domains, the boundary curves are described by using either cubic or tension spline interpolation. For 3-D spatial domains, the boundary surfaces are described by using either linear Coon's interpolation, bi-hyperbolic spline interpolation, or a new technique referred to as 3-D bi-directional Hermite interpolation. Since grid systems generated by algebraic methods can have grid lines that overlap one another, GRID2D/3D contains a graphics package for evaluating the grid systems generated. With the graphics package, the user can generate grid systems in an interactive manner with the grid generation part of GRID2D/3D. GRID2D/3D is written in FORTRAN 77 and can be run on any IBM PC, XT, or AT compatible computer. In order to use GRID2D/3D on workstations or mainframe computers, some minor modifications must be made in the graphics part of the program; no modifications are needed in the grid generation part of the program. This technical memorandum describes the theory and method used in GRID2D/3D.

Measurement and modeling of particulate matter concentrations: Applying spatial analysis and regression techniques to assess air quality.

PubMed

Sajjadi, Seyed Ali; Zolfaghari, Ghasem; Adab, Hamed; Allahabadi, Ahmad; Delsouz, Mehri

2017-01-01

This paper presented the levels of PM 2.5 and PM 10 in different stations at the city of Sabzevar, Iran. Furthermore, this study was an attempt to evaluate spatial interpolation methods for determining the PM 2.5 and PM 10 concentrations in the city of Sabzevar. Particulate matters were measured by Haz-Dust EPAM at 48 stations. Then, four interpolating models, including Radial Basis Functions (RBF), Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Universal Kriging (UK) were used to investigate the status of air pollution in the city. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were employed to compare the four models. The results showed that the PM 2.5 concentrations in the stations were between 10 and 500 μg/m 3 . Furthermore, the PM 10 concentrations for all of 48 stations ranged from 20 to 1500 μg/m 3 . The concentrations obtained for the period of nine months were greater than the standard limits. There was difference in the values of MAPE, RMSE, MBE, and MAE. The results indicated that the MAPE in IDW method was lower than other methods: (41.05 for PM 2.5 and 25.89 for PM 10 ). The best interpolation method for the particulate matter (PM 2.5 and PM 10 ) seemed to be IDW method. •The PM 10 and PM 2.5 concentration measurements were performed in the period of warm and risky in terms of particulate matter at 2016.•Concentrations of PM 2.5 and PM 10 were measured by a monitoring device, environmental dust model Haz-Dust EPAM 5000.•Interpolation is used to convert data from observation points to continuous fields to compare spatial patterns sampled by these measurements with spatial patterns of other spatial entities.

Topsoil pollution forecasting using artificial neural networks on the example of the abnormally distributed heavy metal at Russian subarctic

NASA Astrophysics Data System (ADS)

Tarasov, D. A.; Buevich, A. G.; Sergeev, A. P.; Shichkin, A. V.; Baglaeva, E. M.

2017-06-01

Forecasting the soil pollution is a considerable field of study in the light of the general concern of environmental protection issues. Due to the variation of content and spatial heterogeneity of pollutants distribution at urban areas, the conventional spatial interpolation models implemented in many GIS packages mostly cannot provide appreciate interpolation accuracy. Moreover, the problem of prediction the distribution of the element with high variability in the concentration at the study site is particularly difficult. The work presents two neural networks models forecasting a spatial content of the abnormally distributed soil pollutant (Cr) at a particular location of the subarctic Novy Urengoy, Russia. A method of generalized regression neural network (GRNN) was compared to a common multilayer perceptron (MLP) model. The proposed techniques have been built, implemented and tested using ArcGIS and MATLAB. To verify the models performances, 150 scattered input data points (pollutant concentrations) have been selected from 8.5 km2 area and then split into independent training data set (105 points) and validation data set (45 points). The training data set was generated for the interpolation using ordinary kriging while the validation data set was used to test their accuracies. The networks structures have been chosen during a computer simulation based on the minimization of the RMSE. The predictive accuracy of both models was confirmed to be significantly higher than those achieved by the geostatistical approach (kriging). It is shown that MLP could achieve better accuracy than both kriging and even GRNN for interpolating surfaces.

Accuracy of stream habitat interpolations across spatial scales

USGS Publications Warehouse

Sheehan, Kenneth R.; Welsh, Stuart A.

2013-01-01

Stream habitat data are often collected across spatial scales because relationships among habitat, species occurrence, and management plans are linked at multiple spatial scales. Unfortunately, scale is often a factor limiting insight gained from spatial analysis of stream habitat data. Considerable cost is often expended to collect data at several spatial scales to provide accurate evaluation of spatial relationships in streams. To address utility of single scale set of stream habitat data used at varying scales, we examined the influence that data scaling had on accuracy of natural neighbor predictions of depth, flow, and benthic substrate. To achieve this goal, we measured two streams at gridded resolution of 0.33 × 0.33 meter cell size over a combined area of 934 m2 to create a baseline for natural neighbor interpolated maps at 12 incremental scales ranging from a raster cell size of 0.11 m2 to 16 m2 . Analysis of predictive maps showed a logarithmic linear decay pattern in RMSE values in interpolation accuracy for variables as resolution of data used to interpolate study areas became coarser. Proportional accuracy of interpolated models (r2 ) decreased, but it was maintained up to 78% as interpolation scale moved from 0.11 m2 to 16 m2 . Results indicated that accuracy retention was suitable for assessment and management purposes at various scales different from the data collection scale. Our study is relevant to spatial modeling, fish habitat assessment, and stream habitat management because it highlights the potential of using a single dataset to fulfill analysis needs rather than investing considerable cost to develop several scaled datasets.

Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network.

PubMed

Jia, Zhenyi; Zhou, Shenglu; Su, Quanlong; Yi, Haomin; Wang, Junxiao

2017-12-26

Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution.

Improved interpolation of meteorological forcings for hydrologic applications in a Swiss Alpine region

NASA Astrophysics Data System (ADS)

Tobin, Cara; Nicotina, Ludovico; Parlange, Marc B.; Berne, Alexis; Rinaldo, Andrea

2011-04-01

SummaryThis paper presents a comparative study on the mapping of temperature and precipitation fields in complex Alpine terrain. Its relevance hinges on the major impact that inadequate interpolations of meteorological forcings bear on the accuracy of hydrologic predictions regardless of the specifics of the models, particularly during flood events. Three flood events measured in the Swiss Alps are analyzed in detail to determine the interpolation methods which best capture the distribution of intense, orographically-induced precipitation. The interpolation techniques comparatively examined include: Inverse Distance Weighting (IDW), Ordinary Kriging (OK), and Kriging with External Drift (KED). Geostatistical methods rely on a robust anisotropic variogram for the definition of the spatial rainfall structure. Results indicate that IDW tends to significantly underestimate rainfall volumes whereas OK and KED methods capture spatial patterns and rainfall volumes induced by storm advection. Using numerical weather forecasts and elevation data as covariates for precipitation, we provide evidence for KED to outperform the other methods. Most significantly, the use of elevation as auxiliary information in KED of temperatures demonstrates minimal errors in estimated instantaneous rainfall volumes and provides instantaneous lapse rates which better capture snow/rainfall partitioning. Incorporation of the temperature and precipitation input fields into a hydrological model used for operational management was found to provide vastly improved outputs with respect to measured discharge volumes and flood peaks, with notable implications for flood modeling.

Spatio-temporal patterns of Barmah Forest virus disease in Queensland, Australia.

PubMed

Naish, Suchithra; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

2011-01-01

Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ(2) = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland.

Geostatistical interpolation model selection based on ArcGIS and spatio-temporal variability analysis of groundwater level in piedmont plains, northwest China.

PubMed

Xiao, Yong; Gu, Xiaomin; Yin, Shiyang; Shao, Jingli; Cui, Yali; Zhang, Qiulan; Niu, Yong

2016-01-01

Based on the geo-statistical theory and ArcGIS geo-statistical module, datas of 30 groundwater level observation wells were used to estimate the decline of groundwater level in Beijing piedmont. Seven different interpolation methods (inverse distance weighted interpolation, global polynomial interpolation, local polynomial interpolation, tension spline interpolation, ordinary Kriging interpolation, simple Kriging interpolation and universal Kriging interpolation) were used for interpolating groundwater level between 2001 and 2013. Cross-validation, absolute error and coefficient of determination (R(2)) was applied to evaluate the accuracy of different methods. The result shows that simple Kriging method gave the best fit. The analysis of spatial and temporal variability suggest that the nugget effects from 2001 to 2013 were increasing, which means the spatial correlation weakened gradually under the influence of human activities. The spatial variability in the middle areas of the alluvial-proluvial fan is relatively higher than area in top and bottom. Since the changes of the land use, groundwater level also has a temporal variation, the average decline rate of groundwater level between 2007 and 2013 increases compared with 2001-2006. Urban development and population growth cause over-exploitation of residential and industrial areas. The decline rate of the groundwater level in residential, industrial and river areas is relatively high, while the decreasing of farmland area and development of water-saving irrigation reduce the quantity of water using by agriculture and decline rate of groundwater level in agricultural area is not significant.

Development and validation of segmentation and interpolation techniques in sinograms for metal artifact suppression in CT.

PubMed

Veldkamp, Wouter J H; Joemai, Raoul M S; van der Molen, Aart J; Geleijns, Jacob

2010-02-01

Metal prostheses cause artifacts in computed tomography (CT) images. The purpose of this work was to design an efficient and accurate metal segmentation in raw data to achieve artifact suppression and to improve CT image quality for patients with metal hip or shoulder prostheses. The artifact suppression technique incorporates two steps: metal object segmentation in raw data and replacement of the segmented region by new values using an interpolation scheme, followed by addition of the scaled metal signal intensity. Segmentation of metal is performed directly in sinograms, making it efficient and different from current methods that perform segmentation in reconstructed images in combination with Radon transformations. Metal signal segmentation is achieved by using a Markov random field model (MRF). Three interpolation methods are applied and investigated. To provide a proof of concept, CT data of five patients with metal implants were included in the study, as well as CT data of a PMMA phantom with Teflon, PVC, and titanium inserts. Accuracy was determined quantitatively by comparing mean Hounsfield (HU) values and standard deviation (SD) as a measure of distortion in phantom images with titanium (original and suppressed) and without titanium insert. Qualitative improvement was assessed by comparing uncorrected clinical images with artifact suppressed images. Artifacts in CT data of a phantom and five patients were automatically suppressed. The general visibility of structures clearly improved. In phantom images, the technique showed reduced SD close to the SD for the case where titanium was not inserted, indicating improved image quality. HU values in corrected images were different from expected values for all interpolation methods. Subtle differences between interpolation methods were found. The new artifact suppression design is efficient, for instance, in terms of preserving spatial resolution, as it is applied directly to original raw data. It successfully reduced artifacts in CT images of five patients and in phantom images. Sophisticated interpolation methods are needed to obtain reliable HU values close to the prosthesis.

Comparison Study on the Estimation of the Spatial Distribution of Regional Soil Metal(loid)s Pollution Based on Kriging Interpolation and BP Neural Network

PubMed Central

Zhou, Shenglu; Su, Quanlong; Yi, Haomin

2017-01-01

Soil pollution by metal(loid)s resulting from rapid economic development is a major concern. Accurately estimating the spatial distribution of soil metal(loid) pollution has great significance in preventing and controlling soil pollution. In this study, 126 topsoil samples were collected in Kunshan City and the geo-accumulation index was selected as a pollution index. We used Kriging interpolation and BP neural network methods to estimate the spatial distribution of arsenic (As) and cadmium (Cd) pollution in the study area. Additionally, we introduced a cross-validation method to measure the errors of the estimation results by the two interpolation methods and discussed the accuracy of the information contained in the estimation results. The conclusions are as follows: data distribution characteristics, spatial variability, and mean square errors (MSE) of the different methods showed large differences. Estimation results from BP neural network models have a higher accuracy, the MSE of As and Cd are 0.0661 and 0.1743, respectively. However, the interpolation results show significant skewed distribution, and spatial autocorrelation is strong. Using Kriging interpolation, the MSE of As and Cd are 0.0804 and 0.2983, respectively. The estimation results have poorer accuracy. Combining the two methods can improve the accuracy of the Kriging interpolation and more comprehensively represent the spatial distribution characteristics of metal(loid)s in regional soil. The study may provide a scientific basis and technical support for the regulation of soil metal(loid) pollution. PMID:29278363

Wavefront reconstruction method based on wavelet fractal interpolation for coherent free space optical communication

NASA Astrophysics Data System (ADS)

Zhang, Dai; Hao, Shiqi; Zhao, Qingsong; Zhao, Qi; Wang, Lei; Wan, Xiongfeng

2018-03-01

Existing wavefront reconstruction methods are usually low in resolution, restricted by structure characteristics of the Shack Hartmann wavefront sensor (SH WFS) and the deformable mirror (DM) in the adaptive optics (AO) system, thus, resulting in weak homodyne detection efficiency for free space optical (FSO) communication. In order to solve this problem, we firstly validate the feasibility of liquid crystal spatial light modulator (LC SLM) using in an AO system. Then, wavefront reconstruction method based on wavelet fractal interpolation is proposed after self-similarity analysis of wavefront distortion caused by atmospheric turbulence. Fast wavelet decomposition is operated to multiresolution analyze the wavefront phase spectrum, during which soft threshold denoising is carried out. The resolution of estimated wavefront phase is then improved by fractal interpolation. Finally, fast wavelet reconstruction is taken to recover wavefront phase. Simulation results reflect the superiority of our method in homodyne detection. Compared with minimum variance estimation (MVE) method based on interpolation techniques, the proposed method could obtain superior homodyne detection efficiency with lower operation complexity. Our research findings have theoretical significance in the design of coherent FSO communication system.

Mapping wildfire effects on Ca2+ and Mg2+ released from ash. A microplot analisis.

NASA Astrophysics Data System (ADS)

Pereira, Paulo; Úbeda, Xavier; Martin, Deborah

2010-05-01

Wildland fires have important implications in ecosystems dynamic. Their effects depends on many biophysical components, mainly burned specie, ecosystem affected, amount and spatial distribution of the fuel, relative humidity, slope, aspect and time of residence. These parameters are heterogenic across the landscape, producing a complex mosaic of severities. Wildland fires have a heterogenic impact on ecosystems due their diverse biophysical features. It is widely known that fire impacts can change rapidly even in short distances, producing at microplot scale highly spatial variation. Also after a fire, the most visible thing is ash and his physical and chemical properties are of main importance because here reside the majority of the available nutrients available to the plants. Considering this idea, is of major importance, study their characteristics in order to observe the type and amount of elements available to plants. This study is focused on the study of the spatial variability of two nutrients essential to plant growth, Ca2+ and Mg2+, released from ash after a wildfire at microplot scale. The impacts of fire are highly variable even small distances. This creates many problems at the hour of map the effects of fire in the release of the studied elements. Hence is of major priority identify the less biased interpolation method in order to predict with great accuracy the variable in study. The aim of this study is map the effects of wildfire on the referred elements released from ash at microplot scale, testing several interpolation methods. 16 interpolation techniques were tested, Inverse Distance to a Weight (IDW), with the with the weights of 1,2, 3, 4 and 5, Local Polynomial, with the power of 1 (LP1) and 2 (LP2), Polynomial Regression (PR), Radial Basis Functions, especially, Spline With Tension (SPT), Completely Regularized Spline (CRS), Multiquadratic (MTQ), Inverse Multiquadratic (MTQ), and Thin Plate Spline (TPS). Also geostatistical methods were tested from Kriging family, mainly Ordinary Kriging (OK), Simple Kriging (SK) and Universal Kriging (UK). Interpolation techniques were assessed throughout the Mean Error (ME) and Root Mean Square (RMSE), obtained from the cross validation procedure calculated in all methods. The fire occurred in Portugal, near an urban area and inside the affected area we designed a grid with the dimensions of 9 x 27 m and we collected 40 samples. Before modelling data, we tested their normality with the Shapiro Wilk test. Since the distributions of Ca2+ and Mg2+ did not respect the gaussian distribution we transformed data logarithmically (Ln). With this transformation, data respect the normality and spatial distribution was modelled with the transformed data. On average in the entire plot the ash slurries contained 4371.01 mg/l of Ca2+, however with a higher coefficient of variation (CV%) of 54.05%. From all the tested methods LP1 was the less biased and hence the most accurate to interpolate this element. The most biased was LP2. In relation to Mg2+, considering the entire plot, the ash released in solution on average 1196.01 mg/l, with a CV% of 52.36%, similar to the identified in Ca2+. The best interpolator in this case was SK and the most biased was LP1 and TPS. Comparing all methods in both elements, the quality of the interpolations was higher in Ca2+. These results allowed us to conclude that to achieve the best prediction it is necessary test a wide range of interpolation methods. The best accuracy will permit us to understand with more precision where the studied elements are more available and accessible to plant growth and ecosystem recovers. This spatial pattern of both nutrients is related with ash pH and burned severity evaluated from ash colour and CaCO3 content. These aspects will be also discussed in the work.

Efficient Implementation of an Optimal Interpolator for Large Spatial Data Sets

NASA Technical Reports Server (NTRS)

Memarsadeghi, Nargess; Mount, David M.

2007-01-01

Interpolating scattered data points is a problem of wide ranging interest. A number of approaches for interpolation have been proposed both from theoretical domains such as computational geometry and in applications' fields such as geostatistics. Our motivation arises from geological and mining applications. In many instances data can be costly to compute and are available only at nonuniformly scattered positions. Because of the high cost of collecting measurements, high accuracy is required in the interpolants. One of the most popular interpolation methods in this field is called ordinary kriging. It is popular because it is a best linear unbiased estimator. The price for its statistical optimality is that the estimator is computationally very expensive. This is because the value of each interpolant is given by the solution of a large dense linear system. In practice, kriging problems have been solved approximately by restricting the domain to a small local neighborhood of points that lie near the query point. Determining the proper size for this neighborhood is a solved by ad hoc methods, and it has been shown that this approach leads to undesirable discontinuities in the interpolant. Recently a more principled approach to approximating kriging has been proposed based on a technique called covariance tapering. This process achieves its efficiency by replacing the large dense kriging system with a much sparser linear system. This technique has been applied to a restriction of our problem, called simple kriging, which is not unbiased for general data sets. In this paper we generalize these results by showing how to apply covariance tapering to the more general problem of ordinary kriging. Through experimentation we demonstrate the space and time efficiency and accuracy of approximating ordinary kriging through the use of covariance tapering combined with iterative methods for solving large sparse systems. We demonstrate our approach on large data sizes arising both from synthetic sources and from real applications.

Spatio-Temporal Patterns of Barmah Forest Virus Disease in Queensland, Australia

PubMed Central

Naish, Suchithra; Hu, Wenbiao; Mengersen, Kerrie; Tong, Shilu

2011-01-01

Background Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. Methods/Principal Findings We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. Conclusions/Significance This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland. PMID:22022430

Spatial interpolation quality assessments for soil sensor transect datasets

USDA-ARS?s Scientific Manuscript database

Near-ground geophysical soil sensors provide extremely valuable information for precision agriculture applications. Indeed, their readings can be used as proxy for many soil parameters. Typically, leave-one-out (loo) cross-validation (CV) of spatial interpolation of sensor data returns overly optimi...

Digital image processing of Seabeam bathymetric data for structural studies of seamounts near the East Pacific Rise

NASA Technical Reports Server (NTRS)

Edwards, M. H.; Arvidson, R. E.; Guinness, E. A.

1984-01-01

The problem of displaying information on the seafloor morphology is attacked by utilizing digital image processing techniques to generate images for Seabeam data covering three young seamounts on the eastern flank of the East Pacific Rise. Errors in locations between crossing tracks are corrected by interactively identifying features and translating tracks relative to a control track. Spatial interpolation techniques using moving averages are used to interpolate between gridded depth values to produce images in shaded relief and color-coded forms. The digitally processed images clarify the structural control on seamount growth and clearly show the lateral extent of volcanic materials, including the distribution and fault control of subsidiary volcanic constructional features. The image presentations also clearly show artifacts related to both residual navigational errors and to depth or location differences that depend on ship heading relative to slope orientation in regions with steep slopes.

3-D Interpolation in Object Perception: Evidence from an Objective Performance Paradigm

ERIC Educational Resources Information Center

Kellman, Philip J.; Garrigan, Patrick; Shipley, Thomas F.; Yin, Carol; Machado, Liana

2005-01-01

Object perception requires interpolation processes that connect visible regions despite spatial gaps. Some research has suggested that interpolation may be a 3-D process, but objective performance data and evidence about the conditions leading to interpolation are needed. The authors developed an objective performance paradigm for testing 3-D…

Spatial interpolation of river channel topography using the shortest temporal distance

NASA Astrophysics Data System (ADS)

Zhang, Yanjun; Xian, Cuiling; Chen, Huajin; Grieneisen, Michael L.; Liu, Jiaming; Zhang, Minghua

2016-11-01

It is difficult to interpolate river channel topography due to complex anisotropy. As the anisotropy is often caused by river flow, especially the hydrodynamic and transport mechanisms, it is reasonable to incorporate flow velocity into topography interpolator for decreasing the effect of anisotropy. In this study, two new distance metrics defined as the time taken by water flow to travel between two locations are developed, and replace the spatial distance metric or Euclidean distance that is currently used to interpolate topography. One is a shortest temporal distance (STD) metric. The temporal distance (TD) of a path between two nodes is calculated by spatial distance divided by the tangent component of flow velocity along the path, and the STD is searched using the Dijkstra algorithm in all possible paths between two nodes. The other is a modified shortest temporal distance (MSTD) metric in which both the tangent and normal components of flow velocity were combined. They are used to construct the methods for the interpolation of river channel topography. The proposed methods are used to generate the topography of Wuhan Section of Changjiang River and compared with Universal Kriging (UK) and Inverse Distance Weighting (IDW). The results clearly showed that the STD and MSTD based on flow velocity were reliable spatial interpolators. The MSTD, followed by the STD, presents improvement in prediction accuracy relative to both UK and IDW.

A Residual Kriging method for the reconstruction of 3D high-resolution meteorological fields from airborne and surface observations

NASA Astrophysics Data System (ADS)

Laiti, Lavinia; Zardi, Dino; de Franceschi, Massimiliano; Rampanelli, Gabriele

2013-04-01

Manned light aircrafts and remotely piloted aircrafts represent very valuable and flexible measurement platforms for atmospheric research, as they are able to provide high temporal and spatial resolution observations of the atmosphere above the ground surface. In the present study the application of a geostatistical interpolation technique called Residual Kriging (RK) is proposed for the mapping of airborne measurements of scalar quantities over regularly spaced 3D grids. In RK the dominant (vertical) trend component underlying the original data is first extracted to filter out local anomalies, then the residual field is separately interpolated and finally added back to the trend; the determination of the interpolation weights relies on the estimate of the characteristic covariance function of the residuals, through the computation and modelling of their semivariogram function. RK implementation also allows for the inference of the characteristic spatial scales of variability of the target field and its isotropization, and for an estimate of the interpolation error. The adopted test-bed database consists in a series of flights of an instrumented motorglider exploring the atmosphere of two valleys near the city of Trento (in the southeastern Italian Alps), performed on fair-weather summer days. RK method is used to reconstruct fully 3D high-resolution fields of potential temperature and mixing ratio for specific vertical slices of the valley atmosphere, integrating also ground-based measurements from the nearest surface weather stations. From RK-interpolated meteorological fields, fine-scale features of the atmospheric boundary layer developing over the complex valley topography in connection with the occurrence of thermally-driven slope and valley winds, are detected. The performance of RK mapping is also tested against two other commonly adopted interpolation methods, i.e. the Inverse Distance Weighting and the Delaunay triangulation methods, comparing the results of a cross-validation procedure.

Assessing Fire Weather Index using statistical downscaling and spatial interpolation techniques in Greece

NASA Astrophysics Data System (ADS)

Karali, Anna; Giannakopoulos, Christos; Frias, Maria Dolores; Hatzaki, Maria; Roussos, Anargyros; Casanueva, Ana

2013-04-01

Forest fires have always been present in the Mediterranean ecosystems, thus they constitute a major ecological and socio-economic issue. The last few decades though, the number of forest fires has significantly increased, as well as their severity and impact on the environment. Local fire danger projections are often required when dealing with wild fire research. In the present study the application of statistical downscaling and spatial interpolation methods was performed to the Canadian Fire Weather Index (FWI), in order to assess forest fire risk in Greece. The FWI is used worldwide (including the Mediterranean basin) to estimate the fire danger in a generalized fuel type, based solely on weather observations. The meteorological inputs to the FWI System are noon values of dry-bulb temperature, air relative humidity, 10m wind speed and precipitation during the previous 24 hours. The statistical downscaling methods are based on a statistical model that takes into account empirical relationships between large scale variables (used as predictors) and local scale variables. In the framework of the current study the statistical downscaling portal developed by the Santander Meteorology Group (https://www.meteo.unican.es/downscaling) in the framework of the EU project CLIMRUN (www.climrun.eu) was used to downscale non standard parameters related to forest fire risk. In this study, two different approaches were adopted. Firstly, the analogue downscaling technique was directly performed to the FWI index values and secondly the same downscaling technique was performed indirectly through the meteorological inputs of the index. In both cases, the statistical downscaling portal was used considering the ERA-Interim reanalysis as predictands due to the lack of observations at noon. Additionally, a three-dimensional (3D) interpolation method of position and elevation, based on Thin Plate Splines (TPS) was used, to interpolate the ERA-Interim data used to calculate the index. Results from this method were compared with the statistical downscaling results obtained from the portal. Finally, FWI was computed using weather observations obtained from the Hellenic National Meteorological Service, mainly in the south continental part of Greece and a comparison with the previous results was performed.

The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland

NASA Astrophysics Data System (ADS)

Szymanowski, Mariusz; Kryza, Maciej

2017-02-01

Our study examines the role of auxiliary variables in the process of spatial modelling and mapping of climatological elements, with air temperature in Poland used as an example. The multivariable algorithms are the most frequently applied for spatialization of air temperature, and their results in many studies are proved to be better in comparison to those obtained by various one-dimensional techniques. In most of the previous studies, two main strategies were used to perform multidimensional spatial interpolation of air temperature. First, it was accepted that all variables significantly correlated with air temperature should be incorporated into the model. Second, it was assumed that the more spatial variation of air temperature was deterministically explained, the better was the quality of spatial interpolation. The main goal of the paper was to examine both above-mentioned assumptions. The analysis was performed using data from 250 meteorological stations and for 69 air temperature cases aggregated on different levels: from daily means to 10-year annual mean. Two cases were considered for detailed analysis. The set of potential auxiliary variables covered 11 environmental predictors of air temperature. Another purpose of the study was to compare the results of interpolation given by various multivariable methods using the same set of explanatory variables. Two regression models: multiple linear (MLR) and geographically weighted (GWR) method, as well as their extensions to the regression-kriging form, MLRK and GWRK, respectively, were examined. Stepwise regression was used to select variables for the individual models and the cross-validation method was used to validate the results with a special attention paid to statistically significant improvement of the model using the mean absolute error (MAE) criterion. The main results of this study led to rejection of both assumptions considered. Usually, including more than two or three of the most significantly correlated auxiliary variables does not improve the quality of the spatial model. The effects of introduction of certain variables into the model were not climatologically justified and were seen on maps as unexpected and undesired artefacts. The results confirm, in accordance with previous studies, that in the case of air temperature distribution, the spatial process is non-stationary; thus, the local GWR model performs better than the global MLR if they are specified using the same set of auxiliary variables. If only GWR residuals are autocorrelated, the geographically weighted regression-kriging (GWRK) model seems to be optimal for air temperature spatial interpolation.

Holes in the ocean: Filling voids in bathymetric lidar data

NASA Astrophysics Data System (ADS)

Coleman, John B.; Yao, Xiaobai; Jordan, Thomas R.; Madden, Marguertie

2011-04-01

The mapping of coral reefs may be efficiently accomplished by the use of airborne laser bathymetry. However, there are often data holes within the bathymetry data which must be filled in order to produce a complete representation of the coral habitat. This study presents a method to fill these data holes through data merging and interpolation. The method first merges ancillary digital sounding data with airborne laser bathymetry data in order to populate data points in all areas but particularly those of data holes. What follows is to generate an elevation surface by spatial interpolation based on the merged data points obtained in the first step. We conduct a case study of the Dry Tortugas National Park in Florida and produced an enhanced digital elevation model in the ocean with this method. Four interpolation techniques, including Kriging, natural neighbor, spline, and inverse distance weighted, are implemented and evaluated on their ability to accurately and realistically represent the shallow-water bathymetry of the study area. The natural neighbor technique is found to be the most effective. Finally, this enhanced digital elevation model is used in conjunction with Ikonos imagery to produce a complete, three-dimensional visualization of the study area.

Interpolating precipitation and its relation to runoff and non-point source pollution.

PubMed

Chang, Chia-Ling; Lo, Shang-Lien; Yu, Shaw-L

2005-01-01

When rainfall spatially varies, complete rainfall data for each region with different rainfall characteristics are very important. Numerous interpolation methods have been developed for estimating unknown spatial characteristics. However, no interpolation method is suitable for all circumstances. In this study, several methods, including the arithmetic average method, the Thiessen Polygons method, the traditional inverse distance method, and the modified inverse distance method, were used to interpolate precipitation. The modified inverse distance method considers not only horizontal distances but also differences between the elevations of the region with no rainfall records and of its surrounding rainfall stations. The results show that when the spatial variation of rainfall is strong, choosing a suitable interpolation method is very important. If the rainfall is uniform, the precipitation estimated using any interpolation method would be quite close to the actual precipitation. When rainfall is heavy in locations with high elevation, the rainfall changes with the elevation. In this situation, the modified inverse distance method is much more effective than any other method discussed herein for estimating the rainfall input for WinVAST to estimate runoff and non-point source pollution (NPSP). When the spatial variation of rainfall is random, regardless of the interpolation method used to yield rainfall input, the estimation errors of runoff and NPSP are large. Moreover, the relationship between the relative error of the predicted runoff and predicted pollutant loading of SS is high. However, the pollutant concentration is affected by both runoff and pollutant export, so the relationship between the relative error of the predicted runoff and the predicted pollutant concentration of SS may be unstable.

Evaluation of Statistical Downscaling Skill at Reproducing Extreme Events

NASA Astrophysics Data System (ADS)

McGinnis, S. A.; Tye, M. R.; Nychka, D. W.; Mearns, L. O.

2015-12-01

Climate model outputs usually have much coarser spatial resolution than is needed by impacts models. Although higher resolution can be achieved using regional climate models for dynamical downscaling, further downscaling is often required. The final resolution gap is often closed with a combination of spatial interpolation and bias correction, which constitutes a form of statistical downscaling. We use this technique to downscale regional climate model data and evaluate its skill in reproducing extreme events. We downscale output from the North American Regional Climate Change Assessment Program (NARCCAP) dataset from its native 50-km spatial resolution to the 4-km resolution of University of Idaho's METDATA gridded surface meterological dataset, which derives from the PRISM and NLDAS-2 observational datasets. We operate on the major variables used in impacts analysis at a daily timescale: daily minimum and maximum temperature, precipitation, humidity, pressure, solar radiation, and winds. To interpolate the data, we use the patch recovery method from the Earth System Modeling Framework (ESMF) regridding package. We then bias correct the data using Kernel Density Distribution Mapping (KDDM), which has been shown to exhibit superior overall performance across multiple metrics. Finally, we evaluate the skill of this technique in reproducing extreme events by comparing raw and downscaled output with meterological station data in different bioclimatic regions according to the the skill scores defined by Perkins et al in 2013 for evaluation of AR4 climate models. We also investigate techniques for improving bias correction of values in the tails of the distributions. These techniques include binned kernel density estimation, logspline kernel density estimation, and transfer functions constructed by fitting the tails with a generalized pareto distribution.

New developments in spatial interpolation methods of Sea-Level Anomalies in the Mediterranean Sea

NASA Astrophysics Data System (ADS)

Troupin, Charles; Barth, Alexander; Beckers, Jean-Marie; Pascual, Ananda

2014-05-01

The gridding of along-track Sea-Level Anomalies (SLA) measured by a constellation of satellites has numerous applications in oceanography, such as model validation, data assimilation or eddy tracking. Optimal Interpolation (OI) is often the preferred method for this task, as it leads to the lowest expected error and provides an error field associated to the analysed field. However, the numerical cost of the method may limit its utilization in situations where the number of data points is significant. Furthermore, the separation of non-adjacent regions with OI requires adaptation of the code, leading to a further increase of the numerical cost. To solve these issues, the Data-Interpolating Variational Analysis (DIVA), a technique designed to produce gridded from sparse in situ measurements, is applied on SLA data in the Mediterranean Sea. DIVA and OI have been shown to be equivalent (provided some assumptions on the covariances are made). The main difference lies in the covariance function, which is not explicitly formulated in DIVA. The particular spatial and temporal distributions of measurements required adaptation in the Software tool (data format, parameter determinations, ...). These adaptation are presented in the poster. The daily analysed and error fields obtained with this technique are compared with available products such as the gridded field from the Archiving, Validation and Interpretation of Satellite Oceanographic data (AVISO) data server. The comparison reveals an overall good agreement between the products. The time evolution of the mean error field evidences the need of a large number of simultaneous altimetry satellites: in period during which 4 satellites are available, the mean error is on the order of 17.5%, while when only 2 satellites are available, the error exceeds 25%. Finally, we propose the use sea currents to improve the results of the interpolation, especially in the coastal area. These currents can be constructed from the bathymetry or extracted from a HF radar located in the Balearic Sea.

Investigation of interpolation techniques for the reconstruction of the first dimension of comprehensive two-dimensional liquid chromatography-diode array detector data.

PubMed

Allen, Robert C; Rutan, Sarah C

2011-10-31

Simulated and experimental data were used to measure the effectiveness of common interpolation techniques during chromatographic alignment of comprehensive two-dimensional liquid chromatography-diode array detector (LC×LC-DAD) data. Interpolation was used to generate a sufficient number of data points in the sampled first chromatographic dimension to allow for alignment of retention times from different injections. Five different interpolation methods, linear interpolation followed by cross correlation, piecewise cubic Hermite interpolating polynomial, cubic spline, Fourier zero-filling, and Gaussian fitting, were investigated. The fully aligned chromatograms, in both the first and second chromatographic dimensions, were analyzed by parallel factor analysis to determine the relative area for each peak in each injection. A calibration curve was generated for the simulated data set. The standard error of prediction and percent relative standard deviation were calculated for the simulated peak for each technique. The Gaussian fitting interpolation technique resulted in the lowest standard error of prediction and average relative standard deviation for the simulated data. However, upon applying the interpolation techniques to the experimental data, most of the interpolation methods were not found to produce statistically different relative peak areas from each other. While most of the techniques were not statistically different, the performance was improved relative to the PARAFAC results obtained when analyzing the unaligned data. Copyright © 2011 Elsevier B.V. All rights reserved.

USE OF AUXILIARY DATA FOR SPATIAL INTERPOLATION OF OZONE EXPOSURE IN SOUTHEASTERN FORESTS

EPA Science Inventory

In order to assess the impact of tropospheric ozone on forests, it is necessary to quantify ozone exposure on regional scales. Since ozone monitoring stations are widely scattered and mostly concentrate in urban and suburban areas, some form of spatial interpolation is necessary ...

Ensemble learning for spatial interpolation of soil potassium content based on environmental information.

PubMed

Liu, Wei; Du, Peijun; Wang, Dongchen

2015-01-01

One important method to obtain the continuous surfaces of soil properties from point samples is spatial interpolation. In this paper, we propose a method that combines ensemble learning with ancillary environmental information for improved interpolation of soil properties (hereafter, EL-SP). First, we calculated the trend value for soil potassium contents at the Qinghai Lake region in China based on measured values. Then, based on soil types, geology types, land use types, and slope data, the remaining residual was simulated with the ensemble learning model. Next, the EL-SP method was applied to interpolate soil potassium contents at the study site. To evaluate the utility of the EL-SP method, we compared its performance with other interpolation methods including universal kriging, inverse distance weighting, ordinary kriging, and ordinary kriging combined geographic information. Results show that EL-SP had a lower mean absolute error and root mean square error than the data produced by the other models tested in this paper. Notably, the EL-SP maps can describe more locally detailed information and more accurate spatial patterns for soil potassium content than the other methods because of the combined use of different types of environmental information; these maps are capable of showing abrupt boundary information for soil potassium content. Furthermore, the EL-SP method not only reduces prediction errors, but it also compliments other environmental information, which makes the spatial interpolation of soil potassium content more reasonable and useful.

Geotechnical parameter spatial distribution stochastic analysis based on multi-precision information assimilation

NASA Astrophysics Data System (ADS)

Wang, C.; Rubin, Y.

2014-12-01

Spatial distribution of important geotechnical parameter named compression modulus Es contributes considerably to the understanding of the underlying geological processes and the adequate assessment of the Es mechanics effects for differential settlement of large continuous structure foundation. These analyses should be derived using an assimilating approach that combines in-situ static cone penetration test (CPT) with borehole experiments. To achieve such a task, the Es distribution of stratum of silty clay in region A of China Expo Center (Shanghai) is studied using the Bayesian-maximum entropy method. This method integrates rigorously and efficiently multi-precision of different geotechnical investigations and sources of uncertainty. Single CPT samplings were modeled as a rational probability density curve by maximum entropy theory. Spatial prior multivariate probability density function (PDF) and likelihood PDF of the CPT positions were built by borehole experiments and the potential value of the prediction point, then, preceding numerical integration on the CPT probability density curves, the posterior probability density curve of the prediction point would be calculated by the Bayesian reverse interpolation framework. The results were compared between Gaussian Sequential Stochastic Simulation and Bayesian methods. The differences were also discussed between single CPT samplings of normal distribution and simulated probability density curve based on maximum entropy theory. It is shown that the study of Es spatial distributions can be improved by properly incorporating CPT sampling variation into interpolation process, whereas more informative estimations are generated by considering CPT Uncertainty for the estimation points. Calculation illustrates the significance of stochastic Es characterization in a stratum, and identifies limitations associated with inadequate geostatistical interpolation techniques. This characterization results will provide a multi-precision information assimilation method of other geotechnical parameters.

Rainfall Observed Over Bangladesh 2000-2008: A Comparison of Spatial Interpolation Methods

NASA Astrophysics Data System (ADS)

Pervez, M.; Henebry, G. M.

2010-12-01

In preparation for a hydrometeorological study of freshwater resources in the greater Ganges-Brahmaputra region, we compared the results of four methods of spatial interpolation applied to point measurements of daily rainfall over Bangladesh during a seven year period (2000-2008). Two univariate (inverse distance weighted and spline-regularized and tension) and two multivariate geostatistical (ordinary kriging and kriging with external drift) methods were used to interpolate daily observations from a network of 221 rain gauges across Bangladesh spanning an area of 143,000 sq km. Elevation and topographic index were used as the covariates in the geostatistical methods. The validity of the interpolated maps was analyzed through cross-validation. The quality of the methods was assessed through the Pearson and Spearman correlations and root mean square error measurements of accuracy in cross-validation. Preliminary results indicated that the univariate methods performed better than the geostatistical methods at daily scales, likely due to the relatively dense sampled point measurements and a weak correlation between the rainfall and covariates at daily scales in this region. Inverse distance weighted produced the better results than the spline. For the days with extreme or high rainfall—spatially and quantitatively—the correlation between observed and interpolated estimates appeared to be high (r2 ~ 0.6 RMSE ~ 10mm), although for low rainfall days the correlations were poor (r2 ~ 0.1 RMSE ~ 3mm). The performance quality of these methods was influenced by the density of the sample point measurements, the quantity of the observed rainfall along with spatial extent, and an appropriate search radius defining the neighboring points. Results indicated that interpolated rainfall estimates at daily scales may introduce uncertainties in the successive hydrometeorological analysis. Interpolations at 5-day, 10-day, 15-day, and monthly time scales are currently under investigation.

Distributed optical fiber-based monitoring approach of spatial seepage behavior in dike engineering

NASA Astrophysics Data System (ADS)

Su, Huaizhi; Ou, Bin; Yang, Lifu; Wen, Zhiping

2018-07-01

The failure caused by seepage is the most common one in dike engineering. As to the characteristics of seepage in dike, such as longitudinal extension engineering, the randomness, strong concealment and small initial quantity order, by means of distributed fiber temperature sensor system (DTS), adopting an improved optical fiber layer layout scheme, the location of initial interpolation point of the saturation line is obtained. With the barycentric Lagrange interpolation collocation method (BLICM), the infiltrated surface of dike full-section is generated. Combined with linear optical fiber monitoring seepage method, BLICM is applied in an engineering case, which shows that a real-time seepage monitoring technique is presented in full-section of dike based on the combination method.

Volumetric three-dimensional intravascular ultrasound visualization using shape-based nonlinear interpolation

PubMed Central

2013-01-01

Background Intravascular ultrasound (IVUS) is a standard imaging modality for identification of plaque formation in the coronary and peripheral arteries. Volumetric three-dimensional (3D) IVUS visualization provides a powerful tool to overcome the limited comprehensive information of 2D IVUS in terms of complex spatial distribution of arterial morphology and acoustic backscatter information. Conventional 3D IVUS techniques provide sub-optimal visualization of arterial morphology or lack acoustic information concerning arterial structure due in part to low quality of image data and the use of pixel-based IVUS image reconstruction algorithms. In the present study, we describe a novel volumetric 3D IVUS reconstruction algorithm to utilize IVUS signal data and a shape-based nonlinear interpolation. Methods We developed an algorithm to convert a series of IVUS signal data into a fully volumetric 3D visualization. Intermediary slices between original 2D IVUS slices were generated utilizing the natural cubic spline interpolation to consider the nonlinearity of both vascular structure geometry and acoustic backscatter in the arterial wall. We evaluated differences in image quality between the conventional pixel-based interpolation and the shape-based nonlinear interpolation methods using both virtual vascular phantom data and in vivo IVUS data of a porcine femoral artery. Volumetric 3D IVUS images of the arterial segment reconstructed using the two interpolation methods were compared. Results In vitro validation and in vivo comparative studies with the conventional pixel-based interpolation method demonstrated more robustness of the shape-based nonlinear interpolation algorithm in determining intermediary 2D IVUS slices. Our shape-based nonlinear interpolation demonstrated improved volumetric 3D visualization of the in vivo arterial structure and more realistic acoustic backscatter distribution compared to the conventional pixel-based interpolation method. Conclusions This novel 3D IVUS visualization strategy has the potential to improve ultrasound imaging of vascular structure information, particularly atheroma determination. Improved volumetric 3D visualization with accurate acoustic backscatter information can help with ultrasound molecular imaging of atheroma component distribution. PMID:23651569

Analysis of spatial distribution of land cover maps accuracy

NASA Astrophysics Data System (ADS)

Khatami, R.; Mountrakis, G.; Stehman, S. V.

2017-12-01

Land cover maps have become one of the most important products of remote sensing science. However, classification errors will exist in any classified map and affect the reliability of subsequent map usage. Moreover, classification accuracy often varies over different regions of a classified map. These variations of accuracy will affect the reliability of subsequent analyses of different regions based on the classified maps. The traditional approach of map accuracy assessment based on an error matrix does not capture the spatial variation in classification accuracy. Here, per-pixel accuracy prediction methods are proposed based on interpolating accuracy values from a test sample to produce wall-to-wall accuracy maps. Different accuracy prediction methods were developed based on four factors: predictive domain (spatial versus spectral), interpolation function (constant, linear, Gaussian, and logistic), incorporation of class information (interpolating each class separately versus grouping them together), and sample size. Incorporation of spectral domain as explanatory feature spaces of classification accuracy interpolation was done for the first time in this research. Performance of the prediction methods was evaluated using 26 test blocks, with 10 km × 10 km dimensions, dispersed throughout the United States. The performance of the predictions was evaluated using the area under the curve (AUC) of the receiver operating characteristic. Relative to existing accuracy prediction methods, our proposed methods resulted in improvements of AUC of 0.15 or greater. Evaluation of the four factors comprising the accuracy prediction methods demonstrated that: i) interpolations should be done separately for each class instead of grouping all classes together; ii) if an all-classes approach is used, the spectral domain will result in substantially greater AUC than the spatial domain; iii) for the smaller sample size and per-class predictions, the spectral and spatial domain yielded similar AUC; iv) for the larger sample size (i.e., very dense spatial sample) and per-class predictions, the spatial domain yielded larger AUC; v) increasing the sample size improved accuracy predictions with a greater benefit accruing to the spatial domain; and vi) the function used for interpolation had the smallest effect on AUC.

Empirical performance of interpolation techniques in risk-neutral density (RND) estimation

NASA Astrophysics Data System (ADS)

Bahaludin, H.; Abdullah, M. H.

2017-03-01

The objective of this study is to evaluate the empirical performance of interpolation techniques in risk-neutral density (RND) estimation. Firstly, the empirical performance is evaluated by using statistical analysis based on the implied mean and the implied variance of RND. Secondly, the interpolation performance is measured based on pricing error. We propose using the leave-one-out cross-validation (LOOCV) pricing error for interpolation selection purposes. The statistical analyses indicate that there are statistical differences between the interpolation techniques:second-order polynomial, fourth-order polynomial and smoothing spline. The results of LOOCV pricing error shows that interpolation by using fourth-order polynomial provides the best fitting to option prices in which it has the lowest value error.

Deriving meteorological variables across Africa for the study and control of vector-borne disease: a comparison of remote sensing and spatial interpolation of climate

PubMed Central

Hay, S. I.; Lennon, J. J.

2012-01-01

Summary This paper presents the results of an investigation into the utility of remote sensing (RS) using meteorological satellites sensors and spatial interpolation (SI) of data from meteorological stations, for the prediction of spatial variation in monthly climate across continental Africa in 1990. Information from the Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration’s (NOAA) polar-orbiting meteorological satellites was used to estimate land surface temperature (LST) and atmospheric moisture. Cold cloud duration (CCD) data derived from the High Resolution Radiometer (HRR) on-board the European Meteorological Satellite programme’s (EUMETSAT) Meteosat satellite series were also used as a RS proxy measurement of rainfall. Temperature, atmospheric moisture and rainfall surfaces were independently derived from SI of measurements from the World Meteorological Organization (WMO) member stations of Africa. These meteorological station data were then used to test the accuracy of each methodology, so that the appropriateness of the two techniques for epidemiological research could be compared. SI was a more accurate predictor of temperature, whereas RS provided a better surrogate for rainfall; both were equally accurate at predicting atmospheric moisture. The implications of these results for mapping short and long-term climate change and hence their potential for the study and control of disease vectors are considered. Taking into account logistic and analytical problems, there were no clear conclusions regarding the optimality of either technique, but there was considerable potential for synergy. PMID:10203175

Deriving meteorological variables across Africa for the study and control of vector-borne disease: a comparison of remote sensing and spatial interpolation of climate.

PubMed

Hay, S I; Lennon, J J

1999-01-01

This paper presents the results of an investigation into the utility of remote sensing (RS) using meteorological satellites sensors and spatial interpolation (SI) of data from meteorological stations, for the prediction of spatial variation in monthly climate across continental Africa in 1990. Information from the Advanced Very High Resolution Radiometer (AVHRR) of the National Oceanic and Atmospheric Administration's (NOAA) polar-orbiting meteorological satellites was used to estimate land surface temperature (LST) and atmospheric moisture. Cold cloud duration (CCD) data derived from the High Resolution Radiometer (HRR) on-board the European Meteorological Satellite programme's (EUMETSAT) Meteosat satellite series were also used as a RS proxy measurement of rainfall. Temperature, atmospheric moisture and rainfall surfaces were independently derived from SI of measurements from the World Meteorological Organization (WMO) member stations of Africa. These meteorological station data were then used to test the accuracy of each methodology, so that the appropriateness of the two techniques for epidemiological research could be compared. SI was a more accurate predictor of temperature, whereas RS provided a better surrogate for rainfall; both were equally accurate at predicting atmospheric moisture. The implications of these results for mapping short and long-term climate change and hence their potential for the study and control of disease vectors are considered. Taking into account logistic and analytical problems, there were no clear conclusions regarding the optimality of either technique, but there was considerable potential for synergy.

Object Interpolation in Three Dimensions

ERIC Educational Resources Information Center

Kellman, Philip J.; Garrigan, Patrick; Shipley, Thomas F.

2005-01-01

Perception of objects in ordinary scenes requires interpolation processes connecting visible areas across spatial gaps. Most research has focused on 2-D displays, and models have been based on 2-D, orientation-sensitive units. The authors present a view of interpolation processes as intrinsically 3-D and producing representations of contours and…

Segmentation of arterial vessel wall motion to sub-pixel resolution using M-mode ultrasound.

PubMed

Fancourt, Craig; Azer, Karim; Ramcharan, Sharmilee L; Bunzel, Michelle; Cambell, Barry R; Sachs, Jeffrey R; Walker, Matthew

2008-01-01

We describe a method for segmenting arterial vessel wall motion to sub-pixel resolution, using the returns from M-mode ultrasound. The technique involves measuring the spatial offset between all pairs of scans from their cross-correlation, converting the spatial offsets to relative wall motion through a global optimization, and finally translating from relative to absolute wall motion by interpolation over the M-mode image. The resulting detailed wall distension waveform has the potential to enhance existing vascular biomarkers, such as strain and compliance, as well as enable new ones.

Balancing Conflicting Requirements for Grid and Particle Decomposition in Continuum-Lagrangian Solvers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sitaraman, Hariswaran; Grout, Ray

2015-10-30

The load balancing strategies for hybrid solvers that involve grid based partial differential equation solution coupled with particle tracking are presented in this paper. A typical Message Passing Interface (MPI) based parallelization of grid based solves are done using a spatial domain decomposition while particle tracking is primarily done using either of the two techniques. One of the techniques is to distribute the particles to MPI ranks to whose grid they belong to while the other is to share the particles equally among all ranks, irrespective of their spatial location. The former technique provides spatial locality for field interpolation butmore » cannot assure load balance in terms of number of particles, which is achieved by the latter. The two techniques are compared for a case of particle tracking in a homogeneous isotropic turbulence box as well as a turbulent jet case. We performed a strong scaling study for more than 32,000 cores, which results in particle densities representative of anticipated exascale machines. The use of alternative implementations of MPI collectives and efficient load equalization strategies are studied to reduce data communication overheads.« less

The natural neighbor series manuals and source codes

NASA Astrophysics Data System (ADS)

Watson, Dave

1999-05-01

This software series is concerned with reconstruction of spatial functions by interpolating a set of discrete observations having two or three independent variables. There are three components in this series: (1) nngridr: an implementation of natural neighbor interpolation, 1994, (2) modemap: an implementation of natural neighbor interpolation on the sphere, 1998 and (3) orebody: an implementation of natural neighbor isosurface generation (publication incomplete). Interpolation is important to geologists because it can offer graphical insights into significant geological structure and behavior, which, although inherent in the data, may not be otherwise apparent. It also is the first step in numerical integration, which provides a primary avenue to detailed quantification of the observed spatial function. Interpolation is implemented by selecting a surface-generating rule that controls the form of a `bridge' built across the interstices between adjacent observations. The cataloging and classification of the many such rules that have been reported is a subject in itself ( Watson, 1992), and the merits of various approaches have been debated at length. However, for practical purposes, interpolation methods are usually judged on how satisfactorily they handle problematic data sets. Sparse scattered data or traverse data, especially if the functional values are highly variable, generally tests interpolation methods most severely; but one method, natural neighbor interpolation, usually does produce preferable results for such data.

Comparison of elevation and remote sensing derived products as auxiliary data for climate surface interpolation

USGS Publications Warehouse

Alvarez, Otto; Guo, Qinghua; Klinger, Robert C.; Li, Wenkai; Doherty, Paul

2013-01-01

Climate models may be limited in their inferential use if they cannot be locally validated or do not account for spatial uncertainty. Much of the focus has gone into determining which interpolation method is best suited for creating gridded climate surfaces, which often a covariate such as elevation (Digital Elevation Model, DEM) is used to improve the interpolation accuracy. One key area where little research has addressed is in determining which covariate best improves the accuracy in the interpolation. In this study, a comprehensive evaluation was carried out in determining which covariates were most suitable for interpolating climatic variables (e.g. precipitation, mean temperature, minimum temperature, and maximum temperature). We compiled data for each climate variable from 1950 to 1999 from approximately 500 weather stations across the Western United States (32° to 49° latitude and −124.7° to −112.9° longitude). In addition, we examined the uncertainty of the interpolated climate surface. Specifically, Thin Plate Spline (TPS) was used as the interpolation method since it is one of the most popular interpolation techniques to generate climate surfaces. We considered several covariates, including DEM, slope, distance to coast (Euclidean distance), aspect, solar potential, radar, and two Normalized Difference Vegetation Index (NDVI) products derived from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS). A tenfold cross-validation was applied to determine the uncertainty of the interpolation based on each covariate. In general, the leading covariate for precipitation was radar, while DEM was the leading covariate for maximum, mean, and minimum temperatures. A comparison to other products such as PRISM and WorldClim showed strong agreement across large geographic areas but climate surfaces generated in this study (ClimSurf) had greater variability at high elevation regions, such as in the Sierra Nevada Mountains.

SU-F-T-315: Comparative Studies of Planar Dose with Different Spatial Resolution for Head and Neck IMRT QA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hwang, T; Koo, T

Purpose: To quantitatively investigate the planar dose difference and the γ value between the reference fluence map with the 1 mm detector-to-detector distance and the other fluence maps with less spatial resolution for head and neck intensity modulated radiation (IMRT) therapy. Methods: For ten head and neck cancer patients, the IMRT quality assurance (QA) beams were generated using by the commercial radiation treatment planning system, Pinnacle3 (ver. 8.0.d Philips Medical System, Madison, WI). For each beam, ten fluence maps (detector-to-detector distance: 1 mm to 10 mm by 1 mm) were generated. The fluence maps with larger than 1 mm detector-todetectormore » distance were interpolated using MATLAB (R2014a, the Math Works,Natick, MA) by four different interpolation Methods: for the bilinear, the cubic spline, the bicubic, and the nearest neighbor interpolation, respectively. These interpolated fluence maps were compared with the reference one using the γ value (criteria: 3%, 3 mm) and the relative dose difference. Results: As the detector-to-detector distance increases, the dose difference between the two maps increases. For the fluence map with the same resolution, the cubic spline interpolation and the bicubic interpolation are almost equally best interpolation methods while the nearest neighbor interpolation is the worst.For example, for 5 mm distance fluence maps, γ≤1 are 98.12±2.28%, 99.48±0.66%, 99.45±0.65% and 82.23±0.48% for the bilinear, the cubic spline, the bicubic, and the nearest neighbor interpolation, respectively. For 7 mm distance fluence maps, γ≤1 are 90.87±5.91%, 90.22±6.95%, 91.79±5.97% and 71.93±4.92 for the bilinear, the cubic spline, the bicubic, and the nearest neighbor interpolation, respectively. Conclusion: We recommend that the 2-dimensional detector array with high spatial resolution should be used as an IMRT QA tool and that the measured fluence maps should be interpolated using by the cubic spline interpolation or the bicubic interpolation for head and neck IMRT delivery. This work was supported by Radiation Technology R&D program through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (No. 2013M2A2A7038291)« less

Spatial Interpolation of Reference Evapotranspiration in India: Comparison of IDW and Kriging Methods

NASA Astrophysics Data System (ADS)

Hodam, Sanayanbi; Sarkar, Sajal; Marak, Areor G. R.; Bandyopadhyay, A.; Bhadra, A.

2017-12-01

In the present study, to understand the spatial distribution characteristics of the ETo over India, spatial interpolation was performed on the means of 32 years (1971-2002) monthly data of 131 India Meteorological Department stations uniformly distributed over the country by two methods, namely, inverse distance weighted (IDW) interpolation and kriging. Kriging was found to be better while developing the monthly surfaces during cross-validation. However, in station-wise validation, IDW performed better than kriging in almost all the cases, hence is recommended for spatial interpolation of ETo and its governing meteorological parameters. This study also checked if direct kriging of FAO-56 Penman-Monteith (PM) (Allen et al. in Crop evapotranspiration—guidelines for computing crop water requirements, Irrigation and drainage paper 56, Food and Agriculture Organization of the United Nations (FAO), Rome, 1998) point ETo produced comparable results against ETo estimated with individually kriged weather parameters (indirect kriging). Indirect kriging performed marginally well compared to direct kriging. Point ETo values were extended to areal ETo values by IDW and FAO-56 PM mean ETo maps for India were developed to obtain sufficiently accurate ETo estimates at unknown locations.

Comparison of different wind data interpolation methods for a region with complex terrain in Central Asia

NASA Astrophysics Data System (ADS)

Reinhardt, Katja; Samimi, Cyrus

2018-01-01

While climatological data of high spatial resolution are largely available in most developed countries, the network of climatological stations in many other regions of the world still constitutes large gaps. Especially for those regions, interpolation methods are important tools to fill these gaps and to improve the data base indispensible for climatological research. Over the last years, new hybrid methods of machine learning and geostatistics have been developed which provide innovative prospects in spatial predictive modelling. This study will focus on evaluating the performance of 12 different interpolation methods for the wind components \\overrightarrow{u} and \\overrightarrow{v} in a mountainous region of Central Asia. Thereby, a special focus will be on applying new hybrid methods on spatial interpolation of wind data. This study is the first evaluating and comparing the performance of several of these hybrid methods. The overall aim of this study is to determine whether an optimal interpolation method exists, which can equally be applied for all pressure levels, or whether different interpolation methods have to be used for the different pressure levels. Deterministic (inverse distance weighting) and geostatistical interpolation methods (ordinary kriging) were explored, which take into account only the initial values of \\overrightarrow{u} and \\overrightarrow{v} . In addition, more complex methods (generalized additive model, support vector machine and neural networks as single methods and as hybrid methods as well as regression-kriging) that consider additional variables were applied. The analysis of the error indices revealed that regression-kriging provided the most accurate interpolation results for both wind components and all pressure heights. At 200 and 500 hPa, regression-kriging is followed by the different kinds of neural networks and support vector machines and for 850 hPa it is followed by the different types of support vector machine and ordinary kriging. Overall, explanatory variables improve the interpolation results.

Image interpolation allows accurate quantitative bone morphometry in registered micro-computed tomography scans.

PubMed

Schulte, Friederike A; Lambers, Floor M; Mueller, Thomas L; Stauber, Martin; Müller, Ralph

2014-04-01

Time-lapsed in vivo micro-computed tomography is a powerful tool to analyse longitudinal changes in the bone micro-architecture. Registration can overcome problems associated with spatial misalignment between scans; however, it requires image interpolation which might affect the outcome of a subsequent bone morphometric analysis. The impact of the interpolation error itself, though, has not been quantified to date. Therefore, the purpose of this ex vivo study was to elaborate the effect of different interpolator schemes [nearest neighbour, tri-linear and B-spline (BSP)] on bone morphometric indices. None of the interpolator schemes led to significant differences between interpolated and non-interpolated images, with the lowest interpolation error found for BSPs (1.4%). Furthermore, depending on the interpolator, the processing order of registration, Gaussian filtration and binarisation played a role. Independent from the interpolator, the present findings suggest that the evaluation of bone morphometry should be done with images registered using greyscale information.

Interpolation Approaches for Characterizing Spatial Variability of Soil Properties in Tuz Lake Basin of Turkey

NASA Astrophysics Data System (ADS)

Gorji, Taha; Sertel, Elif; Tanik, Aysegul

2017-12-01

Soil management is an essential concern in protecting soil properties, in enhancing appropriate soil quality for plant growth and agricultural productivity, and in preventing soil erosion. Soil scientists and decision makers require accurate and well-distributed spatially continuous soil data across a region for risk assessment and for effectively monitoring and managing soils. Recently, spatial interpolation approaches have been utilized in various disciplines including soil sciences for analysing, predicting and mapping distribution and surface modelling of environmental factors such as soil properties. The study area selected in this research is Tuz Lake Basin in Turkey bearing ecological and economic importance. Fertile soil plays a significant role in agricultural activities, which is one of the main industries having great impact on economy of the region. Loss of trees and bushes due to intense agricultural activities in some parts of the basin lead to soil erosion. Besides, soil salinization due to both human-induced activities and natural factors has exacerbated its condition regarding agricultural land development. This study aims to compare capability of Local Polynomial Interpolation (LPI) and Radial Basis Functions (RBF) as two interpolation methods for mapping spatial pattern of soil properties including organic matter, phosphorus, lime and boron. Both LPI and RBF methods demonstrated promising results for predicting lime, organic matter, phosphorous and boron. Soil samples collected in the field were used for interpolation analysis in which approximately 80% of data was used for interpolation modelling whereas the remaining for validation of the predicted results. Relationship between validation points and their corresponding estimated values in the same location is examined by conducting linear regression analysis. Eight prediction maps generated from two different interpolation methods for soil organic matter, phosphorus, lime and boron parameters were examined based on R2 and RMSE values. The outcomes indicate that RBF performance in predicting lime, organic matter and boron put forth better results than LPI. However, LPI shows better results for predicting phosphorus.

Stream Kriging: Incremental and recursive ordinary Kriging over spatiotemporal data streams

NASA Astrophysics Data System (ADS)

Zhong, Xu; Kealy, Allison; Duckham, Matt

2016-05-01

Ordinary Kriging is widely used for geospatial interpolation and estimation. Due to the O (n3) time complexity of solving the system of linear equations, ordinary Kriging for a large set of source points is computationally intensive. Conducting real-time Kriging interpolation over continuously varying spatiotemporal data streams can therefore be especially challenging. This paper develops and tests two new strategies for improving the performance of an ordinary Kriging interpolator adapted to a stream-processing environment. These strategies rely on the expectation that, over time, source data points will frequently refer to the same spatial locations (for example, where static sensor nodes are generating repeated observations of a dynamic field). First, an incremental strategy improves efficiency in cases where a relatively small proportion of previously processed spatial locations are absent from the source points at any given iteration. Second, a recursive strategy improves efficiency in cases where there is substantial set overlap between the sets of spatial locations of source points at the current and previous iterations. These two strategies are evaluated in terms of their computational efficiency in comparison to ordinary Kriging algorithm. The results show that these two strategies can reduce the time taken to perform the interpolation by up to 90%, and approach average-case time complexity of O (n2) when most but not all source points refer to the same locations over time. By combining the approaches developed in this paper with existing heuristic ordinary Kriging algorithms, the conclusions indicate how further efficiency gains could potentially be accrued. The work ultimately contributes to the development of online ordinary Kriging interpolation algorithms, capable of real-time spatial interpolation with large streaming data sets.

A comparison of spatial analysis methods for the construction of topographic maps of retinal cell density.

PubMed

Garza-Gisholt, Eduardo; Hemmi, Jan M; Hart, Nathan S; Collin, Shaun P

2014-01-01

Topographic maps that illustrate variations in the density of different neuronal sub-types across the retina are valuable tools for understanding the adaptive significance of retinal specialisations in different species of vertebrates. To date, such maps have been created from raw count data that have been subjected to only limited analysis (linear interpolation) and, in many cases, have been presented as iso-density contour maps with contour lines that have been smoothed 'by eye'. With the use of stereological approach to count neuronal distribution, a more rigorous approach to analysing the count data is warranted and potentially provides a more accurate representation of the neuron distribution pattern. Moreover, a formal spatial analysis of retinal topography permits a more robust comparison of topographic maps within and between species. In this paper, we present a new R-script for analysing the topography of retinal neurons and compare methods of interpolating and smoothing count data for the construction of topographic maps. We compare four methods for spatial analysis of cell count data: Akima interpolation, thin plate spline interpolation, thin plate spline smoothing and Gaussian kernel smoothing. The use of interpolation 'respects' the observed data and simply calculates the intermediate values required to create iso-density contour maps. Interpolation preserves more of the data but, consequently includes outliers, sampling errors and/or other experimental artefacts. In contrast, smoothing the data reduces the 'noise' caused by artefacts and permits a clearer representation of the dominant, 'real' distribution. This is particularly useful where cell density gradients are shallow and small variations in local density may dramatically influence the perceived spatial pattern of neuronal topography. The thin plate spline and the Gaussian kernel methods both produce similar retinal topography maps but the smoothing parameters used may affect the outcome.

Space Weather Activities of IONOLAB Group: TEC Mapping

NASA Astrophysics Data System (ADS)

Arikan, F.; Yilmaz, A.; Arikan, O.; Sayin, I.; Gurun, M.; Akdogan, K. E.; Yildirim, S. A.

2009-04-01

Being a key player in Space Weather, ionospheric variability affects the performance of both communication and navigation systems. To improve the performance of these systems, ionosphere has to be monitored. Total Electron Content (TEC), line integral of the electron density along a ray path, is an important parameter to investigate the ionospheric variability. A cost-effective way of obtaining TEC is by using dual-frequency GPS receivers. Since these measurements are sparse in space, accurate and robust interpolation techniques are needed to interpolate (or map) the TEC distribution for a given region in space. However, the TEC data derived from GPS measurements contain measurement noise, model and computational errors. Thus, it is necessary to analyze the interpolation performance of the techniques on synthetic data sets that can represent various ionospheric states. By this way, interpolation performance of the techniques can be compared over many parameters that can be controlled to represent the desired ionospheric states. In this study, Multiquadrics, Inverse Distance Weighting (IDW), Cubic Splines, Ordinary and Universal Kriging, Random Field Priors (RFP), Multi-Layer Perceptron Neural Network (MLP-NN), and Radial Basis Function Neural Network (RBF-NN) are employed as the spatial interpolation algorithms. These mapping techniques are initially tried on synthetic TEC surfaces for parameter and coefficient optimization and determination of error bounds. Interpolation performance of these methods are compared on synthetic TEC surfaces over the parameters of sampling pattern, number of samples, the variability of the surface and the trend type in the TEC surfaces. By examining the performance of the interpolation methods, it is observed that both Kriging, RFP and NN have important advantages and possible disadvantages depending on the given constraints. It is also observed that the determining parameter in the error performance is the trend in the Ionosphere. Optimization of the algorithms in terms of their performance parameters (like the choice of the semivariogram function for Kriging algorithms and the hidden layer and neuron numbers for MLP-NN) mostly depend on the behavior of the ionosphere at that given time instant for the desired region. The sampling pattern and number of samples are the other important parameters that may contribute to the higher errors in reconstruction. For example, for all of the above listed algorithms, hexagonal regular sampling of the ionosphere provides the lowest reconstruction error and the performance significantly degrades as the samples in the region become sparse and clustered. The optimized models and coefficients are applied to regional GPS-TEC mapping using the IONOLAB-TEC data (www.ionolab.org). Both Kriging combined with Kalman Filter and dynamic modeling of NN are also implemented as first trials of TEC and space weather predictions.

Context dependent anti-aliasing image reconstruction

NASA Technical Reports Server (NTRS)

Beaudet, Paul R.; Hunt, A.; Arlia, N.

1989-01-01

Image Reconstruction has been mostly confined to context free linear processes; the traditional continuum interpretation of digital array data uses a linear interpolator with or without an enhancement filter. Here, anti-aliasing context dependent interpretation techniques are investigated for image reconstruction. Pattern classification is applied to each neighborhood to assign it a context class; a different interpolation/filter is applied to neighborhoods of differing context. It is shown how the context dependent interpolation is computed through ensemble average statistics using high resolution training imagery from which the lower resolution image array data is obtained (simulation). A quadratic least squares (LS) context-free image quality model is described from which the context dependent interpolation coefficients are derived. It is shown how ensembles of high-resolution images can be used to capture the a priori special character of different context classes. As a consequence, a priori information such as the translational invariance of edges along the edge direction, edge discontinuity, and the character of corners is captured and can be used to interpret image array data with greater spatial resolution than would be expected by the Nyquist limit. A Gibb-like artifact associated with this super-resolution is discussed. More realistic context dependent image quality models are needed and a suggestion is made for using a quality model which now is finding application in data compression.

The Choice of Spatial Interpolation Method Affects Research Conclusions

NASA Astrophysics Data System (ADS)

Eludoyin, A. O.; Ijisesan, O. S.; Eludoyin, O. M.

2017-12-01

Studies from developing countries using spatial interpolations in geographical information systems (GIS) are few and recent. Many of the studies have adopted interpolation procedures including kriging, moving average or Inverse Weighted Average (IDW) and nearest point without the necessary recourse to their uncertainties. This study compared the results of modelled representations of popular interpolation procedures from two commonly used GIS software (ILWIS and ArcGIS) at the Obafemi Awolowo University, Ile-Ife, Nigeria. Data used were concentrations of selected biochemical variables (BOD5, COD, SO4, NO3, pH, suspended and dissolved solids) in Ere stream at Ayepe-Olode, in the southwest Nigeria. Water samples were collected using a depth-integrated grab sampling approach at three locations (upstream, downstream and along a palm oil effluent discharge point in the stream); four stations were sited along each location (Figure 1). Data were first subjected to examination of their spatial distributions and associated variogram variables (nugget, sill and range), using the PAleontological STatistics (PAST3), before the mean values were interpolated in selected GIS software for the variables using each of kriging (simple), moving average and nearest point approaches. Further, the determined variogram variables were substituted with the default values in the selected software, and their results were compared. The study showed that the different point interpolation methods did not produce similar results. For example, whereas the values of conductivity was interpolated to vary as 120.1 - 219.5 µScm-1 with kriging interpolation, it varied as 105.6 - 220.0 µScm-1 and 135.0 - 173.9µScm-1 with nearest point and moving average interpolations, respectively (Figure 2). It also showed that whereas the computed variogram model produced the best fit lines (with least associated error value, Sserror) with Gaussian model, the Spherical model was assumed default for all the distributions in the software, such that the value of nugget was assumed as 0.00, when it was rarely so (Figure 3). The study concluded that interpolation procedures may affect decisions and conclusions on modelling inferences.

Nearest neighbor, bilinear interpolation and bicubic interpolation geographic correction effects on LANDSAT imagery

NASA Technical Reports Server (NTRS)

Jayroe, R. R., Jr.

1976-01-01

Geographical correction effects on LANDSAT image data are identified, using the nearest neighbor, bilinear interpolation and bicubic interpolation techniques. Potential impacts of registration on image compression and classification are explored.

Comparing ordinary kriging and inverse distance weighting for soil as pollution in Beijing.

PubMed

Qiao, Pengwei; Lei, Mei; Yang, Sucai; Yang, Jun; Guo, Guanghui; Zhou, Xiaoyong

2018-06-01

Spatial interpolation method is the basis of soil heavy metal pollution assessment and remediation. The existing evaluation index for interpolation accuracy did not combine with actual situation. The selection of interpolation methods needs to be based on specific research purposes and research object characteristics. In this paper, As pollution in soils of Beijing was taken as an example. The prediction accuracy of ordinary kriging (OK) and inverse distance weighted (IDW) were evaluated based on the cross validation results and spatial distribution characteristics of influencing factors. The results showed that, under the condition of specific spatial correlation, the cross validation results of OK and IDW for every soil point and the prediction accuracy of spatial distribution trend are similar. But the prediction accuracy of OK for the maximum and minimum is less than IDW, while the number of high pollution areas identified by OK are less than IDW. It is difficult to identify the high pollution areas fully by OK, which shows that the smoothing effect of OK is obvious. In addition, with increasing of the spatial correlation of As concentration, the cross validation error of OK and IDW decreases, and the high pollution area identified by OK is approaching the result of IDW, which can identify the high pollution areas more comprehensively. However, because the semivariogram constructed by OK interpolation method is more subjective and requires larger number of soil samples, IDW is more suitable for spatial prediction of heavy metal pollution in soils.

Uncertainty estimates of altimetric Global Mean Sea Level timeseries

NASA Astrophysics Data System (ADS)

Scharffenberg, Martin; Hemming, Michael; Stammer, Detlef

2016-04-01

An attempt is being presented concerned with providing uncertainty measures for global mean sea level time series. For this purpose sea surface height (SSH) fields, simulated by the high resolution STORM/NCEP model for the period 1993 - 2010, were subsampled along altimeter tracks and processed similar to techniques used by five working groups to estimate GMSL. Results suggest that the spatial and temporal resolution have a substantial impact on GMSL estimates. Major impacts can especially result from the interpolation technique or the treatment of SSH outliers and easily lead to artificial temporal variability in the resulting time series.

[Downscaling research of spatial distribution of incidence of hand foot and mouth disease based on area-to-area Poisson Kriging method].

PubMed

Wang, J X; Hu, M G; Yu, S C; Xiao, G X

2017-09-10

Objective: To understand the spatial distribution of incidence of hand foot and mouth disease (HFMD) at scale of township and provide evidence for the better prevention and control of HFMD and allocation of medical resources. Methods: The incidence data of HFMD in 108 counties (district) in Shandong province in 2010 were collected. Downscaling interpolation was conducted by using area-to-area Poisson Kriging method. The interpolation results were visualized by using geographic information system (GIS). The county (district) incidence was interpolated into township incidence to get the distribution of spatial distribution of incidence of township. Results: In the downscaling interpolation, the range of the fitting semi-variance equation was 20.38 km. Within the range, the incidence had correlation with each other. The fitting function of scatter diagram of estimated and actual incidence of HFMD at country level was y =1.053 1 x , R (2)=0.99. The incidences at different scale were consistent. Conclusions: The incidence of HFMD had spatial autocorrelation within 20.38 km. When HFMD occurs in one place, it is necessary to strengthen the surveillance and allocation of medical resource in the surrounding area within 20.38 km. Area to area Poisson Kriging method based downscaling research can be used in spatial visualization of HFMD incidence.

A Comparison of Approximation Modeling Techniques: Polynomial Versus Interpolating Models

NASA Technical Reports Server (NTRS)

Giunta, Anthony A.; Watson, Layne T.

1998-01-01

Two methods of creating approximation models are compared through the calculation of the modeling accuracy on test problems involving one, five, and ten independent variables. Here, the test problems are representative of the modeling challenges typically encountered in realistic engineering optimization problems. The first approximation model is a quadratic polynomial created using the method of least squares. This type of polynomial model has seen considerable use in recent engineering optimization studies due to its computational simplicity and ease of use. However, quadratic polynomial models may be of limited accuracy when the response data to be modeled have multiple local extrema. The second approximation model employs an interpolation scheme known as kriging developed in the fields of spatial statistics and geostatistics. This class of interpolating model has the flexibility to model response data with multiple local extrema. However, this flexibility is obtained at an increase in computational expense and a decrease in ease of use. The intent of this study is to provide an initial exploration of the accuracy and modeling capabilities of these two approximation methods.

Using Neural Networks to Improve the Performance of Radiative Transfer Modeling Used for Geometry Dependent LER Calculations

NASA Astrophysics Data System (ADS)

Fasnacht, Z.; Qin, W.; Haffner, D. P.; Loyola, D. G.; Joiner, J.; Krotkov, N. A.; Vasilkov, A. P.; Spurr, R. J. D.

2017-12-01

In order to estimate surface reflectance used in trace gas retrieval algorithms, radiative transfer models (RTM) such as the Vector Linearized Discrete Ordinate Radiative Transfer Model (VLIDORT) can be used to simulate the top of the atmosphere (TOA) radiances with advanced models of surface properties. With large volumes of satellite data, these model simulations can become computationally expensive. Look up table interpolation can improve the computational cost of the calculations, but the non-linear nature of the radiances requires a dense node structure if interpolation errors are to be minimized. In order to reduce our computational effort and improve the performance of look-up tables, neural networks can be trained to predict these radiances. We investigate the impact of using look-up table interpolation versus a neural network trained using the smart sampling technique, and show that neural networks can speed up calculations and reduce errors while using significantly less memory and RTM calls. In future work we will implement a neural network in operational processing to meet growing demands for reflectance modeling in support of high spatial resolution satellite missions.

A boundary value approach for solving three-dimensional elliptic and hyperbolic partial differential equations.

PubMed

Biala, T A; Jator, S N

2015-01-01

In this article, the boundary value method is applied to solve three dimensional elliptic and hyperbolic partial differential equations. The partial derivatives with respect to two of the spatial variables (y, z) are discretized using finite difference approximations to obtain a large system of ordinary differential equations (ODEs) in the third spatial variable (x). Using interpolation and collocation techniques, a continuous scheme is developed and used to obtain discrete methods which are applied via the Block unification approach to obtain approximations to the resulting large system of ODEs. Several test problems are investigated to elucidate the solution process.

The Variable Grid Method, an Approach for the Simultaneous Visualization and Assessment of Spatial Trends and Uncertainty

NASA Astrophysics Data System (ADS)

Rose, K.; Glosser, D.; Bauer, J. R.; Barkhurst, A.

2015-12-01

The products of spatial analyses that leverage the interpolation of sparse, point data to represent continuous phenomena are often presented without clear explanations of the uncertainty associated with the interpolated values. As a result, there is frequently insufficient information provided to effectively support advanced computational analyses and individual research and policy decisions utilizing these results. This highlights the need for a reliable approach capable of quantitatively producing and communicating spatial data analyses and their inherent uncertainties for a broad range of uses. To address this need, we have developed the Variable Grid Method (VGM), and associated Python tool, which is a flexible approach that can be applied to a variety of analyses and use case scenarios where users need a method to effectively study, evaluate, and analyze spatial trends and patterns while communicating the uncertainty in the underlying spatial datasets. The VGM outputs a simultaneous visualization representative of the spatial data analyses and quantification of underlying uncertainties, which can be calculated using data related to sample density, sample variance, interpolation error, uncertainty calculated from multiple simulations, etc. We will present examples of our research utilizing the VGM to quantify key spatial trends and patterns for subsurface data interpolations and their uncertainties and leverage these results to evaluate storage estimates and potential impacts associated with underground injection for CO2 storage and unconventional resource production and development. The insights provided by these examples identify how the VGM can provide critical information about the relationship between uncertainty and spatial data that is necessary to better support their use in advance computation analyses and informing research, management and policy decisions.

The geostatistical approach for structural and stratigraphic framework analysis of offshore NW Bonaparte Basin, Australia

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wahid, Ali, E-mail: ali.wahid@live.com; Salim, Ahmed Mohamed Ahmed, E-mail: mohamed.salim@petronas.com.my; Yusoff, Wan Ismail Wan, E-mail: wanismail-wanyusoff@petronas.com.my

2016-02-01

Geostatistics or statistical approach is based on the studies of temporal and spatial trend, which depend upon spatial relationships to model known information of variable(s) at unsampled locations. The statistical technique known as kriging was used for petrophycial and facies analysis, which help to assume spatial relationship to model the geological continuity between the known data and the unknown to produce a single best guess of the unknown. Kriging is also known as optimal interpolation technique, which facilitate to generate best linear unbiased estimation of each horizon. The idea is to construct a numerical model of the lithofacies and rockmore » properties that honor available data and further integrate with interpreting seismic sections, techtonostratigraphy chart with sea level curve (short term) and regional tectonics of the study area to find the structural and stratigraphic growth history of the NW Bonaparte Basin. By using kriging technique the models were built which help to estimate different parameters like horizons, facies, and porosities in the study area. The variograms were used to determine for identification of spatial relationship between data which help to find the depositional history of the North West (NW) Bonaparte Basin.« less

Monitoring global snow cover

NASA Technical Reports Server (NTRS)

Armstrong, Richard; Hardman, Molly

1991-01-01

A snow model that supports the daily, operational analysis of global snow depth and age has been developed. It provides improved spatial interpolation of surface reports by incorporating digital elevation data, and by the application of regionalized variables (kriging) through the use of a global snow depth climatology. Where surface observations are inadequate, the model applies satellite remote sensing. Techniques for extrapolation into data-void mountain areas and a procedure to compute snow melt are also contained in the model.

Analysis of spatial and temporal rainfall trends in Sicily during the 1921-2012 period

NASA Astrophysics Data System (ADS)

Liuzzo, Lorena; Bono, Enrico; Sammartano, Vincenzo; Freni, Gabriele

2016-10-01

Precipitation patterns worldwide are changing under the effects of global warming. The impacts of these changes could dramatically affect the hydrological cycle and, consequently, the availability of water resources. In order to improve the quality and reliability of forecasting models, it is important to analyse historical precipitation data to account for possible future changes. For these reasons, a large number of studies have recently been carried out with the aim of investigating the existence of statistically significant trends in precipitation at different spatial and temporal scales. In this paper, the existence of statistically significant trends in rainfall from observational datasets, which were measured by 245 rain gauges over Sicily (Italy) during the 1921-2012 period, was investigated. Annual, seasonal and monthly time series were examined using the Mann-Kendall non-parametric statistical test to detect statistically significant trends at local and regional scales, and their significance levels were assessed. Prior to the application of the Mann-Kendall test, the historical dataset was completed using a geostatistical spatial interpolation technique, the residual ordinary kriging, and then processed to remove the influence of serial correlation on the test results, applying the procedure of trend-free pre-whitening. Once the trends at each site were identified, the spatial patterns of the detected trends were examined using spatial interpolation techniques. Furthermore, focusing on the 30 years from 1981 to 2012, the trend analysis was repeated with the aim of detecting short-term trends or possible changes in the direction of the trends. Finally, the effect of climate change on the seasonal distribution of rainfall during the year was investigated by analysing the trend in the precipitation concentration index. The application of the Mann-Kendall test to the rainfall data provided evidence of a general decrease in precipitation in Sicily during the 1921-2012 period. Downward trends frequently occurred during the autumn and winter months. However, an increase in total annual precipitation was detected during the period from 1981 to 2012.

Resolution enhancement in integral microscopy by physical interpolation.

PubMed

Llavador, Anabel; Sánchez-Ortiga, Emilio; Barreiro, Juan Carlos; Saavedra, Genaro; Martínez-Corral, Manuel

2015-08-01

Integral-imaging technology has demonstrated its capability for computing depth images from the microimages recorded after a single shot. This capability has been shown in macroscopic imaging and also in microscopy. Despite the possibility of refocusing different planes from one snap-shot is crucial for the study of some biological processes, the main drawback in integral imaging is the substantial reduction of the spatial resolution. In this contribution we report a technique, which permits to increase the two-dimensional spatial resolution of the computed depth images in integral microscopy by a factor of √2. This is made by a double-shot approach, carried out by means of a rotating glass plate, which shifts the microimages in the sensor plane. We experimentally validate the resolution enhancement as well as we show the benefit of applying the technique to biological specimens.

Resolution enhancement in integral microscopy by physical interpolation

PubMed Central

Llavador, Anabel; Sánchez-Ortiga, Emilio; Barreiro, Juan Carlos; Saavedra, Genaro; Martínez-Corral, Manuel

2015-01-01

Integral-imaging technology has demonstrated its capability for computing depth images from the microimages recorded after a single shot. This capability has been shown in macroscopic imaging and also in microscopy. Despite the possibility of refocusing different planes from one snap-shot is crucial for the study of some biological processes, the main drawback in integral imaging is the substantial reduction of the spatial resolution. In this contribution we report a technique, which permits to increase the two-dimensional spatial resolution of the computed depth images in integral microscopy by a factor of √2. This is made by a double-shot approach, carried out by means of a rotating glass plate, which shifts the microimages in the sensor plane. We experimentally validate the resolution enhancement as well as we show the benefit of applying the technique to biological specimens. PMID:26309749

Interpolation of Water Quality Along Stream Networks from Synoptic Data

NASA Astrophysics Data System (ADS)

Lyon, S. W.; Seibert, J.; Lembo, A. J.; Walter, M. T.; Gburek, W. J.; Thongs, D.; Schneiderman, E.; Steenhuis, T. S.

2005-12-01

Effective catchment management requires water quality monitoring that identifies major pollutant sources and transport and transformation processes. While traditional monitoring schemes involve regular sampling at fixed locations in the stream, there is an interest synoptic or `snapshot' sampling to quantify water quality throughout a catchment. This type of sampling enables insights to biogeochemical behavior throughout a stream network at low flow conditions. Since baseflow concentrations are temporally persistence, they are indicative of the health of the ecosystems. A major problem with snapshot sampling is the lack of analytical techniques to represent the spatially distributed data in a manner that is 1) easily understood, 2) representative of the stream network, and 3) capable of being used to develop land management scenarios. This study presents a kriging application using the landscape composition of the contributing area along a stream network to define a new distance metric. This allows for locations that are more `similar' to stay spatially close together while less similar locations `move' further apart. We analyze a snapshot sampling campaign consisting of 125 manually collected grab samples during a summer recession flow period in the Townbrook Research Watershed. The watershed is located in the Catskill region of New York State and represents the mixed forest-agriculture land uses of the region. Our initial analysis indicated that stream nutrients (nitrogen and phosphorus) and chemical (major cations and anions) concentrations are controlled by the composition of landscape characteristics (landuse classes and soil types) surrounding the stream. Based on these relationships, an intuitively defined distance metric is developed by combining the traditional distance between observations and the relative difference in composition of contributing area. This metric is used to interpolate between the sampling locations with traditional geostatistic techniques (semivariograms and ordinary kriging). The resulting interpolations provide continuous stream nutrient and chemical concentrations with reduced kriging RMSE (i.e., the interpolation fits the actual data better) performed without path restriction to the stream channel (i.e., the current default for most geostatistical packages) or performed with an in-channel, Euclidean distance metric (i.e., `as the fish swims' distance). In addition to being quantifiably better, the new metric also produces maps of stream concentrations that match expected continuous stream concentrations based on expert knowledge of the watershed. This analysis and its resulting stream concentration maps provide a representation of spatially distributed synoptic data that can be used to quantify water quality for more effective catchment management that focuses on pollutant sources and transport and transformation processes.

Rtop - an R package for interpolation of data with a variable spatial support - examples from river networks

NASA Astrophysics Data System (ADS)

Olav Skøien, Jon; Laaha, Gregor; Koffler, Daniel; Blöschl, Günter; Pebesma, Edzer; Parajka, Juraj; Viglione, Alberto

2013-04-01

Geostatistical methods have been applied only to a limited extent for spatial interpolation in applications where the observations have an irregular support, such as runoff characteristics or population health data. Several studies have shown the potential of such methods (Gottschalk 1993, Sauquet et al. 2000, Gottschalk et al. 2006, Skøien et al. 2006, Goovaerts 2008), but these developments have so far not led to easily accessible, versatile, easy to apply and open source software. Based on the top-kriging approach suggested by Skøien et al. (2006), we will here present the package rtop, which has been implemented in the statistical environment R (R Core Team 2012). Taking advantage of the existing methods in R for analysis of spatial objects (Bivand et al. 2008), and the extensive possibilities for visualizing the results, rtop makes it easy to apply geostatistical interpolation methods when observations have a non-point spatial support. Although the package is flexible regarding data input, the main application so far has been for interpolation along river networks. We will present some examples showing how the package can easily be used for such interpolation. The model will soon be uploaded to CRAN, but is in the meantime also available from R-forge and can be installed by: > install.packages("rtop", repos="http://R-Forge.R-project.org") Bivand, R.S., Pebesma, E.J. & Gómez-Rubio, V., 2008. Applied spatial data analysis with r: Springer. Goovaerts, P., 2008. Kriging and semivariogram deconvolution in the presence of irregular geographical units. Mathematical Geosciences, 40 (1), 101-128. Gottschalk, L., 1993. Interpolation of runoff applying objective methods. Stochastic Hydrology and Hydraulics, 7, 269-281. Gottschalk, L., Krasovskaia, I., Leblois, E. & Sauquet, E., 2006. Mapping mean and variance of runoff in a river basin. Hydrology and Earth System Sciences, 10, 469-484. R Core Team, 2012. R: A language and environment for statistical computing. Vienna, Austria, ISBN 3-900051-07-0. Sauquet, E., Gottschalk, L. & Leblois, E., 2000. Mapping average annual runoff: A hierarchical approach applying a stochastic interpolation scheme. Hydrological Sciences Journal, 45 (6), 799-815. Skøien, J.O., Merz, R. & Blöschl, G., 2006. Top-kriging - geostatistics on stream networks. Hydrology and Earth System Sciences, 10, 277-287.

A MAP-based image interpolation method via Viterbi decoding of Markov chains of interpolation functions.

PubMed

Vedadi, Farhang; Shirani, Shahram

2014-01-01

A new method of image resolution up-conversion (image interpolation) based on maximum a posteriori sequence estimation is proposed. Instead of making a hard decision about the value of each missing pixel, we estimate the missing pixels in groups. At each missing pixel of the high resolution (HR) image, we consider an ensemble of candidate interpolation methods (interpolation functions). The interpolation functions are interpreted as states of a Markov model. In other words, the proposed method undergoes state transitions from one missing pixel position to the next. Accordingly, the interpolation problem is translated to the problem of estimating the optimal sequence of interpolation functions corresponding to the sequence of missing HR pixel positions. We derive a parameter-free probabilistic model for this to-be-estimated sequence of interpolation functions. Then, we solve the estimation problem using a trellis representation and the Viterbi algorithm. Using directional interpolation functions and sequence estimation techniques, we classify the new algorithm as an adaptive directional interpolation using soft-decision estimation techniques. Experimental results show that the proposed algorithm yields images with higher or comparable peak signal-to-noise ratios compared with some benchmark interpolation methods in the literature while being efficient in terms of implementation and complexity considerations.

A fully 3D approach for metal artifact reduction in computed tomography.

PubMed

Kratz, Barbel; Weyers, Imke; Buzug, Thorsten M

2012-11-01

In computed tomography imaging metal objects in the region of interest introduce inconsistencies during data acquisition. Reconstructing these data leads to an image in spatial domain including star-shaped or stripe-like artifacts. In order to enhance the quality of the resulting image the influence of the metal objects can be reduced. Here, a metal artifact reduction (MAR) approach is proposed that is based on a recomputation of the inconsistent projection data using a fully three-dimensional Fourier-based interpolation. The success of the projection space restoration depends sensitively on a sensible continuation of neighboring structures into the recomputed area. Fortunately, structural information of the entire data is inherently included in the Fourier space of the data. This can be used for a reasonable recomputation of the inconsistent projection data. The key step of the proposed MAR strategy is the recomputation of the inconsistent projection data based on an interpolation using nonequispaced fast Fourier transforms (NFFT). The NFFT interpolation can be applied in arbitrary dimension. The approach overcomes the problem of adequate neighborhood definitions on irregular grids, since this is inherently given through the usage of higher dimensional Fourier transforms. Here, applications up to the third interpolation dimension are presented and validated. Furthermore, prior knowledge may be included by an appropriate damping of the transform during the interpolation step. This MAR method is applicable on each angular view of a detector row, on two-dimensional projection data as well as on three-dimensional projection data, e.g., a set of sequential acquisitions at different spatial positions, projection data of a spiral acquisition, or cone-beam projection data. Results of the novel MAR scheme based on one-, two-, and three-dimensional NFFT interpolations are presented. All results are compared in projection data space and spatial domain with the well-known one-dimensional linear interpolation strategy. In conclusion, it is recommended to include as much spatial information into the recomputation step as possible. This is realized by increasing the dimension of the NFFT. The resulting image quality can be enhanced considerably.

Remote sensing, geographical information systems, and spatial modeling for analyzing public transit services

NASA Astrophysics Data System (ADS)

Wu, Changshan

Public transit service is a promising transportation mode because of its potential to address urban sustainability. Current ridership of public transit, however, is very low in most urban regions, particularly those in the United States. This woeful transit ridership can be attributed to many factors, among which poor service quality is key. Given this, there is a need for transit planning and analysis to improve service quality. Traditionally, spatially aggregate data are utilized in transit analysis and planning. Examples include data associated with the census, zip codes, states, etc. Few studies, however, address the influences of spatially aggregate data on transit planning results. In this research, previous studies in transit planning that use spatially aggregate data are reviewed. Next, problems associated with the utilization of aggregate data, the so-called modifiable areal unit problem (MAUP), are detailed and the need for fine resolution data to support public transit planning is argued. Fine resolution data is generated using intelligent interpolation techniques with the help of remote sensing imagery. In particular, impervious surface fraction, an important socio-economic indicator, is estimated through a fully constrained linear spectral mixture model using Landsat Enhanced Thematic Mapper Plus (ETM+) data within the metropolitan area of Columbus, Ohio in the United States. Four endmembers, low albedo, high albedo, vegetation, and soil are selected to model heterogeneous urban land cover. Impervious surface fraction is estimated by analyzing low and high albedo endmembers. With the derived impervious surface fraction, three spatial interpolation methods, spatial regression, dasymetric mapping, and cokriging, are developed to interpolate detailed population density. Results suggest that cokriging applied to impervious surface is a better alternative for estimating fine resolution population density. With the derived fine resolution data, a multiple route maximal covering/shortest path (MRMCSP) model is proposed to address the tradeoff between public transit service quality and access coverage in an established bus-based transit system. Results show that it is possible to improve current transit service quality by eliminating redundant or underutilized service stops. This research illustrates that fine resolution data can be efficiently generated to support urban planning, management and analysis. Further, this detailed data may necessitate the development of new spatial optimization models for use in analysis.

Assessment of spatial distribution of fallout radionuclides through geostatistics concept.

PubMed

Mabit, L; Bernard, C

2007-01-01

After introducing geostatistics concept and its utility in environmental science and especially in Fallout Radionuclide (FRN) spatialisation, a case study for cesium-137 ((137)Cs) redistribution at the field scale using geostatistics is presented. On a Canadian agricultural field, geostatistics coupled with a Geographic Information System (GIS) was used to test three different techniques of interpolation [Ordinary Kriging (OK), Inverse Distance Weighting power one (IDW1) and two (IDW2)] to create a (137)Cs map and to establish a radioisotope budget. Following the optimization of variographic parameters, an experimental semivariogram was developed to determine the spatial dependence of (137)Cs. It was adjusted to a spherical isotropic model with a range of 30 m and a very small nugget effect. This (137)Cs semivariogram showed a good autocorrelation (R(2)=0.91) and was well structured ('nugget-to-sill' ratio of 4%). It also revealed that the sampling strategy was adequate to reveal the spatial correlation of (137)Cs. The spatial redistribution of (137)Cs was estimated by Ordinary Kriging and IDW to produce contour maps. A radioisotope budget was established for the 2.16 ha agricultural field under investigation. It was estimated that around 2 x 10(7)Bq of (137)Cs were missing (around 30% of the total initial fallout) and were exported by physical processes (runoff and erosion processes) from the area under investigation. The cross-validation analysis showed that in the case of spatially structured data, OK is a better interpolation method than IDW1 or IDW2 for the assessment of potential radioactive contamination and/or pollution.

A Modified Kriging Method to Interpolate the Soil Moisture Measured by Wireless Sensor Network with the Aid of Remote Sensing Images

NASA Astrophysics Data System (ADS)

Zhang, J.; Liu, Q.; Li, X.; Niu, H.; Cai, E.

2015-12-01

In recent years, wireless sensor network (WSN) emerges to collect Earth observation data at relatively low cost and light labor load, while its observations are still point-data. To learn the spatial distribution of a land surface parameter, interpolating the point data is necessary. Taking soil moisture (SM) for example, its spatial distribution is critical information for agriculture management, hydrological and ecological researches. This study developed a method to interpolate the WSN-measured SM to acquire the spatial distribution in a 5km*5km study area, located in the middle reaches of HEIHE River, western China. As SM is related to many factors such as topology, soil type, vegetation and etc., even the WSN observation grid is not dense enough to reflect the SM distribution pattern. Our idea is to revise the traditional Kriging algorithm, introducing spectral variables, i.e., vegetation index (VI) and abledo, from satellite imagery as supplementary information to aid the interpolation. Thus, the new Extended-Kriging algorithm operates on the spatial & spectral combined space. To run the algorithm, first we need to estimate the SM variance function, which is also extended to the combined space. As the number of WSN samples in the study area is not enough to gather robust statistics, we have to assume that the SM variance function is invariant over time. So, the variance function is estimated from a SM map, derived from the airborne CASI/TASI images acquired in July 10, 2012, and then applied to interpolate WSN data in that season. Data analysis indicates that the new algorithm can provide more details to the variation of land SM. Then, the Leave-one-out cross-validation is adopted to estimate the interpolation accuracy. Although a reasonable accuracy can be achieved, the result is not yet satisfactory. Besides improving the algorithm, the uncertainties in WSN measurements may also need to be controlled in our further work.

A Comparison of Spatial Analysis Methods for the Construction of Topographic Maps of Retinal Cell Density

PubMed Central

Garza-Gisholt, Eduardo; Hemmi, Jan M.; Hart, Nathan S.; Collin, Shaun P.

2014-01-01

Topographic maps that illustrate variations in the density of different neuronal sub-types across the retina are valuable tools for understanding the adaptive significance of retinal specialisations in different species of vertebrates. To date, such maps have been created from raw count data that have been subjected to only limited analysis (linear interpolation) and, in many cases, have been presented as iso-density contour maps with contour lines that have been smoothed ‘by eye’. With the use of stereological approach to count neuronal distribution, a more rigorous approach to analysing the count data is warranted and potentially provides a more accurate representation of the neuron distribution pattern. Moreover, a formal spatial analysis of retinal topography permits a more robust comparison of topographic maps within and between species. In this paper, we present a new R-script for analysing the topography of retinal neurons and compare methods of interpolating and smoothing count data for the construction of topographic maps. We compare four methods for spatial analysis of cell count data: Akima interpolation, thin plate spline interpolation, thin plate spline smoothing and Gaussian kernel smoothing. The use of interpolation ‘respects’ the observed data and simply calculates the intermediate values required to create iso-density contour maps. Interpolation preserves more of the data but, consequently includes outliers, sampling errors and/or other experimental artefacts. In contrast, smoothing the data reduces the ‘noise’ caused by artefacts and permits a clearer representation of the dominant, ‘real’ distribution. This is particularly useful where cell density gradients are shallow and small variations in local density may dramatically influence the perceived spatial pattern of neuronal topography. The thin plate spline and the Gaussian kernel methods both produce similar retinal topography maps but the smoothing parameters used may affect the outcome. PMID:24747568

Morphing of spatial objects in real time with interpolation by functions of radial and orthogonal basis

NASA Astrophysics Data System (ADS)

Kosnikov, Yu N.; Kuzmin, A. V.; Ho, Hoang Thai

2018-05-01

The article is devoted to visualization of spatial objects’ morphing described by the set of unordered reference points. A two-stage model construction is proposed to change object’s form in real time. The first (preliminary) stage is interpolation of the object’s surface by radial basis functions. Initial reference points are replaced by new spatially ordered ones. Reference points’ coordinates change patterns during the process of morphing are assigned. The second (real time) stage is surface reconstruction by blending functions of orthogonal basis. Finite differences formulas are applied to increase the productivity of calculations.

Water Quality Sensing and Spatio-Temporal Monitoring Structure with Autocorrelation Kernel Methods.

PubMed

Vizcaíno, Iván P; Carrera, Enrique V; Muñoz-Romero, Sergio; Cumbal, Luis H; Rojo-Álvarez, José Luis

2017-10-16

Pollution on water resources is usually analyzed with monitoring campaigns, which consist of programmed sampling, measurement, and recording of the most representative water quality parameters. These campaign measurements yields a non-uniform spatio-temporal sampled data structure to characterize complex dynamics phenomena. In this work, we propose an enhanced statistical interpolation method to provide water quality managers with statistically interpolated representations of spatial-temporal dynamics. Specifically, our proposal makes efficient use of the a priori available information of the quality parameter measurements through Support Vector Regression (SVR) based on Mercer's kernels. The methods are benchmarked against previously proposed methods in three segments of the Machángara River and one segment of the San Pedro River in Ecuador, and their different dynamics are shown by statistically interpolated spatial-temporal maps. The best interpolation performance in terms of mean absolute error was the SVR with Mercer's kernel given by either the Mahalanobis spatial-temporal covariance matrix or by the bivariate estimated autocorrelation function. In particular, the autocorrelation kernel provides with significant improvement of the estimation quality, consistently for all the six water quality variables, which points out the relevance of including a priori knowledge of the problem.

Water Quality Sensing and Spatio-Temporal Monitoring Structure with Autocorrelation Kernel Methods

PubMed Central

Vizcaíno, Iván P.; Muñoz-Romero, Sergio; Cumbal, Luis H.

2017-01-01

Pollution on water resources is usually analyzed with monitoring campaigns, which consist of programmed sampling, measurement, and recording of the most representative water quality parameters. These campaign measurements yields a non-uniform spatio-temporal sampled data structure to characterize complex dynamics phenomena. In this work, we propose an enhanced statistical interpolation method to provide water quality managers with statistically interpolated representations of spatial-temporal dynamics. Specifically, our proposal makes efficient use of the a priori available information of the quality parameter measurements through Support Vector Regression (SVR) based on Mercer’s kernels. The methods are benchmarked against previously proposed methods in three segments of the Machángara River and one segment of the San Pedro River in Ecuador, and their different dynamics are shown by statistically interpolated spatial-temporal maps. The best interpolation performance in terms of mean absolute error was the SVR with Mercer’s kernel given by either the Mahalanobis spatial-temporal covariance matrix or by the bivariate estimated autocorrelation function. In particular, the autocorrelation kernel provides with significant improvement of the estimation quality, consistently for all the six water quality variables, which points out the relevance of including a priori knowledge of the problem. PMID:29035333

Spatiotemporal video deinterlacing using control grid interpolation

NASA Astrophysics Data System (ADS)

Venkatesan, Ragav; Zwart, Christine M.; Frakes, David H.; Li, Baoxin

2015-03-01

With the advent of progressive format display and broadcast technologies, video deinterlacing has become an important video-processing technique. Numerous approaches exist in the literature to accomplish deinterlacing. While most earlier methods were simple linear filtering-based approaches, the emergence of faster computing technologies and even dedicated video-processing hardware in display units has allowed higher quality but also more computationally intense deinterlacing algorithms to become practical. Most modern approaches analyze motion and content in video to select different deinterlacing methods for various spatiotemporal regions. We introduce a family of deinterlacers that employs spectral residue to choose between and weight control grid interpolation based spatial and temporal deinterlacing methods. The proposed approaches perform better than the prior state-of-the-art based on peak signal-to-noise ratio, other visual quality metrics, and simple perception-based subjective evaluations conducted by human viewers. We further study the advantages of using soft and hard decision thresholds on the visual performance.

Tensor-guided fitting of subduction slab depths

USGS Publications Warehouse

Bazargani, Farhad; Hayes, Gavin P.

2013-01-01

Geophysical measurements are often acquired at scattered locations in space. Therefore, interpolating or fitting the sparsely sampled data as a uniform function of space (a procedure commonly known as gridding) is a ubiquitous problem in geophysics. Most gridding methods require a model of spatial correlation for data. This spatial correlation model can often be inferred from some sort of secondary information, which may also be sparsely sampled in space. In this paper, we present a new method to model the geometry of a subducting slab in which we use a data‐fitting approach to address the problem. Earthquakes and active‐source seismic surveys provide estimates of depths of subducting slabs but only at scattered locations. In addition to estimates of depths from earthquake locations, focal mechanisms of subduction zone earthquakes also provide estimates of the strikes of the subducting slab on which they occur. We use these spatially sparse strike samples and the Earth’s curved surface geometry to infer a model for spatial correlation that guides a blended neighbor interpolation of slab depths. We then modify the interpolation method to account for the uncertainties associated with the depth estimates.

Application of spatial methods to identify areas with lime requirement in eastern Croatia

NASA Astrophysics Data System (ADS)

Bogunović, Igor; Kisic, Ivica; Mesic, Milan; Zgorelec, Zeljka; Percin, Aleksandra; Pereira, Paulo

2016-04-01

With more than 50% of acid soils in all agricultural land in Croatia, soil acidity is recognized as a big problem. Low soil pH leads to a series of negative phenomena in plant production and therefore as a compulsory measure for reclamation of acid soils is liming, recommended on the base of soil analysis. The need for liming is often erroneously determined only on the basis of the soil pH, because the determination of cation exchange capacity, the hydrolytic acidity and base saturation is a major cost to producers. Therefore, in Croatia, as well as some other countries, the amount of liming material needed to ameliorate acid soils is calculated by considering their hydrolytic acidity. For this research, several interpolation methods were tested to identify the best spatial predictor of hidrolitic acidity. The purpose of this study was to: test several interpolation methods to identify the best spatial predictor of hidrolitic acidity; and to determine the possibility of using multivariate geostatistics in order to reduce the number of needed samples for determination the hydrolytic acidity, all with an aim that the accuracy of the spatial distribution of liming requirement is not significantly reduced. Soil pH (in KCl) and hydrolytic acidity (Y1) is determined in the 1004 samples (from 0-30 cm) randomized collected in agricultural fields near Orahovica in eastern Croatia. This study tested 14 univariate interpolation models (part of ArcGIS software package) in order to provide most accurate spatial map of hydrolytic acidity on a base of: all samples (Y1 100%), and the datasets with 15% (Y1 85%), 30% (Y1 70%) and 50% fewer samples (Y1 50%). Parallel to univariate interpolation methods, the precision of the spatial distribution of the Y1 was tested by the co-kriging method with exchangeable acidity (pH in KCl) as a covariate. The soils at studied area had an average pH (KCl) 4,81, while the average Y1 10,52 cmol+ kg-1. These data suggest that liming is necessary agrotechnical measure for soil conditioning. The results show that ordinary kriging was most accurate univariate interpolation method with smallest error (RMSE) in all four data sets, while the least precise showed Radial Basis Functions (Thin Plate Spline and Inverse Multiquadratic). Furthermore, it is noticeable a trend of increasing errors (RMSE) with a reduced number of samples tested on the most accurate univariate interpolation model: 3,096 (Y1 100%), 3,258 (Y1 85%), 3,317 (Y1 70%), 3,546 (Y1 50%). The best-fit semivariograms show a strong spatial dependence in Y1 100% (Nugget/Sill 20.19) and Y1 85% (Nugget/Sill 23.83), while a further reduction of the number of samples resulted with moderate spatial dependence (Y1 70% -35,85% and Y1 50% - 32,01). Co-kriging method resulted in a reduction in RMSE compared with univariate interpolation methods for each data set with: 2,054, 1,731 and 1,734 for Y1 85%, Y1 70%, Y1 50%, respectively. The results show the possibility for reducing sampling costs by using co-kriging method which is useful from the practical viewpoint. Reduced number of samples by half for determination of hydrolytic acidity in the interaction with the soil pH provides a higher precision for variable liming compared to the univariate interpolation methods of the entire set of data. These data provide new opportunities to reduce costs in the practical plant production in Croatia.

Gradient-based interpolation method for division-of-focal-plane polarimeters.

PubMed

Gao, Shengkui; Gruev, Viktor

2013-01-14

Recent advancements in nanotechnology and nanofabrication have allowed for the emergence of the division-of-focal-plane (DoFP) polarization imaging sensors. These sensors capture polarization properties of the optical field at every imaging frame. However, the DoFP polarization imaging sensors suffer from large registration error as well as reduced spatial-resolution output. These drawbacks can be improved by applying proper image interpolation methods for the reconstruction of the polarization results. In this paper, we present a new gradient-based interpolation method for DoFP polarimeters. The performance of the proposed interpolation method is evaluated against several previously published interpolation methods by using visual examples and root mean square error (RMSE) comparison. We found that the proposed gradient-based interpolation method can achieve better visual results while maintaining a lower RMSE than other interpolation methods under various dynamic ranges of a scene ranging from dim to bright conditions.

Integrating Map Algebra and Statistical Modeling for Spatio- Temporal Analysis of Monthly Mean Daily Incident Photosynthetically Active Radiation (PAR) over a Complex Terrain.

PubMed

Evrendilek, Fatih

2007-12-12

This study aims at quantifying spatio-temporal dynamics of monthly mean dailyincident photosynthetically active radiation (PAR) over a vast and complex terrain such asTurkey. The spatial interpolation method of universal kriging, and the combination ofmultiple linear regression (MLR) models and map algebra techniques were implemented togenerate surface maps of PAR with a grid resolution of 500 x 500 m as a function of fivegeographical and 14 climatic variables. Performance of the geostatistical and MLR modelswas compared using mean prediction error (MPE), root-mean-square prediction error(RMSPE), average standard prediction error (ASE), mean standardized prediction error(MSPE), root-mean-square standardized prediction error (RMSSPE), and adjustedcoefficient of determination (R² adj. ). The best-fit MLR- and universal kriging-generatedmodels of monthly mean daily PAR were validated against an independent 37-year observeddataset of 35 climate stations derived from 160 stations across Turkey by the Jackknifingmethod. The spatial variability patterns of monthly mean daily incident PAR were moreaccurately reflected in the surface maps created by the MLR-based models than in thosecreated by the universal kriging method, in particular, for spring (May) and autumn(November). The MLR-based spatial interpolation algorithms of PAR described in thisstudy indicated the significance of the multifactor approach to understanding and mappingspatio-temporal dynamics of PAR for a complex terrain over meso-scales.

5-D interpolation with wave-front attributes

NASA Astrophysics Data System (ADS)

Xie, Yujiang; Gajewski, Dirk

2017-11-01

Most 5-D interpolation and regularization techniques reconstruct the missing data in the frequency domain by using mathematical transforms. An alternative type of interpolation methods uses wave-front attributes, that is, quantities with a specific physical meaning like the angle of emergence and wave-front curvatures. In these attributes structural information of subsurface features like dip and strike of a reflector are included. These wave-front attributes work on 5-D data space (e.g. common-midpoint coordinates in x and y, offset, azimuth and time), leading to a 5-D interpolation technique. Since the process is based on stacking next to the interpolation a pre-stack data enhancement is achieved, improving the signal-to-noise ratio (S/N) of interpolated and recorded traces. The wave-front attributes are determined in a data-driven fashion, for example, with the Common Reflection Surface (CRS method). As one of the wave-front-attribute-based interpolation techniques, the 3-D partial CRS method was proposed to enhance the quality of 3-D pre-stack data with low S/N. In the past work on 3-D partial stacks, two potential problems were still unsolved. For high-quality wave-front attributes, we suggest a global optimization strategy instead of the so far used pragmatic search approach. In previous works, the interpolation of 3-D data was performed along a specific azimuth which is acceptable for narrow azimuth acquisition but does not exploit the potential of wide-, rich- or full-azimuth acquisitions. The conventional 3-D partial CRS method is improved in this work and we call it as a wave-front-attribute-based 5-D interpolation (5-D WABI) as the two problems mentioned above are addressed. Data examples demonstrate the improved performance by the 5-D WABI method when compared with the conventional 3-D partial CRS approach. A comparison of the rank-reduction-based 5-D seismic interpolation technique with the proposed 5-D WABI method is given. The comparison reveals that there are significant advantages for steep dipping events using the 5-D WABI method when compared to the rank-reduction-based 5-D interpolation technique. Diffraction tails substantially benefit from this improved performance of the partial CRS stacking approach while the CPU time is comparable to the CPU time consumed by the rank-reduction-based method.

3-d interpolation in object perception: evidence from an objective performance paradigm.

PubMed

Kellman, Philip J; Garrigan, Patrick; Shipley, Thomas F; Yin, Carol; Machado, Liana

2005-06-01

Object perception requires interpolation processes that connect visible regions despite spatial gaps. Some research has suggested that interpolation may be a 3-D process, but objective performance data and evidence about the conditions leading to interpolation are needed. The authors developed an objective performance paradigm for testing 3-D interpolation and tested a new theory of 3-D contour interpolation, termed 3-D relatability. The theory indicates for a given edge which orientations and positions of other edges in space may be connected to it by interpolation. Results of 5 experiments showed that processing of orientation relations in 3-D relatable displays was superior to processing in 3-D nonrelatable displays and that these effects depended on object formation. 3-D interpolation and 3-D relatabilty are discussed in terms of their implications for computational and neural models of object perception, which have typically been based on 2-D-orientation-sensitive units. ((c) 2005 APA, all rights reserved).

GEMAS: Spatial pattern analysis of Ni by using digital image processing techniques on European agricultural soil data

NASA Astrophysics Data System (ADS)

Jordan, Gyozo; Petrik, Attila; De Vivo, Benedetto; Albanese, Stefano; Demetriades, Alecos; Sadeghi, Martiya

2017-04-01

Several studies have investigated the spatial distribution of chemical elements in topsoil (0-20 cm) within the framework of the EuroGeoSurveys Geochemistry Expert Group's 'Geochemical Mapping of Agricultural and Grazing Land Soil' project . Most of these studies used geostatistical analyses and interpolated concentration maps, Exploratory and Compositional Data and Analysis to identify anomalous patterns. The objective of our investigation is to demonstrate the use of digital image processing techniques for reproducible spatial pattern recognition and quantitative spatial feature characterisation. A single element (Ni) concentration in agricultural topsoil is used to perform the detailed spatial analysis, and to relate these features to possible underlying processes. In this study, simple univariate statistical methods were implemented first, and Tukey's inner-fence criterion was used to delineate statistical outliers. The linear and triangular irregular network (TIN) interpolation was used on the outlier-free Ni data points, which was resampled to a 10*10 km grid. Successive moving average smoothing was applied to generalise the TIN model and to suppress small- and at the same time enhance significant large-scale features of Nickel concentration spatial distribution patterns in European topsoil. The TIN map smoothed with a moving average filter revealed the spatial trends and patterns without losing much detail, and it was used as the input into digital image processing, such as local maxima and minima determination, digital cross sections, gradient magnitude and gradient direction calculation, second derivative profile curvature calculation, edge detection, local variability assessment, lineament density and directional variogram analyses. The detailed image processing analysis revealed several NE-SW, E-W and NW-SE oriented elongated features, which coincide with different spatial parameter classes and alignment with local maxima and minima. The NE-SW oriented linear pattern is the dominant feature to the south of the last glaciation limit. Some of these linear features are parallel to the suture zone of the Iapetus Ocean, while the others follow the Alpine and Carpathian Chains. The highest variability zones of Ni concentration in topsoil are located in the Alps and in the Balkans where mafic and ultramafic rocks outcrop. The predominant NE-SW oriented pattern is also captured by the strong anisotropy in the semi-variograms in this direction. A single major E-W oriented north-facing feature runs along the southern border of the last glaciation zone. This zone also coincides with a series of local maxima in Ni concentration along the glaciofluvial deposits. The NW-SE elongated spatial features are less dominant and are located in the Pyrenees and Scandinavia. This study demonstrates the efficiency of systematic image processing analysis in identifying and characterising spatial geochemical patterns that often remain uncovered by the usual visual map interpretation techniques.

Using geographical information systems and cartograms as a health service quality improvement tool.

PubMed

Lovett, Derryn A; Poots, Alan J; Clements, Jake T C; Green, Stuart A; Samarasundera, Edgar; Bell, Derek

2014-07-01

Disease prevalence can be spatially analysed to provide support for service implementation and health care planning, these analyses often display geographic variation. A key challenge is to communicate these results to decision makers, with variable levels of Geographic Information Systems (GIS) knowledge, in a way that represents the data and allows for comprehension. The present research describes the combination of established GIS methods and software tools to produce a novel technique of visualising disease admissions and to help prevent misinterpretation of data and less optimal decision making. The aim of this paper is to provide a tool that supports the ability of decision makers and service teams within health care settings to develop services more efficiently and better cater to the population; this tool has the advantage of information on the position of populations, the size of populations and the severity of disease. A standard choropleth of the study region, London, is used to visualise total emergency admission values for Chronic Obstructive Pulmonary Disease and bronchiectasis using ESRI's ArcGIS software. Population estimates of the Lower Super Output Areas (LSOAs) are then used with the ScapeToad cartogram software tool, with the aim of visualising geography at uniform population density. An interpolation surface, in this case ArcGIS' spline tool, allows the creation of a smooth surface over the LSOA centroids for admission values on both standard and cartogram geographies. The final product of this research is the novel Cartogram Interpolation Surface (CartIS). The method provides a series of outputs culminating in the CartIS, applying an interpolation surface to a uniform population density. The cartogram effectively equalises the population density to remove visual bias from areas with a smaller population, while maintaining contiguous borders. CartIS decreases the number of extreme positive values not present in the underlying data as can be found in interpolation surfaces. This methodology provides a technique for combining simple GIS tools to create a novel output, CartIS, in a health service context with the key aim of improving visualisation communication techniques which highlight variation in small scale geographies across large regions. CartIS more faithfully represents the data than interpolation, and visually highlights areas of extreme value more than cartograms, when either is used in isolation. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

Structural response of existing spatial truss roof construction based on Cosserat rod theory

NASA Astrophysics Data System (ADS)

Miśkiewicz, Mikołaj

2018-04-01

Paper presents the application of the Cosserat rod theory and newly developed associated finite elements code as the tools that support in the expert-designing engineering practice. Mechanical principles of the 3D spatially curved rods, dynamics (statics) laws, principle of virtual work are discussed. Corresponding FEM approach with interpolation and accumulation techniques of state variables are shown that enable the formulation of the C0 Lagrangian rod elements with 6-degrees of freedom per node. Two test examples are shown proving the correctness and suitability of the proposed formulation. Next, the developed FEM code is applied to assess the structural response of the spatial truss roof of the "Olivia" Sports Arena Gdansk, Poland. The numerical results are compared with load test results. It is shown that the proposed FEM approach yields correct results.

Spatial Interpolation of Rain-field Dynamic Time-Space Evolution in Hong Kong

NASA Astrophysics Data System (ADS)

Liu, P.; Tung, Y. K.

2017-12-01

Accurate and reliable measurement and prediction of spatial and temporal distribution of rain-field over a wide range of scales are important topics in hydrologic investigations. In this study, geostatistical treatment of precipitation field is adopted. To estimate the rainfall intensity over a study domain with the sample values and the spatial structure from the radar data, the cumulative distribution functions (CDFs) at all unsampled locations were estimated. Indicator Kriging (IK) was used to estimate the exceedance probabilities for different pre-selected cutoff levels and a procedure was implemented for interpolating CDF values between the thresholds that were derived from the IK. Different interpolation schemes of the CDF were proposed and their influences on the performance were also investigated. The performance measures and visual comparison between the observed rain-field and the IK-based estimation suggested that the proposed method can provide fine results of estimation of indicator variables and is capable of producing realistic image.

Combining geostatistics with Moran's I analysis for mapping soil heavy metals in Beijing, China.

PubMed

Huo, Xiao-Ni; Li, Hong; Sun, Dan-Feng; Zhou, Lian-Di; Li, Bao-Guo

2012-03-01

Production of high quality interpolation maps of heavy metals is important for risk assessment of environmental pollution. In this paper, the spatial correlation characteristics information obtained from Moran's I analysis was used to supplement the traditional geostatistics. According to Moran's I analysis, four characteristics distances were obtained and used as the active lag distance to calculate the semivariance. Validation of the optimality of semivariance demonstrated that using the two distances where the Moran's I and the standardized Moran's I, Z(I) reached a maximum as the active lag distance can improve the fitting accuracy of semivariance. Then, spatial interpolation was produced based on the two distances and their nested model. The comparative analysis of estimation accuracy and the measured and predicted pollution status showed that the method combining geostatistics with Moran's I analysis was better than traditional geostatistics. Thus, Moran's I analysis is a useful complement for geostatistics to improve the spatial interpolation accuracy of heavy metals.

Combining Geostatistics with Moran’s I Analysis for Mapping Soil Heavy Metals in Beijing, China

PubMed Central

Huo, Xiao-Ni; Li, Hong; Sun, Dan-Feng; Zhou, Lian-Di; Li, Bao-Guo

2012-01-01

Production of high quality interpolation maps of heavy metals is important for risk assessment of environmental pollution. In this paper, the spatial correlation characteristics information obtained from Moran’s I analysis was used to supplement the traditional geostatistics. According to Moran’s I analysis, four characteristics distances were obtained and used as the active lag distance to calculate the semivariance. Validation of the optimality of semivariance demonstrated that using the two distances where the Moran’s I and the standardized Moran’s I, Z(I) reached a maximum as the active lag distance can improve the fitting accuracy of semivariance. Then, spatial interpolation was produced based on the two distances and their nested model. The comparative analysis of estimation accuracy and the measured and predicted pollution status showed that the method combining geostatistics with Moran’s I analysis was better than traditional geostatistics. Thus, Moran’s I analysis is a useful complement for geostatistics to improve the spatial interpolation accuracy of heavy metals. PMID:22690179

Restoring method for missing data of spatial structural stress monitoring based on correlation

NASA Astrophysics Data System (ADS)

Zhang, Zeyu; Luo, Yaozhi

2017-07-01

Long-term monitoring of spatial structures is of great importance for the full understanding of their performance and safety. The missing part of the monitoring data link will affect the data analysis and safety assessment of the structure. Based on the long-term monitoring data of the steel structure of the Hangzhou Olympic Center Stadium, the correlation between the stress change of the measuring points is studied, and an interpolation method of the missing stress data is proposed. Stress data of correlated measuring points are selected in the 3 months of the season when missing data is required for fitting correlation. Data of daytime and nighttime are fitted separately for interpolation. For a simple linear regression when single point's correlation coefficient is 0.9 or more, the average error of interpolation is about 5%. For multiple linear regression, the interpolation accuracy is not significantly increased after the number of correlated points is more than 6. Stress baseline value of construction step should be calculated before interpolating missing data in the construction stage, and the average error is within 10%. The interpolation error of continuous missing data is slightly larger than that of the discrete missing data. The data missing rate of this method should better not exceed 30%. Finally, a measuring point's missing monitoring data is restored to verify the validity of the method.

Digital x-ray tomosynthesis with interpolated projection data for thin slab objects

NASA Astrophysics Data System (ADS)

Ha, S.; Yun, J.; Kim, H. K.

2017-11-01

In relation with a thin slab-object inspection, we propose a digital tomosynthesis reconstruction with fewer numbers of measured projections in combinations with additional virtual projections, which are produced by interpolating the measured projections. Hence we can reconstruct tomographic images with less few-view artifacts. The projection interpolation assumes that variations in cone-beam ray path-lengths through an object are negligible and the object is rigid. The interpolation is performed in the projection-space domain. Pixel values in the interpolated projection are the weighted sum of pixel values of the measured projections considering their projection angles. The experimental simulation shows that the proposed method can enhance the contrast-to-noise performance in reconstructed images while sacrificing the spatial resolving power.

[Spatial interpolation of soil organic matter using regression Kriging and geographically weighted regression Kriging].

PubMed

Yang, Shun-hua; Zhang, Hai-tao; Guo, Long; Ren, Yan

2015-06-01

Relative elevation and stream power index were selected as auxiliary variables based on correlation analysis for mapping soil organic matter. Geographically weighted regression Kriging (GWRK) and regression Kriging (RK) were used for spatial interpolation of soil organic matter and compared with ordinary Kriging (OK), which acts as a control. The results indicated that soil or- ganic matter was significantly positively correlated with relative elevation whilst it had a significantly negative correlation with stream power index. Semivariance analysis showed that both soil organic matter content and its residuals (including ordinary least square regression residual and GWR resi- dual) had strong spatial autocorrelation. Interpolation accuracies by different methods were esti- mated based on a data set of 98 validation samples. Results showed that the mean error (ME), mean absolute error (MAE) and root mean square error (RMSE) of RK were respectively 39.2%, 17.7% and 20.6% lower than the corresponding values of OK, with a relative-improvement (RI) of 20.63. GWRK showed a similar tendency, having its ME, MAE and RMSE to be respectively 60.6%, 23.7% and 27.6% lower than those of OK, with a RI of 59.79. Therefore, both RK and GWRK significantly improved the accuracy of OK interpolation of soil organic matter due to their in- corporation of auxiliary variables. In addition, GWRK performed obviously better than RK did in this study, and its improved performance should be attributed to the consideration of sample spatial locations.

Classical and neural methods of image sequence interpolation

NASA Astrophysics Data System (ADS)

Skoneczny, Slawomir; Szostakowski, Jaroslaw

2001-08-01

An image interpolation problem is often encountered in many areas. Some examples are interpolation for coding/decoding process for transmission purposes, reconstruction a full frame from two interlaced sub-frames in normal TV or HDTV, or reconstruction of missing frames in old destroyed cinematic sequences. In this paper an overview of interframe interpolation methods is presented. Both direct as well as motion compensated interpolation techniques are given by examples. The used methodology can also be either classical or based on neural networks depending on demand of a specific interpolation problem solving person.

Using rank-order geostatistics for spatial interpolation of highly skewed data in a heavy-metal contaminated site.

PubMed

Juang, K W; Lee, D Y; Ellsworth, T R

2001-01-01

The spatial distribution of a pollutant in contaminated soils is usually highly skewed. As a result, the sample variogram often differs considerably from its regional counterpart and the geostatistical interpolation is hindered. In this study, rank-order geostatistics with standardized rank transformation was used for the spatial interpolation of pollutants with a highly skewed distribution in contaminated soils when commonly used nonlinear methods, such as logarithmic and normal-scored transformations, are not suitable. A real data set of soil Cd concentrations with great variation and high skewness in a contaminated site of Taiwan was used for illustration. The spatial dependence of ranks transformed from Cd concentrations was identified and kriging estimation was readily performed in the standardized-rank space. The estimated standardized rank was back-transformed into the concentration space using the middle point model within a standardized-rank interval of the empirical distribution function (EDF). The spatial distribution of Cd concentrations was then obtained. The probability of Cd concentration being higher than a given cutoff value also can be estimated by using the estimated distribution of standardized ranks. The contour maps of Cd concentrations and the probabilities of Cd concentrations being higher than the cutoff value can be simultaneously used for delineation of hazardous areas of contaminated soils.

Fine-Scale Spatial Variability of Pedestrian-Level Particulate Matters in Compact Urban Commercial Districts in Hong Kong

PubMed Central

Ng, Edward

2017-01-01

Particulate matters (PM) at the pedestrian level significantly raises the health impacts in the compact urban environment of Hong Kong. A detailed investigation of the fine-scale spatial variation of pedestrian-level PM is necessary to assess the health risk to pedestrians in the outdoor environment. However, the collection of PM data is difficult in the compact urban environment of Hong Kong due to the limited amount of roadside monitoring stations and the complicated urban context. In this study, we measured the fine-scale spatial variability of the PM in three of the most representative commercial districts of Hong Kong using a backpack outdoor environmental measuring unit. Based on the measurement data, 13 types of geospatial interpolation methods were examined for the spatial mapping of PM2.5 and PM10 with a group of building geometrical covariates. Geostatistical modelling was adopted as the basis of spatial interpolation of the PM. The results show that the original cokriging with the exponential kernel function provides the best performance in the PM mapping. Using the fine-scale building geometrical features as covariates slightly improves the interpolation performance. The study results also imply that the fine-scale, localized pollution emission sources heavily influence pedestrian exposure to PM. PMID:28869527

Spectral interpolation - Zero fill or convolution. [image processing

NASA Technical Reports Server (NTRS)

Forman, M. L.

1977-01-01

Zero fill, or augmentation by zeros, is a method used in conjunction with fast Fourier transforms to obtain spectral spacing at intervals closer than obtainable from the original input data set. In the present paper, an interpolation technique (interpolation by repetitive convolution) is proposed which yields values accurate enough for plotting purposes and which lie within the limits of calibration accuracies. The technique is shown to operate faster than zero fill, since fewer operations are required. The major advantages of interpolation by repetitive convolution are that efficient use of memory is possible (thus avoiding the difficulties encountered in decimation in time FFTs) and that is is easy to implement.

Accuration of Time Series and Spatial Interpolation Method for Prediction of Precipitation Distribution on the Geographical Information System

NASA Astrophysics Data System (ADS)

Prasetyo, S. Y. J.; Hartomo, K. D.

2018-01-01

The Spatial Plan of the Province of Central Java 2009-2029 identifies that most regencies or cities in Central Java Province are very vulnerable to landslide disaster. The data are also supported by other data from Indonesian Disaster Risk Index (In Indonesia called Indeks Risiko Bencana Indonesia) 2013 that suggest that some areas in Central Java Province exhibit a high risk of natural disasters. This research aims to develop an application architecture and analysis methodology in GIS to predict and to map rainfall distribution. We propose our GIS architectural application of “Multiplatform Architectural Spatiotemporal” and data analysis methods of “Triple Exponential Smoothing” and “Spatial Interpolation” as our significant scientific contribution. This research consists of 2 (two) parts, namely attribute data prediction using TES method and spatial data prediction using Inverse Distance Weight (IDW) method. We conduct our research in 19 subdistricts in the Boyolali Regency, Central Java Province, Indonesia. Our main research data is the biweekly rainfall data in 2000-2016 Climatology, Meteorology, and Geophysics Agency (In Indonesia called Badan Meteorologi, Klimatologi, dan Geofisika) of Central Java Province and Laboratory of Plant Disease Observations Region V Surakarta, Central Java. The application architecture and analytical methodology of “Multiplatform Architectural Spatiotemporal” and spatial data analysis methodology of “Triple Exponential Smoothing” and “Spatial Interpolation” can be developed as a GIS application framework of rainfall distribution for various applied fields. The comparison between the TES and IDW methods show that relative to time series prediction, spatial interpolation exhibit values that are approaching actual. Spatial interpolation is closer to actual data because computed values are the rainfall data of the nearest location or the neighbour of sample values. However, the IDW’s main weakness is that some area might exhibit the rainfall value of 0. The representation of 0 in the spatial interpolation is mainly caused by the absence of rainfall data in the nearest sample point or too far distance that produces smaller weight.

Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event

NASA Astrophysics Data System (ADS)

Haberlandt, Uwe

2007-01-01

SummaryThe methods kriging with external drift (KED) and indicator kriging with external drift (IKED) are used for the spatial interpolation of hourly rainfall from rain gauges using additional information from radar, daily precipitation of a denser network, and elevation. The techniques are illustrated using data from the storm period of the 10th to the 13th of August 2002 that led to the extreme flood event in the Elbe river basin in Germany. Cross-validation is applied to compare the interpolation performance of the KED and IKED methods using different additional information with the univariate reference methods nearest neighbour (NN) or Thiessen polygons, inverse square distance weighting (IDW), ordinary kriging (OK) and ordinary indicator kriging (IK). Special attention is given to the analysis of the impact of the semivariogram estimation on the interpolation performance. Hourly and average semivariograms are inferred from daily, hourly and radar data considering either isotropic or anisotropic behaviour using automatic and manual fitting procedures. The multivariate methods KED and IKED clearly outperform the univariate ones with the most important additional information being radar, followed by precipitation from the daily network and elevation, which plays only a secondary role here. The best performance is achieved when all additional information are used simultaneously with KED. The indicator-based kriging methods provide, in some cases, smaller root mean square errors than the methods, which use the original data, but at the expense of a significant loss of variance. The impact of the semivariogram on interpolation performance is not very high. The best results are obtained using an automatic fitting procedure with isotropic variograms either from hourly or radar data.

A rational interpolation method to compute frequency response

NASA Technical Reports Server (NTRS)

Kenney, Charles; Stubberud, Stephen; Laub, Alan J.

1993-01-01

A rational interpolation method for approximating a frequency response is presented. The method is based on a product formulation of finite differences, thereby avoiding the numerical problems incurred by near-equal-valued subtraction. Also, resonant pole and zero cancellation schemes are developed that increase the accuracy and efficiency of the interpolation method. Selection techniques of interpolation points are also discussed.

Objective Interpolation of Scatterometer Winds

NASA Technical Reports Server (NTRS)

Tang, Wenquing; Liu, W. Timothy

1996-01-01

Global wind fields are produced by successive corrections that use measurements by the European Remote Sensing Satellite (ERS-1) scatterometer. The methodology is described. The wind fields at 10-meter height provided by the European Center for Medium-Range Weather Forecasting (ECMWF) are used to initialize the interpolation process. The interpolated wind field product ERSI is evaluated in terms of its improvement over the initial guess field (ECMWF) and the bin-averaged ERS-1 wind field (ERSB). Spatial and temporal differences between ERSI, ECMWF and ERSB are presented and discussed.

Exploring the Role of Genetic Algorithms and Artificial Neural Networks for Interpolation of Elevation in Geoinformation Models

NASA Astrophysics Data System (ADS)

Bagheri, H.; Sadjadi, S. Y.; Sadeghian, S.

2013-09-01

One of the most significant tools to study many engineering projects is three-dimensional modelling of the Earth that has many applications in the Geospatial Information System (GIS), e.g. creating Digital Train Modelling (DTM). DTM has numerous applications in the fields of sciences, engineering, design and various project administrations. One of the most significant events in DTM technique is the interpolation of elevation to create a continuous surface. There are several methods for interpolation, which have shown many results due to the environmental conditions and input data. The usual methods of interpolation used in this study along with Genetic Algorithms (GA) have been optimised and consisting of polynomials and the Inverse Distance Weighting (IDW) method. In this paper, the Artificial Intelligent (AI) techniques such as GA and Neural Networks (NN) are used on the samples to optimise the interpolation methods and production of Digital Elevation Model (DEM). The aim of entire interpolation methods is to evaluate the accuracy of interpolation methods. Universal interpolation occurs in the entire neighbouring regions can be suggested for larger regions, which can be divided into smaller regions. The results obtained from applying GA and ANN individually, will be compared with the typical method of interpolation for creation of elevations. The resulting had performed that AI methods have a high potential in the interpolation of elevations. Using artificial networks algorithms for the interpolation and optimisation based on the IDW method with GA could be estimated the high precise elevations.

Comparison of geostatistical interpolation and remote sensing techniques for estimating long-term exposure to ambient PM2.5 concentrations across the continental United States.

PubMed

Lee, Seung-Jae; Serre, Marc L; van Donkelaar, Aaron; Martin, Randall V; Burnett, Richard T; Jerrett, Michael

2012-12-01

A better understanding of the adverse health effects of chronic exposure to fine particulate matter (PM2.5) requires accurate estimates of PM2.5 variation at fine spatial scales. Remote sensing has emerged as an important means of estimating PM2.5 exposures, but relatively few studies have compared remote-sensing estimates to those derived from monitor-based data. We evaluated and compared the predictive capabilities of remote sensing and geostatistical interpolation. We developed a space-time geostatistical kriging model to predict PM2.5 over the continental United States and compared resulting predictions to estimates derived from satellite retrievals. The kriging estimate was more accurate for locations that were about 100 km from a monitoring station, whereas the remote sensing estimate was more accurate for locations that were > 100 km from a monitoring station. Based on this finding, we developed a hybrid map that combines the kriging and satellite-based PM2.5 estimates. We found that for most of the populated areas of the continental United States, geostatistical interpolation produced more accurate estimates than remote sensing. The differences between the estimates resulting from the two methods, however, were relatively small. In areas with extensive monitoring networks, the interpolation may provide more accurate estimates, but in the many areas of the world without such monitoring, remote sensing can provide useful exposure estimates that perform nearly as well.

Mapping snow depth return levels: smooth spatial modeling versus station interpolation

NASA Astrophysics Data System (ADS)

Blanchet, J.; Lehning, M.

2010-12-01

For adequate risk management in mountainous countries, hazard maps for extreme snow events are needed. This requires the computation of spatial estimates of return levels. In this article we use recent developments in extreme value theory and compare two main approaches for mapping snow depth return levels from in situ measurements. The first one is based on the spatial interpolation of pointwise extremal distributions (the so-called Generalized Extreme Value distribution, GEV henceforth) computed at station locations. The second one is new and based on the direct estimation of a spatially smooth GEV distribution with the joint use of all stations. We compare and validate the different approaches for modeling annual maximum snow depth measured at 100 sites in Switzerland during winters 1965-1966 to 2007-2008. The results show a better performance of the smooth GEV distribution fitting, in particular where the station network is sparser. Smooth return level maps can be computed from the fitted model without any further interpolation. Their regional variability can be revealed by removing the altitudinal dependent covariates in the model. We show how return levels and their regional variability are linked to the main climatological patterns of Switzerland.

The Use of Geostatistics in the Study of Floral Phenology of Vulpia geniculata (L.) Link

PubMed Central

León Ruiz, Eduardo J.; García Mozo, Herminia; Domínguez Vilches, Eugenio; Galán, Carmen

2012-01-01

Traditionally phenology studies have been focused on changes through time, but there exist many instances in ecological research where it is necessary to interpolate among spatially stratified samples. The combined use of Geographical Information Systems (GIS) and Geostatistics can be an essential tool for spatial analysis in phenological studies. Geostatistics are a family of statistics that describe correlations through space/time and they can be used for both quantifying spatial correlation and interpolating unsampled points. In the present work, estimations based upon Geostatistics and GIS mapping have enabled the construction of spatial models that reflect phenological evolution of Vulpia geniculata (L.) Link throughout the study area during sampling season. Ten sampling points, scattered troughout the city and low mountains in the “Sierra de Córdoba” were chosen to carry out the weekly phenological monitoring during flowering season. The phenological data were interpolated by applying the traditional geostatitical method of Kriging, which was used to ellaborate weekly estimations of V. geniculata phenology in unsampled areas. Finally, the application of Geostatistics and GIS to create phenological maps could be an essential complement in pollen aerobiological studies, given the increased interest in obtaining automatic aerobiological forecasting maps. PMID:22629169

The use of geostatistics in the study of floral phenology of Vulpia geniculata (L.) link.

PubMed

León Ruiz, Eduardo J; García Mozo, Herminia; Domínguez Vilches, Eugenio; Galán, Carmen

2012-01-01

Traditionally phenology studies have been focused on changes through time, but there exist many instances in ecological research where it is necessary to interpolate among spatially stratified samples. The combined use of Geographical Information Systems (GIS) and Geostatistics can be an essential tool for spatial analysis in phenological studies. Geostatistics are a family of statistics that describe correlations through space/time and they can be used for both quantifying spatial correlation and interpolating unsampled points. In the present work, estimations based upon Geostatistics and GIS mapping have enabled the construction of spatial models that reflect phenological evolution of Vulpia geniculata (L.) Link throughout the study area during sampling season. Ten sampling points, scattered throughout the city and low mountains in the "Sierra de Córdoba" were chosen to carry out the weekly phenological monitoring during flowering season. The phenological data were interpolated by applying the traditional geostatitical method of Kriging, which was used to elaborate weekly estimations of V. geniculata phenology in unsampled areas. Finally, the application of Geostatistics and GIS to create phenological maps could be an essential complement in pollen aerobiological studies, given the increased interest in obtaining automatic aerobiological forecasting maps.

Interpolation between spatial frameworks: an application of process convolution to estimating neighbourhood disease prevalence.

PubMed

Congdon, Peter

2014-04-01

Health data may be collected across one spatial framework (e.g. health provider agencies), but contrasts in health over another spatial framework (neighbourhoods) may be of policy interest. In the UK, population prevalence totals for chronic diseases are provided for populations served by general practitioner practices, but not for neighbourhoods (small areas of circa 1500 people), raising the question whether data for one framework can be used to provide spatially interpolated estimates of disease prevalence for the other. A discrete process convolution is applied to this end and has advantages when there are a relatively large number of area units in one or other framework. Additionally, the interpolation is modified to take account of the observed neighbourhood indicators (e.g. hospitalisation rates) of neighbourhood disease prevalence. These are reflective indicators of neighbourhood prevalence viewed as a latent construct. An illustrative application is to prevalence of psychosis in northeast London, containing 190 general practitioner practices and 562 neighbourhoods, including an assessment of sensitivity to kernel choice (e.g. normal vs exponential). This application illustrates how a zero-inflated Poisson can be used as the likelihood model for a reflective indicator.

Correlation of gravestone decay and air quality 1960-2010

NASA Astrophysics Data System (ADS)

Mooers, H. D.; Carlson, M. J.; Harrison, R. M.; Inkpen, R. J.; Loeffler, S.

2017-03-01

Evaluation of spatial and temporal variability in surface recession of lead-lettered Carrara marble gravestones provides a quantitative measure of acid flux to the stone surfaces and is closely related to local land use and air quality. Correlation of stone decay, land use, and air quality for the period after 1960 when reliable estimates of atmospheric pollution are available is evaluated. Gravestone decay and SO2 measurements are interpolated spatially using deterministic and geostatistical techniques. A general lack of spatial correlation was identified and therefore a land-use-based technique for correlation of stone decay and air quality is employed. Decadally averaged stone decay is highly correlated with land use averaged spatially over an optimum radius of ≈7 km even though air quality, determined by records from the UK monitoring network, is not highly correlated with gravestone decay. The relationships among stone decay, air-quality, and land use is complicated by the relatively low spatial density of both gravestone decay and air quality data and the fact that air quality data is available only as annual averages and therefore seasonal dependence cannot be evaluated. However, acid deposition calculated from gravestone decay suggests that the deposition efficiency of SO2 has increased appreciably since 1980 indicating an increase in the SO2 oxidation process possibly related to reactions with ammonia.

Visual distraction and visuo-spatial memory: a sandwich effect.

PubMed

Tremblay, Sébastien; Nicholls, Alastair P; Parmentier, Fabrice B R; Jones, Dylan M

2005-01-01

The functional characteristics of visuo-spatial serial memory and its sensitivity to irrelevant visual information are examined in the present study, through the investigation of the sandwich effect (e.g., Hitch, 1975). The memory task was one of serial recall for the position of a sequence of seven spatially and temporally separated dots. The presence of irrelevant dots interpolated with to-be-remembered dots affected performance over most serial positions (Experiment 1) but that effect was significantly reduced when the interpolated dots were distinct from the to-be-remembered dots by colour and shape (Experiment 2). Parallels are made between verbal and spatial serial memory, and the reduction of the sandwich effect is discussed in terms of the contribution of perceptual organisation and attentional factors in short-term memory.

An Extended Kriging Method to Interpolate Near-Surface Soil Moisture Data Measured by Wireless Sensor Networks

PubMed Central

Zhang, Jialin; Li, Xiuhong; Yang, Rongjin; Liu, Qiang; Zhao, Long; Dou, Baocheng

2017-01-01

In the practice of interpolating near-surface soil moisture measured by a wireless sensor network (WSN) grid, traditional Kriging methods with auxiliary variables, such as Co-kriging and Kriging with external drift (KED), cannot achieve satisfactory results because of the heterogeneity of soil moisture and its low correlation with the auxiliary variables. This study developed an Extended Kriging method to interpolate with the aid of remote sensing images. The underlying idea is to extend the traditional Kriging by introducing spectral variables, and operating on spatial and spectral combined space. The algorithm has been applied to WSN-measured soil moisture data in HiWATER campaign to generate daily maps from 10 June to 15 July 2012. For comparison, three traditional Kriging methods are applied: Ordinary Kriging (OK), which used WSN data only, Co-kriging and KED, both of which integrated remote sensing data as covariate. Visual inspections indicate that the result from Extended Kriging shows more spatial details than that of OK, Co-kriging, and KED. The Root Mean Square Error (RMSE) of Extended Kriging was found to be the smallest among the four interpolation results. This indicates that the proposed method has advantages in combining remote sensing information and ground measurements in soil moisture interpolation. PMID:28617351

Efficient Craig Interpolation for Linear Diophantine (Dis)Equations and Linear Modular Equations

DTIC Science & Technology

2008-02-01

Craig interpolants has enabled the development of powerful hardware and software model checking techniques. Efficient algorithms are known for computing...interpolants in rational and real linear arithmetic. We focus on subsets of integer linear arithmetic. Our main results are polynomial time algorithms ...congruences), and linear diophantine disequations. We show the utility of the proposed interpolation algorithms for discovering modular/divisibility predicates

A Comparative Study of Three Spatial Interpolation Methodologies for the Analysis of Air Pollution Concentrations in Athens, Greece

NASA Astrophysics Data System (ADS)

Deligiorgi, Despina; Philippopoulos, Kostas; Thanou, Lelouda; Karvounis, Georgios

2010-01-01

Spatial interpolation in air pollution modeling is the procedure for estimating ambient air pollution concentrations at unmonitored locations based on available observations. The selection of the appropriate methodology is based on the nature and the quality of the interpolated data. In this paper, an assessment of three widely used interpolation methodologies is undertaken in order to estimate the errors involved. For this purpose, air quality data from January 2001 to December 2005, from a network of seventeen monitoring stations, operating at the greater area of Athens in Greece, are used. The Nearest Neighbor and the Liner schemes were applied to the mean hourly observations, while the Inverse Distance Weighted (IDW) method to the mean monthly concentrations. The discrepancies of the estimated and measured values are assessed for every station and pollutant, using the correlation coefficient, the scatter diagrams and the statistical residuals. The capability of the methods to estimate air quality data in an area with multiple land-use types and pollution sources, such as Athens, is discussed.

Spatial Interpolation of Fine Particulate Matter Concentrations Using the Shortest Wind-Field Path Distance

PubMed Central

Li, Longxiang; Gong, Jianhua; Zhou, Jieping

2014-01-01

Effective assessments of air-pollution exposure depend on the ability to accurately predict pollutant concentrations at unmonitored locations, which can be achieved through spatial interpolation. However, most interpolation approaches currently in use are based on the Euclidean distance, which cannot account for the complex nonlinear features displayed by air-pollution distributions in the wind-field. In this study, an interpolation method based on the shortest path distance is developed to characterize the impact of complex urban wind-field on the distribution of the particulate matter concentration. In this method, the wind-field is incorporated by first interpolating the observed wind-field from a meteorological-station network, then using this continuous wind-field to construct a cost surface based on Gaussian dispersion model and calculating the shortest wind-field path distances between locations, and finally replacing the Euclidean distances typically used in Inverse Distance Weighting (IDW) with the shortest wind-field path distances. This proposed methodology is used to generate daily and hourly estimation surfaces for the particulate matter concentration in the urban area of Beijing in May 2013. This study demonstrates that wind-fields can be incorporated into an interpolation framework using the shortest wind-field path distance, which leads to a remarkable improvement in both the prediction accuracy and the visual reproduction of the wind-flow effect, both of which are of great importance for the assessment of the effects of pollutants on human health. PMID:24798197

Spatial interpolation of fine particulate matter concentrations using the shortest wind-field path distance.

PubMed

Li, Longxiang; Gong, Jianhua; Zhou, Jieping

2014-01-01

Effective assessments of air-pollution exposure depend on the ability to accurately predict pollutant concentrations at unmonitored locations, which can be achieved through spatial interpolation. However, most interpolation approaches currently in use are based on the Euclidean distance, which cannot account for the complex nonlinear features displayed by air-pollution distributions in the wind-field. In this study, an interpolation method based on the shortest path distance is developed to characterize the impact of complex urban wind-field on the distribution of the particulate matter concentration. In this method, the wind-field is incorporated by first interpolating the observed wind-field from a meteorological-station network, then using this continuous wind-field to construct a cost surface based on Gaussian dispersion model and calculating the shortest wind-field path distances between locations, and finally replacing the Euclidean distances typically used in Inverse Distance Weighting (IDW) with the shortest wind-field path distances. This proposed methodology is used to generate daily and hourly estimation surfaces for the particulate matter concentration in the urban area of Beijing in May 2013. This study demonstrates that wind-fields can be incorporated into an interpolation framework using the shortest wind-field path distance, which leads to a remarkable improvement in both the prediction accuracy and the visual reproduction of the wind-flow effect, both of which are of great importance for the assessment of the effects of pollutants on human health.

Clouds and the Earth's Radiant Energy System (CERES) algorithm theoretical basis document. volume 4; Determination of surface and atmosphere fluxes and temporally and spatially averaged products (subsystems 5-12); Determination of surface and atmosphere fluxes and temporally and spatially averaged products

NASA Technical Reports Server (NTRS)

Wielicki, Bruce A. (Principal Investigator); Barkstrom, Bruce R. (Principal Investigator); Baum, Bryan A.; Charlock, Thomas P.; Green, Richard N.; Lee, Robert B., III; Minnis, Patrick; Smith, G. Louis; Coakley, J. A.; Randall, David R.

1995-01-01

The theoretical bases for the Release 1 algorithms that will be used to process satellite data for investigation of the Clouds and the Earth's Radiant Energy System (CERES) are described. The architecture for software implementation of the methodologies is outlined. Volume 4 details the advanced CERES techniques for computing surface and atmospheric radiative fluxes (using the coincident CERES cloud property and top-of-the-atmosphere (TOA) flux products) and for averaging the cloud properties and TOA, atmospheric, and surface radiative fluxes over various temporal and spatial scales. CERES attempts to match the observed TOA fluxes with radiative transfer calculations that use as input the CERES cloud products and NOAA National Meteorological Center analyses of temperature and humidity. Slight adjustments in the cloud products are made to obtain agreement of the calculated and observed TOA fluxes. The computed products include shortwave and longwave fluxes from the surface to the TOA. The CERES instantaneous products are averaged on a 1.25-deg latitude-longitude grid, then interpolated to produce global, synoptic maps to TOA fluxes and cloud properties by using 3-hourly, normalized radiances from geostationary meteorological satellites. Surface and atmospheric fluxes are computed by using these interpolated quantities. Clear-sky and total fluxes and cloud properties are then averaged over various scales.

Evaluation of Fuzzy-Logic Framework for Spatial Statistics Preserving Methods for Estimation of Missing Precipitation Data

NASA Astrophysics Data System (ADS)

El Sharif, H.; Teegavarapu, R. S.

2012-12-01

Spatial interpolation methods used for estimation of missing precipitation data at a site seldom check for their ability to preserve site and regional statistics. Such statistics are primarily defined by spatial correlations and other site-to-site statistics in a region. Preservation of site and regional statistics represents a means of assessing the validity of missing precipitation estimates at a site. This study evaluates the efficacy of a fuzzy-logic methodology for infilling missing historical daily precipitation data in preserving site and regional statistics. Rain gauge sites in the state of Kentucky, USA, are used as a case study for evaluation of this newly proposed method in comparison to traditional data infilling techniques. Several error and performance measures will be used to evaluate the methods and trade-offs in accuracy of estimation and preservation of site and regional statistics.

Modeling of earthquake ground motion in the frequency domain

NASA Astrophysics Data System (ADS)

Thrainsson, Hjortur

In recent years, the utilization of time histories of earthquake ground motion has grown considerably in the design and analysis of civil structures. It is very unlikely, however, that recordings of earthquake ground motion will be available for all sites and conditions of interest. Hence, there is a need for efficient methods for the simulation and spatial interpolation of earthquake ground motion. In addition to providing estimates of the ground motion at a site using data from adjacent recording stations, spatially interpolated ground motions can also be used in design and analysis of long-span structures, such as bridges and pipelines, where differential movement is important. The objective of this research is to develop a methodology for rapid generation of horizontal earthquake ground motion at any site for a given region, based on readily available source, path and site characteristics, or (sparse) recordings. The research includes two main topics: (i) the simulation of earthquake ground motion at a given site, and (ii) the spatial interpolation of earthquake ground motion. In topic (i), models are developed to simulate acceleration time histories using the inverse discrete Fourier transform. The Fourier phase differences, defined as the difference in phase angle between adjacent frequency components, are simulated conditional on the Fourier amplitude. Uniformly processed recordings from recent California earthquakes are used to validate the simulation models, as well as to develop prediction formulas for the model parameters. The models developed in this research provide rapid simulation of earthquake ground motion over a wide range of magnitudes and distances, but they are not intended to replace more robust geophysical models. In topic (ii), a model is developed in which Fourier amplitudes and Fourier phase angles are interpolated separately. A simple dispersion relationship is included in the phase angle interpolation. The accuracy of the interpolation model is assessed using data from the SMART-1 array in Taiwan. The interpolation model provides an effective method to estimate ground motion at a site using recordings from stations located up to several kilometers away. Reliable estimates of differential ground motion are restricted to relatively limited ranges of frequencies and inter-station spacings.

Bayesian maximum entropy integration of ozone observations and model predictions: an application for attainment demonstration in North Carolina.

PubMed

de Nazelle, Audrey; Arunachalam, Saravanan; Serre, Marc L

2010-08-01

States in the USA are required to demonstrate future compliance of criteria air pollutant standards by using both air quality monitors and model outputs. In the case of ozone, the demonstration tests aim at relying heavily on measured values, due to their perceived objectivity and enforceable quality. Weight given to numerical models is diminished by integrating them in the calculations only in a relative sense. For unmonitored locations, the EPA has suggested the use of a spatial interpolation technique to assign current values. We demonstrate that this approach may lead to erroneous assignments of nonattainment and may make it difficult for States to establish future compliance. We propose a method that combines different sources of information to map air pollution, using the Bayesian Maximum Entropy (BME) Framework. The approach gives precedence to measured values and integrates modeled data as a function of model performance. We demonstrate this approach in North Carolina, using the State's ozone monitoring network in combination with outputs from the Multiscale Air Quality Simulation Platform (MAQSIP) modeling system. We show that the BME data integration approach, compared to a spatial interpolation of measured data, improves the accuracy and the precision of ozone estimations across the state.

An Improved DOA Estimation Approach Using Coarray Interpolation and Matrix Denoising

PubMed Central

Guo, Muran; Chen, Tao; Wang, Ben

2017-01-01

Co-prime arrays can estimate the directions of arrival (DOAs) of O(MN) sources with O(M+N) sensors, and are convenient to analyze due to their closed-form expression for the locations of virtual lags. However, the number of degrees of freedom is limited due to the existence of holes in difference coarrays if subspace-based algorithms such as the spatial smoothing multiple signal classification (MUSIC) algorithm are utilized. To address this issue, techniques such as positive definite Toeplitz completion and array interpolation have been proposed in the literature. Another factor that compromises the accuracy of DOA estimation is the limitation of the number of snapshots. Coarray-based processing is particularly sensitive to the discrepancy between the sample covariance matrix and the ideal covariance matrix due to the finite number of snapshots. In this paper, coarray interpolation based on matrix completion (MC) followed by a denoising operation is proposed to detect more sources with a higher accuracy. The effectiveness of the proposed method is based on the capability of MC to fill in holes in the virtual sensors and that of MC denoising operation to reduce the perturbation in the sample covariance matrix. The results of numerical simulations verify the superiority of the proposed approach. PMID:28509886

An Improved DOA Estimation Approach Using Coarray Interpolation and Matrix Denoising.

PubMed

Guo, Muran; Chen, Tao; Wang, Ben

2017-05-16

Co-prime arrays can estimate the directions of arrival (DOAs) of O ( M N ) sources with O ( M + N ) sensors, and are convenient to analyze due to their closed-form expression for the locations of virtual lags. However, the number of degrees of freedom is limited due to the existence of holes in difference coarrays if subspace-based algorithms such as the spatial smoothing multiple signal classification (MUSIC) algorithm are utilized. To address this issue, techniques such as positive definite Toeplitz completion and array interpolation have been proposed in the literature. Another factor that compromises the accuracy of DOA estimation is the limitation of the number of snapshots. Coarray-based processing is particularly sensitive to the discrepancy between the sample covariance matrix and the ideal covariance matrix due to the finite number of snapshots. In this paper, coarray interpolation based on matrix completion (MC) followed by a denoising operation is proposed to detect more sources with a higher accuracy. The effectiveness of the proposed method is based on the capability of MC to fill in holes in the virtual sensors and that of MC denoising operation to reduce the perturbation in the sample covariance matrix. The results of numerical simulations verify the superiority of the proposed approach.

Fourier-interpolation superresolution optical fluctuation imaging (fSOFi) (Conference Presentation)

NASA Astrophysics Data System (ADS)

Enderlein, Joerg; Stein, Simon C.; Huss, Anja; Hähnel, Dirk; Gregor, Ingo

2016-02-01

Stochastic Optical Fluctuation Imaging (SOFI) is a superresolution fluorescence microscopy technique which allows to enhance the spatial resolution of an image by evaluating the temporal fluctuations of blinking fluorescent emitters. SOFI is not based on the identification and localization of single molecules such as in the widely used Photoactivation Localization Microsopy (PALM) or Stochastic Optical Reconstruction Microscopy (STORM), but computes a superresolved image via temporal cumulants from a recorded movie. A technical challenge hereby is that, when directly applying the SOFI algorithm to a movie of raw images, the pixel size of the final SOFI image is the same as that of the original images, which becomes problematic when the final SOFI resolution is much smaller than this value. In the past, sophisticated cross-correlation schemes have been used for tackling this problem. Here, we present an alternative, exact, straightforward, and simple solution using an interpolation scheme based on Fourier transforms. We exemplify the method on simulated and experimental data.

[Kriging analysis of vegetation index depression in peak cluster karst area].

PubMed

Yang, Qi-Yong; Jiang, Zhong-Cheng; Ma, Zu-Lu; Cao, Jian-Hua; Luo, Wei-Qun; Li, Wen-Jun; Duan, Xiao-Fang

2012-04-01

In order to master the spatial variability of the normal different vegetation index (NDVI) of the peak cluster karst area, taking into account the problem of the mountain shadow "missing" information of remote sensing images existing in the karst area, NDVI of the non-shaded area were extracted in Guohua Ecological Experimental Area, in Pingguo County, Guangxi applying image processing software, ENVI. The spatial variability of NDVI was analyzed applying geostatistical method, and the NDVI of the mountain shadow areas was predicted and validated. The results indicated that the NDVI of the study area showed strong spatial variability and spatial autocorrelation resulting from the impact of intrinsic factors, and the range was 300 m. The spatial distribution maps of the NDVI interpolated by Kriging interpolation method showed that the mean of NDVI was 0.196, apparently strip and block. The higher NDVI values distributed in the area where the slope was greater than 25 degrees of the peak cluster area, while the lower values distributed in the area such as foot of the peak cluster and depression, where slope was less than 25 degrees. Kriging method validation results show that interpolation has a very high prediction accuracy and could predict the NDVI of the shadow area, which provides a new idea and method for monitoring and evaluation of the karst rocky desertification.

Edge directed image interpolation with Bamberger pyramids

NASA Astrophysics Data System (ADS)

Rosiles, Jose Gerardo

2005-08-01

Image interpolation is a standard feature in digital image editing software, digital camera systems and printers. Classical methods for resizing produce blurred images with unacceptable quality. Bamberger Pyramids and filter banks have been successfully used for texture and image analysis. They provide excellent multiresolution and directional selectivity. In this paper we present an edge-directed image interpolation algorithm which takes advantage of the simultaneous spatial-directional edge localization at the subband level. The proposed algorithm outperform classical schemes like bilinear and bicubic schemes from the visual and numerical point of views.

Enhancing GIS Capabilities for High Resolution Earth Science Grids

NASA Astrophysics Data System (ADS)

Koziol, B. W.; Oehmke, R.; Li, P.; O'Kuinghttons, R.; Theurich, G.; DeLuca, C.

2017-12-01

Applications for high performance GIS will continue to increase as Earth system models pursue more realistic representations of Earth system processes. Finer spatial resolution model input and output, unstructured or irregular modeling grids, data assimilation, and regional coordinate systems present novel challenges for GIS frameworks operating in the Earth system modeling domain. This presentation provides an overview of two GIS-driven applications that combine high performance software with big geospatial datasets to produce value-added tools for the modeling and geoscientific community. First, a large-scale interpolation experiment using National Hydrography Dataset (NHD) catchments, a high resolution rectilinear CONUS grid, and the Earth System Modeling Framework's (ESMF) conservative interpolation capability will be described. ESMF is a parallel, high-performance software toolkit that provides capabilities (e.g. interpolation) for building and coupling Earth science applications. ESMF is developed primarily by the NOAA Environmental Software Infrastructure and Interoperability (NESII) group. The purpose of this experiment was to test and demonstrate the utility of high performance scientific software in traditional GIS domains. Special attention will be paid to the nuanced requirements for dealing with high resolution, unstructured grids in scientific data formats. Second, a chunked interpolation application using ESMF and OpenClimateGIS (OCGIS) will demonstrate how spatial subsetting can virtually remove computing resource ceilings for very high spatial resolution interpolation operations. OCGIS is a NESII-developed Python software package designed for the geospatial manipulation of high-dimensional scientific datasets. An overview of the data processing workflow, why a chunked approach is required, and how the application could be adapted to meet operational requirements will be discussed here. In addition, we'll provide a general overview of OCGIS's parallel subsetting capabilities including challenges in the design and implementation of a scientific data subsetter.

Geostatistical interpolation of available copper in orchard soil as influenced by planting duration.

PubMed

Fu, Chuancheng; Zhang, Haibo; Tu, Chen; Li, Lianzhen; Luo, Yongming

2018-01-01

Mapping the spatial distribution of available copper (A-Cu) in orchard soils is important in agriculture and environmental management. However, data on the distribution of A-Cu in orchard soils is usually highly variable and severely skewed due to the continuous input of fungicides. In this study, ordinary kriging combined with planting duration (OK_PD) is proposed as a method for improving the interpolation of soil A-Cu. Four normal distribution transformation methods, namely, the Box-Cox, Johnson, rank order, and normal score methods, were utilized prior to interpolation. A total of 317 soil samples were collected in the orchards of the Northeast Jiaodong Peninsula. Moreover, 1472 orchards were investigated to obtain a map of planting duration using Voronoi tessellations. The soil A-Cu content ranged from 0.09 to 106.05 with a mean of 18.10 mg kg -1 , reflecting the high availability of Cu in the soils. Soil A-Cu concentrations exhibited a moderate spatial dependency and increased significantly with increasing planting duration. All the normal transformation methods successfully decreased the skewness and kurtosis of the soil A-Cu and the associated residuals, and also computed more robust variograms. OK_PD could generate better spatial prediction accuracy than ordinary kriging (OK) for all transformation methods tested, and it also provided a more detailed map of soil A-Cu. Normal score transformation produced satisfactory accuracy and showed an advantage in ameliorating smoothing effect derived from the interpolation methods. Thus, normal score transformation prior to kriging combined with planting duration (NSOK_PD) is recommended for the interpolation of soil A-Cu in this area.

Daily air temperature interpolated at high spatial resolution over a large mountainous region

USGS Publications Warehouse

Dodson, R.; Marks, D.

1997-01-01

Two methods are investigated for interpolating daily minimum and maximum air temperatures (Tmin and Tmax) at a 1 km spatial resolution over a large mountainous region (830 000 km2) in the U.S. Pacific Northwest. The methods were selected because of their ability to (1) account for the effect of elevation on temperature and (2) efficiently handle large volumes of data. The first method, the neutral stability algorithm (NSA), used the hydrostatic and potential temperature equations to convert measured temperatures and elevations to sea-level potential temperatures. The potential temperatures were spatially interpolated using an inverse-squared-distance algorithm and then mapped to the elevation surface of a digital elevation model (DEM). The second method, linear lapse rate adjustment (LLRA), involved the same basic procedure as the NSA, but used a constant linear lapse rate instead of the potential temperature equation. Cross-validation analyses were performed using the NSA and LLRA methods to interpolate Tmin and Tmax each day for the 1990 water year, and the methods were evaluated based on mean annual interpolation error (IE). The NSA method showed considerable bias for sites associated with vertical extrapolation. A correction based on climate station/grid cell elevation differences was developed and found to successfully remove the bias. The LLRA method was tested using 3 lapse rates, none of which produced a serious extrapolation bias. The bias-adjusted NSA and the 3 LLRA methods produced almost identical levels of accuracy (mean absolute errors between 1.2 and 1.3??C), and produced very similar temperature surfaces based on image difference statistics. In terms of accuracy, speed, and ease of implementation, LLRA was chosen as the best of the methods tested.

High Resolution Image Reconstruction from Projection of Low Resolution Images DIffering in Subpixel Shifts

NASA Technical Reports Server (NTRS)

Mareboyana, Manohar; Le Moigne-Stewart, Jacqueline; Bennett, Jerome

2016-01-01

In this paper, we demonstrate a simple algorithm that projects low resolution (LR) images differing in subpixel shifts on a high resolution (HR) also called super resolution (SR) grid. The algorithm is very effective in accuracy as well as time efficiency. A number of spatial interpolation techniques using nearest neighbor, inverse-distance weighted averages, Radial Basis Functions (RBF) etc. used in projection yield comparable results. For best accuracy of reconstructing SR image by a factor of two requires four LR images differing in four independent subpixel shifts. The algorithm has two steps: i) registration of low resolution images and (ii) shifting the low resolution images to align with reference image and projecting them on high resolution grid based on the shifts of each low resolution image using different interpolation techniques. Experiments are conducted by simulating low resolution images by subpixel shifts and subsampling of original high resolution image and the reconstructing the high resolution images from the simulated low resolution images. The results of accuracy of reconstruction are compared by using mean squared error measure between original high resolution image and reconstructed image. The algorithm was tested on remote sensing images and found to outperform previously proposed techniques such as Iterative Back Projection algorithm (IBP), Maximum Likelihood (ML), and Maximum a posterior (MAP) algorithms. The algorithm is robust and is not overly sensitive to the registration inaccuracies.

Assessment of water quality monitoring for the optimal sensor placement in lake Yahuarcocha using pattern recognition techniques and geographical information systems.

PubMed

Jácome, Gabriel; Valarezo, Carla; Yoo, Changkyoo

2018-03-30

Pollution and the eutrophication process are increasing in lake Yahuarcocha and constant water quality monitoring is essential for a better understanding of the patterns occurring in this ecosystem. In this study, key sensor locations were determined using spatial and temporal analyses combined with geographical information systems (GIS) to assess the influence of weather features, anthropogenic activities, and other non-point pollution sources. A water quality monitoring network was established to obtain data on 14 physicochemical and microbiological parameters at each of seven sample sites over a period of 13 months. A spatial and temporal statistical approach using pattern recognition techniques, such as cluster analysis (CA) and discriminant analysis (DA), was employed to classify and identify the most important water quality parameters in the lake. The original monitoring network was reduced to four optimal sensor locations based on a fuzzy overlay of the interpolations of concentration variations of the most important parameters.

Quantum interpolation for high-resolution sensing

PubMed Central

Ajoy, Ashok; Liu, Yi-Xiang; Saha, Kasturi; Marseglia, Luca; Jaskula, Jean-Christophe; Bissbort, Ulf; Cappellaro, Paola

2017-01-01

Recent advances in engineering and control of nanoscale quantum sensors have opened new paradigms in precision metrology. Unfortunately, hardware restrictions often limit the sensor performance. In nanoscale magnetic resonance probes, for instance, finite sampling times greatly limit the achievable sensitivity and spectral resolution. Here we introduce a technique for coherent quantum interpolation that can overcome these problems. Using a quantum sensor associated with the nitrogen vacancy center in diamond, we experimentally demonstrate that quantum interpolation can achieve spectroscopy of classical magnetic fields and individual quantum spins with orders of magnitude finer frequency resolution than conventionally possible. Not only is quantum interpolation an enabling technique to extract structural and chemical information from single biomolecules, but it can be directly applied to other quantum systems for superresolution quantum spectroscopy. PMID:28196889

Quantum interpolation for high-resolution sensing.

PubMed

Ajoy, Ashok; Liu, Yi-Xiang; Saha, Kasturi; Marseglia, Luca; Jaskula, Jean-Christophe; Bissbort, Ulf; Cappellaro, Paola

2017-02-28

Recent advances in engineering and control of nanoscale quantum sensors have opened new paradigms in precision metrology. Unfortunately, hardware restrictions often limit the sensor performance. In nanoscale magnetic resonance probes, for instance, finite sampling times greatly limit the achievable sensitivity and spectral resolution. Here we introduce a technique for coherent quantum interpolation that can overcome these problems. Using a quantum sensor associated with the nitrogen vacancy center in diamond, we experimentally demonstrate that quantum interpolation can achieve spectroscopy of classical magnetic fields and individual quantum spins with orders of magnitude finer frequency resolution than conventionally possible. Not only is quantum interpolation an enabling technique to extract structural and chemical information from single biomolecules, but it can be directly applied to other quantum systems for superresolution quantum spectroscopy.

Enhancement of low sampling frequency recordings for ECG biometric matching using interpolation.

PubMed

Sidek, Khairul Azami; Khalil, Ibrahim

2013-01-01

Electrocardiogram (ECG) based biometric matching suffers from high misclassification error with lower sampling frequency data. This situation may lead to an unreliable and vulnerable identity authentication process in high security applications. In this paper, quality enhancement techniques for ECG data with low sampling frequency has been proposed for person identification based on piecewise cubic Hermite interpolation (PCHIP) and piecewise cubic spline interpolation (SPLINE). A total of 70 ECG recordings from 4 different public ECG databases with 2 different sampling frequencies were applied for development and performance comparison purposes. An analytical method was used for feature extraction. The ECG recordings were segmented into two parts: the enrolment and recognition datasets. Three biometric matching methods, namely, Cross Correlation (CC), Percent Root-Mean-Square Deviation (PRD) and Wavelet Distance Measurement (WDM) were used for performance evaluation before and after applying interpolation techniques. Results of the experiments suggest that biometric matching with interpolated ECG data on average achieved higher matching percentage value of up to 4% for CC, 3% for PRD and 94% for WDM. These results are compared with the existing method when using ECG recordings with lower sampling frequency. Moreover, increasing the sample size from 56 to 70 subjects improves the results of the experiment by 4% for CC, 14.6% for PRD and 0.3% for WDM. Furthermore, higher classification accuracy of up to 99.1% for PCHIP and 99.2% for SPLINE with interpolated ECG data as compared of up to 97.2% without interpolation ECG data verifies the study claim that applying interpolation techniques enhances the quality of the ECG data. Crown Copyright © 2012. Published by Elsevier Ireland Ltd. All rights reserved.

GMI-IPS: Python Processing Software for Aircraft Campaigns

NASA Technical Reports Server (NTRS)

Damon, M. R.; Strode, S. A.; Steenrod, S. D.; Prather, M. J.

2018-01-01

NASA's Atmospheric Tomography Mission (ATom) seeks to understand the impact of anthropogenic air pollution on gases in the Earth's atmosphere. Four flight campaigns are being deployed on a seasonal basis to establish a continuous global-scale data set intended to improve the representation of chemically reactive gases in global atmospheric chemistry models. The Global Modeling Initiative (GMI), is creating chemical transport simulations on a global scale for each of the ATom flight campaigns. To meet the computational demands required to translate the GMI simulation data to grids associated with the flights from the ATom campaigns, the GMI ICARTT Processing Software (GMI-IPS) has been developed and is providing key functionality for data processing and analysis in this ongoing effort. The GMI-IPS is written in Python and provides computational kernels for data interpolation and visualization tasks on GMI simulation data. A key feature of the GMI-IPS, is its ability to read ICARTT files, a text-based file format for airborne instrument data, and extract the required flight information that defines regional and temporal grid parameters associated with an ATom flight. Perhaps most importantly, the GMI-IPS creates ICARTT files containing GMI simulated data, which are used in collaboration with ATom instrument teams and other modeling groups. The initial main task of the GMI-IPS is to interpolate GMI model data to the finer temporal resolution (1-10 seconds) of a given flight. The model data includes basic fields such as temperature and pressure, but the main focus of this effort is to provide species concentrations of chemical gases for ATom flights. The software, which uses parallel computation techniques for data intensive tasks, linearly interpolates each of the model fields to the time resolution of the flight. The temporally interpolated data is then saved to disk, and is used to create additional derived quantities. In order to translate the GMI model data to the spatial grid of the flight path as defined by the pressure, latitude, and longitude points at each flight time record, a weighted average is then calculated from the nearest neighbors in two dimensions (latitude, longitude). Using SciPya's Regular Grid Interpolator, interpolation functions are generated for the GMI model grid and the calculated weighted averages. The flight path points are then extracted from the ATom ICARTT instrument file, and are sent to the multi-dimensional interpolating functions to generate GMI field quantities along the spatial path of the flight. The interpolated field quantities are then written to a ICARTT data file, which is stored for further manipulation. The GMI-IPS is aware of a generic ATom ICARTT header format, containing basic information for all flight campaigns. The GMI-IPS includes logic to edit metadata for the derived field quantities, as well as modify the generic header data such as processing dates and associated instrument files. The ICARTT interpolated data is then appended to the modified header data, and the ICARTT processing is complete for the given flight and ready for collaboration. The output ICARTT data adheres to the ICARTT file format standards V1.1. The visualization component of the GMI-IPS uses Matplotlib extensively and has several functions ranging in complexity. First, it creates a model background curtain for the flight (time versus model eta levels) with the interpolated flight data superimposed on the curtain. Secondly, it creates a time-series plot of the interpolated flight data. Lastly, the visualization component creates averaged 2D model slices (longitude versus latitude) with overlaid flight track circles at key pressure levels. The GMI-IPS consists of a handful of classes and supporting functionality that have been generalized to be compatible with any ICARTT file that adheres to the base class definition. The base class represents a generic ICARTT entry, only defining a single time entry and 3D spatial positioning parameters. Other classes inherit from this base class; several classes for input ICARTT instrument files, which contain the necessary flight positioning information as a basis for data processing, as well as other classes for output ICARTT files, which contain the interpolated model data. Utility classes provide functionality for routine procedures such as: comparing field names among ICARTT files, reading ICARTT entries from a data file and storing them in data structures, and returning a reduced spatial grid based on a collection of ICARTT entries. Although the GMI-IPS is compatible with GMI model data, it can be adapted with reasonable effort for any simulation that creates Hierarchical Data Format (HDF) files. The same can be said of its adaptability to ICARTT files outside of the context of the ATom mission. The GMI-IPS contains just under 30,000 lines of code, eight classes, and a dozen drivers and utility programs. It is maintained with GIT source code management and has been used to deliver processed GMI model data for the ATom campaigns that have taken place to date.

Impact of geocoding methods on associations between long-term exposure to urban air pollution and lung function.

PubMed

Jacquemin, Bénédicte; Lepeule, Johanna; Boudier, Anne; Arnould, Caroline; Benmerad, Meriem; Chappaz, Claire; Ferran, Joane; Kauffmann, Francine; Morelli, Xavier; Pin, Isabelle; Pison, Christophe; Rios, Isabelle; Temam, Sofia; Künzli, Nino; Slama, Rémy; Siroux, Valérie

2013-09-01

Errors in address geocodes may affect estimates of the effects of air pollution on health. We investigated the impact of four geocoding techniques on the association between urban air pollution estimated with a fine-scale (10 m × 10 m) dispersion model and lung function in adults. We measured forced expiratory volume in 1 sec (FEV1) and forced vital capacity (FVC) in 354 adult residents of Grenoble, France, who were participants in two well-characterized studies, the Epidemiological Study on the Genetics and Environment on Asthma (EGEA) and the European Community Respiratory Health Survey (ECRHS). Home addresses were geocoded using individual building matching as the reference approach and three spatial interpolation approaches. We used a dispersion model to estimate mean PM10 and nitrogen dioxide concentrations at each participant's address during the 12 months preceding their lung function measurements. Associations between exposures and lung function parameters were adjusted for individual confounders and same-day exposure to air pollutants. The geocoding techniques were compared with regard to geographical distances between coordinates, exposure estimates, and associations between the estimated exposures and health effects. Median distances between coordinates estimated using the building matching and the three interpolation techniques were 26.4, 27.9, and 35.6 m. Compared with exposure estimates based on building matching, PM10 concentrations based on the three interpolation techniques tended to be overestimated. When building matching was used to estimate exposures, a one-interquartile range increase in PM10 (3.0 μg/m3) was associated with a 3.72-point decrease in FVC% predicted (95% CI: -0.56, -6.88) and a 3.86-point decrease in FEV1% predicted (95% CI: -0.14, -3.24). The magnitude of associations decreased when other geocoding approaches were used [e.g., for FVC% predicted -2.81 (95% CI: -0.26, -5.35) using NavTEQ, or 2.08 (95% CI -4.63, 0.47, p = 0.11) using Google Maps]. Our findings suggest that the choice of geocoding technique may influence estimated health effects when air pollution exposures are estimated using a fine-scale exposure model.

Interpolative modeling of GaAs FET S-parameter data bases for use in Monte Carlo simulations

NASA Technical Reports Server (NTRS)

Campbell, L.; Purviance, J.

1992-01-01

A statistical interpolation technique is presented for modeling GaAs FET S-parameter measurements for use in the statistical analysis and design of circuits. This is accomplished by interpolating among the measurements in a GaAs FET S-parameter data base in a statistically valid manner.

Space-time interpolation of satellite winds in the tropics

NASA Astrophysics Data System (ADS)

Patoux, Jérôme; Levy, Gad

2013-09-01

A space-time interpolator for creating average geophysical fields from satellite measurements is presented and tested. It is designed for optimal spatiotemporal averaging of heterogeneous data. While it is illustrated with satellite surface wind measurements in the tropics, the methodology can be useful for interpolating, analyzing, and merging a wide variety of heterogeneous and satellite data in the atmosphere and ocean over the entire globe. The spatial and temporal ranges of the interpolator are determined by averaging satellite and in situ measurements over increasingly larger space and time windows and matching the corresponding variability at each scale. This matching provides a relationship between temporal and spatial ranges, but does not provide a unique pair of ranges as a solution to all averaging problems. The pair of ranges most appropriate for a given application can be determined by performing a spectral analysis of the interpolated fields and choosing the smallest values that remove any or most of the aliasing due to the uneven sampling by the satellite. The methodology is illustrated with the computation of average divergence fields over the equatorial Pacific Ocean from SeaWinds-on-QuikSCAT surface wind measurements, for which 72 h and 510 km are suggested as optimal interpolation windows. It is found that the wind variability is reduced over the cold tongue and enhanced over the Pacific warm pool, consistent with the notion that the unstably stratified boundary layer has generally more variable winds and more gustiness than the stably stratified boundary layer. It is suggested that the spectral analysis optimization can be used for any process where time-space correspondence can be assumed.

Integrated GIS and multivariate statistical analysis for regional scale assessment of heavy metal soil contamination: A critical review.

PubMed

Hou, Deyi; O'Connor, David; Nathanail, Paul; Tian, Li; Ma, Yan

2017-12-01

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0-0.10 m, or 0-0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km 2 , with a median of 0.4 samples per km 2 . The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis (PCA) and cluster analysis (CA). Copyright © 2017 Elsevier Ltd. All rights reserved.

An efficient interpolation technique for jump proposals in reversible-jump Markov chain Monte Carlo calculations

PubMed Central

Farr, W. M.; Mandel, I.; Stevens, D.

2015-01-01

Selection among alternative theoretical models given an observed dataset is an important challenge in many areas of physics and astronomy. Reversible-jump Markov chain Monte Carlo (RJMCMC) is an extremely powerful technique for performing Bayesian model selection, but it suffers from a fundamental difficulty and it requires jumps between model parameter spaces, but cannot efficiently explore both parameter spaces at once. Thus, a naive jump between parameter spaces is unlikely to be accepted in the Markov chain Monte Carlo (MCMC) algorithm and convergence is correspondingly slow. Here, we demonstrate an interpolation technique that uses samples from single-model MCMCs to propose intermodel jumps from an approximation to the single-model posterior of the target parameter space. The interpolation technique, based on a kD-tree data structure, is adaptive and efficient in modest dimensionality. We show that our technique leads to improved convergence over naive jumps in an RJMCMC, and compare it to other proposals in the literature to improve the convergence of RJMCMCs. We also demonstrate the use of the same interpolation technique as a way to construct efficient ‘global’ proposal distributions for single-model MCMCs without prior knowledge of the structure of the posterior distribution, and discuss improvements that permit the method to be used in higher dimensional spaces efficiently. PMID:26543580

Simulation of Pellet Ablation

NASA Astrophysics Data System (ADS)

Parks, P. B.; Ishizaki, Ryuichi

2000-10-01

In order to clarify the structure of the ablation flow, 2D simulation is carried out with a fluid code solving temporal evolution of MHD equations. The code includes electrostatic sheath effect at the cloud interface.(P.B. Parks et al.), Plasma Phys. Contr. Fusion 38, 571 (1996). An Eulerian cylindrical coordinate system (r,z) is used with z in a spherical pellet. The code uses the Cubic-Interpolated Psudoparticle (CIP) method(H. Takewaki and T. Yabe, J. Comput. Phys. 70), 355 (1987). that divides the fluid equations into non-advection and advection phases. The most essential element of the CIP method is in calculation of the advection phase. In this phase, a cubic interpolated spatial profile is shifted in space according to the total derivative equations, similarly to a particle scheme. Since the profile is interpolated by using the value and the spatial derivative value at each grid point, there is no numerical oscillation in space, that often appears in conventional spline interpolation. A free boundary condition is used in the code. The possibility of a stationary shock will also be shown in the presentation because the supersonic ablation flow across the magnetic field is impeded.

Selection of Optimal Auxiliary Soil Nutrient Variables for Cokriging Interpolation

PubMed Central

Song, Genxin; Zhang, Jing; Wang, Ke

2014-01-01

In order to explore the selection of the best auxiliary variables (BAVs) when using the Cokriging method for soil attribute interpolation, this paper investigated the selection of BAVs from terrain parameters, soil trace elements, and soil nutrient attributes when applying Cokriging interpolation to soil nutrients (organic matter, total N, available P, and available K). In total, 670 soil samples were collected in Fuyang, and the nutrient and trace element attributes of the soil samples were determined. Based on the spatial autocorrelation of soil attributes, the Digital Elevation Model (DEM) data for Fuyang was combined to explore the coordinate relationship among terrain parameters, trace elements, and soil nutrient attributes. Variables with a high correlation to soil nutrient attributes were selected as BAVs for Cokriging interpolation of soil nutrients, and variables with poor correlation were selected as poor auxiliary variables (PAVs). The results of Cokriging interpolations using BAVs and PAVs were then compared. The results indicated that Cokriging interpolation with BAVs yielded more accurate results than Cokriging interpolation with PAVs (the mean absolute error of BAV interpolation results for organic matter, total N, available P, and available K were 0.020, 0.002, 7.616, and 12.4702, respectively, and the mean absolute error of PAV interpolation results were 0.052, 0.037, 15.619, and 0.037, respectively). The results indicated that Cokriging interpolation with BAVs can significantly improve the accuracy of Cokriging interpolation for soil nutrient attributes. This study provides meaningful guidance and reference for the selection of auxiliary parameters for the application of Cokriging interpolation to soil nutrient attributes. PMID:24927129

Short term spatio-temporal variability of soil water-extractable calcium and magnesium after a low severity grassland fire in Lithuania.

NASA Astrophysics Data System (ADS)

Pereira, Paulo; Martin, David

2014-05-01

Fire has important impacts on soil nutrient spatio-temporal distribution (Outeiro et al., 2008). This impact depends on fire severity, topography of the burned area, type of soil and vegetation affected, and the meteorological conditions post-fire. Fire produces a complex mosaic of impacts in soil that can be extremely variable at small plot scale in the space and time. In order to assess and map such a heterogeneous distribution, the test of interpolation methods is fundamental to identify the best estimator and to have a better understanding of soil nutrients spatial distribution. The objective of this work is to identify the short-term spatial variability of water-extractable calcium and magnesium after a low severity grassland fire. The studied area is located near Vilnius (Lithuania) at 54° 42' N, 25° 08 E, 158 masl. Four days after the fire, it was designed in a burned area a plot with 400 m2 (20 x 20 m with 5 m space between sampling points). Twenty five samples from top soil (0-5 cm) were collected immediately after the fire (IAF), 2, 5, 7 and 9 months after the fire (a total of 125 in all sampling dates). The original data of water-extractable calcium and magnesium did not respected the Gaussian distribution, thus a neperian logarithm (ln) was applied in order to normalize data. Significant differences of water-extractable calcium and magnesium among sampling dates were carried out with the Anova One-way test using the ln data. In order to assess the spatial variability of water-extractable calcium and magnesium, we tested several interpolation methods as Ordinary Kriging (OK), Inverse Distance to a Weight (IDW) with the power of 1, 2, 3 and 4, Radial Basis Functions (RBF) - Inverse Multiquadratic (IMT), Multilog (MTG), Multiquadratic (MTQ) Natural Cubic Spline (NCS) and Thin Plate Spline (TPS) - and Local Polynomial (LP) with the power of 1 and 2. Interpolation tests were carried out with Ln data. The best interpolation method was assessed using the cross validation method. Cross-validation was obtained by taking each observation in turn out of the sample pool and estimating from the remaining ones. The errors produced (observed-predicted) are used to evaluate the performance of each method. With these data, the mean error (ME) and root mean square error (RMSE) were calculated. The best method was the one which had the lower RMSE (Pereira et al. in press). The results shown significant differences among sampling dates in the water-extractable calcium (F= 138.78, p< 0.001) and extractable magnesium (F= 160.66; p< 0.001). Water-extractable calcium and magnesium was high IAF decreasing until 7 months after the fire, rising in the last sampling date. Among the tested methods, the most accurate to interpolate the water-extractable calcium were: IAF-IDW1; 2 Months-IDW1; 5 months-OK; 7 Months-IDW4 and 9 Months-IDW3. In relation to water-extractable magnesium the best interpolation techniques were: IAF-IDW2; 2 Months-IDW1; 5 months- IDW3; 7 Months-TPS and 9 Months-IDW1. These results suggested that the spatial variability of these water-extractable is variable with the time. The causes of this variability will be discussed during the presentation. References Outeiro, L., Aspero, F., Ubeda, X. (2008) Geostatistical methods to study spatial variability of soil cation after a prescribed fire and rainfall. Catena, 74: 310-320. Pereira, P., Cerdà, A., Úbeda, X., Mataix-Solera, J. Arcenegui, V., Zavala, L. Modelling the impacts of wildfire on ash thickness in a short-term period, Land Degradation and Development, (In Press), DOI: 10.1002/ldr.2195

Bayesian Tracking of Emerging Epidemics Using Ensemble Optimal Statistical Interpolation

PubMed Central

Cobb, Loren; Krishnamurthy, Ashok; Mandel, Jan; Beezley, Jonathan D.

2014-01-01

We present a preliminary test of the Ensemble Optimal Statistical Interpolation (EnOSI) method for the statistical tracking of an emerging epidemic, with a comparison to its popular relative for Bayesian data assimilation, the Ensemble Kalman Filter (EnKF). The spatial data for this test was generated by a spatial susceptible-infectious-removed (S-I-R) epidemic model of an airborne infectious disease. Both tracking methods in this test employed Poisson rather than Gaussian noise, so as to handle epidemic data more accurately. The EnOSI and EnKF tracking methods worked well on the main body of the simulated spatial epidemic, but the EnOSI was able to detect and track a distant secondary focus of infection that the EnKF missed entirely. PMID:25113590

Signal-to-noise ratio estimation on SEM images using cubic spline interpolation with Savitzky-Golay smoothing.

PubMed

Sim, K S; Kiani, M A; Nia, M E; Tso, C P

2014-01-01

A new technique based on cubic spline interpolation with Savitzky-Golay noise reduction filtering is designed to estimate signal-to-noise ratio of scanning electron microscopy (SEM) images. This approach is found to present better result when compared with two existing techniques: nearest neighbourhood and first-order interpolation. When applied to evaluate the quality of SEM images, noise can be eliminated efficiently with optimal choice of scan rate from real-time SEM images, without generating corruption or increasing scanning time. © 2013 The Authors Journal of Microscopy © 2013 Royal Microscopical Society.

Nonanalytic function generation routines for 16-bit microprocessors

NASA Technical Reports Server (NTRS)

Soeder, J. F.; Shaufl, M.

1980-01-01

Interpolation techniques for three types (univariate, bivariate, and map) of nonanalytic functions are described. These interpolation techniques are then implemented in scaled fraction arithmetic on a representative 16 bit microprocessor. A FORTRAN program is described that facilitates the scaling, documentation, and organization of data for use by these routines. Listings of all these programs are included in an appendix.

High-Resolution Spatial Distribution and Estimation of Access to Improved Sanitation in Kenya.

PubMed

Jia, Peng; Anderson, John D; Leitner, Michael; Rheingans, Richard

2016-01-01

Access to sanitation facilities is imperative in reducing the risk of multiple adverse health outcomes. A distinct disparity in sanitation exists among different wealth levels in many low-income countries, which may hinder the progress across each of the Millennium Development Goals. The surveyed households in 397 clusters from 2008-2009 Kenya Demographic and Health Surveys were divided into five wealth quintiles based on their national asset scores. A series of spatial analysis methods including excess risk, local spatial autocorrelation, and spatial interpolation were applied to observe disparities in coverage of improved sanitation among different wealth categories. The total number of the population with improved sanitation was estimated by interpolating, time-adjusting, and multiplying the surveyed coverage rates by high-resolution population grids. A comparison was then made with the annual estimates from United Nations Population Division and World Health Organization /United Nations Children's Fund Joint Monitoring Program for Water Supply and Sanitation. The Empirical Bayesian Kriging interpolation produced minimal root mean squared error for all clusters and five quintiles while predicting the raw and spatial coverage rates of improved sanitation. The coverage in southern regions was generally higher than in the north and east, and the coverage in the south decreased from Nairobi in all directions, while Nyanza and North Eastern Province had relatively poor coverage. The general clustering trend of high and low sanitation improvement among surveyed clusters was confirmed after spatial smoothing. There exists an apparent disparity in sanitation among different wealth categories across Kenya and spatially smoothed coverage rates resulted in a closer estimation of the available statistics than raw coverage rates. Future intervention activities need to be tailored for both different wealth categories and nationally where there are areas of greater needs when resources are limited.

Tracking the dispersion of Scaphoideus titanus Ball (Hemiptera: Cicadellidae) from wild to cultivated grapevine: use of a novel mark-capture technique.

PubMed

Lessio, F; Tota, F; Alma, A

2014-08-01

The dispersion of Scaphoideus titanus Ball adults from wild to cultivated grapevines was studied using a novel mark-capture technique. The crowns of wild grapevines located at a distance from vineyards ranging from 5 to 330 m were sprayed with a water solution of either cow milk (marker: casein) or chicken egg whites (marker: albumin) and insects captured in yellow sticky traps placed on the canopy of grapes were analyzed via an indirect ELISA for markers' identification. Data were subject to exponential regression as a function of distance from wild grapevine, and to spatial interpolation (Inverse Distance Weighted and Kernel interpolation with barriers) using ArcGIS Desktop 10.1 software. The influence of rainfall and time elapsed after marking on markers' effectiveness, and the different dispersion of males and females were studied with regression analyses. Of a total of 5417 insects analyzed, 43% were positive to egg; whereas 18% of 536 tested resulted marked with milk. No influence of rainfall or time elapsed was observed for egg, whereas milk was affected by time. Males and females showed no difference in dispersal. Marked adults decreased exponentially along with distance from wild grapevine and up to 80% of them were captured within 30 m. However, there was evidence of long-range dispersal up to 330 m. The interpolation maps showed a clear clustering of marked S. titanus close to the treated wild grapevine, and the pathways to the vineyards did not always seem to go along straight lines but mainly along ecological corridors. S. titanus adults are therefore capable of dispersing from wild to cultivated grapevine, and this may affect pest management strategies.

Spatial Estimation of Sub-Hour Global Horizontal Irradiance Based on Official Observations and Remote Sensors

PubMed Central

Gutierrez-Corea, Federico-Vladimir; Manso-Callejo, Miguel-Angel; Moreno-Regidor, María-Pilar; Velasco-Gómez, Jesús

2014-01-01

This study was motivated by the need to improve densification of Global Horizontal Irradiance (GHI) observations, increasing the number of surface weather stations that observe it, using sensors with a sub-hour periodicity and examining the methods of spatial GHI estimation (by interpolation) with that periodicity in other locations. The aim of the present research project is to analyze the goodness of 15-minute GHI spatial estimations for five methods in the territory of Spain (three geo-statistical interpolation methods, one deterministic method and the HelioSat2 method, which is based on satellite images). The research concludes that, when the work area has adequate station density, the best method for estimating GHI every 15 min is Regression Kriging interpolation using GHI estimated from satellite images as one of the input variables. On the contrary, when station density is low, the best method is estimating GHI directly from satellite images. A comparison between the GHI observed by volunteer stations and the estimation model applied concludes that 67% of the volunteer stations analyzed present values within the margin of error (average of ±2 standard deviations). PMID:24732102

Spatial estimation of sub-hour Global Horizontal Irradiance based on official observations and remote sensors.

PubMed

Gutierrez-Corea, Federico-Vladimir; Manso-Callejo, Miguel-Angel; Moreno-Regidor, María-Pilar; Velasco-Gómez, Jesús

2014-04-11

This study was motivated by the need to improve densification of Global Horizontal Irradiance (GHI) observations, increasing the number of surface weather stations that observe it, using sensors with a sub-hour periodicity and examining the methods of spatial GHI estimation (by interpolation) with that periodicity in other locations. The aim of the present research project is to analyze the goodness of 15-minute GHI spatial estimations for five methods in the territory of Spain (three geo-statistical interpolation methods, one deterministic method and the HelioSat2 method, which is based on satellite images). The research concludes that, when the work area has adequate station density, the best method for estimating GHI every 15 min is Regression Kriging interpolation using GHI estimated from satellite images as one of the input variables. On the contrary, when station density is low, the best method is estimating GHI directly from satellite images. A comparison between the GHI observed by volunteer stations and the estimation model applied concludes that 67% of the volunteer stations analyzed present values within the margin of error (average of ±2 standard deviations).

Spatial interpolation and simulation of post-burn duff thickness after prescribed fire

Treesearch

Peter R. Robichaud; S. M. Miller

1999-01-01

Prescribed fire is used as a site treatment after timber harvesting. These fires result in spatial patterns with some portions consuming all of the forest floor material (duff) and others consuming little. Prior to the burn, spatial sampling of duff thickness and duff water content can be used to generate geostatistical spatial simulations of these characteristics....

Optimal design of the first stage of the plate-fin heat exchanger for the EAST cryogenic system

NASA Astrophysics Data System (ADS)

Qingfeng, JIANG; Zhigang, ZHU; Qiyong, ZHANG; Ming, ZHUANG; Xiaofei, LU

2018-03-01

The size of the heat exchanger is an important factor determining the dimensions of the cold box in helium cryogenic systems. In this paper, a counter-flow multi-stream plate-fin heat exchanger is optimized by means of a spatial interpolation method coupled with a hybrid genetic algorithm. Compared with empirical correlations, this spatial interpolation algorithm based on a kriging model can be adopted to more precisely predict the Colburn heat transfer factors and Fanning friction factors of offset-strip fins. Moreover, strict computational fluid dynamics simulations can be carried out to predict the heat transfer and friction performance in the absence of reliable experimental data. Within the constraints of heat exchange requirements, maximum allowable pressure drop, existing manufacturing techniques and structural strength, a mathematical model of an optimized design with discrete and continuous variables based on a hybrid genetic algorithm is established in order to minimize the volume. The results show that for the first-stage heat exchanger in the EAST refrigerator, the structural size could be decreased from the original 2.200 × 0.600 × 0.627 (m3) to the optimized 1.854 × 0.420 × 0.340 (m3), with a large reduction in volume. The current work demonstrates that the proposed method could be a useful tool to achieve optimization in an actual engineering project during the practical design process.

Evaluation of statistical and rainfall-runoff models for predicting historical daily streamflow time series in the Des Moines and Iowa River watersheds

USGS Publications Warehouse

Farmer, William H.; Knight, Rodney R.; Eash, David A.; Kasey J. Hutchinson,; Linhart, S. Mike; Christiansen, Daniel E.; Archfield, Stacey A.; Over, Thomas M.; Kiang, Julie E.

2015-08-24

Daily records of streamflow are essential to understanding hydrologic systems and managing the interactions between human and natural systems. Many watersheds and locations lack streamgages to provide accurate and reliable records of daily streamflow. In such ungaged watersheds, statistical tools and rainfall-runoff models are used to estimate daily streamflow. Previous work compared 19 different techniques for predicting daily streamflow records in the southeastern United States. Here, five of the better-performing methods are compared in a different hydroclimatic region of the United States, in Iowa. The methods fall into three classes: (1) drainage-area ratio methods, (2) nonlinear spatial interpolations using flow duration curves, and (3) mechanistic rainfall-runoff models. The first two classes are each applied with nearest-neighbor and map-correlated index streamgages. Using a threefold validation and robust rank-based evaluation, the methods are assessed for overall goodness of fit of the hydrograph of daily streamflow, the ability to reproduce a daily, no-fail storage-yield curve, and the ability to reproduce key streamflow statistics. As in the Southeast study, a nonlinear spatial interpolation of daily streamflow using flow duration curves is found to be a method with the best predictive accuracy. Comparisons with previous work in Iowa show that the accuracy of mechanistic models with at-site calibration is substantially degraded in the ungaged framework.

Spatiotemporal Interpolation of Elevation Changes Derived from Satellite Altimetry for Jakobshavn Isbrae, Greenland

NASA Technical Reports Server (NTRS)

Hurkmans, R.T.W.L.; Bamber, J.L.; Sorensen, L. S.; Joughin, I. R.; Davis, C. H.; Krabill, W. B.

2012-01-01

Estimation of ice sheet mass balance from satellite altimetry requires interpolation of point-scale elevation change (dHdt) data over the area of interest. The largest dHdt values occur over narrow, fast-flowing outlet glaciers, where data coverage of current satellite altimetry is poorest. In those areas, straightforward interpolation of data is unlikely to reflect the true patterns of dHdt. Here, four interpolation methods are compared and evaluated over Jakobshavn Isbr, an outlet glacier for which widespread airborne validation data are available from NASAs Airborne Topographic Mapper (ATM). The four methods are ordinary kriging (OK), kriging with external drift (KED), where the spatial pattern of surface velocity is used as a proxy for that of dHdt, and their spatiotemporal equivalents (ST-OK and ST-KED).

Satellite radiothermovision of atmospheric mesoscale processes: case study of tropical cyclones

NASA Astrophysics Data System (ADS)

Ermakov, D. M.; Sharkov, E. A.; Chernushich, A. P.

2015-04-01

Satellite radiothermovision is a set of processing techniques applicable for multisource data of radiothermal monitoring of oceanatmosphere system, which allows creating dynamic description of mesoscale and synoptic atmospheric processes and estimating physically meaningful integral characteristics of the observed processes (like avdective flow of the latent heat through a given border). The approach is based on spatiotemporal interpolation of the satellite measurements which allows reconstructing the radiothermal fields (as well as the fields of geophysical parameters) of the ocean-atmosphere system at global scale with spatial resolution of about 0.125° and temporal resolution of 1.5 hour. The accuracy of spatiotemporal interpolation was estimated by direct comparison of interpolated data with the data of independent asynchronous measurements and was shown to correspond to the best achievable as reported in literature (for total precipitable water fields the accuracy is about 0.8 mm). The advantages of the implemented interpolation scheme are: closure under input radiothermal data, homogeneity in time scale (all data are interpolated through the same time intervals), automatic estimation of both the intermediate states of scalar field of the studied geophysical parameter and of vector field of effective velocity of advection (horizontal movements). Using this pair of fields one can calculate the flow of a given geophysical quantity though any given border. For example, in case of total precipitable water field, this flow (under proper calibration) has the meaning of latent heat advective flux. This opportunity was used to evaluate the latent heat flux though a set of circular contours, enclosing a tropical cyclone and drifting with it during its evolution. A remarkable interrelation was observed between the calculated magnitude and sign of advective latent flux and the intensity of a tropical cyclone. This interrelation is demonstrated in several examples of hurricanes and tropical cyclones of August, 2000, and typhoons of November, 2013, including super typhoon Haiyan.

Studying the spatial variability of Cr in agricultural field using both particle induced X-ray emission (PIXE) and instrumental neutron activation analysis (INAA) technique

NASA Astrophysics Data System (ADS)

Cruvinel, Paulo E.; Crestana, Sílvio; Artaxo, Paulo; Martins, JoséV.; Armelin, Maria JoséA.

1996-04-01

In the field of soil physics, a technique which permits a non-destructive, accurate and fast elemental analysis with a minimum of sample preparation effort is often desired. Although trace elements are minor components of the solid phase, they play an important role in soil fertility. Cr is of nutritional importance because it is a required element in human and animal nutrition. The immobility of Cr may be responsible for an inadequate Cr supply to plants. This work not only demonstrates the suitability of PIXE as a fast and non-destructive technique, useful to measure Cr content in soil samples, but also outlines a study of spatial variability of that element in agricultural field. To demonstrate the capability of the method soil samples were collected in a 5000 m 2 agricultural field. The soil samples were analyzed using both PIXE and INAA techniques. Besides, a Fourier interpolation technique was used to verify the distribution of Cr along of the sampled field. INAA was carried out by means of the γ-ray emitted by 51Cr(320 keV). Results show that there is a good linear relationship between the elemental concentration of Cr obtained using those techniques, i.e. a correlation coefficient of r2 = 0.82 was achieved.

View-interpolation of sparsely sampled sinogram using convolutional neural network

NASA Astrophysics Data System (ADS)

Lee, Hoyeon; Lee, Jongha; Cho, Suengryong

2017-02-01

Spare-view sampling and its associated iterative image reconstruction in computed tomography have actively investigated. Sparse-view CT technique is a viable option to low-dose CT, particularly in cone-beam CT (CBCT) applications, with advanced iterative image reconstructions with varying degrees of image artifacts. One of the artifacts that may occur in sparse-view CT is the streak artifact in the reconstructed images. Another approach has been investigated for sparse-view CT imaging by use of the interpolation methods to fill in the missing view data and that reconstructs the image by an analytic reconstruction algorithm. In this study, we developed an interpolation method using convolutional neural network (CNN), which is one of the widely used deep-learning methods, to find missing projection data and compared its performances with the other interpolation techniques.

An algebraic algorithm for nonuniformity correction in focal-plane arrays.

PubMed

Ratliff, Bradley M; Hayat, Majeed M; Hardie, Russell C

2002-09-01

A scene-based algorithm is developed to compensate for bias nonuniformity in focal-plane arrays. Nonuniformity can be extremely problematic, especially for mid- to far-infrared imaging systems. The technique is based on use of estimates of interframe subpixel shifts in an image sequence, in conjunction with a linear-interpolation model for the motion, to extract information on the bias nonuniformity algebraically. The performance of the proposed algorithm is analyzed by using real infrared and simulated data. One advantage of this technique is its simplicity; it requires relatively few frames to generate an effective correction matrix, thereby permitting the execution of frequent on-the-fly nonuniformity correction as drift occurs. Additionally, the performance is shown to exhibit considerable robustness with respect to lack of the common types of temporal and spatial irradiance diversity that are typically required by statistical scene-based nonuniformity correction techniques.

An efficient technique for the numerical solution of the bidomain equations.

PubMed

Whiteley, Jonathan P

2008-08-01

Computing the numerical solution of the bidomain equations is widely accepted to be a significant computational challenge. In this study we extend a previously published semi-implicit numerical scheme with good stability properties that has been used to solve the bidomain equations (Whiteley, J.P. IEEE Trans. Biomed. Eng. 53:2139-2147, 2006). A new, efficient numerical scheme is developed which utilizes the observation that the only component of the ionic current that must be calculated on a fine spatial mesh and updated frequently is the fast sodium current. Other components of the ionic current may be calculated on a coarser mesh and updated less frequently, and then interpolated onto the finer mesh. Use of this technique to calculate the transmembrane potential and extracellular potential induces very little error in the solution. For the simulations presented in this study an increase in computational efficiency of over two orders of magnitude over standard numerical techniques is obtained.

Spatial analysis of soil organic carbon in Zhifanggou catchment of the Loess Plateau.

PubMed

Li, Mingming; Zhang, Xingchang; Zhen, Qing; Han, Fengpeng

2013-01-01

Soil organic carbon (SOC) reflects soil quality and plays a critical role in soil protection, food safety, and global climate changes. This study involved grid sampling at different depths (6 layers) between 0 and 100 cm in a catchment. A total of 1282 soil samples were collected from 215 plots over 8.27 km(2). A combination of conventional analytical methods and geostatistical methods were used to analyze the data for spatial variability and soil carbon content patterns. The mean SOC content in the 1282 samples from the study field was 3.08 g · kg(-1). The SOC content of each layer decreased with increasing soil depth by a power function relationship. The SOC content of each layer was moderately variable and followed a lognormal distribution. The semi-variograms of the SOC contents of the six different layers were fit with the following models: exponential, spherical, exponential, Gaussian, exponential, and exponential, respectively. A moderate spatial dependence was observed in the 0-10 and 10-20 cm layers, which resulted from stochastic and structural factors. The spatial distribution of SOC content in the four layers between 20 and 100 cm exhibit were mainly restricted by structural factors. Correlations within each layer were observed between 234 and 562 m. A classical Kriging interpolation was used to directly visualize the spatial distribution of SOC in the catchment. The variability in spatial distribution was related to topography, land use type, and human activity. Finally, the vertical distribution of SOC decreased. Our results suggest that the ordinary Kriging interpolation can directly reveal the spatial distribution of SOC and the sample distance about this study is sufficient for interpolation or plotting. More research is needed, however, to clarify the spatial variability on the bigger scale and better understand the factors controlling spatial variability of soil carbon in the Loess Plateau region.

Adapting Better Interpolation Methods to Model Amphibious MT Data Along the Cascadian Subduction Zone.

NASA Astrophysics Data System (ADS)

Parris, B. A.; Egbert, G. D.; Key, K.; Livelybrooks, D.

2016-12-01

Magnetotellurics (MT) is an electromagnetic technique used to model the inner Earth's electrical conductivity structure. MT data can be analyzed using iterative, linearized inversion techniques to generate models imaging, in particular, conductive partial melts and aqueous fluids that play critical roles in subduction zone processes and volcanism. For example, the Magnetotelluric Observations of Cascadia using a Huge Array (MOCHA) experiment provides amphibious data useful for imaging subducted fluids from trench to mantle wedge corner. When using MOD3DEM(Egbert et al. 2012), a finite difference inversion package, we have encountered problems inverting, particularly, sea floor stations due to the strong, nearby conductivity gradients. As a work-around, we have found that denser, finer model grids near the land-sea interface produce better inversions, as characterized by reduced data residuals. This is partly to be due to our ability to more accurately capture topography and bathymetry. We are experimenting with improved interpolation schemes that more accurately track EM fields across cell boundaries, with an eye to enhancing the accuracy of the simulated responses and, thus, inversion results. We are adapting how MOD3DEM interpolates EM fields in two ways. The first seeks to improve weighting functions for interpolants to better address current continuity across grid boundaries. Electric fields are interpolated using a tri-linear spline technique, where the eight nearest electrical field estimates are each given weights determined by the technique, a kind of weighted average. We are modifying these weights to include cross-boundary conductivity ratios to better model current continuity. We are also adapting some of the techniques discussed in Shantsev et al (2014) to enhance the accuracy of the interpolated fields calculated by our forward solver, as well as to better approximate the sensitivities passed to the software's Jacobian that are used to generate a new forward model during each iteration of the inversion.

Signal-to-noise ratio estimation using adaptive tuning on the piecewise cubic Hermite interpolation model for images.

PubMed

Sim, K S; Yeap, Z X; Tso, C P

2016-11-01

An improvement to the existing technique of quantifying signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images using piecewise cubic Hermite interpolation (PCHIP) technique is proposed. The new technique uses an adaptive tuning onto the PCHIP, and is thus named as ATPCHIP. To test its accuracy, 70 images are corrupted with noise and their autocorrelation functions are then plotted. The ATPCHIP technique is applied to estimate the uncorrupted noise-free zero offset point from a corrupted image. Three existing methods, the nearest neighborhood, first order interpolation and original PCHIP, are used to compare with the performance of the proposed ATPCHIP method, with respect to their calculated SNR values. Results show that ATPCHIP is an accurate and reliable method to estimate SNR values from SEM images. SCANNING 38:502-514, 2016. © 2015 Wiley Periodicals, Inc. © Wiley Periodicals, Inc.

High Performance Geostatistical Modeling of Biospheric Resources

NASA Astrophysics Data System (ADS)

Pedelty, J. A.; Morisette, J. T.; Smith, J. A.; Schnase, J. L.; Crosier, C. S.; Stohlgren, T. J.

2004-12-01

We are using parallel geostatistical codes to study spatial relationships among biospheric resources in several study areas. For example, spatial statistical models based on large- and small-scale variability have been used to predict species richness of both native and exotic plants (hot spots of diversity) and patterns of exotic plant invasion. However, broader use of geostastics in natural resource modeling, especially at regional and national scales, has been limited due to the large computing requirements of these applications. To address this problem, we implemented parallel versions of the kriging spatial interpolation algorithm. The first uses the Message Passing Interface (MPI) in a master/slave paradigm on an open source Linux Beowulf cluster, while the second is implemented with the new proprietary Xgrid distributed processing system on an Xserve G5 cluster from Apple Computer, Inc. These techniques are proving effective and provide the basis for a national decision support capability for invasive species management that is being jointly developed by NASA and the US Geological Survey.

Interactive algebraic grid-generation technique

NASA Technical Reports Server (NTRS)

Smith, R. E.; Wiese, M. R.

1986-01-01

An algebraic grid generation technique and use of an associated interactive computer program are described. The technique, called the two boundary technique, is based on Hermite cubic interpolation between two fixed, nonintersecting boundaries. The boundaries are referred to as the bottom and top, and they are defined by two ordered sets of points. Left and right side boundaries which intersect the bottom and top boundaries may also be specified by two ordered sets of points. when side boundaries are specified, linear blending functions are used to conform interior interpolation to the side boundaries. Spacing between physical grid coordinates is determined as a function of boundary data and uniformly space computational coordinates. Control functions relating computational coordinates to parametric intermediate variables that affect the distance between grid points are embedded in the interpolation formulas. A versatile control function technique with smooth-cubic-spline functions is presented. The technique works best in an interactive graphics environment where computational displays and user responses are quickly exchanged. An interactive computer program based on the technique and called TBGG (two boundary grid generation) is also described.

Spatial Distribution of Socioeconomic Characteristics in Rural’s Java: A Case from Three Different Rural Areas in Central Java

NASA Astrophysics Data System (ADS)

Rudiarto, I.; Handayani, W.; Wijaya, H. B.; Insani, T. D.

2018-02-01

The intention of this paper is to combine the socio economic development into spatial aspect with the fact that the divided sub study areas are differed in its socio-economic characteristic. The research was conducted in three different rural areas, i.e.; coastal area in Sayung sub district - Demak Regency, plain area in Delanggu sub district - Klaten Regency, and mountain area in Kledung sub district - Temanggung regency. Spatial interpolation technique has been applied in order to identify the spatial distribution of socioeconomic data. The results show that socioeconomic characteristic in plain area and coastal area is more varied and regularly distributed as compared to the mountain area. Educated people are less found in the plain area while in coastal and mountain area the condition is better. Coastal area is identified as the prone area to the disaster issues and therefore socioeconomically vulnerable. The result of this research is very important to the development policies that need to undertake regarding to socioeconomic development in each associated location.

a Climatology of Global Precipitation.

NASA Astrophysics Data System (ADS)

Legates, David Russell

A global climatology of mean monthly precipitation has been developed using traditional land-based gage measurements as well as derived oceanic data. These data have been screened for coding errors and redundant entries have been removed. Oceanic precipitation estimates are most often extrapolated from coastal and island observations because few gage estimates of oceanic precipitation exist. One such procedure, developed by Dorman and Bourke and used here, employs a derived relationship between observed rainfall totals and the "current weather" at coastal stations. The combined data base contains 24,635 independent terrestial station records and 2223 oceanic grid-point records. Raingage catches are known to underestimate actual precipitation. Errors in the gage catch result from wind -field deformation, wetting losses, and evaporation from the gage and can amount to nearly 8, 2, and 1 percent of the global catch, respectively. A procedure has been developed to correct many of these errors and has been used to adjust the gage estimates of global precipitation. Space-time variations in gage type, air temperature, wind speed, and natural vegetation were incorporated into the correction procedure. Corrected data were then interpolated to the nodes of a 0.5^circ of latitude by 0.5^circ of longitude lattice using a spherically-based interpolation algorithm. Interpolation errors are largest in areas of low station density, rugged topography, and heavy precipitation. Interpolated estimates also were compared with a digital filtering technique to access the aliasing of high-frequency "noise" into the lower frequency signals. Isohyetal maps displaying the mean annual, seasonal, and monthly precipitation are presented. Gage corrections and the standard error of the corrected estimates also are mapped. Results indicate that mean annual global precipitation is 1123 mm with 1251 mm falling over the oceans and 820 mm over land. Spatial distributions of monthly precipitation generally are consistent with existing precipitation climatologies.

Traffic volume estimation using network interpolation techniques.

DOT National Transportation Integrated Search

2013-12-01

Kriging method is a frequently used interpolation methodology in geography, which enables estimations of unknown values at : certain places with the considerations of distances among locations. When it is used in transportation field, network distanc...

A Brief History of the use of Electromagnetic Induction Techniques in Soil Survey

NASA Astrophysics Data System (ADS)

Brevik, Eric C.; Doolittle, James

2017-04-01

Electromagnetic induction (EMI) has been used to characterize the spatial variability of soil properties since the late 1970s. Initially used to assess soil salinity, the use of EMI in soil studies has expanded to include: mapping soil types; characterizing soil water content and flow patterns; assessing variations in soil texture, compaction, organic matter content, and pH; and determining the depth to subsurface horizons, stratigraphic layers or bedrock, among other uses. In all cases the soil property being investigated must influence soil apparent electrical conductivity (ECa) either directly or indirectly for EMI techniques to be effective. An increasing number and diversity of EMI sensors have been developed in response to users' needs and the availability of allied technologies, which have greatly improved the functionality of these tools and increased the amount and types of data that can be gathered with a single pass. EMI investigations provide several benefits for soil studies. The large amount of georeferenced data that can be rapidly and inexpensively collected with EMI provides more complete characterization of the spatial variations in soil properties than traditional sampling techniques. In addition, compared to traditional soil survey methods, EMI can more effectively characterize diffuse soil boundaries and identify included areas of dissimilar soils within mapped soil units, giving soil scientists greater confidence when collecting spatial soil information. EMI techniques do have limitations; results are site-specific and can vary depending on the complex interactions among multiple and variable soil properties. Despite this, EMI techniques are increasingly being used to investigate the spatial variability of soil properties at field and landscape scales. The future should witness a greater use of multiple-frequency and multiple-coil EMI sensors and integration with other sensors to assess the spatial variability of soil properties. Data analysis will be improved with advanced processing and presentation systems and more sophisticated geostatistical modeling algorithms will be developed and used to interpolate EMI data, improve the resolution of subsurface features, and assess soil properties.

Evaluation of a data fusion approach to estimate daily PM2.5 levels in North China

PubMed Central

Liang, Fengchao; Gao, Meng; Xiao, Qingyang; Carmichael, Gregory R.

2017-01-01

PM2.5 air pollution has been a growing concern worldwide. Previous studies have conducted several techniques to estimate PM2.5 exposure spatiotemporally in China, but all these have limitations. This study was to develop a data fusion approach and compare it with kriging and Chemistry Module. Two techniques were applied to create daily spatial cover of PM2.5 in grid cells with a resolution of 10 km in North China in 2013, respectively, which was kriging with an external drift (KED) and Weather Research and Forecast Model with Chemistry Module (WRF-Chem). A data fusion technique was developed by fusing PM2.5 concentration predicted by KED and WRF-Chem, accounting for the distance from the central of grid cell to the nearest ground observations and daily spatial correlations between WRF-Chem and observations. Model performances were evaluated by comparing them with ground observations and the spatial prediction errors. KED and data fusion performed better at monitoring sites with a daily model R2 of 0.95 and 0.94, respectively and PM2.5 was overestimated by WRF-Chem (R2=0.51). KED and data fusion performed better around the ground monitors, WRF-Chem performed relative worse with high prediction errors in the central of study domain. In our study, both KED and data fusion technique provided highly accurate PM2.5. Current monitoring network in North China was dense enough to provide a reliable PM2.5 prediction by interpolation technique. PMID:28599195

Evaluation of a data fusion approach to estimate daily PM2.5 levels in North China.

PubMed

Liang, Fengchao; Gao, Meng; Xiao, Qingyang; Carmichael, Gregory R; Pan, Xiaochuan; Liu, Yang

2017-10-01

PM 2.5 air pollution has been a growing concern worldwide. Previous studies have conducted several techniques to estimate PM 2.5 exposure spatiotemporally in China, but all these have limitations. This study was to develop a data fusion approach and compare it with kriging and Chemistry Module. Two techniques were applied to create daily spatial cover of PM 2.5 in grid cells with a resolution of 10km in North China in 2013, respectively, which was kriging with an external drift (KED) and Weather Research and Forecast Model with Chemistry Module (WRF-Chem). A data fusion technique was developed by fusing PM 2.5 concentration predicted by KED and WRF-Chem, accounting for the distance from the central of grid cell to the nearest ground observations and daily spatial correlations between WRF-Chem and observations. Model performances were evaluated by comparing them with ground observations and the spatial prediction errors. KED and data fusion performed better at monitoring sites with a daily model R 2 of 0.95 and 0.94, respectively and PM 2.5 was overestimated by WRF-Chem (R 2 =0.51). KED and data fusion performed better around the ground monitors, WRF-Chem performed relative worse with high prediction errors in the central of study domain. In our study, both KED and data fusion technique provided highly accurate PM 2.5 . Current monitoring network in North China was dense enough to provide a reliable PM 2.5 prediction by interpolation technique. Copyright © 2017. Published by Elsevier Inc.

Interpolation of unevenly spaced data using a parabolic leapfrog correction method and cubic splines

Treesearch

Julio L. Guardado; William T. Sommers

1977-01-01

The technique proposed allows interpolation of data recorded at unevenly spaced sites to a regular grid or to other sites. Known data are interpolated to an initial guess field grid of unevenly spaced rows and columns by a simple distance weighting procedure. The initial guess field is then adjusted by using a parabolic leapfrog correction and the known data. The final...

Novel Digital Signal Processing and Detection Techniques.

DTIC Science & Technology

1980-09-01

decimation and interpolation [11, 1 2]. * Submitted by: Bede Liu Department of Electrical .l Engineering and Computer Science Princeton University ...on the use of recursive filters for decimation and interpolation. 4- UNCL.ASSIFIED~ SECURITY CLASSIFICATION OF PAGEfW1,en Data Fneprd) ...filter structure for realizing low-pass filter is developed 16,7]. By employing decimation and interpolation, the filter uses only coefficients 0, +1, and

The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes

USGS Publications Warehouse

Funk, Chris; Peterson, Pete; Landsfeld, Martin; Pedreros, Diego; Verdin, James; Shukla, Shraddhanand; Husak, Gregory; Rowland, James; Harrison, Laura; Hoell, Andrew; Michaelsen, Joel

2015-01-01

The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.

The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes

PubMed Central

Funk, Chris; Peterson, Pete; Landsfeld, Martin; Pedreros, Diego; Verdin, James; Shukla, Shraddhanand; Husak, Gregory; Rowland, James; Harrison, Laura; Hoell, Andrew; Michaelsen, Joel

2015-01-01

The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia. PMID:26646728

Impact of rain gauge quality control and interpolation on streamflow simulation: an application to the Warwick catchment, Australia

NASA Astrophysics Data System (ADS)

Liu, Shulun; Li, Yuan; Pauwels, Valentijn R. N.; Walker, Jeffrey P.

2017-12-01

Rain gauges are widely used to obtain temporally continuous point rainfall records, which are then interpolated into spatially continuous data to force hydrological models. However, rainfall measurements and interpolation procedure are subject to various uncertainties, which can be reduced by applying quality control and selecting appropriate spatial interpolation approaches. Consequently, the integrated impact of rainfall quality control and interpolation on streamflow simulation has attracted increased attention but not been fully addressed. This study applies a quality control procedure to the hourly rainfall measurements obtained in the Warwick catchment in eastern Australia. The grid-based daily precipitation from the Australian Water Availability Project was used as a reference. The Pearson correlation coefficient between the daily accumulation of gauged rainfall and the reference data was used to eliminate gauges with significant quality issues. The unrealistic outliers were censored based on a comparison between gauged rainfall and the reference. Four interpolation methods, including the inverse distance weighting (IDW), nearest neighbors (NN), linear spline (LN), and ordinary Kriging (OK), were implemented. The four methods were firstly assessed through a cross-validation using the quality-controlled rainfall data. The impacts of the quality control and interpolation on streamflow simulation were then evaluated through a semi-distributed hydrological model. The results showed that the Nash–Sutcliffe model efficiency coefficient (NSE) and Bias of the streamflow simulations were significantly improved after quality control. In the cross-validation, the IDW and OK methods resulted in good interpolation rainfall, while the NN led to the worst result. In term of the impact on hydrological prediction, the IDW led to the most consistent streamflow predictions with the observations, according to the validation at five streamflow-gauged locations. The OK method performed second best according to streamflow predictions at the five gauges in the calibration period (01/01/2007–31/12/2011) and four gauges during the validation period (01/01/2012–30/06/2014). However, NN produced the worst prediction at the outlet of the catchment in the validation period, indicating a low robustness. While the IDW exhibited the best performance in the study catchment in terms of accuracy, robustness and efficiency, more general recommendations on the selection of rainfall interpolation methods need to be further explored.

Impact of rain gauge quality control and interpolation on streamflow simulation: an application to the Warwick catchment, Australia

NASA Astrophysics Data System (ADS)

Liu, Shulun; Li, Yuan; Pauwels, Valentijn R. N.; Walker, Jeffrey P.

2018-01-01

Rain gauges are widely used to obtain temporally continuous point rainfall records, which are then interpolated into spatially continuous data to force hydrological models. However, rainfall measurements and interpolation procedure are subject to various uncertainties, which can be reduced by applying quality control and selecting appropriate spatial interpolation approaches. Consequently, the integrated impact of rainfall quality control and interpolation on streamflow simulation has attracted increased attention but not been fully addressed. This study applies a quality control procedure to the hourly rainfall measurements obtained in the Warwick catchment in eastern Australia. The grid-based daily precipitation from the Australian Water Availability Project was used as a reference. The Pearson correlation coefficient between the daily accumulation of gauged rainfall and the reference data was used to eliminate gauges with significant quality issues. The unrealistic outliers were censored based on a comparison between gauged rainfall and the reference. Four interpolation methods, including the inverse distance weighting (IDW), nearest neighbors (NN), linear spline (LN), and ordinary Kriging (OK), were implemented. The four methods were firstly assessed through a cross-validation using the quality-controlled rainfall data. The impacts of the quality control and interpolation on streamflow simulation were then evaluated through a semi-distributed hydrological model. The results showed that the Nash–Sutcliffe model efficiency coefficient (NSE) and Bias of the streamflow simulations were significantly improved after quality control. In the cross-validation, the IDW and OK methods resulted in good interpolation rainfall, while the NN led to the worst result. In term of the impact on hydrological prediction, the IDW led to the most consistent streamflow predictions with the observations, according to the validation at five streamflow-gauged locations. The OK method performed second best according to streamflow predictions at the five gauges in the calibration period (01/01/2007–31/12/2011) and four gauges during the validation period (01/01/2012–30/06/2014). However, NN produced the worst prediction at the outlet of the catchment in the validation period, indicating a low robustness. While the IDW exhibited the best performance in the study catchment in terms of accuracy, robustness and efficiency, more general recommendations on the selection of rainfall interpolation methods need to be further explored.

An interpolation method for stream habitat assessments

USGS Publications Warehouse

Sheehan, Kenneth R.; Welsh, Stuart A.

2015-01-01

Interpolation of stream habitat can be very useful for habitat assessment. Using a small number of habitat samples to predict the habitat of larger areas can reduce time and labor costs as long as it provides accurate estimates of habitat. The spatial correlation of stream habitat variables such as substrate and depth improves the accuracy of interpolated data. Several geographical information system interpolation methods (natural neighbor, inverse distance weighted, ordinary kriging, spline, and universal kriging) were used to predict substrate and depth within a 210.7-m2 section of a second-order stream based on 2.5% and 5.0% sampling of the total area. Depth and substrate were recorded for the entire study site and compared with the interpolated values to determine the accuracy of the predictions. In all instances, the 5% interpolations were more accurate for both depth and substrate than the 2.5% interpolations, which achieved accuracies up to 95% and 92%, respectively. Interpolations of depth based on 2.5% sampling attained accuracies of 49–92%, whereas those based on 5% percent sampling attained accuracies of 57–95%. Natural neighbor interpolation was more accurate than that using the inverse distance weighted, ordinary kriging, spline, and universal kriging approaches. Our findings demonstrate the effective use of minimal amounts of small-scale data for the interpolation of habitat over large areas of a stream channel. Use of this method will provide time and cost savings in the assessment of large sections of rivers as well as functional maps to aid the habitat-based management of aquatic species.

An objective isobaric/isentropic technique for upper air analysis

NASA Technical Reports Server (NTRS)

Mancuso, R. L.; Endlich, R. M.; Ehernberger, L. J.

1981-01-01

An objective meteorological analysis technique is presented whereby both horizontal and vertical upper air analyses are performed. The process used to interpolate grid-point values from the upper-air station data is the same as for grid points on both an isobaric surface and a vertical cross-sectional plane. The nearby data surrounding each grid point are used in the interpolation by means of an anisotropic weighting scheme, which is described. The interpolation for a grid-point potential temperature is performed isobarically; whereas wind, mixing-ratio, and pressure height values are interpolated from data that lie on the isentropic surface that passes through the grid point. Two versions (A and B) of the technique are evaluated by qualitatively comparing computer analyses with subjective handdrawn analyses. The objective products of version A generally have fair correspondence with the subjective analyses and with the station data, and depicted the structure of the upper fronts, tropopauses, and jet streams fairly well. The version B objective products correspond more closely to the subjective analyses, and show the same strong gradients across the upper front with only minor smoothing.

Application of Gaussian Elimination to Determine Field Components within Unmeasured Regions in the UCN τ Trap

NASA Astrophysics Data System (ADS)

Felkins, Joseph; Holley, Adam

2017-09-01

Determining the average lifetime of a neutron gives information about the fundamental parameters of interactions resulting from the charged weak current. It is also an input for calculations of the abundance of light elements in the early cosmos, which are also directly measured. Experimentalists have devised two major approaches to measure the lifespan of the neutron, the beam experiment, and the bottle experiment. For the bottle experiment, I have designed a computational algorithm based on a numerical technique that interpolates magnetic field values in between measured points. This algorithm produces interpolated fields that satisfy the Maxwell-Heaviside equations for use in a simulation that will investigate the rate of depolarization in magnetic traps used for bottle experiments, such as the UCN τ experiment at Los Alamos National Lab. I will present how UCN depolarization can cause a systematic error in experiments like UCN τ. I will then describe the technique that I use for the interpolation, and will discuss the accuracy of interpolation for changes with the number of measured points and the volume of the interpolated region. Supported by NSF Grant 1553861.

Spatial adaptive sampling in multiscale simulation

NASA Astrophysics Data System (ADS)

Rouet-Leduc, Bertrand; Barros, Kipton; Cieren, Emmanuel; Elango, Venmugil; Junghans, Christoph; Lookman, Turab; Mohd-Yusof, Jamaludin; Pavel, Robert S.; Rivera, Axel Y.; Roehm, Dominic; McPherson, Allen L.; Germann, Timothy C.

2014-07-01

In a common approach to multiscale simulation, an incomplete set of macroscale equations must be supplemented with constitutive data provided by fine-scale simulation. Collecting statistics from these fine-scale simulations is typically the overwhelming computational cost. We reduce this cost by interpolating the results of fine-scale simulation over the spatial domain of the macro-solver. Unlike previous adaptive sampling strategies, we do not interpolate on the potentially very high dimensional space of inputs to the fine-scale simulation. Our approach is local in space and time, avoids the need for a central database, and is designed to parallelize well on large computer clusters. To demonstrate our method, we simulate one-dimensional elastodynamic shock propagation using the Heterogeneous Multiscale Method (HMM); we find that spatial adaptive sampling requires only ≈ 50 ×N0.14 fine-scale simulations to reconstruct the stress field at all N grid points. Related multiscale approaches, such as Equation Free methods, may also benefit from spatial adaptive sampling.

Reconstruction of reflectance data using an interpolation technique.

PubMed

Abed, Farhad Moghareh; Amirshahi, Seyed Hossein; Abed, Mohammad Reza Moghareh

2009-03-01

A linear interpolation method is applied for reconstruction of reflectance spectra of Munsell as well as ColorChecker SG color chips from the corresponding colorimetric values under a given set of viewing conditions. Hence, different types of lookup tables (LUTs) have been created to connect the colorimetric and spectrophotometeric data as the source and destination spaces in this approach. To optimize the algorithm, different color spaces and light sources have been used to build different types of LUTs. The effects of applied color datasets as well as employed color spaces are investigated. Results of recovery are evaluated by the mean and the maximum color difference values under other sets of standard light sources. The mean and the maximum values of root mean square (RMS) error between the reconstructed and the actual spectra are also calculated. Since the speed of reflectance reconstruction is a key point in the LUT algorithm, the processing time spent for interpolation of spectral data has also been measured for each model. Finally, the performance of the suggested interpolation technique is compared with that of the common principal component analysis method. According to the results, using the CIEXYZ tristimulus values as a source space shows priority over the CIELAB color space. Besides, the colorimetric position of a desired sample is a key point that indicates the success of the approach. In fact, because of the nature of the interpolation technique, the colorimetric position of the desired samples should be located inside the color gamut of available samples in the dataset. The resultant spectra that have been reconstructed by this technique show considerable improvement in terms of RMS error between the actual and the reconstructed reflectance spectra as well as CIELAB color differences under the other light source in comparison with those obtained from the standard PCA technique.

Guided filter and principal component analysis hybrid method for hyperspectral pansharpening

NASA Astrophysics Data System (ADS)

Qu, Jiahui; Li, Yunsong; Dong, Wenqian

2018-01-01

Hyperspectral (HS) pansharpening aims to generate a fused HS image with high spectral and spatial resolution through integrating an HS image with a panchromatic (PAN) image. A guided filter (GF) and principal component analysis (PCA) hybrid HS pansharpening method is proposed. First, the HS image is interpolated and the PCA transformation is performed on the interpolated HS image. The first principal component (PC1) channel concentrates on the spatial information of the HS image. Different from the traditional PCA method, the proposed method sharpens the PAN image and utilizes the GF to obtain the spatial information difference between the HS image and the enhanced PAN image. Then, in order to reduce spectral and spatial distortion, an appropriate tradeoff parameter is defined and the spatial information difference is injected into the PC1 channel through multiplying by this tradeoff parameter. Once the new PC1 channel is obtained, the fused image is finally generated by the inverse PCA transformation. Experiments performed on both synthetic and real datasets show that the proposed method outperforms other several state-of-the-art HS pansharpening methods in both subjective and objective evaluations.

Soft bilateral filtering volumetric shadows using cube shadow maps

PubMed Central

Ali, Hatam H.; Sunar, Mohd Shahrizal; Kolivand, Hoshang

2017-01-01

Volumetric shadows often increase the realism of rendered scenes in computer graphics. Typical volumetric shadows techniques do not provide a smooth transition effect in real-time with conservation on crispness of boundaries. This research presents a new technique for generating high quality volumetric shadows by sampling and interpolation. Contrary to conventional ray marching method, which requires extensive time, this proposed technique adopts downsampling in calculating ray marching. Furthermore, light scattering is computed in High Dynamic Range buffer to generate tone mapping. The bilateral interpolation is used along a view rays to smooth transition of volumetric shadows with respect to preserving-edges. In addition, this technique applied a cube shadow map to create multiple shadows. The contribution of this technique isreducing the number of sample points in evaluating light scattering and then introducing bilateral interpolation to improve volumetric shadows. This contribution is done by removing the inherent deficiencies significantly in shadow maps. This technique allows obtaining soft marvelous volumetric shadows, having a good performance and high quality, which show its potential for interactive applications. PMID:28632740

Color characterization of cine film

NASA Astrophysics Data System (ADS)

Noriega, Leonardo; Morovic, Jan; MacDonald, Lindsay W.; Lempp, Wolfgang

2002-06-01

This paper describes the characterization of cine film, by identifying the relationship between the Status A density values of positive print film and the XYZ values of conventional colorimetry. Several approaches are tried including least-squares modeling, tetrahedral interpolation, and distance weighted interpolation. The distance weighted technique has been improved by the use of the Mahalanobis distance metric in order to perform the interpolation, and this is presented as an innovation.

The Canadian Precipitation Analysis (CaPA): Evaluation of the statistical interpolation scheme

NASA Astrophysics Data System (ADS)

Evans, Andrea; Rasmussen, Peter; Fortin, Vincent

2013-04-01

CaPA (Canadian Precipitation Analysis) is a data assimilation system which employs statistical interpolation to combine observed precipitation with gridded precipitation fields produced by Environment Canada's Global Environmental Multiscale (GEM) climate model into a final gridded precipitation analysis. Precipitation is important in many fields and applications, including agricultural water management projects, flood control programs, and hydroelectric power generation planning. Precipitation is a key input to hydrological models, and there is a desire to have access to the best available information about precipitation in time and space. The principal goal of CaPA is to produce this type of information. In order to perform the necessary statistical interpolation, CaPA requires the estimation of a semi-variogram. This semi-variogram is used to describe the spatial correlations between precipitation innovations, defined as the observed precipitation amounts minus the GEM forecasted amounts predicted at the observation locations. Currently, CaPA uses a single isotropic variogram across the entire analysis domain. The present project investigates the implications of this choice by first conducting a basic variographic analysis of precipitation innovation data across the Canadian prairies, with specific interest in identifying and quantifying potential anisotropy within the domain. This focus is further expanded by identifying the effect of storm type on the variogram. The ultimate goal of the variographic analysis is to develop improved semi-variograms for CaPA that better capture the spatial complexities of precipitation over the Canadian prairies. CaPA presently applies a Box-Cox data transformation to both the observations and the GEM data, prior to the calculation of the innovations. The data transformation is necessary to satisfy the normal distribution assumption, but introduces a significant bias. The second part of the investigation aims at devising a bias correction scheme based on a moving-window averaging technique. For both the variogram and bias correction components of this investigation, a series of trial runs are conducted to evaluate the impact of these changes on the resulting CaPA precipitation analyses.

Mapping soil particle-size fractions: A comparison of compositional kriging and log-ratio kriging

NASA Astrophysics Data System (ADS)

Wang, Zong; Shi, Wenjiao

2017-03-01

Soil particle-size fractions (psf) as basic physical variables need to be accurately predicted for regional hydrological, ecological, geological, agricultural and environmental studies frequently. Some methods had been proposed to interpolate the spatial distributions of soil psf, but the performance of compositional kriging and different log-ratio kriging methods is still unclear. Four log-ratio transformations, including additive log-ratio (alr), centered log-ratio (clr), isometric log-ratio (ilr), and symmetry log-ratio (slr), combined with ordinary kriging (log-ratio kriging: alr_OK, clr_OK, ilr_OK and slr_OK) were selected to be compared with compositional kriging (CK) for the spatial prediction of soil psf in Tianlaochi of Heihe River Basin, China. Root mean squared error (RMSE), Aitchison's distance (AD), standardized residual sum of squares (STRESS) and right ratio of the predicted soil texture types (RR) were chosen to evaluate the accuracy for different interpolators. The results showed that CK had a better accuracy than the four log-ratio kriging methods. The RMSE (sand, 9.27%; silt, 7.67%; clay, 4.17%), AD (0.45), STRESS (0.60) of CK were the lowest and the RR (58.65%) was the highest in the five interpolators. The clr_OK achieved relatively better performance than the other log-ratio kriging methods. In addition, CK presented reasonable and smooth transition on mapping soil psf according to the environmental factors. The study gives insights for mapping soil psf accurately by comparing different methods for compositional data interpolation. Further researches of methods combined with ancillary variables are needed to be implemented to improve the interpolation performance.

Generic Sensor Modeling Using Pulse Method

NASA Technical Reports Server (NTRS)

Helder, Dennis L.; Choi, Taeyoung

2005-01-01

Recent development of high spatial resolution satellites such as IKONOS, Quickbird and Orbview enable observation of the Earth's surface with sub-meter resolution. Compared to the 30 meter resolution of Landsat 5 TM, the amount of information in the output image was dramatically increased. In this era of high spatial resolution, the estimation of spatial quality of images is gaining attention. Historically, the Modulation Transfer Function (MTF) concept has been used to estimate an imaging system's spatial quality. Sometimes classified by target shapes, various methods were developed in laboratory environment utilizing sinusoidal inputs, periodic bar patterns and narrow slits. On-orbit sensor MTF estimation was performed on 30-meter GSD Landsat4 Thematic Mapper (TM) data from the bridge pulse target as a pulse input . Because of a high resolution sensor s small Ground Sampling Distance (GSD), reasonably sized man-made edge, pulse, and impulse targets can be deployed on a uniform grassy area with accurate control of ground targets using tarps and convex mirrors. All the previous work cited calculated MTF without testing the MTF estimator's performance. In previous report, a numerical generic sensor model had been developed to simulate and improve the performance of on-orbit MTF estimating techniques. Results from the previous sensor modeling report that have been incorporated into standard MTF estimation work include Fermi edge detection and the newly developed 4th order modified Savitzky-Golay (MSG) interpolation technique. Noise sensitivity had been studied by performing simulations on known noise sources and a sensor model. Extensive investigation was done to characterize multi-resolution ground noise. Finally, angle simulation was tested by using synthetic pulse targets with angles from 2 to 15 degrees, several brightness levels, and different noise levels from both ground targets and imaging system. As a continuing research activity using the developed sensor model, this report was dedicated to MTF estimation via pulse input method characterization using the Fermi edge detection and 4th order MSG interpolation method. The relationship between pulse width and MTF value at Nyquist was studied including error detection and correction schemes. Pulse target angle sensitivity was studied by using synthetic targets angled from 2 to 12 degrees. In this report, from the ground and system noise simulation, a minimum SNR value was suggested for a stable MTF value at Nyquist for the pulse method. Target width error detection and adjustment technique based on a smooth transition of MTF profile is presented, which is specifically applicable only to the pulse method with 3 pixel wide targets.

Geographically weighted regression based methods for merging satellite and gauge precipitation

NASA Astrophysics Data System (ADS)

Chao, Lijun; Zhang, Ke; Li, Zhijia; Zhu, Yuelong; Wang, Jingfeng; Yu, Zhongbo

2018-03-01

Real-time precipitation data with high spatiotemporal resolutions are crucial for accurate hydrological forecasting. To improve the spatial resolution and quality of satellite precipitation, a three-step satellite and gauge precipitation merging method was formulated in this study: (1) bilinear interpolation is first applied to downscale coarser satellite precipitation to a finer resolution (PS); (2) the (mixed) geographically weighted regression methods coupled with a weighting function are then used to estimate biases of PS as functions of gauge observations (PO) and PS; and (3) biases of PS are finally corrected to produce a merged precipitation product. Based on the above framework, eight algorithms, a combination of two geographically weighted regression methods and four weighting functions, are developed to merge CMORPH (CPC MORPHing technique) precipitation with station observations on a daily scale in the Ziwuhe Basin of China. The geographical variables (elevation, slope, aspect, surface roughness, and distance to the coastline) and a meteorological variable (wind speed) were used for merging precipitation to avoid the artificial spatial autocorrelation resulting from traditional interpolation methods. The results show that the combination of the MGWR and BI-square function (MGWR-BI) has the best performance (R = 0.863 and RMSE = 7.273 mm/day) among the eight algorithms. The MGWR-BI algorithm was then applied to produce hourly merged precipitation product. Compared to the original CMORPH product (R = 0.208 and RMSE = 1.208 mm/hr), the quality of the merged data is significantly higher (R = 0.724 and RMSE = 0.706 mm/hr). The developed merging method not only improves the spatial resolution and quality of the satellite product but also is easy to implement, which is valuable for hydrological modeling and other applications.

Spatial and temporal allocation of ship exhaust emissions in Australian coastal waters using AIS data: Analysis and treatment of data gaps

NASA Astrophysics Data System (ADS)

Goldsworthy, Brett

2017-08-01

Ship exhaust emissions need to be allocated accurately in both space and time in order to examine many of the associated impacts, including on air quality and health. Data on ship activity from the Automatic Identification System (AIS) allow ship exhaust emissions to be calculated with fine spatial and temporal resolution. However, there are spatial gaps in the coverage afforded by the coastal network of ground stations that are used to collect the AIS data. This paper focuses on the problem of allocating emissions to the coastal gap regions. Allocating emissions to these regions involves generating interpolated ship tracks that both span the gaps and avoid coming too close to land. In most cases, a simple shortest path or straight line interpolation produces tracks that do not overlap or come too close to land. Where the simple method does not produce acceptable results, vessel tracks are steered around land on shortest available paths using a combination of visibility graphs and Dijkstra's algorithm. A geographical cluster analysis is first used to identify the boundary regions of the data gaps. The properties of the data gaps are summarised in terms of the length, duration and speed of the spanning tracks. The interpolation methods are used to improve the spatial distribution of emissions. It is also shown that emissions in the gap regions can contribute substantially to the total ship exhaust emissions in close proximity to highly populated areas.

A Tracking Analyst for large 3D spatiotemporal data from multiple sources (case study: Tracking volcanic eruptions in the atmosphere)

NASA Astrophysics Data System (ADS)

Gad, Mohamed A.; Elshehaly, Mai H.; Gračanin, Denis; Elmongui, Hicham G.

2018-02-01

This research presents a novel Trajectory-based Tracking Analyst (TTA) that can track and link spatiotemporally variable data from multiple sources. The proposed technique uses trajectory information to determine the positions of time-enabled and spatially variable scatter data at any given time through a combination of along trajectory adjustment and spatial interpolation. The TTA is applied in this research to track large spatiotemporal data of volcanic eruptions (acquired using multi-sensors) in the unsteady flow field of the atmosphere. The TTA enables tracking injections into the atmospheric flow field, the reconstruction of the spatiotemporally variable data at any desired time, and the spatiotemporal join of attribute data from multiple sources. In addition, we were able to create a smooth animation of the volcanic ash plume at interactive rates. The initial results indicate that the TTA can be applied to a wide range of multiple-source data.

Arc Length Based Grid Distribution For Surface and Volume Grids

NASA Technical Reports Server (NTRS)

Mastin, C. Wayne

1996-01-01

Techniques are presented for distributing grid points on parametric surfaces and in volumes according to a specified distribution of arc length. Interpolation techniques are introduced which permit a given distribution of grid points on the edges of a three-dimensional grid block to be propagated through the surface and volume grids. Examples demonstrate how these methods can be used to improve the quality of grids generated by transfinite interpolation.

Comparison of interpolation functions to improve a rebinning-free CT-reconstruction algorithm.

PubMed

de las Heras, Hugo; Tischenko, Oleg; Xu, Yuan; Hoeschen, Christoph

2008-01-01

The robust algorithm OPED for the reconstruction of images from Radon data has been recently developed. This reconstructs an image from parallel data within a special scanning geometry that does not need rebinning but only a simple re-ordering, so that the acquired fan data can be used directly for the reconstruction. However, if the number of rays per fan view is increased, there appear empty cells in the sinogram. These cells need to be filled by interpolation before the reconstruction can be carried out. The present paper analyzes linear interpolation, cubic splines and parametric (or "damped") splines for the interpolation task. The reconstruction accuracy in the resulting images was measured by the Normalized Mean Square Error (NMSE), the Hilbert Angle, and the Mean Relative Error. The spatial resolution was measured by the Modulation Transfer Function (MTF). Cubic splines were confirmed to be the most recommendable method. The reconstructed images resulting from cubic spline interpolation show a significantly lower NMSE than the ones from linear interpolation and have the largest MTF for all frequencies. Parametric splines proved to be advantageous only for small sinograms (below 50 fan views).

Spatial dynamics of the invasive defoliator amber-marked birch leafminer across the Anchorage landscape

Treesearch

J.E. Lundquist; R.M. Reich; M. Tuffly

2012-01-01

The amber-marked birch leafminer has caused severe infestations of birch species in Anchorage, AK, since 2002. Its spatial distribution has been monitored since 2006 and summarized using interpolated surfaces based on simple kriging. In this study, we developed methods of assessing and describing spatial distribution of the leafminer as they vary from year to year, and...

Interpolated twitches in fatiguing single mouse muscle fibres: implications for the assessment of central fatigue

PubMed Central

Place, Nicolas; Yamada, Takashi; Bruton, Joseph D; Westerblad, Håkan

2008-01-01

An electrically evoked twitch during a maximal voluntary contraction (twitch interpolation) is frequently used to assess central fatigue. In this study we used intact single muscle fibres to determine if intramuscular mechanisms could affect the force increase with the twitch interpolation technique. Intact single fibres from flexor digitorum brevis of NMRI mice were dissected and mounted in a chamber equipped with a force transducer. Free myoplasmic [Ca2+] ([Ca2+]i) was measured with the fluorescent Ca2+ indicator indo-1. Seven fibres were fatigued with repeated 70 Hz tetani until 40% initial force with an interpolated pulse evoked every fifth tetanus. Results showed that the force generated by the interpolated twitch increased throughout fatigue, being 9 ± 1% of tetanic force at the start and 19 ± 1% at the end (P < 0.001). This was not due to a larger increase in [Ca2+]i induced by the interpolated twitch during fatigue but rather to the fact that the force–[Ca2+]i relationship is sigmoidal and fibres entered a steeper part of the relationship during fatigue. In another set of experiments, we observed that repeated tetani evoked at 150 Hz resulted in more rapid fatigue development than at 70 Hz and there was a decrease in force (‘sag’) during contractions, which was not observed at 70 Hz. In conclusion, the extent of central fatigue is difficult to assess and it may be overestimated when using the twitch interpolation technique. PMID:18403421

Spatiotemporal Visualization of Time-Series Satellite-Derived CO2 Flux Data Using Volume Rendering and Gpu-Based Interpolation on a Cloud-Driven Digital Earth

NASA Astrophysics Data System (ADS)

Wu, S.; Yan, Y.; Du, Z.; Zhang, F.; Liu, R.

2017-10-01

The ocean carbon cycle has a significant influence on global climate, and is commonly evaluated using time-series satellite-derived CO2 flux data. Location-aware and globe-based visualization is an important technique for analyzing and presenting the evolution of climate change. To achieve realistic simulation of the spatiotemporal dynamics of ocean carbon, a cloud-driven digital earth platform is developed to support the interactive analysis and display of multi-geospatial data, and an original visualization method based on our digital earth is proposed to demonstrate the spatiotemporal variations of carbon sinks and sources using time-series satellite data. Specifically, a volume rendering technique using half-angle slicing and particle system is implemented to dynamically display the released or absorbed CO2 gas. To enable location-aware visualization within the virtual globe, we present a 3D particlemapping algorithm to render particle-slicing textures onto geospace. In addition, a GPU-based interpolation framework using CUDA during real-time rendering is designed to obtain smooth effects in both spatial and temporal dimensions. To demonstrate the capabilities of the proposed method, a series of satellite data is applied to simulate the air-sea carbon cycle in the China Sea. The results show that the suggested strategies provide realistic simulation effects and acceptable interactive performance on the digital earth.

Image processing of aerodynamic data

NASA Technical Reports Server (NTRS)

Faulcon, N. D.

1985-01-01

The use of digital image processing techniques in analyzing and evaluating aerodynamic data is discussed. An image processing system that converts images derived from digital data or from transparent film into black and white, full color, or false color pictures is described. Applications to black and white images of a model wing with a NACA 64-210 section in simulated rain and to computed low properties for transonic flow past a NACA 0012 airfoil are presented. Image processing techniques are used to visualize the variations of water film thicknesses on the wing model and to illustrate the contours of computed Mach numbers for the flow past the NACA 0012 airfoil. Since the computed data for the NACA 0012 airfoil are available only at discrete spatial locations, an interpolation method is used to provide values of the Mach number over the entire field.

Patch-Based Super-Resolution of MR Spectroscopic Images: Application to Multiple Sclerosis

PubMed Central

Jain, Saurabh; Sima, Diana M.; Sanaei Nezhad, Faezeh; Hangel, Gilbert; Bogner, Wolfgang; Williams, Stephen; Van Huffel, Sabine; Maes, Frederik; Smeets, Dirk

2017-01-01

Purpose: Magnetic resonance spectroscopic imaging (MRSI) provides complementary information to conventional magnetic resonance imaging. Acquiring high resolution MRSI is time consuming and requires complex reconstruction techniques. Methods: In this paper, a patch-based super-resolution method is presented to increase the spatial resolution of metabolite maps computed from MRSI. The proposed method uses high resolution anatomical MR images (T1-weighted and Fluid-attenuated inversion recovery) to regularize the super-resolution process. The accuracy of the method is validated against conventional interpolation techniques using a phantom, as well as simulated and in vivo acquired human brain images of multiple sclerosis subjects. Results: The method preserves tissue contrast and structural information, and matches well with the trend of acquired high resolution MRSI. Conclusions: These results suggest that the method has potential for clinically relevant neuroimaging applications. PMID:28197066

Scale effects on the evapotranspiration estimation over a water-controlled Mediterranean ecosystem and its influence on hydrological modelling

NASA Astrophysics Data System (ADS)

Carpintero, Elisabet; González-Dugo, María P.; José Polo, María; Hain, Christopher; Nieto, Héctor; Gao, Feng; Andreu, Ana; Kustas, William; Anderson, Martha

2017-04-01

The integration of currently available satellite data into surface energy balance models can provide estimates of evapotranspiration (ET) with spatial and temporal resolutions determined by sensor characteristics. The use of data fusion techniques may increase the temporal resolution of these estimates using multiple satellites, providing a more frequent ET monitoring for hydrological purposes. The objective of this work is to analyze the effects of pixel resolution on the estimation of evapotranspiration using different remote sensing platforms, and to provide continuous monitoring of ET over a water-controlled ecosystem, the Holm oak savanna woodland known as dehesa. It is an agroforestry system with a complex canopy structure characterized by widely-spaced oak trees combined with crops, pasture and shrubs. The study was carried out during two years, 2013 and 2014, combining ET estimates at different spatial and temporal resolutions and applying data fusion techniques for a frequent monitoring of water use at fine spatial resolution. A global and daily ET product at 5 km resolution, developed with the ALEXI model using MODIS day-night temperature difference (Anderson et al., 2015a) was used as a starting point. The associated flux disaggregation scheme, DisALEXI (Norman et al., 2003), was later applied to constrain higher resolution ET from both MODIS and Landsat 7/8 images. The Climate Forecast System Reanalysis (CFSR) provided the meteorological data. Finally, a data fusion technique, the STARFM model (Gao et al., 2006), was applied to fuse MODIS and Landsat ET maps in order to obtain daily ET at 30 m resolution. These estimates were validated and analyzed at two different scales: at local scale over a dehesa experimental site and at watershed scale with a predominant Mediterranean oak savanna landscape, both located in Southern Spain. Local ET estimates from the modeling system were validated with measurements provided by an eddy covariance tower installed in the dehesa (38 ° 12 'N, 4 ° 17' W, 736 m a.s.l.). The results supported the ability of ALEXI/DisALEXI model to accurately estimate turbulent and radiative fluxes over this complex landscape, both at 1 Km and at 30 m spatial resolution. The application of the STARFM model gave significant improvement in capturing the spatio-temporal heterogeneity of ET over the different seasons, compared with traditional interpolation methods using MODIS and Landsat ET data. At basin scale, the physically-based distributed hydrological model WiMMed has been applied to evaluate ET estimates. This model focuses on the spatial interpolation of the meteorological variables and the physical modelling of the daily water balance at the cell and watershed scale, using daily streamflow rates measured at the watershed outlet for final comparison.

Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wood, Andrew W; Leung, Lai R; Sridhar, V

Six approaches for downscaling climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the NCAR-DOE Parallel Climate Model (PCM), and the implications of the comparison for a future (2040–2060) PCM climate scenario were also explored. The six approaches were made up of three relatively simple statistical downscaling methods – linear interpolation (LI), spatial disaggregationmore » (SD), and bias-correction and spatial disaggregation (BCSD) – each applied to both PCM output directly (at T42 spatial resolution), and after dynamical downscaling via a Regional Climate Model (RCM – at ½-degree spatial resolution), for downscaling the climate model outputs to the 1/8-degree spatial resolution of the hydrological model. For the retrospective climate simulation, results were compared to an observed gridded climatology of temperature and precipitation, and gridded hydrologic variables resulting from forcing the hydrologic model with observations. The most significant findings are that the BCSD method was successful in reproducing the main features of the observed hydrometeorology from the retrospective climate simulation, when applied to both PCM and RCM outputs. Linear interpolation produced better results using RCM output than PCM output, but both methods (PCM-LI and RCM-LI) lead to unacceptably biased hydrologic simulations. Spatial disaggregation of the PCM output produced results similar to those achieved with the RCM interpolated output; nonetheless, neither PCM nor RCM output was useful for hydrologic simulation purposes without a bias-correction step. For the future climate scenario, only the BCSD-method (using PCM or RCM) was able to produce hydrologically plausible results. With the BCSD method, the RCM-derived hydrology was more sensitive to climate change than the PCM-derived hydrology.« less

Locally adaptive, spatially explicit projection of US population for 2030 and 2050.

PubMed

McKee, Jacob J; Rose, Amy N; Bright, Edward A; Huynh, Timmy; Bhaduri, Budhendra L

2015-02-03

Localized adverse events, including natural hazards, epidemiological events, and human conflict, underscore the criticality of quantifying and mapping current population. Building on the spatial interpolation technique previously developed for high-resolution population distribution data (LandScan Global and LandScan USA), we have constructed an empirically informed spatial distribution of projected population of the contiguous United States for 2030 and 2050, depicting one of many possible population futures. Whereas most current large-scale, spatially explicit population projections typically rely on a population gravity model to determine areas of future growth, our projection model departs from these by accounting for multiple components that affect population distribution. Modeled variables, which included land cover, slope, distances to larger cities, and a moving average of current population, were locally adaptive and geographically varying. The resulting weighted surface was used to determine which areas had the greatest likelihood for future population change. Population projections of county level numbers were developed using a modified version of the US Census's projection methodology, with the US Census's official projection as the benchmark. Applications of our model include incorporating multiple various scenario-driven events to produce a range of spatially explicit population futures for suitability modeling, service area planning for governmental agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations.

A novel iterative modified bicubic interpolation method enables high-contrast and high-resolution image generation for F-18 FDG-PET.

PubMed

Okizaki, Atsutaka; Nakayama, Michihiro; Nakajima, Kaori; Takahashi, Koji

2017-12-01

Positron emission tomography (PET) has become a useful and important technique in oncology. However, spatial resolution of PET is not high; therefore, small abnormalities can sometimes be overlooked with PET. To address this problem, we devised a novel algorithm, iterative modified bicubic interpolation method (IMBIM). IMBIM generates high resolution and -contrast image. The purpose of this study was to investigate the utility of IMBIM for clinical FDG positron emission tomography/X-ray computed tomography (PET/CT) imaging.We evaluated PET images from 1435 patients with malignant tumor and compared the contrast (uptake ratio of abnormal lesions to background) in high resolution image with the standard bicubic interpolation method (SBIM) and IMBIM. In addition to the contrast analysis, 340 out of 1435 patients were selected for visual evaluation by nuclear medicine physicians to investigate lesion detectability. Abnormal uptakes on the images were categorized as either absolutely abnormal or equivocal finding.The average of contrast with IMBIM was significantly higher than that with SBIM (P < .001). The improvements were prominent with large matrix sizes and small lesions. SBIM images showed abnormalities in 198 of 340 lesions (58.2%), while IMBIM indicated abnormalities in 312 (91.8%). There was statistically significant improvement in lesion detectability with IMBIM (P < .001).In conclusion, IMBIM generates high-resolution images with improved contrast and, therefore, may facilitate more accurate diagnoses in clinical practice. Copyright © 2017 The Authors. Published by Wolters Kluwer Health, Inc. All rights reserved.

Present and future responses of growing degree days for Crete Island in Greece

NASA Astrophysics Data System (ADS)

Paparrizos, Spyridon; Matzarakis, Andreas

2017-02-01

Climate affects practically all the physiological processes that determine plant life (IPCC, 2014). A major challenge and objective of the agricultural science is to predict the occurrences of specific physical or biological events. For this reason, flower phenology has been widely used to study the flowering in plant species of economic interest, and in this concept, temperature and heat units have been widely accepted as the most important factors affecting processes leading to flowering. The determination of heat requirements in the first developing phases of plants has been expressed as Growing Degree Days (GDD). Determination of GDD is useful for achieving a better understanding of the flowering season development in several plant species, and for forecasting when flowering will occur (Paparrizos and Matzarakis, 2017). Temperature and GDD represent two important spatially-dynamic climatic variables, as they both play vital roles in influencing forest development by directly affecting plant functions such as evapotranspiration, photosynthesis and plant transpiration. Understanding the spatial distribution of GDD is crucial to the practice of sustainable agricultural and forest management, as GDD relates to the integration of growth and provides precise point estimates (Hasan et al., 2007; Matzarakis et al., 2007). The aim of the current study was to estimate and map through downscaling spatial interpolation and multi-linear regression techniques, the future variation of GDD for the periods 2021-2050 and 2071-2100, under the A1B and B1 IPCC emission scenarios in relation with the reference periods for Crete Island in Greece. Future temperature data were obtained, validated and analysed from the ENSEMBLES European project. A combination of dynamical and statistical approach was conducted in order to downscale and perform the spatial interpolation of GDD through ArcGIS 10.2.1. The results indicated that in the future, GDD will be increased and the existing cultivations can reach maturity sooner. Nevertheless, rough topography will act as an inhibitor towards the expansion of the existing cultivations in higher altitudes.

Blotch removal for old movie restoration using epitome analysis

NASA Astrophysics Data System (ADS)

Rashwan, Abdullah M.

2011-10-01

Automatic blotch removal in old movies is important in film restoration. Blotches are black or white spots randomly occurring along the movie frames. Removing these spots are obtained by first automatically detecting the blotches then interpolating them using the spatial and temporal information in current, succeeding, and preceding frames. In this paper, simplified Rank Order Detector (sROD) is used with tweaked parameters to over detect the blotches, Epitome Analysis is used for interpolating the detected blotches.

The analysis of composite laminated beams using a 2D interpolating meshless technique

NASA Astrophysics Data System (ADS)

Sadek, S. H. M.; Belinha, J.; Parente, M. P. L.; Natal Jorge, R. M.; de Sá, J. M. A. César; Ferreira, A. J. M.

2018-02-01

Laminated composite materials are widely implemented in several engineering constructions. For its relative light weight, these materials are suitable for aerospace, military, marine, and automotive structural applications. To obtain safe and economical structures, the modelling analysis accuracy is highly relevant. Since meshless methods in the recent years achieved a remarkable progress in computational mechanics, the present work uses one of the most flexible and stable interpolation meshless technique available in the literature—the Radial Point Interpolation Method (RPIM). Here, a 2D approach is considered to numerically analyse composite laminated beams. Both the meshless formulation and the equilibrium equations ruling the studied physical phenomenon are presented with detail. Several benchmark beam examples are studied and the results are compared with exact solutions available in the literature and the results obtained from a commercial finite element software. The results show the efficiency and accuracy of the proposed numeric technique.

Evaluation of rainfall structure on hydrograph simulation: Comparison of radar and interpolated methods, a study case in a tropical catchment

NASA Astrophysics Data System (ADS)

Velasquez, N.; Ochoa, A.; Castillo, S.; Hoyos Ortiz, C. D.

2017-12-01

The skill of river discharge simulation using hydrological models strongly depends on the quality and spatio-temporal representativeness of precipitation during storm events. All precipitation measurement strategies have their own strengths and weaknesses that translate into discharge simulation uncertainties. Distributed hydrological models are based on evolving rainfall fields in the same time scale as the hydrological simulation. In general, rainfall measurements from a dense and well maintained rain gauge network provide a very good estimation of the total volume for each rainfall event, however, the spatial structure relies on interpolation strategies introducing considerable uncertainty in the simulation process. On the other hand, rainfall retrievals from radar reflectivity achieve a better spatial structure representation but with higher uncertainty in the surface precipitation intensity and volume depending on the vertical rainfall characteristics and radar scan strategy. To assess the impact of both rainfall measurement methodologies on hydrological simulations, and in particular the effects of the rainfall spatio-temporal variability, a numerical modeling experiment is proposed including the use of a novel QPE (Quantitative Precipitation Estimation) method based on disdrometer data in order to estimate surface rainfall from radar reflectivity. The experiment is based on the simulation of 84 storms, the hydrological simulations are carried out using radar QPE and two different interpolation methods (IDW and TIN), and the assessment of simulated peak flow. Results show significant rainfall differences between radar QPE and the interpolated fields, evidencing a poor representation of storms in the interpolated fields, which tend to miss the precise location of the intense precipitation cores, and to artificially generate rainfall in some areas of the catchment. Regarding streamflow modelling, the potential improvement achieved by using radar QPE depends on the density of the rain gauge network and its distribution relative to the precipitation events. The results for the 84 storms show a better model skill using radar QPE than the interpolated fields. Results using interpolated fields are highly affected by the dominant rainfall type and the basin scale.

Development of a low-budget, remote, solar powered, and self-operating rain gauge for spatial rainfall real time data monitoring in pristine and urban areas

NASA Astrophysics Data System (ADS)

Shafiei Shiva, J.; Chandler, D. G.; Nucera, K. J.; Valinski, N.

2016-12-01

Precipitation is one of the main components of the hydrological cycle and simulations and it is generally stated as an average value for the study area. However, due to high spatial variability of precipitation in some situations, more precise local data is required. In order to acquire the precipitation data, interpolation of neighbor gauged precipitation data is used which is the most affordable technique for a watershed scale study. Moreover, novel spatial rain measurements such as Doppler radars and satellite image processing have been widely used in recent studies. Although, due to impediments in the radar data processing and the effect of the local setting on the accuracy of the interpolated data, the local measurement of the precipitation remains as one of the most reliable approaches in attaining rain data. In this regard, development of a low-budget, remote, solar powered, and self-operating rain gauge for spatial rainfall real time data monitoring for pristine and urban areas has been presented in this research. The proposed rain gauge consists of two main parts: (a) hydraulic instruments and (b) electrical devices. The hydraulic instruments will collect the rain fall and store it in a PVC container which is connected to the high sensitivity pressure transducer systems. These electrical devices will transmit the data via cellphone networks which will be available for further analysis in less than one minute, after processing. The above-mentioned real time rain fall data can be employed in the precipitation measurement and the evaporation estimation. Due to the installed solar panel for battery recharging and designed siphon system for draining cumulative rain, this device is considered as a self-operating rain gauge. At this time, more than ten rain gauges are built and installed in the urban area of Syracuse, NY. Furthermore, these data are also useful for calibration and validation of data obtained by other rain gauging devices and estimation techniques. Moreover, remote data communication challenges in urban area are demonstrated and the solution for these problems have been addressed. Finally, the rainfall data obtained from the presented rain gauge has been compared with other measuring systems.

Spatial variability of soil available phosphorous and potassium at three different soils located in Pannonian Croatia

NASA Astrophysics Data System (ADS)

Bogunović, Igor; Pereira, Paulo; Đurđević, Boris

2017-04-01

Information on spatial distribution of soil nutrients in agroecosystems is critical for improving productivity and reducing environmental pressures in intensive farmed soils. In this context, spatial prediction of soil properties should be accurate. In this study we analyse 704 data of soil available phosphorus (AP) and potassium (AK); the data derive from soil samples collected across three arable fields in Baranja region (Croatia) in correspondence of different soil types: Cambisols (169 samples), Chernozems (131 samples) and Gleysoils (404 samples). The samples are collected in a regular sampling grid (distance 225 x 225 m). Several geostatistical techniques (Inverse Distance to a Weight (IDW) with the power of 1, 2 and 3; Radial Basis Functions (RBF) - Inverse Multiquadratic (IMT), Multiquadratic (MTQ), Completely Regularized Spline (CRS), Spline with Tension (SPT) and Thin Plate Spline (TPS); and Local Polynomial (LP) with the power of 1 and 2; two geostatistical techniques -Ordinary Kriging - OK and Simple Kriging - SK) were tested in order to evaluate the most accurate spatial variability maps using criteria of lowest RMSE during cross validation technique. Soil parameters varied considerably throughout the studied fields and their coefficient of variations ranged from 31.4% to 37.7% and from 19.3% to 27.1% for soil AP and AK, respectively. The experimental variograms indicate a moderate spatial dependence for AP and strong spatial dependence for all three locations. The best spatial predictor for AP at Chernozem field was Simple kriging (RMSE=61.711), and for AK inverse multiquadratic (RMSE=44.689). The least accurate technique was Thin plate spline (AP) and Inverse distance to a weight with a power of 1 (AK). Radial basis function models (Spline with Tension for AP at Gleysoil and Cambisol and Completely Regularized Spline for AK at Gleysol) were the best predictors, while Thin Plate Spline models were the least accurate in all three cases. The best interpolator for AK at Cambisol was the local polynomial with the power of 2 (RMSE=33.943), while the least accurate was Thin Plate Spline (RMSE=39.572).

Mapping Atmospheric Moisture Climatologies across the Conterminous United States

PubMed Central

Daly, Christopher; Smith, Joseph I.; Olson, Keith V.

2015-01-01

Spatial climate datasets of 1981–2010 long-term mean monthly average dew point and minimum and maximum vapor pressure deficit were developed for the conterminous United States at 30-arcsec (~800m) resolution. Interpolation of long-term averages (twelve monthly values per variable) was performed using PRISM (Parameter-elevation Relationships on Independent Slopes Model). Surface stations available for analysis numbered only 4,000 for dew point and 3,500 for vapor pressure deficit, compared to 16,000 for previously-developed grids of 1981–2010 long-term mean monthly minimum and maximum temperature. Therefore, a form of Climatologically-Aided Interpolation (CAI) was used, in which the 1981–2010 temperature grids were used as predictor grids. For each grid cell, PRISM calculated a local regression function between the interpolated climate variable and the predictor grid. Nearby stations entering the regression were assigned weights based on the physiographic similarity of the station to the grid cell that included the effects of distance, elevation, coastal proximity, vertical atmospheric layer, and topographic position. Interpolation uncertainties were estimated using cross-validation exercises. Given that CAI interpolation was used, a new method was developed to allow uncertainties in predictor grids to be accounted for in estimating the total interpolation error. Local land use/land cover properties had noticeable effects on the spatial patterns of atmospheric moisture content and deficit. An example of this was relatively high dew points and low vapor pressure deficits at stations located in or near irrigated fields. The new grids, in combination with existing temperature grids, enable the user to derive a full suite of atmospheric moisture variables, such as minimum and maximum relative humidity, vapor pressure, and dew point depression, with accompanying assumptions. All of these grids are available online at http://prism.oregonstate.edu, and include 800-m and 4-km resolution data, images, metadata, pedigree information, and station inventory files. PMID:26485026

Estimation of lunar surface maturity and ferrous oxide from Moon Mineralogy Mapper (M3) data through data interpolation techniques

NASA Astrophysics Data System (ADS)

Ajith Kumar, P.; Kumar, Shashi

2016-04-01

Surface maturity estimation of the lunar regolith revealed selenological process behind the formation of lunar surface, which might be provided vital information regarding the geological evolution of earth, because lunar surface is being considered as 8-9 times older than as that of the earth. Spectral reflectances data from Moon mineralogy mapper (M3), the hyperspectral sensor of chandrayan-1 coupled with the standard weight percentages of FeO from lunar returned samples of Apollo and Luna landing sites, through data interpolation techniques to generate the weight percentage FeO map of the target lunar locations. With the interpolated data mineral maps were prepared and the results are analyzed.

[Monitoring the thermal plume from coastal nuclear power plant using satellite remote sensing data: modeling, and validation].

PubMed

Zhu, Li; Zhao, Li-Min; Wang, Qiao; Zhang, Ai-Ling; Wu, Chuan-Qing; Li, Jia-Guo; Shi, Ji-Xiang

2014-11-01

Thermal plume from coastal nuclear power plant is a small-scale human activity, mornitoring of which requires high-frequency and high-spatial remote sensing data. The infrared scanner (IRS), on board of HJ-1B, has an infrared channel IRS4 with 300 m and 4-days as its spatial and temporal resolution. Remote sensing data aquired using IRS4 is an available source for mornitoring thermal plume. Retrieval pattern for coastal sea surface temperature (SST) was built to monitor the thermal plume from nuclear power plant. The research area is located near Guangdong Daya Bay Nuclear Power Station (GNPS), where synchronized validations were also implemented. The National Centers for Environmental Prediction (NCEP) data was interpolated spatially and temporally. The interpolated data as well as surface weather conditions were subsequently employed into radiative transfer model for the atmospheric correction of IRS4 thermal image. A look-up-table (LUT) was built for the inversion between IRS4 channel radiance and radiometric temperature, and a fitted function was also built from the LUT data for the same purpose. The SST was finally retrieved based on those preprocessing procedures mentioned above. The bulk temperature (BT) of 84 samples distributed near GNPS was shipboard collected synchronically using salinity-temperature-deepness (CTD) instruments. The discrete sample data was surface interpolated and compared with the satellite retrieved SST. Results show that the average BT over the study area is 0.47 degrees C higher than the retrieved skin temperature (ST). For areas far away from outfall, the ST is higher than BT, with differences less than 1.0 degrees C. The main driving force for temperature variations in these regions is solar radiation. For areas near outfall, on the contrary, the retrieved ST is lower than BT, and greater differences between the two (meaning > 1.0 degrees C) happen when it gets closer to the outfall. Unlike the former case, the convective heat transfer resulting from the thermal plume is the primary reason leading to the temperature variations. Temperature rising (TR) distributions obtained from remote sensing data and in-situ measurements are consistent, except that the interpolated BT shows more level details (> 5 levels) than that of the ST (up to 4 levels). The areas with higher TR levels (> 2) are larger on BT maps, while for lower TR levels (≤ 2), the two methods perform with no obvious differences. Minimal errors for satellite-derived SST occur regularly around local time 10 a. m. This makes the remote sensing results to be substitutes for in-situ measurements. Therefore, for operational applications of HJ-1B IRS4, remote sensing technique can be a practical approach to monitoring the nuclear plant thermal pollution around this time period.

Minimized-Laplacian residual interpolation for color image demosaicking

NASA Astrophysics Data System (ADS)

Kiku, Daisuke; Monno, Yusuke; Tanaka, Masayuki; Okutomi, Masatoshi

2014-03-01

A color difference interpolation technique is widely used for color image demosaicking. In this paper, we propose a minimized-laplacian residual interpolation (MLRI) as an alternative to the color difference interpolation, where the residuals are differences between observed and tentatively estimated pixel values. In the MLRI, we estimate the tentative pixel values by minimizing the Laplacian energies of the residuals. This residual image transfor- mation allows us to interpolate more easily than the standard color difference transformation. We incorporate the proposed MLRI into the gradient based threshold free (GBTF) algorithm, which is one of current state-of- the-art demosaicking algorithms. Experimental results demonstrate that our proposed demosaicking algorithm can outperform the state-of-the-art algorithms for the 30 images of the IMAX and the Kodak datasets.

Error analysis of filtering operations in pixel-duplicated images of diabetic retinopathy

NASA Astrophysics Data System (ADS)

Mehrubeoglu, Mehrube; McLauchlan, Lifford

2010-08-01

In this paper, diabetic retinopathy is chosen for a sample target image to demonstrate the effectiveness of image enlargement through pixel duplication in identifying regions of interest. Pixel duplication is presented as a simpler alternative to data interpolation techniques for detecting small structures in the images. A comparative analysis is performed on different image processing schemes applied to both original and pixel-duplicated images. Structures of interest are detected and and classification parameters optimized for minimum false positive detection in the original and enlarged retinal pictures. The error analysis demonstrates the advantages as well as shortcomings of pixel duplication in image enhancement when spatial averaging operations (smoothing filters) are also applied.

Vector Doppler: spatial sampling analysis and presentation techniques for real-time systems

NASA Astrophysics Data System (ADS)

Capineri, Lorenzo; Scabia, Marco; Masotti, Leonardo F.

2001-05-01

The aim of the vector Doppler (VD) technique is the quantitative reconstruction of a velocity field independently of the ultrasonic probe axis to flow angle. In particular vector Doppler is interesting for studying vascular pathologies related to complex blood flow conditions. Clinical applications require a real-time operating mode and the capability to perform Doppler measurements over a defined volume. The combination of these two characteristics produces a real-time vector velocity map. In previous works the authors investigated the theory of pulsed wave (PW) vector Doppler and developed an experimental system capable of producing off-line 3D vector velocity maps. Afterwards, for producing dynamic velocity vector maps, we realized a new 2D vector Doppler system based on a modified commercial echograph. The measurement and presentation of a vector velocity field requires a correct spatial sampling that must satisfy the Shannon criterion. In this work we tackled this problem, establishing a relationship between sampling steps and scanning system characteristics. Another problem posed by the vector Doppler technique is the data representation in real-time that should be easy to interpret for the physician. With this in mine we attempted a multimedia solution that uses both interpolated images and sound to represent the information of the measured vector velocity map. These presentation techniques were experimented for real-time scanning on flow phantoms and preliminary measurements in vivo on a human carotid artery.

Automated brain tumor segmentation using spatial accuracy-weighted hidden Markov Random Field.

PubMed

Nie, Jingxin; Xue, Zhong; Liu, Tianming; Young, Geoffrey S; Setayesh, Kian; Guo, Lei; Wong, Stephen T C

2009-09-01

A variety of algorithms have been proposed for brain tumor segmentation from multi-channel sequences, however, most of them require isotropic or pseudo-isotropic resolution of the MR images. Although co-registration and interpolation of low-resolution sequences, such as T2-weighted images, onto the space of the high-resolution image, such as T1-weighted image, can be performed prior to the segmentation, the results are usually limited by partial volume effects due to interpolation of low-resolution images. To improve the quality of tumor segmentation in clinical applications where low-resolution sequences are commonly used together with high-resolution images, we propose the algorithm based on Spatial accuracy-weighted Hidden Markov random field and Expectation maximization (SHE) approach for both automated tumor and enhanced-tumor segmentation. SHE incorporates the spatial interpolation accuracy of low-resolution images into the optimization procedure of the Hidden Markov Random Field (HMRF) to segment tumor using multi-channel MR images with different resolutions, e.g., high-resolution T1-weighted and low-resolution T2-weighted images. In experiments, we evaluated this algorithm using a set of simulated multi-channel brain MR images with known ground-truth tissue segmentation and also applied it to a dataset of MR images obtained during clinical trials of brain tumor chemotherapy. The results show that more accurate tumor segmentation results can be obtained by comparing with conventional multi-channel segmentation algorithms.

TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015.

PubMed

Abatzoglou, John T; Dobrowski, Solomon Z; Parks, Sean A; Hegewisch, Katherine C

2018-01-09

We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958-2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.

TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015

NASA Astrophysics Data System (ADS)

Abatzoglou, John T.; Dobrowski, Solomon Z.; Parks, Sean A.; Hegewisch, Katherine C.

2018-01-01

We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958-2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from other sources to produce a monthly dataset of precipitation, maximum and minimum temperature, wind speed, vapor pressure, and solar radiation. TerraClimate additionally produces monthly surface water balance datasets using a water balance model that incorporates reference evapotranspiration, precipitation, temperature, and interpolated plant extractable soil water capacity. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time varying climate and climatic water balance data. We validated spatiotemporal aspects of TerraClimate using annual temperature, precipitation, and calculated reference evapotranspiration from station data, as well as annual runoff from streamflow gauges. TerraClimate datasets showed noted improvement in overall mean absolute error and increased spatial realism relative to coarser resolution gridded datasets.

Temporal and spatial characterization of zenith total delay (ZTD) in North Europe

NASA Astrophysics Data System (ADS)

Stoew, B.; Elgered, G.

2003-04-01

The estimates of ZTD are often treated as realizations of random walk stochastic processes. We derive the corresponding process parameters for 34 different locations in North Europe using two measurement techniques - Global Positioning System (GPS) and Water Vapor Radiometer (WVR). GPS-estimated ZTD is an excellent candidate for data assimilation in numerical weather prediction (NWP) models in terms of both spatial and temporal resolution. We characterize the long term behavior of the ZTD as a function of site latitude and height. The spatial characteristics of the ZTD are studied as a function of site separation and season. We investigate the influence of the time-interpolated atmospheric pressure data used for the estimation of zenith wet delay (ZWD) from ZTD. Characterization of extreme atmospheric events can aid the development of an early warning system. We consider two types of extreme meteorological phenomena with regard to their spatial scales. The first type concerns larger regions (including several GPS sites); the extreme weather is characterized by intense precipitation which may result in a flood. The second type is related to local variations in the ZWD/ZTD and can be used for detection/monitoring of passing atmospheric fronts.

Feasibility and validation of virtual autopsy for dental identification using the Interpol dental codes.

PubMed

Franco, Ademir; Thevissen, Patrick; Coudyzer, Walter; Develter, Wim; Van de Voorde, Wim; Oyen, Raymond; Vandermeulen, Dirk; Jacobs, Reinhilde; Willems, Guy

2013-05-01

Virtual autopsy is a medical imaging technique, using full body computed tomography (CT), allowing for a noninvasive and permanent observation of all body parts. For dental identification clinically and radiologically observed ante-mortem (AM) and post-mortem (PM) oral identifiers are compared. The study aimed to verify if a PM dental charting can be performed on virtual reconstructions of full-body CT's using the Interpol dental codes. A sample of 103 PM full-body CT's was collected from the forensic autopsy files of the Department of Forensic Medicine University Hospitals, KU Leuven, Belgium. For validation purposes, 3 of these bodies underwent a complete dental autopsy, a dental radiological and a full-body CT examination. The bodies were scanned in a Siemens Definition Flash CT Scanner (Siemens Medical Solutions, Germany). The images were examined on 8- and 12-bit screen resolution as three-dimensional (3D) reconstructions and as axial, coronal and sagittal slices. InSpace(®) (Siemens Medical Solutions, Germany) software was used for 3D reconstruction. The dental identifiers were charted on pink PM Interpol forms (F1, F2), using the related dental codes. Optimal dental charting was obtained by combining observations on 3D reconstructions and CT slices. It was not feasible to differentiate between different kinds of dental restoration materials. The 12-bit resolution enabled to collect more detailed evidences, mainly related to positions within a tooth. Oral identifiers, not implemented in the Interpol dental coding were observed. Amongst these, the observed (3D) morphological features of dental and maxillofacial structures are important identifiers. The latter can become particularly more relevant towards the future, not only because of the inherent spatial features, yet also because of the increasing preventive dental treatment, and the decreasing application of dental restorations. In conclusion, PM full-body CT examinations need to be implemented in the PM dental charting protocols and the Interpol dental codes should be adapted accordingly. Copyright © 2012 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.

Compact cell-centered discretization stencils at fine-coarse block structured grid interfaces

NASA Astrophysics Data System (ADS)

Pletzer, Alexander; Jamroz, Ben; Crockett, Robert; Sides, Scott

2014-03-01

Different strategies for coupling fine-coarse grid patches are explored in the context of the adaptive mesh refinement (AMR) method. We show that applying linear interpolation to fill in the fine grid ghost values can produce a finite volume stencil of comparable accuracy to quadratic interpolation provided the cell volumes are adjusted. The volume of fine cells expands whereas the volume of neighboring coarse cells contracts. The amount by which the cells contract/expand depends on whether the interface is a face, an edge, or a corner. It is shown that quadratic or better interpolation is required when the conductivity is spatially varying, anisotropic, the refinement ratio is other than two, or when the fine-coarse interface is concave.

Assimilation of the AVISO Altimetry Data into the Ocean Dynamics Model with a High Spatial Resolution Using Ensemble Optimal Interpolation (EnOI)

NASA Astrophysics Data System (ADS)

Kaurkin, M. N.; Ibrayev, R. A.; Belyaev, K. P.

2018-01-01

A parallel realization of the Ensemble Optimal Interpolation (EnOI) data assimilation (DA) method in conjunction with the eddy-resolving global circulation model is implemented. The results of DA experiments in the North Atlantic with the assimilation of the Archiving, Validation and Interpretation of Satellite Oceanographic (AVISO) data from the Jason-1 satellite are analyzed. The results of simulation are compared with the independent temperature and salinity data from the ARGO drifters.

Assessing the Suitability and Limitations of Satellite-based Measurements for Estimating CO, CO2, NO2 and O3 Concentrations over the Niger Delta

NASA Astrophysics Data System (ADS)

Fagbeja, M. A.; Hill, J. L.; Chatterton, T. J.; Longhurst, J. W.; Akinyede, J. O.

2011-12-01

Space-based satellite sensor technology may provide important tools in the study and assessment of national, regional and local air pollution. However, the application of optical satellite sensor observation of atmospheric trace gases, including those considered to be 'air pollutants', within the lower latitudes is limited due to prevailing climatic conditions. The lack of appropriate air pollution ground monitoring stations within the tropical belt reduces the ability to verify and calibrate space-based measurements. This paper considers the suitability of satellite remotely sensed data in estimating concentrations of atmospheric trace gases in view of the prevailing climate over the Niger Delta region. The methodological approach involved identifying suitable satellite data products and using the ArcGIS Geostatistical Analyst kriging interpolation technique to generate surface concentrations from satellite column measurements. The observed results are considered in the context of the climate of the study area. Using data from January 2001 to December 2005, an assessment of the suitability of satellite sensor data to interpolate column concentrations of trace gases over the Niger Delta has been undertaken and indicates varying degrees of reliability. The level of reliability of the interpolated surfaces is predicated on the number and spatial distributions of column measurements. Accounting for the two climatic seasons in the region, the interpolation of total column concentrations of CO and CO2 from SCIAMACHY produced both reliable and unreliable results over inland parts of the region during the dry season, while mainly unreliable results are observed over the coastal parts especially during the rainy season due to inadequate column measurements. The interpolation of tropospheric measurements of NO2 and O3 from GOME and OMI respectively produced reliable results all year. This is thought to be due to the spatial distribution of available column measurements, which were more regularly distributed over the region than the total column measurements of CO and CO2. Observations also indicated higher concentrations during the dry season than the wet seasons. The observed trend in the concentration of tropospheric O3 was as expected, considering the observed concentrations of precursor gases of CO and NO2. Whilst satellites currently play a significant role in the assessment of global air pollution and the long-range transport of air pollutants, the technology is faced with limitations in assessing ground level concentrations of pollutants. These limitations restrict the extent to which both pollution emissions and impacts of receptors can be accurately assessed. Further research is required to improve the capability of satellite sensors to observe atmospheric pollutants within the lower troposphere, where pollution has the most direct impacts on humans and ecosystems.

Transfer of Satellite Rainfall Uncertainty from Gauged to Ungauged Regions at Regional and Seasonal Timescales

NASA Technical Reports Server (NTRS)

Tang, Ling; Hossain, Faisal; Huffman, George J.

2010-01-01

Hydrologists and other users need to know the uncertainty of the satellite rainfall data sets across the range of time/space scales over the whole domain of the data set. Here, uncertainty' refers to the general concept of the deviation' of an estimate from the reference (or ground truth) where the deviation may be defined in multiple ways. This uncertainty information can provide insight to the user on the realistic limits of utility, such as hydrologic predictability, that can be achieved with these satellite rainfall data sets. However, satellite rainfall uncertainty estimation requires ground validation (GV) precipitation data. On the other hand, satellite data will be most useful over regions that lack GV data, for example developing countries. This paper addresses the open issues for developing an appropriate uncertainty transfer scheme that can routinely estimate various uncertainty metrics across the globe by leveraging a combination of spatially-dense GV data and temporally sparse surrogate (or proxy) GV data, such as the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar and the Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar. The TRMM Multi-satellite Precipitation Analysis (TMPA) products over the US spanning a record of 6 years are used as a representative example of satellite rainfall. It is shown that there exists a quantifiable spatial structure in the uncertainty of satellite data for spatial interpolation. Probabilistic analysis of sampling offered by the existing constellation of passive microwave sensors indicate that transfer of uncertainty for hydrologic applications may be effective at daily time scales or higher during the GPM era. Finally, a commonly used spatial interpolation technique (kriging), that leverages the spatial correlation of estimation uncertainty, is assessed at climatologic, seasonal, monthly and weekly timescales. It is found that the effectiveness of kriging is sensitive to the type of uncertainty metric, time scale of transfer and the density of GV data within the transfer domain. Transfer accuracy is lowest at weekly timescales with the error doubling from monthly to weekly.However, at very low GV data density (<20% of the domain), the transfer accuracy is too low to show any distinction as a function of the timescale of transfer.

An operator calculus for surface and volume modeling

NASA Technical Reports Server (NTRS)

Gordon, W. J.

1984-01-01

The mathematical techniques which form the foundation for most of the surface and volume modeling techniques used in practice are briefly described. An outline of what may be termed an operator calculus for the approximation and interpolation of functions of more than one independent variable is presented. By considering the linear operators associated with bivariate and multivariate interpolation/approximation schemes, it is shown how they can be compounded by operator multiplication and Boolean addition to obtain a distributive lattice of approximation operators. It is then demonstrated via specific examples how this operator calculus leads to practical techniques for sculptured surface and volume modeling.

Geostatistical uncertainty of assessing air quality using high-spatial-resolution lichen data: A health study in the urban area of Sines, Portugal.

PubMed

Ribeiro, Manuel C; Pinho, P; Branquinho, C; Llop, Esteve; Pereira, Maria J

2016-08-15

In most studies correlating health outcomes with air pollution, personal exposure assignments are based on measurements collected at air-quality monitoring stations not coinciding with health data locations. In such cases, interpolators are needed to predict air quality in unsampled locations and to assign personal exposures. Moreover, a measure of the spatial uncertainty of exposures should be incorporated, especially in urban areas where concentrations vary at short distances due to changes in land use and pollution intensity. These studies are limited by the lack of literature comparing exposure uncertainty derived from distinct spatial interpolators. Here, we addressed these issues with two interpolation methods: regression Kriging (RK) and ordinary Kriging (OK). These methods were used to generate air-quality simulations with a geostatistical algorithm. For each method, the geostatistical uncertainty was drawn from generalized linear model (GLM) analysis. We analyzed the association between air quality and birth weight. Personal health data (n=227) and exposure data were collected in Sines (Portugal) during 2007-2010. Because air-quality monitoring stations in the city do not offer high-spatial-resolution measurements (n=1), we used lichen data as an ecological indicator of air quality (n=83). We found no significant difference in the fit of GLMs with any of the geostatistical methods. With RK, however, the models tended to fit better more often and worse less often. Moreover, the geostatistical uncertainty results showed a marginally higher mean and precision with RK. Combined with lichen data and land-use data of high spatial resolution, RK is a more effective geostatistical method for relating health outcomes with air quality in urban areas. This is particularly important in small cities, which generally do not have expensive air-quality monitoring stations with high spatial resolution. Further, alternative ways of linking human activities with their environment are needed to improve human well-being. Copyright © 2016 Elsevier B.V. All rights reserved.

Accurate B-spline-based 3-D interpolation scheme for digital volume correlation

NASA Astrophysics Data System (ADS)

Ren, Maodong; Liang, Jin; Wei, Bin

2016-12-01

An accurate and efficient 3-D interpolation scheme, based on sampling theorem and Fourier transform technique, is proposed to reduce the sub-voxel matching error caused by intensity interpolation bias in digital volume correlation. First, the influence factors of the interpolation bias are investigated theoretically using the transfer function of an interpolation filter (henceforth filter) in the Fourier domain. A law that the positional error of a filter can be expressed as a function of fractional position and wave number is found. Then, considering the above factors, an optimized B-spline-based recursive filter, combining B-spline transforms and least squares optimization method, is designed to virtually eliminate the interpolation bias in the process of sub-voxel matching. Besides, given each volumetric image containing different wave number ranges, a Gaussian weighting function is constructed to emphasize or suppress certain of wave number ranges based on the Fourier spectrum analysis. Finally, a novel software is developed and series of validation experiments were carried out to verify the proposed scheme. Experimental results show that the proposed scheme can reduce the interpolation bias to an acceptable level.

Full-field stress determination in photoelasticity with phase shifting technique

NASA Astrophysics Data System (ADS)

Guo, Enhai; Liu, Yonggang; Han, Yongsheng; Arola, Dwayne; Zhang, Dongsheng

2018-04-01

Photoelasticity is an effective method for evaluating the stress and its spatial variations within a stressed body. In the present study, a method to determine the stress distribution by means of phase shifting and a modified shear-difference is proposed. First, the orientation of the first principal stress and the retardation between the principal stresses are determined in the full-field through phase shifting. Then, through bicubic interpolation and derivation of a modified shear-difference method, the internal stress is calculated from the point with a free boundary along its normal direction. A method to reduce integration error in the shear difference scheme is proposed and compared to the existing methods; the integration error is reduced when using theoretical photoelastic parameters to calculate the stress component with the same points. Results show that when the value of Δx/Δy approaches one, the error is minimum, and although the interpolation error is inevitable, it has limited influence on the accuracy of the result. Finally, examples are presented for determining the stresses in a circular plate and ring subjected to diametric loading. Results show that the proposed approach provides a complete solution for determining the full-field stresses in photoelastic models.

Uncertainty in regional temperatures inferred from sparse global observations: Application to a probabilistic classification of El Niño

NASA Astrophysics Data System (ADS)

Ilyas, Maryam; Brierley, Christopher M.; Guillas, Serge

2017-09-01

Instrumental records showing increases in surface temperature are some of the robust and iconic evidence of climate change. But how much should we trust regional temperature estimates interpolated from sparse observations? Here we quantify the uncertainty in the instrumental record by applying multiresolution lattice kriging, a recently developed interpolation technique that leverages the multiple spatial scales of temperature anomalies. The probability of monthly anomalies across the globe is represented by an ensemble, based on HadCRUT4 and accounting for observational and coverage uncertainties. To demonstrate the potential of these new data, we investigate the area-averaged temperature anomalies over the Niño 3.4 region in the equatorial Pacific. Having developed a definition of the El Niño-Southern Oscillation (ENSO) able to cope with probability distribution functions, we classify the ENSO state for each year since 1851. We find that for many years it is ambiguous as to whether there was an El Niño or not from the Niño 3.4 region alone. These years are mainly before 1920, but also just after World War II.

Simulation and Spectrum Extraction in the Spectroscopic Channel of the SNAP Experiment

NASA Astrophysics Data System (ADS)

Tilquin, Andre; Bonissent, A.; Gerdes, D.; Ealet, A.; Prieto, E.; Macaire, C.; Aumenier, M. H.

2007-05-01

A pixel-level simulation software is described. It is composed of two modules. The first module applies Fourier optics at each active element of the system to construct the PSF at a large variety of wavelengths and spatial locations of the point source. The input is provided by the engineer's design program (Zemax). It describes the optical path and the distortions. The PSF properties are compressed and interpolated using shapelets decomposition and neural network techniques. A second module is used for production jobs. It uses the output of the first module to reconstruct the relevant PSF and integrate it on the detector pixels. Extended and polychromatic sources are approximated by a combination of monochromatic point sources. For the spectrum extraction, we use a fast simulator based on a multidimensional linear interpolation of the pixel response tabulated on a grid of values of wavelength, position on sky and slice number. The prediction of the fast simulator is compared to the observed pixel content, and a chi-square minimization where the parameters are the bin contents is used to build the extracted spectrum. The visible and infrared arms are combined in the same chi-square, providing a single spectrum.

Spatial and temporal patterns of locally-acquired dengue transmission in northern Queensland, Australia, 1993-2012.

PubMed

Naish, Suchithra; Dale, Pat; Mackenzie, John S; McBride, John; Mengersen, Kerrie; Tong, Shilu

2014-01-01

Dengue has been a major public health concern in Australia since it re-emerged in Queensland in 1992-1993. We explored spatio-temporal characteristics of locally-acquired dengue cases in northern tropical Queensland, Australia during the period 1993-2012. Locally-acquired notified cases of dengue were collected for northern tropical Queensland from 1993 to 2012. Descriptive spatial and temporal analyses were conducted using geographic information system tools and geostatistical techniques. 2,398 locally-acquired dengue cases were recorded in northern tropical Queensland during the study period. The areas affected by the dengue cases exhibited spatial and temporal variation over the study period. Notified cases of dengue occurred more frequently in autumn. Mapping of dengue by statistical local areas (census units) reveals the presence of substantial spatio-temporal variation over time and place. Statistically significant differences in dengue incidence rates among males and females (with more cases in females) (χ(2) = 15.17, d.f.  = 1, p<0.01). Differences were observed among age groups, but these were not statistically significant. There was a significant positive spatial autocorrelation of dengue incidence for the four sub-periods, with the Moran's I statistic ranging from 0.011 to 0.463 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the northern Queensland. Tropical areas are potential high-risk areas for mosquito-borne diseases such as dengue. This study demonstrated that the locally-acquired dengue cases have exhibited a spatial and temporal variation over the past twenty years in northern tropical Queensland, Australia. Therefore, this study provides an impetus for further investigation of clusters and risk factors in these high-risk areas.

Spatial and Temporal Patterns of Locally-Acquired Dengue Transmission in Northern Queensland, Australia, 1993–2012

PubMed Central

Naish, Suchithra; Dale, Pat; Mackenzie, John S.; McBride, John; Mengersen, Kerrie; Tong, Shilu

2014-01-01

Background Dengue has been a major public health concern in Australia since it re-emerged in Queensland in 1992–1993. We explored spatio-temporal characteristics of locally-acquired dengue cases in northern tropical Queensland, Australia during the period 1993–2012. Methods Locally-acquired notified cases of dengue were collected for northern tropical Queensland from 1993 to 2012. Descriptive spatial and temporal analyses were conducted using geographic information system tools and geostatistical techniques. Results 2,398 locally-acquired dengue cases were recorded in northern tropical Queensland during the study period. The areas affected by the dengue cases exhibited spatial and temporal variation over the study period. Notified cases of dengue occurred more frequently in autumn. Mapping of dengue by statistical local areas (census units) reveals the presence of substantial spatio-temporal variation over time and place. Statistically significant differences in dengue incidence rates among males and females (with more cases in females) (χ2 = 15.17, d.f. = 1, p<0.01). Differences were observed among age groups, but these were not statistically significant. There was a significant positive spatial autocorrelation of dengue incidence for the four sub-periods, with the Moran's I statistic ranging from 0.011 to 0.463 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the northern Queensland. Conclusions Tropical areas are potential high-risk areas for mosquito-borne diseases such as dengue. This study demonstrated that the locally-acquired dengue cases have exhibited a spatial and temporal variation over the past twenty years in northern tropical Queensland, Australia. Therefore, this study provides an impetus for further investigation of clusters and risk factors in these high-risk areas. PMID:24691549

Interpretation of heavy rainfall spatial distribution in mountain watersheds by copula functions

NASA Astrophysics Data System (ADS)

Grossi, Giovanna; Balistrocchi, Matteo

2016-04-01

The spatial distribution of heavy rainfalls can strongly influence flood dynamics in mountain watersheds, depending on their geomorphologic features, namely orography, slope, land covers and soil types. Unfortunately, the direct observation of rainfall fields by meteorological radar is very difficult in this situation, so that interpolation of rain gauge observations or downscaling of meteorological predictions must be adopted to derive spatial rainfall distributions. To do so, various stochastic and physically based approaches are already available, even though the first one is the most familiar in hydrology. Indeed, Kriging interpolation procedures represent very popular techniques to face this problem by means of a stochastic approach. A certain number of restrictive assumptions and parameter uncertainties however affects Kriging. Many alternative formulations and additional procedures were therefore developed during the last decades. More recently, copula functions (Joe, 1997; Nelsen, 2006; Salvadori et al. 2007) were suggested to provide a more straightforward solution to carry out spatial interpolations of hydrologic variables (Bardossy & Pegram; 2009). Main advantages lie in the possibility of i) assessing the dependence structure relating to rainfall variables independently of marginal distributions, ii) expressing the association degree through rank correlation coefficients, iii) implementing marginal distributions and copula functions belonging to different models to develop complex joint distribution functions, iv) verifying the model reliability by effective statistical tests (Genest et al., 2009). A suitable case study to verify these potentialities is provided by the Taro River, a right-bank tributary of the Po River (northern Italy), whose contributing area amounts to about 2˙000 km2. The mountain catchment area is divided into two similar watersheds, so that spatial distribution is crucial in extreme flood event generation. A quite well diffused hydro-meteorological network, consisting of about 30 rain gauges and 10 hydrometers, monitors this medium-size watershed. A decade of rainfall-runoff event observations are available. Severe rainfall events were identified with reference to a main raingauge station, by using an interevent time definition and a depth threshold. Rainfall depths were thus derived and the spatial variability of their association degree was represented by using the Kendall coefficient. A unique copula model based on Gumbel copula function was finally found to be suitable to represent the dependence structure relating to rainfall depths observed in distinct raingauges. Bardossy A., Pegram G. (2009), Copula based multisite model for daily precipitation simulation, Hydrol. Earth Syst. Sci., 13, 2299-2314. Genest C., Rémilland B., Beaudoin D. (2009), Goodness-of-fit tests for copulas: a review and a power study, Insur. Math. Econ., 44(2), 199-213. Joe H. (1997), Multivariate models and dependence concepts, Chapman and Hall, London. Nelsen R. B. (2006), An introduction to copulas, second ed., Springer, New York. Salvadori G., De Michele C., Kottegoda N. T., Rosso R. (2007), Extremes in nature: an approach using copulas, Springer, Dordrecht, The Nederlands.

Quadratic trigonometric B-spline for image interpolation using GA

PubMed Central

Abbas, Samreen; Irshad, Misbah

2017-01-01

In this article, a new quadratic trigonometric B-spline with control parameters is constructed to address the problems related to two dimensional digital image interpolation. The newly constructed spline is then used to design an image interpolation scheme together with one of the soft computing techniques named as Genetic Algorithm (GA). The idea of GA has been formed to optimize the control parameters in the description of newly constructed spline. The Feature SIMilarity (FSIM), Structure SIMilarity (SSIM) and Multi-Scale Structure SIMilarity (MS-SSIM) indices along with traditional Peak Signal-to-Noise Ratio (PSNR) are employed as image quality metrics to analyze and compare the outcomes of approach offered in this work, with three of the present digital image interpolation schemes. The upshots show that the proposed scheme is better choice to deal with the problems associated to image interpolation. PMID:28640906

Quadratic trigonometric B-spline for image interpolation using GA.

PubMed

Hussain, Malik Zawwar; Abbas, Samreen; Irshad, Misbah

2017-01-01

In this article, a new quadratic trigonometric B-spline with control parameters is constructed to address the problems related to two dimensional digital image interpolation. The newly constructed spline is then used to design an image interpolation scheme together with one of the soft computing techniques named as Genetic Algorithm (GA). The idea of GA has been formed to optimize the control parameters in the description of newly constructed spline. The Feature SIMilarity (FSIM), Structure SIMilarity (SSIM) and Multi-Scale Structure SIMilarity (MS-SSIM) indices along with traditional Peak Signal-to-Noise Ratio (PSNR) are employed as image quality metrics to analyze and compare the outcomes of approach offered in this work, with three of the present digital image interpolation schemes. The upshots show that the proposed scheme is better choice to deal with the problems associated to image interpolation.

A Quadtree-gridding LBM with Immersed Boundary for Two-dimension Viscous Flows

NASA Astrophysics Data System (ADS)

Yao, Jieke; Feng, Wenliang; Chen, Bin; Zhou, Wei; Cao, Shikun

2017-07-01

An un-uniform quadtree grids lattice Boltzmann method (LBM) with immersed boundary is presented in this paper. In overlapping for different level grids, temporal and spatial interpolation are necessary to ensure the continuity of physical quantity. In order to take advantage of the equation for temporal and spatial step in the same level grids, equal interval interpolation, which is simple to apply to any refined boundary grids in the LBM, is adopted in temporal and spatial aspects to obtain second-order accuracy. The velocity correction, which can guarantee more preferably no-slip boundary condition than the direct forcing method and the momentum exchange method in the traditional immersed-boundary LBM, is used for solid boundary to make the best of Cartesian grid. In present quadtree-gridding immersed-boundary LBM, large eddy simulation (LES) is adopted to simulate the flows over obstacle in higher Reynolds number (Re). The incompressible viscous flows over circular cylinder are carried out, and a great agreement is obtained.

Spatial Copula Model for Imputing Traffic Flow Data from Remote Microwave Sensors.

PubMed

Ma, Xiaolei; Luan, Sen; Du, Bowen; Yu, Bin

2017-09-21

Issues of missing data have become increasingly serious with the rapid increase in usage of traffic sensors. Analyses of the Beijing ring expressway have showed that up to 50% of microwave sensors pose missing values. The imputation of missing traffic data must be urgently solved although a precise solution that cannot be easily achieved due to the significant number of missing portions. In this study, copula-based models are proposed for the spatial interpolation of traffic flow from remote traffic microwave sensors. Most existing interpolation methods only rely on covariance functions to depict spatial correlation and are unsuitable for coping with anomalies due to Gaussian consumption. Copula theory overcomes this issue and provides a connection between the correlation function and the marginal distribution function of traffic flow. To validate copula-based models, a comparison with three kriging methods is conducted. Results indicate that copula-based models outperform kriging methods, especially on roads with irregular traffic patterns. Copula-based models demonstrate significant potential to impute missing data in large-scale transportation networks.

A comparison of spatial interpolation methods for soil temperature over a complex topographical region

NASA Astrophysics Data System (ADS)

Wu, Wei; Tang, Xiao-Ping; Ma, Xue-Qing; Liu, Hong-Bin

2016-08-01

Soil temperature variability data provide valuable information on understanding land-surface ecosystem processes and climate change. This study developed and analyzed a spatial dataset of monthly mean soil temperature at a depth of 10 cm over a complex topographical region in southwestern China. The records were measured at 83 stations during the period of 1961-2000. Nine approaches were compared for interpolating soil temperature. The accuracy indicators were root mean square error (RMSE), modelling efficiency (ME), and coefficient of residual mass (CRM). The results indicated that thin plate spline with latitude, longitude, and elevation gave the best performance with RMSE varying between 0.425 and 0.592 °C, ME between 0.895 and 0.947, and CRM between -0.007 and 0.001. A spatial database was developed based on the best model. The dataset showed that larger seasonal changes of soil temperature were from autumn to winter over the region. The northern and eastern areas with hilly and low-middle mountains experienced larger seasonal changes.

Estimator banks: a new tool for direction-of-arrival estimation

NASA Astrophysics Data System (ADS)

Gershman, Alex B.; Boehme, Johann F.

1997-10-01

A new powerful tool for improving the threshold performance of direction-of-arrival (DOA) estimation is considered. The essence of our approach is to reduce the number of outliers in the threshold domain using the so-called estimator bank containing multiple 'parallel' underlying DOA estimators which are based on pseudorandom resampling of the MUSIC spatial spectrum for given data batch or sample covariance matrix. To improve the threshold performance relative to conventional MUSIC, evolutionary principles are used, i.e., only 'successful' underlying estimators (having no failure in the preliminary estimated source localization sectors) are exploited in the final estimate. An efficient beamspace root implementation of the estimator bank approach is developed, combined with the array interpolation technique which enables the application to arbitrary arrays. A higher-order extension of our approach is also presented, where the cumulant-based MUSIC estimator is exploited as a basic technique for spatial spectrum resampling. Simulations and experimental data processing show that our algorithm performs well below the MUSIC threshold, namely, has the threshold performance similar to that of the stochastic ML method. At the same time, the computational cost of our algorithm is much lower than that of stochastic ML because no multidimensional optimization is involved.

Efficacy of Distortion Correction on Diffusion Imaging: Comparison of FSL Eddy and Eddy_Correct Using 30 and 60 Directions Diffusion Encoding

PubMed Central

Yamada, Haruyasu; Abe, Osamu; Shizukuishi, Takashi; Kikuta, Junko; Shinozaki, Takahiro; Dezawa, Ko; Nagano, Akira; Matsuda, Masayuki; Haradome, Hiroki; Imamura, Yoshiki

2014-01-01

Diffusion imaging is a unique noninvasive tool to detect brain white matter trajectory and integrity in vivo. However, this technique suffers from spatial distortion and signal pileup or dropout originating from local susceptibility gradients and eddy currents. Although there are several methods to mitigate these problems, most techniques can be applicable either to susceptibility or eddy-current induced distortion alone with a few exceptions. The present study compared the correction efficiency of FSL tools, “eddy_correct” and the combination of “eddy” and “topup” in terms of diffusion-derived fractional anisotropy (FA). The brain diffusion images were acquired from 10 healthy subjects using 30 and 60 directions encoding schemes based on the electrostatic repulsive forces. For the 30 directions encoding, 2 sets of diffusion images were acquired with the same parameters, except for the phase-encode blips which had opposing polarities along the anteroposterior direction. For the 60 directions encoding, non–diffusion-weighted and diffusion-weighted images were obtained with forward phase-encoding blips and non–diffusion-weighted images with the same parameter, except for the phase-encode blips, which had opposing polarities. FA images without and with distortion correction were compared in a voxel-wise manner with tract-based spatial statistics. We showed that images corrected with eddy and topup possessed higher FA values than images uncorrected and corrected with eddy_correct with trilinear (FSL default setting) or spline interpolation in most white matter skeletons, using both encoding schemes. Furthermore, the 60 directions encoding scheme was superior as measured by increased FA values to the 30 directions encoding scheme, despite comparable acquisition time. This study supports the combination of eddy and topup as a superior correction tool in diffusion imaging rather than the eddy_correct tool, especially with trilinear interpolation, using 60 directions encoding scheme. PMID:25405472

Efficacy of distortion correction on diffusion imaging: comparison of FSL eddy and eddy_correct using 30 and 60 directions diffusion encoding.

PubMed

Yamada, Haruyasu; Abe, Osamu; Shizukuishi, Takashi; Kikuta, Junko; Shinozaki, Takahiro; Dezawa, Ko; Nagano, Akira; Matsuda, Masayuki; Haradome, Hiroki; Imamura, Yoshiki

2014-01-01

Diffusion imaging is a unique noninvasive tool to detect brain white matter trajectory and integrity in vivo. However, this technique suffers from spatial distortion and signal pileup or dropout originating from local susceptibility gradients and eddy currents. Although there are several methods to mitigate these problems, most techniques can be applicable either to susceptibility or eddy-current induced distortion alone with a few exceptions. The present study compared the correction efficiency of FSL tools, "eddy_correct" and the combination of "eddy" and "topup" in terms of diffusion-derived fractional anisotropy (FA). The brain diffusion images were acquired from 10 healthy subjects using 30 and 60 directions encoding schemes based on the electrostatic repulsive forces. For the 30 directions encoding, 2 sets of diffusion images were acquired with the same parameters, except for the phase-encode blips which had opposing polarities along the anteroposterior direction. For the 60 directions encoding, non-diffusion-weighted and diffusion-weighted images were obtained with forward phase-encoding blips and non-diffusion-weighted images with the same parameter, except for the phase-encode blips, which had opposing polarities. FA images without and with distortion correction were compared in a voxel-wise manner with tract-based spatial statistics. We showed that images corrected with eddy and topup possessed higher FA values than images uncorrected and corrected with eddy_correct with trilinear (FSL default setting) or spline interpolation in most white matter skeletons, using both encoding schemes. Furthermore, the 60 directions encoding scheme was superior as measured by increased FA values to the 30 directions encoding scheme, despite comparable acquisition time. This study supports the combination of eddy and topup as a superior correction tool in diffusion imaging rather than the eddy_correct tool, especially with trilinear interpolation, using 60 directions encoding scheme.

Interpolation/extrapolation technique with application to hypervelocity impact of space debris

NASA Technical Reports Server (NTRS)

Rule, William K.

1992-01-01

A new technique for the interpolation/extrapolation of engineering data is described. The technique easily allows for the incorporation of additional independent variables, and the most suitable data in the data base is automatically used for each prediction. The technique provides diagnostics for assessing the reliability of the prediction. Two sets of predictions made for known 5-degree-of-freedom, 15-parameter functions using the new technique produced an average coefficient of determination of 0.949. Here, the technique is applied to the prediction of damage to the Space Station from hypervelocity impact of space debris. A new set of impact data is presented for this purpose. Reasonable predictions for bumper damage were obtained, but predictions of pressure wall and multilayer insulation damage were poor.

High-temperature behavior of a deformed Fermi gas obeying interpolating statistics.

PubMed

Algin, Abdullah; Senay, Mustafa

2012-04-01

An outstanding idea originally introduced by Greenberg is to investigate whether there is equivalence between intermediate statistics, which may be different from anyonic statistics, and q-deformed particle algebra. Also, a model to be studied for addressing such an idea could possibly provide us some new consequences about the interactions of particles as well as their internal structures. Motivated mainly by this idea, in this work, we consider a q-deformed Fermi gas model whose statistical properties enable us to effectively study interpolating statistics. Starting with a generalized Fermi-Dirac distribution function, we derive several thermostatistical functions of a gas of these deformed fermions in the thermodynamical limit. We study the high-temperature behavior of the system by analyzing the effects of q deformation on the most important thermostatistical characteristics of the system such as the entropy, specific heat, and equation of state. It is shown that such a deformed fermion model in two and three spatial dimensions exhibits the interpolating statistics in a specific interval of the model deformation parameter 0 < q < 1. In particular, for two and three spatial dimensions, it is found from the behavior of the third virial coefficient of the model that the deformation parameter q interpolates completely between attractive and repulsive systems, including the free boson and fermion cases. From the results obtained in this work, we conclude that such a model could provide much physical insight into some interacting theories of fermions, and could be useful to further study the particle systems with intermediate statistics.

[Research on Kalman interpolation prediction model based on micro-region PM2.5 concentration].

PubMed

Wang, Wei; Zheng, Bin; Chen, Binlin; An, Yaoming; Jiang, Xiaoming; Li, Zhangyong

2018-02-01

In recent years, the pollution problem of particulate matter, especially PM2.5, is becoming more and more serious, which has attracted many people's attention from all over the world. In this paper, a Kalman prediction model combined with cubic spline interpolation is proposed, which is applied to predict the concentration of PM2.5 in the micro-regional environment of campus, and to realize interpolation simulation diagram of concentration of PM2.5 and simulate the spatial distribution of PM2.5. The experiment data are based on the environmental information monitoring system which has been set up by our laboratory. And the predicted and actual values of PM2.5 concentration data have been checked by the way of Wilcoxon signed-rank test. We find that the value of bilateral progressive significance probability was 0.527, which is much greater than the significant level α = 0.05. The mean absolute error (MEA) of Kalman prediction model was 1.8 μg/m 3 , the average relative error (MER) was 6%, and the correlation coefficient R was 0.87. Thus, the Kalman prediction model has a better effect on the prediction of concentration of PM2.5 than those of the back propagation (BP) prediction and support vector machine (SVM) prediction. In addition, with the combination of Kalman prediction model and the spline interpolation method, the spatial distribution and local pollution characteristics of PM2.5 can be simulated.

Quadratic polynomial interpolation on triangular domain

NASA Astrophysics Data System (ADS)

Li, Ying; Zhang, Congcong; Yu, Qian

2018-04-01

In the simulation of natural terrain, the continuity of sample points are not in consonance with each other always, traditional interpolation methods often can't faithfully reflect the shape information which lie in data points. So, a new method for constructing the polynomial interpolation surface on triangular domain is proposed. Firstly, projected the spatial scattered data points onto a plane and then triangulated them; Secondly, A C1 continuous piecewise quadric polynomial patch was constructed on each vertex, all patches were required to be closed to the line-interpolation one as far as possible. Lastly, the unknown quantities were gotten by minimizing the object functions, and the boundary points were treated specially. The result surfaces preserve as many properties of data points as possible under conditions of satisfying certain accuracy and continuity requirements, not too convex meantime. New method is simple to compute and has a good local property, applicable to shape fitting of mines and exploratory wells and so on. The result of new surface is given in experiments.

Optimization and comparison of three spatial interpolation methods for electromagnetic levels in the AM band within an urban area.

PubMed

Rufo, Montaña; Antolín, Alicia; Paniagua, Jesús M; Jiménez, Antonio

2018-04-01

A comparative study was made of three methods of interpolation - inverse distance weighting (IDW), spline and ordinary kriging - after optimization of their characteristic parameters. These interpolation methods were used to represent the electric field levels for three emission frequencies (774kHz, 900kHz, and 1107kHz) and for the electrical stimulation quotient, Q E , characteristic of complex electromagnetic environments. Measurements were made with a spectrum analyser in a village in the vicinity of medium-wave radio broadcasting antennas. The accuracy of the models was quantified by comparing their predictions with levels measured at the control points not used to generate the models. The results showed that optimizing the characteristic parameters of each interpolation method allows any of them to be used. However, the best results in terms of the regression coefficient between each model's predictions and the actual control point field measurements were for the IDW method. Copyright © 2018 Elsevier Inc. All rights reserved.

Preliminary results of spatial modeling of selected forest health variables in Georgia

Treesearch

Brock Stewart; Chris J. Cieszewski

2009-01-01

Variables relating to forest health monitoring, such as mortality, are difficult to predict and model. We present here the results of fitting various spatial regression models to these variables. We interpolate plot-level values compiled from the Forest Inventory and Analysis National Information Management System (FIA-NIMS) data that are related to forest health....

Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed

USGS Publications Warehouse

Balk, Benjamin; Elder, Kelly

2000-01-01

We model the spatial distribution of snow across a mountain basin using an approach that combines binary decision tree and geostatistical techniques. In April 1997 and 1998, intensive snow surveys were conducted in the 6.9‐km2 Loch Vale watershed (LVWS), Rocky Mountain National Park, Colorado. Binary decision trees were used to model the large‐scale variations in snow depth, while the small‐scale variations were modeled through kriging interpolation methods. Binary decision trees related depth to the physically based independent variables of net solar radiation, elevation, slope, and vegetation cover type. These decision tree models explained 54–65% of the observed variance in the depth measurements. The tree‐based modeled depths were then subtracted from the measured depths, and the resulting residuals were spatially distributed across LVWS through kriging techniques. The kriged estimates of the residuals were added to the tree‐based modeled depths to produce a combined depth model. The combined depth estimates explained 60–85% of the variance in the measured depths. Snow densities were mapped across LVWS using regression analysis. Snow‐covered area was determined from high‐resolution aerial photographs. Combining the modeled depths and densities with a snow cover map produced estimates of the spatial distribution of snow water equivalence (SWE). This modeling approach offers improvement over previous methods of estimating SWE distribution in mountain basins.

Parametric Grid Information in the DOE Knowledge Base: Data Preparation, Storage, and Access

DOE Office of Scientific and Technical Information (OSTI.GOV)

HIPP,JAMES R.; MOORE,SUSAN G.; MYERS,STEPHEN C.

The parametric grid capability of the Knowledge Base provides an efficient, robust way to store and access interpolatable information which is needed to monitor the Comprehensive Nuclear Test Ban Treaty. To meet both the accuracy and performance requirements of operational monitoring systems, we use a new approach which combines the error estimation of kriging with the speed and robustness of Natural Neighbor Interpolation (NNI). The method involves three basic steps: data preparation (DP), data storage (DS), and data access (DA). The goal of data preparation is to process a set of raw data points to produce a sufficient basis formore » accurate NNI of value and error estimates in the Data Access step. This basis includes a set of nodes and their connectedness, collectively known as a tessellation, and the corresponding values and errors that map to each node, which we call surfaces. In many cases, the raw data point distribution is not sufficiently dense to guarantee accurate error estimates from the NNI, so the original data set must be densified using a newly developed interpolation technique known as Modified Bayesian Kriging. Once appropriate kriging parameters have been determined by variogram analysis, the optimum basis for NNI is determined in a process they call mesh refinement, which involves iterative kriging, new node insertion, and Delauny triangle smoothing. The process terminates when an NNI basis has been calculated which will fir the kriged values within a specified tolerance. In the data storage step, the tessellations and surfaces are stored in the Knowledge Base, currently in a binary flatfile format but perhaps in the future in a spatially-indexed database. Finally, in the data access step, a client application makes a request for an interpolated value, which triggers a data fetch from the Knowledge Base through the libKBI interface, a walking triangle search for the containing triangle, and finally the NNI interpolation.« less

Real-time image-based B-mode ultrasound image simulation of needles using tensor-product interpolation.

PubMed

Zhu, Mengchen; Salcudean, Septimiu E

2011-07-01

In this paper, we propose an interpolation-based method for simulating rigid needles in B-mode ultrasound images in real time. We parameterize the needle B-mode image as a function of needle position and orientation. We collect needle images under various spatial configurations in a water-tank using a needle guidance robot. Then we use multidimensional tensor-product interpolation to simulate images of needles with arbitrary poses and positions using collected images. After further processing, the interpolated needle and seed images are superimposed on top of phantom or tissue image backgrounds. The similarity between the simulated and the real images is measured using a correlation metric. A comparison is also performed with in vivo images obtained during prostate brachytherapy. Our results, carried out for both the convex (transverse plane) and linear (sagittal/para-sagittal plane) arrays of a trans-rectal transducer indicate that our interpolation method produces good results while requiring modest computing resources. The needle simulation method we present can be extended to the simulation of ultrasound images of other wire-like objects. In particular, we have shown that the proposed approach can be used to simulate brachytherapy seeds.

Probabilistic surface reconstruction from multiple data sets: An example for the Australian Moho

NASA Astrophysics Data System (ADS)

Bodin, T.; Salmon, M.; Kennett, B. L. N.; Sambridge, M.

2012-10-01

Interpolation of spatial data is a widely used technique across the Earth sciences. For example, the thickness of the crust can be estimated by different active and passive seismic source surveys, and seismologists reconstruct the topography of the Moho by interpolating these different estimates. Although much research has been done on improving the quantity and quality of observations, the interpolation algorithms utilized often remain standard linear regression schemes, with three main weaknesses: (1) the level of structure in the surface, or smoothness, has to be predefined by the user; (2) different classes of measurements with varying and often poorly constrained uncertainties are used together, and hence it is difficult to give appropriate weight to different data types with standard algorithms; (3) there is typically no simple way to propagate uncertainties in the data to uncertainty in the estimated surface. Hence the situation can be expressed by Mackenzie (2004): "We use fantastic telescopes, the best physical models, and the best computers. The weak link in this chain is interpreting our data using 100 year old mathematics". Here we use recent developments made in Bayesian statistics and apply them to the problem of surface reconstruction. We show how the reversible jump Markov chain Monte Carlo (rj-McMC) algorithm can be used to let the degree of structure in the surface be directly determined by the data. The solution is described in probabilistic terms, allowing uncertainties to be fully accounted for. The method is illustrated with an application to Moho depth reconstruction in Australia.

Spatial and Temporal Variation of Japanese encephalitis Disease and Detection of Disease Hotspots: a Case Study of Gorakhpur District, Uttar Pradesh, India

NASA Astrophysics Data System (ADS)

Verma, S.; Gupta, R. D.

2014-11-01

In recent times, Japanese Encephalitis (JE) has emerged as a serious public health problem. In India, JE outbreaks were recently reported in Uttar Pradesh, Gorakhpur. The present study presents an approach to use GIS for analyzing the reported cases of JE in the Gorakhpur district based on spatial analysis to bring out the spatial and temporal dynamics of the JE epidemic. The study investigates spatiotemporal pattern of the occurrence of disease and detection of the JE hotspot. Spatial patterns of the JE disease can provide an understanding of geographical changes. Geospatial distribution of the JE disease outbreak is being investigated since 2005 in this study. The JE incidence data for the years 2005 to 2010 is used. The data is then geo-coded at block level. Spatial analysis is used to evaluate autocorrelation in JE distribution and to test the cases that are clustered or dispersed in space. The Inverse Distance Weighting interpolation technique is used to predict the pattern of JE incidence distribution prevalent across the study area. Moran's I Index (Moran's I) statistics is used to evaluate autocorrelation in spatial distribution. The Getis-Ord Gi*(d) is used to identify the disease areas. The results represent spatial disease patterns from 2005 to 2010, depicting spatially clustered patterns with significant differences between the blocks. It is observed that the blocks on the built up areas reported higher incidences.

Locally-Adaptive, Spatially-Explicit Projection of U.S. Population for 2030 and 2050

DOE PAGES

McKee, Jacob J.; Rose, Amy N.; Bright, Eddie A.; ...

2015-02-03

Localized adverse events, including natural hazards, epidemiological events, and human conflict, underscore the criticality of quantifying and mapping current population. Moreover, knowing the spatial distribution of future population allows for increased preparation in the event of an emergency. Building on the spatial interpolation technique previously developed for high resolution population distribution data (LandScan Global and LandScan USA), we have constructed an empirically-informed spatial distribution of the projected population of the contiguous U.S. for 2030 and 2050. Whereas most current large-scale, spatially explicit population projections typically rely on a population gravity model to determine areas of future growth, our projection modelmore » departs from these by accounting for multiple components that affect population distribution. Modelled variables, which included land cover, slope, distances to larger cities, and a moving average of current population, were locally adaptive and geographically varying. The resulting weighted surface was used to determine which areas had the greatest likelihood for future population change. Population projections of county level numbers were developed using a modified version of the U.S. Census s projection methodology with the U.S. Census s official projection as the benchmark. Applications of our model include, but are not limited to, suitability modelling, service area planning for governmental agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations.« less

Locally-Adaptive, Spatially-Explicit Projection of U.S. Population for 2030 and 2050

DOE Office of Scientific and Technical Information (OSTI.GOV)

McKee, Jacob J.; Rose, Amy N.; Bright, Eddie A.

Localized adverse events, including natural hazards, epidemiological events, and human conflict, underscore the criticality of quantifying and mapping current population. Moreover, knowing the spatial distribution of future population allows for increased preparation in the event of an emergency. Building on the spatial interpolation technique previously developed for high resolution population distribution data (LandScan Global and LandScan USA), we have constructed an empirically-informed spatial distribution of the projected population of the contiguous U.S. for 2030 and 2050. Whereas most current large-scale, spatially explicit population projections typically rely on a population gravity model to determine areas of future growth, our projection modelmore » departs from these by accounting for multiple components that affect population distribution. Modelled variables, which included land cover, slope, distances to larger cities, and a moving average of current population, were locally adaptive and geographically varying. The resulting weighted surface was used to determine which areas had the greatest likelihood for future population change. Population projections of county level numbers were developed using a modified version of the U.S. Census s projection methodology with the U.S. Census s official projection as the benchmark. Applications of our model include, but are not limited to, suitability modelling, service area planning for governmental agencies, consequence assessment, mitigation planning and implementation, and assessment of spatially vulnerable populations.« less

An optical systems analysis approach to image resampling

NASA Technical Reports Server (NTRS)

Lyon, Richard G.

1997-01-01

All types of image registration require some type of resampling, either during the registration or as a final step in the registration process. Thus the image(s) must be regridded into a spatially uniform, or angularly uniform, coordinate system with some pre-defined resolution. Frequently the ending resolution is not the resolution at which the data was observed with. The registration algorithm designer and end product user are presented with a multitude of possible resampling methods each of which modify the spatial frequency content of the data in some way. The purpose of this paper is threefold: (1) to show how an imaging system modifies the scene from an end to end optical systems analysis approach, (2) to develop a generalized resampling model, and (3) empirically apply the model to simulated radiometric scene data and tabulate the results. A Hanning windowed sinc interpolator method will be developed based upon the optical characterization of the system. It will be discussed in terms of the effects and limitations of sampling, aliasing, spectral leakage, and computational complexity. Simulated radiometric scene data will be used to demonstrate each of the algorithms. A high resolution scene will be "grown" using a fractal growth algorithm based on mid-point recursion techniques. The result scene data will be convolved with a point spread function representing the optical response. The resultant scene will be convolved with the detection systems response and subsampled to the desired resolution. The resultant data product will be subsequently resampled to the correct grid using the Hanning windowed sinc interpolator and the results and errors tabulated and discussed.

Geospatial interpolation and mapping of tropospheric ozone pollution using geostatistics.

PubMed

Kethireddy, Swatantra R; Tchounwou, Paul B; Ahmad, Hafiz A; Yerramilli, Anjaneyulu; Young, John H

2014-01-10

Tropospheric ozone (O3) pollution is a major problem worldwide, including in the United States of America (USA), particularly during the summer months. Ozone oxidative capacity and its impact on human health have attracted the attention of the scientific community. In the USA, sparse spatial observations for O3 may not provide a reliable source of data over a geo-environmental region. Geostatistical Analyst in ArcGIS has the capability to interpolate values in unmonitored geo-spaces of interest. In this study of eastern Texas O3 pollution, hourly episodes for spring and summer 2012 were selectively identified. To visualize the O3 distribution, geostatistical techniques were employed in ArcMap. Using ordinary Kriging, geostatistical layers of O3 for all the studied hours were predicted and mapped at a spatial resolution of 1 kilometer. A decent level of prediction accuracy was achieved and was confirmed from cross-validation results. The mean prediction error was close to 0, the root mean-standardized-prediction error was close to 1, and the root mean square and average standard errors were small. O3 pollution map data can be further used in analysis and modeling studies. Kriging results and O3 decadal trends indicate that the populace in Houston-Sugar Land-Baytown, Dallas-Fort Worth-Arlington, Beaumont-Port Arthur, San Antonio, and Longview are repeatedly exposed to high levels of O3-related pollution, and are prone to the corresponding respiratory and cardiovascular health effects. Optimization of the monitoring network proves to be an added advantage for the accurate prediction of exposure levels.

The Effect of Administrative Boundaries and Geocoding Error on Cancer Rates in California

PubMed Central

Goldberg, Daniel W.; Cockburn, Myles G.

2012-01-01

Geocoding is often used to produce maps of disease rates from the diagnosis addresses of incident cases to assist with disease surveillance, prevention, and control. In this process, diagnosis addresses are converted into latitude/longitude pairs which are then aggregated to produce rates at varying geographic scales such as Census tracts, neighborhoods, cities, counties, and states. The specific techniques used within geocoding systems have an impact on where the output geocode is located and can therefore have an effect on the derivation of disease rates at different geographic aggregations. This paper investigates how county-level cancer rates are affected by the choice of interpolation method when case data are geocoded to the ZIP code level. Four commonly used areal unit interpolation techniques are applied and the output of each is used to compute crude county-level five-year incidence rates of all cancers in California. We found that the rates observed for 44 out of the 58 counties in California vary based on which interpolation method is used, with rates in some counties increasing by nearly 400% between interpolation methods. PMID:22469490

Investigations of interpolation errors of angle encoders for high precision angle metrology

NASA Astrophysics Data System (ADS)

Yandayan, Tanfer; Geckeler, Ralf D.; Just, Andreas; Krause, Michael; Asli Akgoz, S.; Aksulu, Murat; Grubert, Bernd; Watanabe, Tsukasa

2018-06-01

Interpolation errors at small angular scales are caused by the subdivision of the angular interval between adjacent grating lines into smaller intervals when radial gratings are used in angle encoders. They are often a major error source in precision angle metrology and better approaches for determining them at low levels of uncertainty are needed. Extensive investigations of interpolation errors of different angle encoders with various interpolators and interpolation schemes were carried out by adapting the shearing method to the calibration of autocollimators with angle encoders. The results of the laboratories with advanced angle metrology capabilities are presented which were acquired by the use of four different high precision angle encoders/interpolators/rotary tables. State of the art uncertainties down to 1 milliarcsec (5 nrad) were achieved for the determination of the interpolation errors using the shearing method which provides simultaneous access to the angle deviations of the autocollimator and of the angle encoder. Compared to the calibration and measurement capabilities (CMC) of the participants for autocollimators, the use of the shearing technique represents a substantial improvement in the uncertainty by a factor of up to 5 in addition to the precise determination of interpolation errors or their residuals (when compensated). A discussion of the results is carried out in conjunction with the equipment used.

Robust chemical and chemical-resistant material detection using hyper-spectral imager and a new bend interpolation and local scaling HSI sharpening method

NASA Astrophysics Data System (ADS)

Chen, Hai-Wen; McGurr, Michael; Brickhouse, Mark

2015-05-01

We present new results from our ongoing research activity for chemical threat detection using hyper-spectral imager (HSI) detection techniques by detecting nontraditional threat spectral signatures of agent usage, such as protective equipment, coatings, paints, spills, and stains that are worn by human or on trucks or other objects. We have applied several current state-of-the-art HSI target detection methods such as Matched Filter (MF), Adaptive Coherence Estimator (ACE), Constrained Energy Minimization (CEM), and Spectral Angle Mapper (SAM). We are interested in detecting several chemical related materials: (a) Tyvek clothing is chemical resistance and Tyvek coveralls are one-piece garments for protecting human body from harmful chemicals, and (b) ammonium salts from background could be representative of spills from scrubbers or related to other chemical activities. The HSI dataset that we used for detection covers a chemical test field with more than 50 different kinds of chemicals, protective materials, coatings, and paints. Among them, there are four different kinds of Tyvek material, three types of ammonium salts, and one yellow jugs. The imagery cube data were collected by a HSI sensor with a spectral range of 400-2,500nm. Preliminary testing results are promising, and very high probability of detection (Pd) and low probability of false detection are achieved with the usage of full spectral range (400- 2,500nm). In the second part of this paper, we present our newly developed HSI sharpening technique. A new Band Interpolation and Local Scaling (BILS) method has been developed to improve HSI spatial resolution by 4-16 times with a low-cost high-resolution pen-chromatic camera and a RGB camera. Preliminary results indicate that this new technique is promising.

Spatially continuous interpolation of water stage and water depths using the Everglades depth estimation network (EDEN)

USGS Publications Warehouse

Pearlstine, Leonard; Higer, Aaron; Palaseanu, Monica; Fujisaki, Ikuko; Mazzotti, Frank

2007-01-01

The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level monitoring, ground-elevation modeling, and water-surface modeling that provides scientists and managers with current (2000-present), online water-stage and water-depth information for the entire freshwater portion of the Greater Everglades. Continuous daily spatial interpolations of the EDEN network stage data are presented on a 400-square-meter grid spacing. EDEN offers a consistent and documented dataset that can be used by scientists and managers to (1) guide large-scale field operations, (2) integrate hydrologic and ecological responses, and (3) support biological and ecological assessments that measure ecosystem responses to the implementation of the Comprehensive Everglades Restoration Plan (CERP) The target users are biologists and ecologists examining trophic level responses to hydrodynamic changes in the Everglades.

Integrationof Remote Sensing and Geographic information system in Ground Water Quality Assessment and Management

NASA Astrophysics Data System (ADS)

Shakak, N.

2015-04-01

Spatial variations in ground water quality in the Khartoum state, Sudan, have been studied using geographic information system (GIS) and remote sensing technique. Gegraphical informtion system a tool which is used for storing, analyzing and displaying spatial data is also used for investigating ground water quality information. Khartoum landsat mosac image aquired in 2013was used, Arc/Gis software applied to extract the boundary of the study area, the image was classified to create land use/land cover map. The land use map,geological and soil map are used for correlation between land use , geological formations, and soil types to understand the source of natural pollution that can lower the ground water quality. For this study, the global positioning system (GPS), used in the field to identify the borehole location in a three dimentional coordinate (Latitude, longitude, and altitude), water samples were collected from 156 borehole wells, and analyzed for physico-chemical parameters like electrical conductivity, Total dissolved solid,Chloride, Nitrate, Sodium, Magnisium, Calcium,and Flouride, using standard techniques in the laboratory and compared with the standards.The ground water quality maps of the entire study area have been prepared using spatial interpolation technique for all the above parameters.then the created maps used to visualize, analyze, and understand the relationship among the measured points. Mapping was coded for potable zones, non-potable zones in the study area, in terms of water quality sutability for drinking water and sutability for irrigation. In general satellite remote sensing in conjunction with geographical information system (GIS) offers great potential for water resource development and management.

Response Surface Methods For Spatially-Resolved Optical Measurement Techniques

NASA Technical Reports Server (NTRS)

Danehy, P. M.; Dorrington, A. A.; Cutler, A. D.; DeLoach, R.

2003-01-01

Response surface methods (or methodology), RSM, have been applied to improve data quality for two vastly different spatially-resolved optical measurement techniques. In the first application, modern design of experiments (MDOE) methods, including RSM, are employed to map the temperature field in a direct-connect supersonic combustion test facility at NASA Langley Research Center. The laser-based measurement technique known as coherent anti-Stokes Raman spectroscopy (CARS) is used to measure temperature at various locations in the combustor. RSM is then used to develop temperature maps of the flow. Even though the temperature fluctuations at a single point in the flowfield have a standard deviation on the order of 300 K, RSM provides analytic fits to the data having 95% confidence interval half width uncertainties in the fit as low as +/- 30 K. Methods of optimizing future CARS experiments are explored. The second application of RSM is to quantify the shape of a 5-meter diameter, ultra-lightweight, inflatable space antenna at NASA Langley Research Center. Photogrammetry is used to simultaneously measure the shape of the antenna at approximately 500 discrete spatial locations. RSM allows an analytic model to be developed that describes the shape of the majority of the antenna with an uncertainty of 0.4 mm, with 95% confidence. This model would allow a quantitative comparison between the actual shape of the antenna and the original design shape. Accurately determining this shape also allows confident interpolation between the measured points. Such a model could, for example, be used for ray tracing of radio-frequency waves up to 95 GHz. to predict the performance of the antenna.

Well-posedness and decay for the dissipative system modeling electro-hydrodynamics in negative Besov spaces

NASA Astrophysics Data System (ADS)

Zhao, Jihong; Liu, Qiao

2017-07-01

In Guo and Wang (2012) [10], Y. Guo and Y. Wang developed a general new energy method for proving the optimal time decay rates of the solutions to dissipative equations. In this paper, we generalize this method in the framework of homogeneous Besov spaces. Moreover, we apply this method to a model arising from electro-hydrodynamics, which is a strongly coupled system of the Navier-Stokes equations and the Poisson-Nernst-Planck equations through charge transport and external forcing terms. We show that some weighted negative Besov norms of solutions are preserved along time evolution, and obtain the optimal time decay rates of the higher-order spatial derivatives of solutions by the Fourier splitting approach and the interpolation techniques.

High resolution multi-facies realizations of sedimentary reservoir and aquifer analogs

PubMed Central

Bayer, Peter; Comunian, Alessandro; Höyng, Dominik; Mariethoz, Gregoire

2015-01-01

Geological structures are by nature inaccessible to direct observation. This can cause difficulties in applications where a spatially explicit representation of such structures is required, in particular when modelling fluid migration in geological formations. An increasing trend in recent years has been to use analogs to palliate this lack of knowledge, i.e., exploiting the spatial information from sites where the geology is accessible (outcrops, quarry sites) and transferring the observed properties to a study site deemed geologically similar. While this approach is appealing, it is difficult to put in place because of the lack of access to well-documented analog data. In this paper we present comprehensive analog data sets which characterize sedimentary structures from important groundwater hosting formations in Germany and Brazil. Multiple 2-D outcrop faces are described in terms of hydraulic, thermal and chemical properties and interpolated in 3-D using stochastic techniques. These unique data sets can be used by the wider community to implement analog approaches for characterizing reservoir and aquifer formations. PMID:26175910

A Review on Medical Image Registration as an Optimization Problem

PubMed Central

Song, Guoli; Han, Jianda; Zhao, Yiwen; Wang, Zheng; Du, Huibin

2017-01-01

Objective: In the course of clinical treatment, several medical media are required by a phy-sician in order to provide accurate and complete information about a patient. Medical image registra-tion techniques can provide a richer diagnosis and treatment information to doctors and to provide a comprehensive reference source for the researchers involved in image registration as an optimization problem. Methods: The essence of image registration is associating two or more different images spatial asso-ciation, and getting the translation of their spatial relationship. For medical image registration, its pro-cess is not absolute. Its core purpose is finding the conversion relationship between different images. Result: The major step of image registration includes the change of geometrical dimensions, and change of the image of the combination, image similarity measure, iterative optimization and interpo-lation process. Conclusion: The contribution of this review is sort of related image registration research methods, can provide a brief reference for researchers about image registration. PMID:28845149

An integral conservative gridding--algorithm using Hermitian curve interpolation.

PubMed

Volken, Werner; Frei, Daniel; Manser, Peter; Mini, Roberto; Born, Ernst J; Fix, Michael K

2008-11-07

The problem of re-sampling spatially distributed data organized into regular or irregular grids to finer or coarser resolution is a common task in data processing. This procedure is known as 'gridding' or 're-binning'. Depending on the quantity the data represents, the gridding-algorithm has to meet different requirements. For example, histogrammed physical quantities such as mass or energy have to be re-binned in order to conserve the overall integral. Moreover, if the quantity is positive definite, negative sampling values should be avoided. The gridding process requires a re-distribution of the original data set to a user-requested grid according to a distribution function. The distribution function can be determined on the basis of the given data by interpolation methods. In general, accurate interpolation with respect to multiple boundary conditions of heavily fluctuating data requires polynomial interpolation functions of second or even higher order. However, this may result in unrealistic deviations (overshoots or undershoots) of the interpolation function from the data. Accordingly, the re-sampled data may overestimate or underestimate the given data by a significant amount. The gridding-algorithm presented in this work was developed in order to overcome these problems. Instead of a straightforward interpolation of the given data using high-order polynomials, a parametrized Hermitian interpolation curve was used to approximate the integrated data set. A single parameter is determined by which the user can control the behavior of the interpolation function, i.e. the amount of overshoot and undershoot. Furthermore, it is shown how the algorithm can be extended to multidimensional grids. The algorithm was compared to commonly used gridding-algorithms using linear and cubic interpolation functions. It is shown that such interpolation functions may overestimate or underestimate the source data by about 10-20%, while the new algorithm can be tuned to significantly reduce these interpolation errors. The accuracy of the new algorithm was tested on a series of x-ray CT-images (head and neck, lung, pelvis). The new algorithm significantly improves the accuracy of the sampled images in terms of the mean square error and a quality index introduced by Wang and Bovik (2002 IEEE Signal Process. Lett. 9 81-4).

Spatial analysis of lettuce downy mildew using geostatistics and geographic information systems.

PubMed

Wu, B M; van Bruggen, A H; Subbarao, K V; Pennings, G G

2001-02-01

ABSTRACT The epidemiology of lettuce downy mildew has been investigated extensively in coastal California. However, the spatial patterns of the disease and the distance that Bremia lactucae spores can be transported have not been determined. During 1995 to 1998, we conducted several field- and valley-scale surveys to determine spatial patterns of this disease in the Salinas valley. Geostatistical analyses of the survey data at both scales showed that the influence range of downy mildew incidence at one location on incidence at other locations was between 80 and 3,000 m. A linear relationship was detected between semivariance and lag distance at the field scale, although no single statistical model could fit the semi-variograms at the valley scale. Spatial interpolation by the inverse distance weighting method with a power of 2 resulted in plausible estimates of incidence throughout the valley. Cluster analysis in geographic information systems on the interpolated disease incidence from different dates demonstrated that the Salinas valley could be divided into two areas, north and south of Salinas City, with high and low disease pressure, respectively. Seasonal and spatial trends along the valley suggested that the distinction between the downy mildew conducive and nonconducive areas might be determined by environmental factors.

Spatial Resolution Requirements for Traffic-Related Air Pollutant Exposure Evaluations

PubMed Central

Batterman, Stuart; Chambliss, Sarah; Isakov, Vlad

2014-01-01

Vehicle emissions represent one of the most important air pollution sources in most urban areas, and elevated concentrations of pollutants found near major roads have been associated with many adverse health impacts. To understand these impacts, exposure estimates should reflect the spatial and temporal patterns observed for traffic-related air pollutants. This paper evaluates the spatial resolution and zonal systems required to estimate accurately intraurban and near-road exposures of traffic-related air pollutants. The analyses use the detailed information assembled for a large (800 km2) area centered on Detroit, Michigan, USA. Concentrations of nitrogen oxides (NOx) due to vehicle emissions were estimated using hourly traffic volumes and speeds on 9,700 links representing all but minor roads in the city, the MOVES2010 emission model, the RLINE dispersion model, local meteorological data, a temporal resolution of 1 hr, and spatial resolution as low as 10 m. Model estimates were joined with the corresponding shape files to estimate residential exposures for 700,000 individuals at property parcel, census block, census tract, and ZIP code levels. We evaluate joining methods, the spatial resolution needed to meet specific error criteria, and the extent of exposure misclassification. To portray traffic-related air pollutant exposure, raster or inverse distance-weighted interpolations are superior to nearest neighbor approaches, and interpolations between receptors and points of interest should not exceed about 40 m near major roads, and 100 m at larger distances. For census tracts and ZIP codes, average exposures are overestimated since few individuals live very near major roads, the range of concentrations is compressed, most exposures are misclassified, and high concentrations near roads are entirely omitted. While smaller zones improve performance considerably, even block-level data can misclassify many individuals. To estimate exposures and impacts of traffic-related pollutants accurately, data should be geocoded or estimated at the most-resolved spatial level; census tract and larger zones have little if any ability to represent intraurban variation in traffic-related air pollutant concentrations. These results are based on one of the most comprehensive intraurban modeling studies in the literature and results are robust. Recommendations address the value of dispersion models to portray spatial and temporal variation of air pollutants in epidemiology and other studies; techniques to improve accuracy and reduce the computational burden in urban scale modeling; the necessary spatial resolution for health surveillance, demographic, and pollution data; and the consequences of low resolution data in terms of exposure misclassification. PMID:25132794

Surface modeling of soil antibiotics.

PubMed

Shi, Wen-jiao; Yue, Tian-xiang; Du, Zheng-ping; Wang, Zong; Li, Xue-wen

2016-02-01

Large numbers of livestock and poultry feces are continuously applied into soils in intensive vegetable cultivation areas, and then some veterinary antibiotics are persistent existed in soils and cause health risk. For the spatial heterogeneity of antibiotic residues, developing a suitable technique to interpolate soil antibiotic residues is still a challenge. In this study, we developed an effective interpolator, high accuracy surface modeling (HASM) combined vegetable types, to predict the spatial patterns of soil antibiotics, using 100 surface soil samples collected from an intensive vegetable cultivation area located in east of China, and the fluoroquinolones (FQs), including ciprofloxacin (CFX), enrofloxacin (EFX) and norfloxacin (NFX), were analyzed as the target antibiotics. The results show that vegetable type is an effective factor to be combined to improve the interpolator performance. HASM achieves less mean absolute errors (MAEs) and root mean square errors (RMSEs) for total FQs (NFX+CFX+EFX), NFX, CFX and EFX than kriging with external drift (KED), stratified kriging (StK), ordinary kriging (OK) and inverse distance weighting (IDW). The MAE of HASM for FQs is 55.1 μg/kg, and the MAEs of KED, StK, OK and IDW are 99.0 μg/kg, 102.8 μg/kg, 106.3 μg/kg and 108.7 μg/kg, respectively. Further, RMSE simulated by HASM for FQs (CFX, EFX and NFX) are 106.2 μg/kg (88.6 μg/kg, 20.4 μg/kg and 39.2 μg/kg), and less 30% (27%, 22% and 36%), 33% (27%, 27% and 43%), 38% (34%, 23% and 41%) and 42% (32%, 35% and 51%) than the ones by KED, StK, OK and IDW, respectively. HASM also provides better maps with more details and more consistent maximum and minimum values of soil antibiotics compared with the measured data. The better performance can be concluded that HASM takes the vegetable type information as global approximate information, and takes local sampling data as its optimum control constraints. Copyright © 2015 Elsevier B.V. All rights reserved.

Assimilation of ground and satellite snow observations in a distributed hydrologic model to improve water supply forecasts in the Upper Colorado River Basin

NASA Astrophysics Data System (ADS)

Micheletty, P. D.; Day, G. N.; Quebbeman, J.; Carney, S.; Park, G. H.

2016-12-01

The Upper Colorado River Basin above Lake Powell is a major source of water supply for 25 million people and provides irrigation water for 3.5 million acres. Approximately 85% of the annual runoff is produced from snowmelt. Water supply forecasts of the April-July runoff produced by the National Weather Service (NWS) Colorado Basin River Forecast Center (CBRFC), are critical to basin water management. This project leverages advanced distributed models, datasets, and snow data assimilation techniques to improve operational water supply forecasts made by CBRFC in the Upper Colorado River Basin. The current work will specifically focus on improving water supply forecasts through the implementation of a snow data assimilation process coupled with the Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM). Three types of observations will be used in the snow data assimilation system: satellite Snow Covered Area (MODSCAG), satellite Dust Radiative Forcing in Snow (MODDRFS), and SNOTEL Snow Water Equivalent (SWE). SNOTEL SWE provides the main source of high elevation snowpack information during the snow season, however, these point measurement sites are carefully selected to provide consistent indices of snowpack, and may not be representative of the surrounding watershed. We address this problem by transforming the SWE observations to standardized deviates and interpolating the standardized deviates using a spatial regression model. The interpolation process will also take advantage of the MODIS Snow Covered Area and Grainsize (MODSCAG) product to inform the model on the spatial distribution of snow. The interpolated standardized deviates are back-transformed and used in an Ensemble Kalman Filter (EnKF) to update the model simulated SWE. The MODIS Dust Radiative Forcing in Snow (MODDRFS) product will be used more directly through temporary adjustments to model snowmelt parameters, which should improve melt estimates in areas affected by dust on snow. In order to assess the value of different data sources, reforecasts will be produced for a historical period and performance measures will be computed to assess forecast skill. The existing CBRFC Ensemble Streamflow Prediction (ESP) reforecasts will provide a baseline for comparison to determine the added-value of the data assimilation process.

Proceedings of the Third Annual Symposium on Mathematical Pattern Recognition and Image Analysis

NASA Technical Reports Server (NTRS)

Guseman, L. F., Jr.

1985-01-01

Topics addressed include: multivariate spline method; normal mixture analysis applied to remote sensing; image data analysis; classifications in spatially correlated environments; probability density functions; graphical nonparametric methods; subpixel registration analysis; hypothesis integration in image understanding systems; rectification of satellite scanner imagery; spatial variation in remotely sensed images; smooth multidimensional interpolation; and optimal frequency domain textural edge detection filters.

[Spatial distribution prediction of surface soil Pb in a battery contaminated site].

PubMed

Liu, Geng; Niu, Jun-Jie; Zhang, Chao; Zhao, Xin; Guo, Guan-Lin

2014-12-01

In order to enhance the reliability of risk estimation and to improve the accuracy of pollution scope determination in a battery contaminated site with the soil characteristic pollutant Pb, four spatial interpolation models, including Combination Prediction Model (OK(LG) + TIN), kriging model (OK(BC)), Inverse Distance Weighting model (IDW), and Spline model were employed to compare their effects on the spatial distribution and pollution assessment of soil Pb. The results showed that Pb concentration varied significantly and the data was severely skewed. The variation coefficient of the site was higher in the local region. OK(LG) + TIN was found to be more accurate than the other three models in predicting the actual pollution situations of the contaminated site. The prediction accuracy of other models was lower, due to the effect of the principle of different models and datum feature. The interpolation results of OK(BC), IDW and Spline could not reflect the detailed characteristics of seriously contaminated areas, and were not suitable for mapping and spatial distribution prediction of soil Pb in this site. This study gives great contributions and provides useful references for defining the remediation boundary and making remediation decision of contaminated sites.

GENIE - Generation of computational geometry-grids for internal-external flow configurations

NASA Technical Reports Server (NTRS)

Soni, B. K.

1988-01-01

Progress realized in the development of a master geometry-grid generation code GENIE is presented. The grid refinement process is enhanced by developing strategies to utilize bezier curves/surfaces and splines along with weighted transfinite interpolation technique and by formulating new forcing function for the elliptic solver based on the minimization of a non-orthogonality functional. A two step grid adaptation procedure is developed by optimally blending adaptive weightings with weighted transfinite interpolation technique. Examples of 2D-3D grids are provided to illustrate the success of these methods.

Geometric and shading correction for images of printed materials using boundary.

PubMed

Brown, Michael S; Tsoi, Yau-Chat

2006-06-01

A novel technique that uses boundary interpolation to correct geometric distortion and shading artifacts present in images of printed materials is presented. Unlike existing techniques, our algorithm can simultaneously correct a variety of geometric distortions, including skew, fold distortion, binder curl, and combinations of these. In addition, the same interpolation framework can be used to estimate the intrinsic illumination component of the distorted image to correct shading artifacts. We detail our algorithm for geometric and shading correction and demonstrate its usefulness on real-world and synthetic data.

Improved Visualization of Gastrointestinal Slow Wave Propagation Using a Novel Wavefront-Orientation Interpolation Technique.

PubMed

Mayne, Terence P; Paskaranandavadivel, Niranchan; Erickson, Jonathan C; OGrady, Gregory; Cheng, Leo K; Angeli, Timothy R

2018-02-01

High-resolution mapping of gastrointestinal (GI) slow waves is a valuable technique for research and clinical applications. Interpretation of high-resolution GI mapping data relies on animations of slow wave propagation, but current methods remain as rudimentary, pixelated electrode activation animations. This study aimed to develop improved methods of visualizing high-resolution slow wave recordings that increases ease of interpretation. The novel method of "wavefront-orientation" interpolation was created to account for the planar movement of the slow wave wavefront, negate any need for distance calculations, remain robust in atypical wavefronts (i.e., dysrhythmias), and produce an appropriate interpolation boundary. The wavefront-orientation method determines the orthogonal wavefront direction and calculates interpolated values as the mean slow wave activation-time (AT) of the pair of linearly adjacent electrodes along that direction. Stairstep upsampling increased smoothness and clarity. Animation accuracy of 17 human high-resolution slow wave recordings (64-256 electrodes) was verified by visual comparison to the prior method showing a clear improvement in wave smoothness that enabled more accurate interpretation of propagation, as confirmed by an assessment of clinical applicability performed by eight GI clinicians. Quantitatively, the new method produced accurate interpolation values compared to experimental data (mean difference 0.02 ± 0.05 s) and was accurate when applied solely to dysrhythmic data (0.02 ± 0.06 s), both within the error in manual AT marking (mean 0.2 s). Mean interpolation processing time was 6.0 s per wave. These novel methods provide a validated visualization platform that will improve analysis of high-resolution GI mapping in research and clinical translation.

Empirical Assessment of Spatial Prediction Methods for Location Cost Adjustment Factors

PubMed Central

Migliaccio, Giovanni C.; Guindani, Michele; D'Incognito, Maria; Zhang, Linlin

2014-01-01

In the feasibility stage, the correct prediction of construction costs ensures that budget requirements are met from the start of a project's lifecycle. A very common approach for performing quick-order-of-magnitude estimates is based on using Location Cost Adjustment Factors (LCAFs) that compute historically based costs by project location. Nowadays, numerous LCAF datasets are commercially available in North America, but, obviously, they do not include all locations. Hence, LCAFs for un-sampled locations need to be inferred through spatial interpolation or prediction methods. Currently, practitioners tend to select the value for a location using only one variable, namely the nearest linear-distance between two sites. However, construction costs could be affected by socio-economic variables as suggested by macroeconomic theories. Using a commonly used set of LCAFs, the City Cost Indexes (CCI) by RSMeans, and the socio-economic variables included in the ESRI Community Sourcebook, this article provides several contributions to the body of knowledge. First, the accuracy of various spatial prediction methods in estimating LCAF values for un-sampled locations was evaluated and assessed in respect to spatial interpolation methods. Two Regression-based prediction models were selected, a Global Regression Analysis and a Geographically-weighted regression analysis (GWR). Once these models were compared against interpolation methods, the results showed that GWR is the most appropriate way to model CCI as a function of multiple covariates. The outcome of GWR, for each covariate, was studied for all the 48 states in the contiguous US. As a direct consequence of spatial non-stationarity, it was possible to discuss the influence of each single covariate differently from state to state. In addition, the article includes a first attempt to determine if the observed variability in cost index values could be, at least partially explained by independent socio-economic variables. PMID:25018582

Evaluation of a spatial rainfall generator and an interpolation methods for the creation of future gridded data sets over complex terrains

NASA Astrophysics Data System (ADS)

Camera, Corrado; Bruggeman, Adriana; Hadjinicolaou, Panos; Michaelides, Silas; Lange, Manfred A.

2015-04-01

Space-time variability of precipitation plays a key role as a driver of many processes in different environmental fields like hydrology, ecology, biology, agriculture, and natural hazards. The objective of this study was to compare two approaches for statistical downscaling of precipitation from climate models. The study was applied to the island of Cyprus, an orographically complex terrain. The first approach makes use of a spatial temporal Neyman-Scott Rectangular Pulses (NSRP) model and a previously tested interpolation scheme (Camera et al., 2014). The second approach is based on the use of the single site NSRP model and a simplified gridded scheme based on scaling coefficients obtained from past observations. The rainfall generators were evaluated on the period 1980-2010. Both approaches were subsequently used to downscale three RCMs from the EU ENSEMBLE project to calculate climate projections (2020-2050). The main advantage of the spatial-temporal approach is that it allows creating spatially consistent daily maps of precipitation. On the other hand, due to the assumptions made using a stochastic generator based on homogeneous Poisson processes, it shows a smoothing out of all the rainfall statistics (except mean and variance) all over the study area. This leads to high errors when analyzing indices related to extremes. Examples are the number of days with rainfall over 50 mm (R50 - mean error 65%), the 95th percentile value of rainy days (RT95 - mean error 19%), and the mean annual rainfall recorded on days with rainfall above the 95th percentile (RA95 - mean error 22%). The single site approach excludes the possibility of using the created gridded data sets for case studies involving spatial connection between grid cells (e.g. hydrologic modelling), but it leads to a better reproduction of rainfall statistics and properties. The errors for the extreme indices are in fact much lower: 17% for R50, 4% for RT95, and 2% for RA95. Future projections show a decrease of the mean annual rainfall (for both approaches) over the study area between 70 mm (≈15%) and 5 mm (≈1%), in comparison to the reference period 1980-2010. Regarding extremes, calculated only with the single site approach, the projections show a decrease of the R50 index between 25% and 7%, and of the RT95 between 8% and 0%. Thus, these projections indicate that a slight reduction in the number and intensity of extremes can be expected. Further research will be done to adapt and evaluate the use of a spatial-temporal generator with nonhomogeneous spatial activation of raincells (Burton et al., 2010) to the study area. Burton, A., Fowler, H.J., Kilsby, C.G., O'Connell, P. E., 2010a. A stochastic model for the spatial-temporal simulation of non-homogeneous rainfall occurrence and amounts, Water Resour. Res. 46, W11501. DOI: 10.1029/2009WR008884 Camera, C., Bruggeman, A., Hadjinicolaou, P., Pashiardis, S., Lange, M. A., 2014. Evaluation of interpolation techniques for the creation of gridded daily precipitation (1 × 1 km2); Cyprus, 1980-2010. J. Geophys. Res. Atmos., 119, 693-712. DOI: 10.1002/2013JD020611.

A novel interpolation approach for the generation of 3D-geometric digital bone models from image stacks

PubMed Central

Mittag, U.; Kriechbaumer, A.; Rittweger, J.

2017-01-01

The authors propose a new 3D interpolation algorithm for the generation of digital geometric 3D-models of bones from existing image stacks obtained by peripheral Quantitative Computed Tomography (pQCT) or Magnetic Resonance Imaging (MRI). The technique is based on the interpolation of radial gray value profiles of the pQCT cross sections. The method has been validated by using an ex-vivo human tibia and by comparing interpolated pQCT images with images from scans taken at the same position. A diversity index of <0.4 (1 meaning maximal diversity) even for the structurally complex region of the epiphysis, along with the good agreement of mineral-density-weighted cross-sectional moment of inertia (CSMI), demonstrate the high quality of our interpolation approach. Thus the authors demonstrate that this interpolation scheme can substantially improve the generation of 3D models from sparse scan sets, not only with respect to the outer shape but also with respect to the internal gray-value derived material property distribution. PMID:28574415

Interpolation of extensive routine water pollution monitoring datasets: methodology and discussion of implications for aquifer management

NASA Astrophysics Data System (ADS)

Yuval; Rimon, Y.; Graber, E. R.; Furman, A.

2013-07-01

A large fraction of the fresh water available for human use is stored in groundwater aquifers. Since human activities such as mining, agriculture, industry and urbanization often result in incursion of various pollutants to groundwater, routine monitoring of water quality is an indispensable component of judicious aquifer management. Unfortunately, groundwater pollution monitoring is expensive and usually cannot cover an aquifer with the spatial resolution necessary for making adequate management decisions. Interpolation of monitoring data between points is thus an important tool for supplementing measured data. However, interpolating routine groundwater pollution data poses a special problem due to the nature of the observations. The data from a producing aquifer usually includes many zero pollution concentration values from the clean parts of the aquifer but may span a wide range (up to a few orders of magnitude) of values in the polluted areas. This manuscript presents a methodology that can cope with such datasets and use them to produce maps that present the pollution plumes but also delineates the clean areas that are fit for production. A method for assessing the quality of mapping in a way which is suitable to the data's dynamic range of values is also presented. Local variant of inverse distance weighting is employed to interpolate the data. Inclusion zones around the interpolation points ensure that only relevant observations contribute to each interpolated concentration. Using inclusion zones improves the accuracy of the mapping but results in interpolation grid points which are not assigned a value. That inherent trade-off between the interpolation accuracy and coverage is demonstrated using both circular and elliptical inclusion zones. A leave-one-out cross testing is used to assess and compare the performance of the interpolations. The methodology is demonstrated using groundwater pollution monitoring data from the Coastal aquifer along the Israeli shoreline.

Interpolation of extensive routine water pollution monitoring datasets: methodology and discussion of implications for aquifer management.

PubMed

Yuval, Yuval; Rimon, Yaara; Graber, Ellen R; Furman, Alex

2014-08-01

A large fraction of the fresh water available for human use is stored in groundwater aquifers. Since human activities such as mining, agriculture, industry and urbanisation often result in incursion of various pollutants to groundwater, routine monitoring of water quality is an indispensable component of judicious aquifer management. Unfortunately, groundwater pollution monitoring is expensive and usually cannot cover an aquifer with the spatial resolution necessary for making adequate management decisions. Interpolation of monitoring data is thus an important tool for supplementing monitoring observations. However, interpolating routine groundwater pollution data poses a special problem due to the nature of the observations. The data from a producing aquifer usually includes many zero pollution concentration values from the clean parts of the aquifer but may span a wide range of values (up to a few orders of magnitude) in the polluted areas. This manuscript presents a methodology that can cope with such datasets and use them to produce maps that present the pollution plumes but also delineates the clean areas that are fit for production. A method for assessing the quality of mapping in a way which is suitable to the data's dynamic range of values is also presented. A local variant of inverse distance weighting is employed to interpolate the data. Inclusion zones around the interpolation points ensure that only relevant observations contribute to each interpolated concentration. Using inclusion zones improves the accuracy of the mapping but results in interpolation grid points which are not assigned a value. The inherent trade-off between the interpolation accuracy and coverage is demonstrated using both circular and elliptical inclusion zones. A leave-one-out cross testing is used to assess and compare the performance of the interpolations. The methodology is demonstrated using groundwater pollution monitoring data from the coastal aquifer along the Israeli shoreline. The implications for aquifer management are discussed.

Validation of China-wide interpolated daily climate variables from 1960 to 2011

NASA Astrophysics Data System (ADS)

Yuan, Wenping; Xu, Bing; Chen, Zhuoqi; Xia, Jiangzhou; Xu, Wenfang; Chen, Yang; Wu, Xiaoxu; Fu, Yang

2015-02-01

Temporally and spatially continuous meteorological variables are increasingly in demand to support many different types of applications related to climate studies. Using measurements from 600 climate stations, a thin-plate spline method was applied to generate daily gridded climate datasets for mean air temperature, maximum temperature, minimum temperature, relative humidity, sunshine duration, wind speed, atmospheric pressure, and precipitation over China for the period 1961-2011. A comprehensive evaluation of interpolated climate was conducted at 150 independent validation sites. The results showed superior performance for most of the estimated variables. Except for wind speed, determination coefficients ( R 2) varied from 0.65 to 0.90, and interpolations showed high consistency with observations. Most of the estimated climate variables showed relatively consistent accuracy among all seasons according to the root mean square error, R 2, and relative predictive error. The interpolated data correctly predicted the occurrence of daily precipitation at validation sites with an accuracy of 83 %. Moreover, the interpolation data successfully explained the interannual variability trend for the eight meteorological variables at most validation sites. Consistent interannual variability trends were observed at 66-95 % of the sites for the eight meteorological variables. Accuracy in distinguishing extreme weather events differed substantially among the meteorological variables. The interpolated data identified extreme events for the three temperature variables, relative humidity, and sunshine duration with an accuracy ranging from 63 to 77 %. However, for wind speed, air pressure, and precipitation, the interpolation model correctly identified only 41, 48, and 58 % of extreme events, respectively. The validation indicates that the interpolations can be applied with high confidence for the three temperatures variables, as well as relative humidity and sunshine duration based on the performance of these variables in estimating daily variations, interannual variability, and extreme events. Although longitude, latitude, and elevation data are included in the model, additional information, such as topography and cloud cover, should be integrated into the interpolation algorithm to improve performance in estimating wind speed, atmospheric pressure, and precipitation.

Spacecraft Orbit Anomaly Representation Using Thrust-Fourier-Coefficients with Orbit Determination Toolbox

NASA Astrophysics Data System (ADS)

Ko, H.; Scheeres, D.

2014-09-01

Representing spacecraft orbit anomalies between two separate states is a challenging but an important problem in achieving space situational awareness for an active spacecraft. Incorporation of such a capability could play an essential role in analyzing satellite behaviors as well as trajectory estimation of the space object. A general way to deal with the anomaly problem is to add an estimated perturbing acceleration such as dynamic model compensation (DMC) into an orbit determination process based on pre- and post-anomaly tracking data. It is a time-consuming numerical process to find valid coefficients to compensate for unknown dynamics for the anomaly. Even if the orbit determination filter with DMC can crudely estimate an unknown acceleration, this approach does not consider any fundamental element of the unknown dynamics for a given anomaly. In this paper, a new way of representing a spacecraft anomaly using an interpolation technique with the Thrust-Fourier-Coefficients (TFCs) is introduced and several anomaly cases are studied using this interpolation method. It provides a very efficient way of reconstructing the fundamental elements of the dynamics for a given spacecraft anomaly. Any maneuver performed by a satellite transitioning between two arbitrary orbital states can be represented as an equivalent maneuver using an interpolation technique with the TFCs. Given unconnected orbit states between two epochs due to a spacecraft anomaly, it is possible to obtain a unique control law using the TFCs that is able to generate the desired secular behavior for the given orbital changes. This interpolation technique can capture the fundamental elements of combined unmodeled anomaly events. The interpolated orbit trajectory, using the TFCs compensating for a given anomaly, can be used to improve the quality of orbit fits through the anomaly period and therefore help to obtain a good orbit determination solution after the anomaly. Orbit Determination Toolbox (ODTBX) is modified to adapt this technique in order to verify the performance of this interpolation approach. Spacecraft anomaly cases are based on either single or multiple low or high thrust maneuvers and the unknown thrust accelerations are recovered and compared with the true thrust acceleration. The advantage of this approach is to easily append TFCs and its dynamics to the pre-built ODTBX, which enables us to blend post-anomaly tracking data to improve the performance of the interpolation representation in the absence of detailed information about a maneuver. It allows us to improve space situational awareness in the areas of uncertainty propagation, anomaly characterization and track correlation.

Interpreting the Geochemistry of the Northern Peninsula Ranges Batholith Using Principle Component Analysis and Spatial Interpolation

NASA Astrophysics Data System (ADS)

Pompe, L.; Clausen, B. L.; Morton, D. M.

2014-12-01

The Cretaceous northern Peninsular Ranges batholith (PRB) exemplifies emplacement in a combination oceanic arc / continental margin arc setting. Two approaches that can aid in understanding its statistical and spatial geochemistry variation are principle component analysis (PCA) and GIS interpolation mapping. The data analysis primarily used 287 samples from the large granitoid geochemical data set systematically collected by Baird and Welday. Of these, 80 points fell in the western Santa Ana block, 108 in the transitional Perris block, and 99 in the eastern San Jacinto block. In the statistical analysis, multivariate outliers were identified using Mahalanobis distance and excluded. A centered log ratio transformation was used to facilitate working with geochemical concentration values that range over many orders of magnitude. The data was then analyzed using PCA with IBM SPSS 21 reducing 40 geochemical variables to 4 components which are approximately related to the compatible, HFS, HRE, and LIL elements. The 4 components were interpreted as follows: (1) compatible [and negatively correlated incompatible] elements indicate extent of differentiation as typified by SiO2, (2) HFS elements indicate crustal contamination as typified by Sri and Nb/Yb ratios, (3) HRE elements indicate source depth as typified by Sr/Y and Gd/Yb ratios, and (4) LIL elements indicate alkalinity as typified by the K2O/SiO2ratio. Spatial interpolation maps of the 4 components were created with Esri ArcGIS for Desktop 10.2 by interpolating between the sample points using kriging and inverse distance weighting. Across-arc trends on the interpolation maps indicate a general increase from west to east for each of the 4 components, but with local exceptions as follows. The 15km offset on the San Jacinto Fault may be affecting the contours. South of San Jacinto is a west-east band of low Nb/Yb, Gd/Yb, and Sr/Y ratios. The highest Sr/Y ratios in the north central area that decrease further east may be due to the far eastern granitoids being transported above a shear zone. Along the western edge of the PRB, high SiO2 and K2O/SiO2 are interpreted to result from sampling shallow levels in the batholith (2-3 kb), as compared to deeper levels in the central (5-6 kb) and eastern (4.5 kb) areas.

Repeated surveys by acoustic Doppler current profiler for flow and sediment dynamics in a tidal river

USGS Publications Warehouse

Dinehart, R.L.; Burau, J.R.

2005-01-01

A strategy of repeated surveys by acoustic Doppler current profiler (ADCP) was applied in a tidal river to map velocity vectors and suspended-sediment indicators. The Sacramento River at the junction with the Delta Cross Channel at Walnut Grove, California, was surveyed over several tidal cycles in the Fall of 2000 and 2001 with a vessel-mounted ADCP. Velocity profiles were recorded along flow-defining survey paths, with surveys repeated every 27 min through a diurnal tidal cycle. Velocity vectors along each survey path were interpolated to a three-dimensional Cartesian grid that conformed to local bathymetry. A separate array of vectors was interpolated onto a grid from each survey. By displaying interpolated vector grids sequentially with computer animation, flow dynamics of the reach could be studied in three-dimensions as flow responded to the tidal cycle. Velocity streamtraces in the grid showed the upwelling of flow from the bottom of the Sacramento River channel into the Delta Cross Channel. The sequential display of vector grids showed that water in the canal briefly returned into the Sacramento River after peak flood tides, which had not been known previously. In addition to velocity vectors, ADCP data were processed to derive channel bathymetry and a spatial indicator for suspended-sediment concentration. Individual beam distances to bed, recorded by the ADCP, were transformed to yield bathymetry accurate enough to resolve small bedforms within the study reach. While recording velocity, ADCPs also record the intensity of acoustic backscatter from particles suspended in the flow. Sequential surveys of backscatter intensity were interpolated to grids and animated to indicate the spatial movement of suspended sediment through the study reach. Calculation of backscatter flux through cross-sectional grids provided a first step for computation of suspended-sediment discharge, the second step being a calibrated relation between backscatter intensity and sediment concentration. Spatial analyses of ADCP data showed that a strategy of repeated surveys and flow-field interpolation has the potential to simplify computation of flow and sediment discharge through complex waterways. The use of trade, product, industry, or firm names in this report is for descriptive purposes only and does not constitute endorsement of products by the US Government. ?? 2005 Elsevier B.V. All rights reserved.

Spatial electromagnetic field intensity modelling of global system for mobile communication base stations in the Istanbul Technical University Ayazaga campus area.

PubMed

Boz, Kubra; Denli, Hayri Hakan

2018-05-07

The rapid development of the global system for mobile communication services and the consequent increased electromagnetic field (EMF) exposure to the human body have generated debate on the potential danger with respect to human health. The many research studies focused on this subject have, however, not provided any certain evidence about harmful consequences due to mobile communication systems. On the other hand, there are still views suggesting such exposure might affect the human body in different ways. To reduce such effects to a minimum, the International Commission on Non-Ionizing Radiation Protection (ICNIRP) has declared boundary values for the energy released by the base stations, which are the main source of the electromagnetic fields. These values are accepted by many countries in various parts of the world. The aim of this study was to create EMF intensity maps for the area covered by Istanbul Technical University (ITU) and find areas of potential risk with regard to health considering the current situation and future trends. In this study, the field intensities of electromagnetic signals issued at the frequencies of 900 and 1800 MHz were measured in V/m at 29 pre-specified survey points using a spectrum analyzer (Spectran HF-6065). Geographic information systems and spatial interpolation techniques were used to produce EMF intensity maps. Three different spatial interpolation methods, minimum mean square error, Radial Basis and Empirical Bayesian Kriging, were compared. The results were geographically analyzed and the measurements expressed as heat maps covering the study area. Using these maps, the values measured were compared with the EMF intensity standards issued by ICNIRP. The results showed that the exposure levels to the EMF intensities were all within the ICNIRP limits at the ITU study area. However, since the EMF intensity level with respect to human health is not known, it is not possible to confirm if these levels are safe or not.

On the influence of zero-padding on the nonlinear operations in Quantitative Susceptibility Mapping

PubMed Central

Eskreis-Winkler, Sarah; Zhou, Dong; Liu, Tian; Gupta, Ajay; Gauthier, Susan A.; Wang, Yi; Spincemaille, Pascal

2016-01-01

Purpose Zero padding is a well-studied interpolation technique that improves image visualization without increasing image resolution. This interpolation is often performed as a last step before images are displayed on clinical workstations. Here, we seek to demonstrate the importance of zero padding before rather than after performing non-linear post-processing algorithms, such as Quantitative Susceptibility Mapping (QSM). To do so, we evaluate apparent spatial resolution, relative error and depiction of multiple sclerosis (MS) lesions on images that were zero padded prior to, in the middle of, and after the application of the QSM algorithm. Materials and Methods High resolution gradient echo (GRE) data were acquired on twenty MS patients, from which low resolution data were derived using k-space cropping. Pre-, mid-, and post-zero padded QSM images were reconstructed from these low resolution data by zero padding prior to field mapping, after field mapping, and after susceptibility mapping, respectively. Using high resolution QSM as the gold standard, apparent spatial resolution, relative error, and image quality of the pre-, mid-, and post-zero padded QSM images were measured and compared. Results Both the accuracy and apparent spatial resolution of the pre-zero padded QSM was higher than that of mid-zero padded QSM (p < 0.001; p < 0.001), which was higher than that of post-zero padded QSM (p < 0.001; p < 0.001). The image quality of pre-zero padded reconstructions was higher than that of mid- and post-zero padded reconstructions (p = 0.004; p < 0.001). Conclusion Zero padding of the complex GRE data prior to nonlinear susceptibility mapping improves image accuracy and apparent resolution compared to zero padding afterwards. It also provides better delineation of MS lesion geometry, which may improve lesion subclassification and disease monitoring in MS patients. PMID:27587225

On the influence of zero-padding on the nonlinear operations in Quantitative Susceptibility Mapping.

PubMed

Eskreis-Winkler, Sarah; Zhou, Dong; Liu, Tian; Gupta, Ajay; Gauthier, Susan A; Wang, Yi; Spincemaille, Pascal

2017-01-01

Zero padding is a well-studied interpolation technique that improves image visualization without increasing image resolution. This interpolation is often performed as a last step before images are displayed on clinical workstations. Here, we seek to demonstrate the importance of zero padding before rather than after performing non-linear post-processing algorithms, such as Quantitative Susceptibility Mapping (QSM). To do so, we evaluate apparent spatial resolution, relative error and depiction of multiple sclerosis (MS) lesions on images that were zero padded prior to, in the middle of, and after the application of the QSM algorithm. High resolution gradient echo (GRE) data were acquired on twenty MS patients, from which low resolution data were derived using k-space cropping. Pre-, mid-, and post-zero padded QSM images were reconstructed from these low resolution data by zero padding prior to field mapping, after field mapping, and after susceptibility mapping, respectively. Using high resolution QSM as the gold standard, apparent spatial resolution, relative error, and image quality of the pre-, mid-, and post-zero padded QSM images were measured and compared. Both the accuracy and apparent spatial resolution of the pre-zero padded QSM was higher than that of mid-zero padded QSM (p<0.001; p<0.001), which was higher than that of post-zero padded QSM (p<0.001; p<0.001). The image quality of pre-zero padded reconstructions was higher than that of mid- and post-zero padded reconstructions (p=0.004; p<0.001). Zero padding of the complex GRE data prior to nonlinear susceptibility mapping improves image accuracy and apparent resolution compared to zero padding afterwards. It also provides better delineation of MS lesion geometry, which may improve lesion subclassification and disease monitoring in MS patients. Copyright © 2016 Elsevier Inc. All rights reserved.

Interpolation strategies for reducing IFOV artifacts in microgrid polarimeter imagery.

PubMed

Ratliff, Bradley M; LaCasse, Charles F; Tyo, J Scott

2009-05-25

Microgrid polarimeters are composed of an array of micro-polarizing elements overlaid upon an FPA sensor. In the past decade systems have been designed and built in all regions of the optical spectrum. These systems have rugged, compact designs and the ability to obtain a complete set of polarimetric measurements during a single image capture. However, these systems acquire the polarization measurements through spatial modulation and each measurement has a varying instantaneous field-of-view (IFOV). When these measurements are combined to estimate the polarization images, strong edge artifacts are present that severely degrade the estimated polarization imagery. These artifacts can be reduced when interpolation strategies are first applied to the intensity data prior to Stokes vector estimation. Here we formally study IFOV error and the performance of several bilinear interpolation strategies used for reducing it.

Long-term Observations of Intense Precipitation Small-scale Spatial Variability in a Semi-arid Catchment

NASA Astrophysics Data System (ADS)

Cropp, E. L.; Hazenberg, P.; Castro, C. L.; Demaria, E. M.

2017-12-01

In the southwestern US, the summertime North American Monsoon (NAM) provides about 60% of the region's annual precipitation. Recent research using high-resolution atmospheric model simulations and retrospective predictions has shown that since the 1950's, and more specifically in the last few decades, the mean daily precipitation in the southwestern U.S. during the NAM has followed a decreasing trend. Furthermore, days with more extreme precipitation have intensified. The current work focuses the impact of these long-term changes on the observed small-scale spatial variability of intense precipitation. Since limited long-term high-resolution observational data exist to support such climatological-induced spatial changes in precipitation frequency and intensity, the current work utilizes observations from the USDA-ARS Walnut Gulch Experimental Watershed (WGEW) in southeastern Arizona. Within this 150 km^2 catchment over 90 rain gauges have been installed since the 1950s, measuring at sub-hourly resolution. We have applied geospatial analyses and the kriging interpolation technique to identify long-term changes in the spatial and temporal correlation and anisotropy of intense precipitation. The observed results will be compared with the previously model simulated results, as well as related to large-scale variations in climate patterns, such as the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO).

Application of Design Methodologies for Feedback Compensation Associated with Linear Systems

NASA Technical Reports Server (NTRS)

Smith, Monty J.

1996-01-01

The work that follows is concerned with the application of design methodologies for feedback compensation associated with linear systems. In general, the intent is to provide a well behaved closed loop system in terms of stability and robustness (internal signals remain bounded with a certain amount of uncertainty) and simultaneously achieve an acceptable level of performance. The approach here has been to convert the closed loop system and control synthesis problem into the interpolation setting. The interpolation formulation then serves as our mathematical representation of the design process. Lifting techniques have been used to solve the corresponding interpolation and control synthesis problems. Several applications using this multiobjective design methodology have been included to show the effectiveness of these techniques. In particular, the mixed H 2-H performance criteria with algorithm has been used on several examples including an F-18 HARV (High Angle of Attack Research Vehicle) for sensitivity performance.

Evaluation of interpolation techniques for the creation of gridded daily precipitation (1 × 1 km2); Cyprus, 1980-2010

NASA Astrophysics Data System (ADS)

Camera, Corrado; Bruggeman, Adriana; Hadjinicolaou, Panos; Pashiardis, Stelios; Lange, Manfred A.

2014-01-01

High-resolution gridded daily data sets are essential for natural resource management and the analyses of climate changes and their effects. This study aims to evaluate the performance of 15 simple or complex interpolation techniques in reproducing daily precipitation at a resolution of 1 km2 over topographically complex areas. Methods are tested considering two different sets of observation densities and different rainfall amounts. We used rainfall data that were recorded at 74 and 145 observational stations, respectively, spread over the 5760 km2 of the Republic of Cyprus, in the Eastern Mediterranean. Regression analyses utilizing geographical copredictors and neighboring interpolation techniques were evaluated both in isolation and combined. Linear multiple regression (LMR) and geographically weighted regression methods (GWR) were tested. These included a step-wise selection of covariables, as well as inverse distance weighting (IDW), kriging, and 3D-thin plate splines (TPS). The relative rank of the different techniques changes with different station density and rainfall amounts. Our results indicate that TPS performs well for low station density and large-scale events and also when coupled with regression models. It performs poorly for high station density. The opposite is observed when using IDW. Simple IDW performs best for local events, while a combination of step-wise GWR and IDW proves to be the best method for large-scale events and high station density. This study indicates that the use of step-wise regression with a variable set of geographic parameters can improve the interpolation of large-scale events because it facilitates the representation of local climate dynamics.

LPV Controller Interpolation for Improved Gain-Scheduling Control Performance

NASA Technical Reports Server (NTRS)

Wu, Fen; Kim, SungWan

2002-01-01

In this paper, a new gain-scheduling control design approach is proposed by combining LPV (linear parameter-varying) control theory with interpolation techniques. The improvement of gain-scheduled controllers can be achieved from local synthesis of Lyapunov functions and continuous construction of a global Lyapunov function by interpolation. It has been shown that this combined LPV control design scheme is capable of improving closed-loop performance derived from local performance improvement. The gain of the LPV controller will also change continuously across parameter space. The advantages of the newly proposed LPV control is demonstrated through a detailed AMB controller design example.

Student Support for Research in Hierarchical Control and Trajectory Planning

NASA Technical Reports Server (NTRS)

Martin, Clyde F.

1999-01-01

Generally, classical polynomial splines tend to exhibit unwanted undulations. In this work, we discuss a technique, based on control principles, for eliminating these undulations and increasing the smoothness properties of the spline interpolants. We give a generalization of the classical polynomial splines and show that this generalization is, in fact, a family of splines that covers the broad spectrum of polynomial, trigonometric and exponential splines. A particular element in this family is determined by the appropriate control data. It is shown that this technique is easy to implement. Several numerical and curve-fitting examples are given to illustrate the advantages of this technique over the classical approach. Finally, we discuss the convergence properties of the interpolant.

Operational Interoperable Web Coverage Service for Earth Observing Satellite Data: Issues and Lessons Learned

NASA Astrophysics Data System (ADS)

Yang, W.; Min, M.; Bai, Y.; Lynnes, C.; Holloway, D.; Enloe, Y.; di, L.

2008-12-01

In the past few years, there have been growing interests, among major earth observing satellite (EOS) data providers, in serving data through the interoperable Web Coverage Service (WCS) interface protocol, developed by the Open Geospatial Consortium (OGC). The interface protocol defined in WCS specifications allows client software to make customized requests of multi-dimensional EOS data, including spatial and temporal subsetting, resampling and interpolation, and coordinate reference system (CRS) transformation. A WCS server describes an offered coverage, i.e., a data product, through a response to a client's DescribeCoverage request. The description includes the offered coverage's spatial/temporal extents and resolutions, supported CRSs, supported interpolation methods, and supported encoding formats. Based on such information, a client can request the entire or a subset of coverage in any spatial/temporal resolutions and in any one of the supported CRSs, formats, and interpolation methods. When implementing a WCS server, a data provider has different approaches to present its data holdings to clients. One of the most straightforward, and commonly used, approaches is to offer individual physical data files as separate coverages. Such implementation, however, will result in too many offered coverages for large data holdings and it also cannot fully present the relationship among different, but spatially and/or temporally associated, data files. It is desirable to disconnect offered coverages from physical data files so that the former is more coherent, especially in spatial and temporal domains. Therefore, some servers offer one single coverage for a set of spatially coregistered time series data files such as a daily global precipitation coverage linked to many global single- day precipitation files; others offer one single coverage for multiple temporally coregistered files together forming a large spatial extent. In either case, a server needs to assemble an output coverage real-time by combining potentially large number of physical files, which can be operationally difficult. The task becomes more challenging if an offered coverage involves spatially and temporally un-registered physical files. In this presentation, we will discuss issues and lessons learned in providing NASA's AIRS Level 2 atmospheric products, which are in satellite swath CRS and in 6-minute segment granule files, as virtual global coverages. We"ll discuss the WCS server's on- the-fly georectification, mosaicking, quality screening, performance, and scalability.

Roles of Spatial Scale and Rarity on the Relationship between Butterfly Species Richness and Human Density in South Africa

PubMed Central

Mecenero, Silvia; Altwegg, Res; Colville, Jonathan F.; Beale, Colin M.

2015-01-01

Wildlife and humans tend to prefer the same productive environments, yet high human densities often lead to reduced biodiversity. Species richness is often positively correlated with human population density at broad scales, but this correlation could also be caused by unequal sampling effort leading to higher species tallies in areas of dense human activity. We examined the relationships between butterfly species richness and human population density at five spatial resolutions ranging from 2' to 60' across South Africa. We used atlas-type data and spatial interpolation techniques aimed at reducing the effect of unequal spatial sampling. Our results confirm the general positive correlation between total species richness and human population density. Contrary to our expectations, the strength of this positive correlation did not weaken at finer spatial resolutions. The patterns observed using total species richness were driven mostly by common species. The richness of threatened and restricted range species was not correlated to human population density. None of the correlations we examined were particularly strong, with much unexplained variance remaining, suggesting that the overlap between butterflies and humans is not strong compared to other factors not accounted for in our analyses. Special consideration needs to be made regarding conservation goals and variables used when investigating the overlap between species and humans for biodiversity conservation. PMID:25915899

Roles of Spatial Scale and Rarity on the Relationship between Butterfly Species Richness and Human Density in South Africa.

PubMed

Mecenero, Silvia; Altwegg, Res; Colville, Jonathan F; Beale, Colin M

2015-01-01

Wildlife and humans tend to prefer the same productive environments, yet high human densities often lead to reduced biodiversity. Species richness is often positively correlated with human population density at broad scales, but this correlation could also be caused by unequal sampling effort leading to higher species tallies in areas of dense human activity. We examined the relationships between butterfly species richness and human population density at five spatial resolutions ranging from 2' to 60' across South Africa. We used atlas-type data and spatial interpolation techniques aimed at reducing the effect of unequal spatial sampling. Our results confirm the general positive correlation between total species richness and human population density. Contrary to our expectations, the strength of this positive correlation did not weaken at finer spatial resolutions. The patterns observed using total species richness were driven mostly by common species. The richness of threatened and restricted range species was not correlated to human population density. None of the correlations we examined were particularly strong, with much unexplained variance remaining, suggesting that the overlap between butterflies and humans is not strong compared to other factors not accounted for in our analyses. Special consideration needs to be made regarding conservation goals and variables used when investigating the overlap between species and humans for biodiversity conservation.

Lung Cancer Mortality and Radon Concentration in a Chronically Exposed Neighborhood in Chihuahua, Mexico: A Geospatial Analysis

PubMed Central

Hinojosa de la Garza, Octavio R.; Sanín, Luz H.; Montero Cabrera, María Elena; Serrano Ramirez, Korina Ivette; Martínez Meyer, Enrique; Reyes Cortés, Manuel

2014-01-01

This study correlated lung cancer (LC) mortality with statistical data obtained from government public databases. In order to asses a relationship between LC deaths and radon accumulation in dwellings, indoor radon concentrations were measured with passive detectors randomly distributed in Chihuahua City. Kriging (K) and Inverse-Distance Weighting (IDW) spatial interpolations were carried out. Deaths were georeferenced and Moran's I correlation coefficients were calculated. The mean values (over n = 171) of the interpolation of radon concentrations of deceased's dwellings were 247.8 and 217.1 Bq/m3, for K and IDW, respectively. Through the Moran's I values obtained, correspondingly equal to 0.56 and 0.61, it was evident that LC mortality was directly associated with locations with high levels of radon, considering a stable population for more than 25 years, suggesting spatial clustering of LC deaths due to indoor radon concentrations. PMID:25165752

Bayesian hierarchical models for regional climate reconstructions of the last glacial maximum

NASA Astrophysics Data System (ADS)

Weitzel, Nils; Hense, Andreas; Ohlwein, Christian

2017-04-01

Spatio-temporal reconstructions of past climate are important for the understanding of the long term behavior of the climate system and the sensitivity to forcing changes. Unfortunately, they are subject to large uncertainties, have to deal with a complex proxy-climate structure, and a physically reasonable interpolation between the sparse proxy observations is difficult. Bayesian Hierarchical Models (BHMs) are a class of statistical models that is well suited for spatio-temporal reconstructions of past climate because they permit the inclusion of multiple sources of information (e.g. records from different proxy types, uncertain age information, output from climate simulations) and quantify uncertainties in a statistically rigorous way. BHMs in paleoclimatology typically consist of three stages which are modeled individually and are combined using Bayesian inference techniques. The data stage models the proxy-climate relation (often named transfer function), the process stage models the spatio-temporal distribution of the climate variables of interest, and the prior stage consists of prior distributions of the model parameters. For our BHMs, we translate well-known proxy-climate transfer functions for pollen to a Bayesian framework. In addition, we can include Gaussian distributed local climate information from preprocessed proxy records. The process stage combines physically reasonable spatial structures from prior distributions with proxy records which leads to a multivariate posterior probability distribution for the reconstructed climate variables. The prior distributions that constrain the possible spatial structure of the climate variables are calculated from climate simulation output. We present results from pseudoproxy tests as well as new regional reconstructions of temperatures for the last glacial maximum (LGM, ˜ 21,000 years BP). These reconstructions combine proxy data syntheses with information from climate simulations for the LGM that were performed in the PMIP3 project. The proxy data syntheses consist either of raw pollen data or of normally distributed climate data from preprocessed proxy records. Future extensions of our method contain the inclusion of other proxy types (transfer functions), the implementation of other spatial interpolation techniques, the use of age uncertainties, and the extension to spatio-temporal reconstructions of the last deglaciation. Our work is part of the PalMod project funded by the German Federal Ministry of Education and Science (BMBF).

Spatial Copula Model for Imputing Traffic Flow Data from Remote Microwave Sensors

PubMed Central

Ma, Xiaolei; Du, Bowen; Yu, Bin

2017-01-01

Issues of missing data have become increasingly serious with the rapid increase in usage of traffic sensors. Analyses of the Beijing ring expressway have showed that up to 50% of microwave sensors pose missing values. The imputation of missing traffic data must be urgently solved although a precise solution that cannot be easily achieved due to the significant number of missing portions. In this study, copula-based models are proposed for the spatial interpolation of traffic flow from remote traffic microwave sensors. Most existing interpolation methods only rely on covariance functions to depict spatial correlation and are unsuitable for coping with anomalies due to Gaussian consumption. Copula theory overcomes this issue and provides a connection between the correlation function and the marginal distribution function of traffic flow. To validate copula-based models, a comparison with three kriging methods is conducted. Results indicate that copula-based models outperform kriging methods, especially on roads with irregular traffic patterns. Copula-based models demonstrate significant potential to impute missing data in large-scale transportation networks. PMID:28934164

Spatial interpolation of solar global radiation

NASA Astrophysics Data System (ADS)

Lussana, C.; Uboldi, F.; Antoniazzi, C.

2010-09-01

Solar global radiation is defined as the radiant flux incident onto an area element of the terrestrial surface. Its direct knowledge plays a crucial role in many applications, from agrometeorology to environmental meteorology. The ARPA Lombardia's meteorological network includes about one hundred of pyranometers, mostly distributed in the southern part of the Alps and in the centre of the Po Plain. A statistical interpolation method based on an implementation of the Optimal Interpolation is applied to the hourly average of the solar global radiation observations measured by the ARPA Lombardia's network. The background field is obtained using SMARTS (The Simple Model of the Atmospheric Radiative Transfer of Sunshine, Gueymard, 2001). The model is initialised by assuming clear sky conditions and it takes into account the solar position and orography related effects (shade and reflection). The interpolation of pyranometric observations introduces in the analysis fields information about cloud presence and influence. A particular effort is devoted to prevent observations affected by large errors of different kinds (representativity errors, systematic errors, gross errors) from entering the analysis procedure. The inclusion of direct cloud information from satellite observations is also planned.

The Atmospheric Data Acquisition And Interpolation Process For Center-TRACON Automation System

NASA Technical Reports Server (NTRS)

Jardin, M. R.; Erzberger, H.; Denery, Dallas G. (Technical Monitor)

1995-01-01

The Center-TRACON Automation System (CTAS), an advanced new air traffic automation program, requires knowledge of spatial and temporal atmospheric conditions such as the wind speed and direction, the temperature and the pressure in order to accurately predict aircraft trajectories. Real-time atmospheric data is available in a grid format so that CTAS must interpolate between the grid points to estimate the atmospheric parameter values. The atmospheric data grid is generally not in the same coordinate system as that used by CTAS so that coordinate conversions are required. Both the interpolation and coordinate conversion processes can introduce errors into the atmospheric data and reduce interpolation accuracy. More accurate algorithms may be computationally expensive or may require a prohibitively large amount of data storage capacity so that trade-offs must be made between accuracy and the available computational and data storage resources. The atmospheric data acquisition and processing employed by CTAS will be outlined in this report. The effects of atmospheric data processing on CTAS trajectory prediction will also be analyzed, and several examples of the trajectory prediction process will be given.

Delimiting Areas of Endemism through Kernel Interpolation

PubMed Central

Oliveira, Ubirajara; Brescovit, Antonio D.; Santos, Adalberto J.

2015-01-01

We propose a new approach for identification of areas of endemism, the Geographical Interpolation of Endemism (GIE), based on kernel spatial interpolation. This method differs from others in being independent of grid cells. This new approach is based on estimating the overlap between the distribution of species through a kernel interpolation of centroids of species distribution and areas of influence defined from the distance between the centroid and the farthest point of occurrence of each species. We used this method to delimit areas of endemism of spiders from Brazil. To assess the effectiveness of GIE, we analyzed the same data using Parsimony Analysis of Endemism and NDM and compared the areas identified through each method. The analyses using GIE identified 101 areas of endemism of spiders in Brazil GIE demonstrated to be effective in identifying areas of endemism in multiple scales, with fuzzy edges and supported by more synendemic species than in the other methods. The areas of endemism identified with GIE were generally congruent with those identified for other taxonomic groups, suggesting that common processes can be responsible for the origin and maintenance of these biogeographic units. PMID:25611971

When Distractors and To-Be-Remembered Items Compete for the Control of Action: A New Perspective on Serial Memory for Spatial Information

ERIC Educational Resources Information Center

Guerard, Katherine; Tremblay, Sebastien

2011-01-01

In serial memory for spatial information, performance is impaired when distractors are interpolated between to-be-remembered (TBR) stimuli (Tremblay, Nicholls, Parmentier, & Jones, 2005). The so-called sandwich effect, combined with the use of eye tracking, served as a tool for examining the role of the oculomotor system in serial memory for…

Linking Meteorology, Air Quality Models and Observations to ...

EPA Pesticide Factsheets

Epidemiologic studies are critical in establishing the association between exposure to air pollutants and adverse health effects. Results of epidemiologic studies are used by U.S. EPA in developing air quality standards to protect the public from the health effects of air pollutants. A major challenge in environmental epidemiology is adequate exposure characterization. Numerous health studies have used measurements from a few central-site ambient monitors to characterize air pollution exposures. Relying solely on central-site ambient monitors does not account for the spatial-heterogeneity of ambient air pollution patterns, the temporal variability in ambient concentrations, nor the influence of infiltration and indoor sources. Central-site monitoring becomes even more problematic for certain air pollutants that exhibit significant spatial heterogeneity. Statistical interpolation techniques and passive monitoring methods can provide additional spatial resolution in ambient concentration estimates. In addition, spatio-temporal models, which integrate GIS data and other factors, such as meteorology, have also been developed to produce more resolved estimates of ambient concentrations. Models, such as the Community Multi-Scale Air Quality (CMAQ) model, estimate ambient concentrations by combining information on meteorology, source emissions, and chemical-fate and transport. Hybrid modeling approaches, which integrate regional scale models with local scale dispersion

Comparison of spatial interpolation of rainfall with emphasis on extreme events

NASA Astrophysics Data System (ADS)

Amin, Kanwal; Duan, Zheng; Disse, Markus

2017-04-01

The sparse network of rain-gauges has always motivated the scientists to find more robust ways to include the spatial variability of precipitation. Turning Bands Simulation, External Drift Kriging, Copula and Random Mixing are amongst one of them. Remote sensing Technologies i.e., radar and satellite estimations are widely known to provide a spatial profile of the precipitation, however during extreme events the accuracy of the resulted areal precipitation is still under discussion. The aim is to compare the areal hourly precipitation results of a flood event from RADOLAN (Radar online adjustment) with the gridded rainfall obtained via Turning Bands Simulation (TBM) and Inverse Distance Weighting (IDW) method. The comparison is mainly focused on performing the uncertainty analysis of the areal precipitation through the said simulation and remote sensing technique for the Upper Main Catchment. The comparison of the results obtained from TBM, IDW and RADOLAN show considerably similar results near the rain gauge stations, but the degree of ambiguity elevates with the increasing distance from the gauge stations. Future research will be carried out to compare the forecasted gridded precipitation simulations with the real-time rainfall forecast system (RADVOR) to make the flood evacuation process more robust and efficient.

Optimal Interpolation scheme to generate reference crop evapotranspiration

NASA Astrophysics Data System (ADS)

Tomas-Burguera, Miquel; Beguería, Santiago; Vicente-Serrano, Sergio; Maneta, Marco

2018-05-01

We used an Optimal Interpolation (OI) scheme to generate a reference crop evapotranspiration (ETo) grid, forcing meteorological variables, and their respective error variance in the Iberian Peninsula for the period 1989-2011. To perform the OI we used observational data from the Spanish Meteorological Agency (AEMET) and outputs from a physically-based climate model. To compute ETo we used five OI schemes to generate grids for the five observed climate variables necessary to compute ETo using the FAO-recommended form of the Penman-Monteith equation (FAO-PM). The granularity of the resulting grids are less sensitive to variations in the density and distribution of the observational network than those generated by other interpolation methods. This is because our implementation of the OI method uses a physically-based climate model as prior background information about the spatial distribution of the climatic variables, which is critical for under-observed regions. This provides temporal consistency in the spatial variability of the climatic fields. We also show that increases in the density and improvements in the distribution of the observational network reduces substantially the uncertainty of the climatic and ETo estimates. Finally, a sensitivity analysis of observational uncertainties and network densification suggests the existence of a trade-off between quantity and quality of observations.

An approach to unbiased subsample interpolation for motion tracking.

PubMed

McCormick, Matthew M; Varghese, Tomy

2013-04-01

Accurate subsample displacement estimation is necessary for ultrasound elastography because of the small deformations that occur and the subsequent application of a derivative operation on local displacements. Many of the commonly used subsample estimation techniques introduce significant bias errors. This article addresses a reduced bias approach to subsample displacement estimations that consists of a two-dimensional windowed-sinc interpolation with numerical optimization. It is shown that a Welch or Lanczos window with a Nelder-Mead simplex or regular-step gradient-descent optimization is well suited for this purpose. Little improvement results from a sinc window radius greater than four data samples. The strain signal-to-noise ratio (SNR) obtained in a uniformly elastic phantom is compared with other parabolic and cosine interpolation methods; it is found that the strain SNR ratio is improved over parabolic interpolation from 11.0 to 13.6 in the axial direction and 0.7 to 1.1 in the lateral direction for an applied 1% axial deformation. The improvement was most significant for small strains and displacement tracking in the lateral direction. This approach does not rely on special properties of the image or similarity function, which is demonstrated by its effectiveness with the application of a previously described regularization technique.

Analysis and simulation of wireless signal propagation applying geostatistical interpolation techniques

NASA Astrophysics Data System (ADS)

Kolyaie, S.; Yaghooti, M.; Majidi, G.

2011-12-01

This paper is a part of an ongoing research to examine the capability of geostatistical analysis for mobile networks coverage prediction, simulation and tuning. Mobile network coverage predictions are used to find network coverage gaps and areas with poor serviceability. They are essential data for engineering and management in order to make better decision regarding rollout, planning and optimisation of mobile networks.The objective of this research is to evaluate different interpolation techniques in coverage prediction. In method presented here, raw data collected from drive testing a sample of roads in study area is analysed and various continuous surfaces are created using different interpolation methods. Two general interpolation methods are used in this paper with different variables; first, Inverse Distance Weighting (IDW) with various powers and number of neighbours and second, ordinary kriging with Gaussian, spherical, circular and exponential semivariogram models with different number of neighbours. For the result comparison, we have used check points coming from the same drive test data. Prediction values for check points are extracted from each surface and the differences with actual value are computed. The output of this research helps finding an optimised and accurate model for coverage prediction.

The effect of administrative boundaries and geocoding error on cancer rates in California.

PubMed

Goldberg, Daniel W; Cockburn, Myles G

2012-04-01

Geocoding is often used to produce maps of disease rates from the diagnosis addresses of incident cases to assist with disease surveillance, prevention, and control. In this process, diagnosis addresses are converted into latitude/longitude pairs which are then aggregated to produce rates at varying geographic scales such as Census tracts, neighborhoods, cities, counties, and states. The specific techniques used within geocoding systems have an impact on where the output geocode is located and can therefore have an effect on the derivation of disease rates at different geographic aggregations. This paper investigates how county-level cancer rates are affected by the choice of interpolation method when case data are geocoded to the ZIP code level. Four commonly used areal unit interpolation techniques are applied and the output of each is used to compute crude county-level five-year incidence rates of all cancers in California. We found that the rates observed for 44 out of the 58 counties in California vary based on which interpolation method is used, with rates in some counties increasing by nearly 400% between interpolation methods. Copyright © 2012 Elsevier Ltd. All rights reserved.

Estimating impacts of plantation forestry on plant biodiversity in southern Chile-a spatially explicit modelling approach.

PubMed

Braun, Andreas Christian; Koch, Barbara

2016-10-01

Monitoring the impacts of land-use practices is of particular importance with regard to biodiversity hotspots in developing countries. Here, conserving the high level of unique biodiversity is challenged by limited possibilities for data collection on site. Especially for such scenarios, assisting biodiversity assessments by remote sensing has proven useful. Remote sensing techniques can be applied to interpolate between biodiversity assessments taken in situ. Through this approach, estimates of biodiversity for entire landscapes can be produced, relating land-use intensity to biodiversity conditions. Such maps are a valuable basis for developing biodiversity conservation plans. Several approaches have been published so far to interpolate local biodiversity assessments in remote sensing data. In the following, a new approach is proposed. Instead of inferring biodiversity using environmental variables or the variability of spectral values, a hypothesis-based approach is applied. Empirical knowledge about biodiversity in relation to land-use is formalized and applied as ascription rules for image data. The method is exemplified for a large study site (over 67,000 km(2)) in central Chile, where forest industry heavily impacts plant diversity. The proposed approach yields a coefficient of correlation of 0.73 and produces a convincing estimate of regional biodiversity. The framework is broad enough to be applied to other study sites.

Fast high resolution reconstruction in multi-slice and multi-view cMRI

NASA Astrophysics Data System (ADS)

Velasco Toledo, Nelson; Romero Castro, Eduardo

2015-01-01

Cardiac magnetic resonance imaging (cMRI) is an useful tool in diagnosis, prognosis and research since it functionally tracks the heart structure. Although useful, this imaging technique is limited in spatial resolution because heart is a constant moving organ, also there are other non controled conditions such as patient movements and volumetric changes during apnea periods when data is acquired, those conditions limit the time to capture high quality information. This paper presents a very fast and simple strategy to reconstruct high resolution 3D images from a set of low resolution series of 2D images. The strategy is based on an information reallocation algorithm which uses the DICOM header to relocate voxel intensities in a regular grid. An interpolation method is applied to fill empty places with estimated data, the interpolation resamples the low resolution information to estimate the missing information. As a final step a gaussian filter that denoises the final result. A reconstructed image evaluation is performed using as a reference a super-resolution reconstructed image. The evaluation reveals that the method maintains the general heart structure with a small loss in detailed information (edge sharpening and blurring), some artifacts related with input information quality are detected. The proposed method requires low time and computational resources.

Optimum interpolation analysis of basin-scale ¹³⁷Cs transport in surface seawater in the North Pacific Ocean.

PubMed

Inomata, Y; Aoyama, M; Tsumune, D; Motoi, T; Nakano, H

2012-12-01

¹³⁷Cs is one of the conservative tracers applied to the study of oceanic circulation processes on decadal time scales. To investigate the spatial distribution and the temporal variation of ¹³⁷Cs concentrations in surface seawater in the North Pacific Ocean after 1957, a technique for optimum interpolation (OI) was applied to understand the behaviour of ¹³⁷Cs that revealed the basin-scale circulation of Cs ¹³⁷Cs in surface seawater in the North Pacific Ocean: ¹³⁷Cs deposited in the western North Pacific Ocean from global fallout (late 1950s and early 1960s) and from local fallout (transported from the Bikini and Enewetak Atolls during the late 1950s) was further transported eastward with the Kuroshio and North Pacific Currents within several years of deposition and was accumulated in the eastern North Pacific Ocean until 1967. Subsequently, ¹³⁷Cs concentrations in the eastern North Pacific Ocean decreased due to southward transport. Less radioactively contaminated seawater was also transported northward, upstream of the North Equatorial Current in the western North Pacific Ocean in the 1970s, indicating seawater re-circulation in the North Pacific Gyre.

Integration of Geophysical Data into Structural Geological Modelling through Bayesian Networks

NASA Astrophysics Data System (ADS)

de la Varga, Miguel; Wellmann, Florian; Murdie, Ruth

2016-04-01

Structural geological models are widely used to represent the spatial distribution of relevant geological features. Several techniques exist to construct these models on the basis of different assumptions and different types of geological observations (e.g. Jessell et al., 2014). However, two problems are prevalent when constructing models: (i) observations and assumptions, and therefore also the constructed model, are subject to uncertainties, and (ii) additional information, such as geophysical data, is often available, but cannot be considered directly in the geological modelling step. In our work, we propose the integration of all available data into a Bayesian network including the generation of the implicit geological method by means of interpolation functions (Mallet, 1992; Lajaunie et al., 1997; Mallet, 2004; Carr et al., 2001; Hillier et al., 2014). As a result, we are able to increase the certainty of the resultant models as well as potentially learn features of our regional geology through data mining and information theory techniques. MCMC methods are used in order to optimize computational time and assure the validity of the results. Here, we apply the aforementioned concepts in a 3-D model of the Sandstone Greenstone Belt in the Archean Yilgarn Craton in Western Australia. The example given, defines the uncertainty in the thickness of greenstone as limited by Bouguer anomaly and the internal structure of the greenstone as limited by the magnetic signature of a banded iron formation. The incorporation of the additional data and specially the gravity provides an important reduction of the possible outcomes and therefore the overall uncertainty. References Carr, C. J., K. R. Beatson, B. J. Cherrie, J. T. Mitchell, R. W. Fright, C. B. McCallum, and R. T. Evans, 2001, Reconstruction and representation of 3D objects with radial basis functions: Proceedings of the 28th annual conference on Computer graphics and interactive techniques, 67-76. Jessell, M., Aillères, L., de Kemp, E., Lindsay, M., Wellmann, F., Hillier, M., ... & Martin, R. (2014). Next Generation Three-Dimensional Geologic Modeling and Inversion. Lajaunie, C., G. Courrioux, and L. Manuel, 1997, Foliation fields and 3D cartography in geology: Principles of a method based on potential interpolation: Mathematical Geology, 29, 571-584. Mallet, J.-L., 1992, Discrete smooth interpolation in geometric modelling: Computer-Aided Design, 24, 178-191 Mallet, L. J., 2004, Space-time mathematical framework for sedimentary geology: Mathematical Geology, 36, 1-32.

Optimising rigid motion compensation for small animal brain PET imaging

NASA Astrophysics Data System (ADS)

Spangler-Bickell, Matthew G.; Zhou, Lin; Kyme, Andre Z.; De Laat, Bart; Fulton, Roger R.; Nuyts, Johan

2016-10-01

Motion compensation (MC) in PET brain imaging of awake small animals is attracting increased attention in preclinical studies since it avoids the confounding effects of anaesthesia and enables behavioural tests during the scan. A popular MC technique is to use multiple external cameras to track the motion of the animal’s head, which is assumed to be represented by the motion of a marker attached to its forehead. In this study we have explored several methods to improve the experimental setup and the reconstruction procedures of this method: optimising the camera-marker separation; improving the temporal synchronisation between the motion tracker measurements and the list-mode stream; post-acquisition smoothing and interpolation of the motion data; and list-mode reconstruction with appropriately selected subsets. These techniques have been tested and verified on measurements of a moving resolution phantom and brain scans of an awake rat. The proposed techniques improved the reconstructed spatial resolution of the phantom by 27% and of the rat brain by 14%. We suggest a set of optimal parameter values to use for awake animal PET studies and discuss the relative significance of each parameter choice.

Evaluation of Bias Correction Method for Satellite-Based Rainfall Data

PubMed Central

Bhatti, Haris Akram; Rientjes, Tom; Haile, Alemseged Tamiru; Habib, Emad; Verhoef, Wouter

2016-01-01

With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration’s (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003–2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW’s) of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach. PMID:27314363

Evaluation of Bias Correction Method for Satellite-Based Rainfall Data.

PubMed

Bhatti, Haris Akram; Rientjes, Tom; Haile, Alemseged Tamiru; Habib, Emad; Verhoef, Wouter

2016-06-15

With the advances in remote sensing technology, satellite-based rainfall estimates are gaining attraction in the field of hydrology, particularly in rainfall-runoff modeling. Since estimates are affected by errors correction is required. In this study, we tested the high resolution National Oceanic and Atmospheric Administration's (NOAA) Climate Prediction Centre (CPC) morphing technique (CMORPH) satellite rainfall product (CMORPH) in the Gilgel Abbey catchment, Ethiopia. CMORPH data at 8 km-30 min resolution is aggregated to daily to match in-situ observations for the period 2003-2010. Study objectives are to assess bias of the satellite estimates, to identify optimum window size for application of bias correction and to test effectiveness of bias correction. Bias correction factors are calculated for moving window (MW) sizes and for sequential windows (SW's) of 3, 5, 7, 9, …, 31 days with the aim to assess error distribution between the in-situ observations and CMORPH estimates. We tested forward, central and backward window (FW, CW and BW) schemes to assess the effect of time integration on accumulated rainfall. Accuracy of cumulative rainfall depth is assessed by Root Mean Squared Error (RMSE). To systematically correct all CMORPH estimates, station based bias factors are spatially interpolated to yield a bias factor map. Reliability of interpolation is assessed by cross validation. The uncorrected CMORPH rainfall images are multiplied by the interpolated bias map to result in bias corrected CMORPH estimates. Findings are evaluated by RMSE, correlation coefficient (r) and standard deviation (SD). Results showed existence of bias in the CMORPH rainfall. It is found that the 7 days SW approach performs best for bias correction of CMORPH rainfall. The outcome of this study showed the efficiency of our bias correction approach.

Robust sampling-sourced numerical retrieval algorithm for optical energy loss function based on log-log mesh optimization and local monotonicity preserving Steffen spline

NASA Astrophysics Data System (ADS)

Maglevanny, I. I.; Smolar, V. A.

2016-01-01

We introduce a new technique of interpolation of the energy-loss function (ELF) in solids sampled by empirical optical spectra. Finding appropriate interpolation methods for ELFs poses several challenges. The sampled ELFs are usually very heterogeneous, can originate from various sources thus so called "data gaps" can appear, and significant discontinuities and multiple high outliers can be present. As a result an interpolation based on those data may not perform well at predicting reasonable physical results. Reliable interpolation tools, suitable for ELF applications, should therefore satisfy several important demands: accuracy and predictive power, robustness and computational efficiency, and ease of use. We examined the effect on the fitting quality due to different interpolation schemes with emphasis on ELF mesh optimization procedures and we argue that the optimal fitting should be based on preliminary log-log scaling data transforms by which the non-uniformity of sampled data distribution may be considerably reduced. The transformed data are then interpolated by local monotonicity preserving Steffen spline. The result is a piece-wise smooth fitting curve with continuous first-order derivatives that passes through all data points without spurious oscillations. Local extrema can occur only at grid points where they are given by the data, but not in between two adjacent grid points. It is found that proposed technique gives the most accurate results and also that its computational time is short. Thus, it is feasible using this simple method to address practical problems associated with interaction between a bulk material and a moving electron. A compact C++ implementation of our algorithm is also presented.

The Interpolation Theory of Radial Basis Functions

NASA Astrophysics Data System (ADS)

Baxter, Brad

2010-06-01

In this dissertation, it is first shown that, when the radial basis function is a p-norm and 1 < p < 2, interpolation is always possible when the points are all different and there are at least two of them. We then show that interpolation is not always possible when p > 2. Specifically, for every p > 2, we construct a set of different points in some Rd for which the interpolation matrix is singular. The greater part of this work investigates the sensitivity of radial basis function interpolants to changes in the function values at the interpolation points. Our early results show that it is possible to recast the work of Ball, Narcowich and Ward in the language of distributional Fourier transforms in an elegant way. We then use this language to study the interpolation matrices generated by subsets of regular grids. In particular, we are able to extend the classical theory of Toeplitz operators to calculate sharp bounds on the spectra of such matrices. Applying our understanding of these spectra, we construct preconditioners for the conjugate gradient solution of the interpolation equations. Our main result is that the number of steps required to achieve solution of the linear system to within a required tolerance can be independent of the number of interpolation points. The Toeplitz structure allows us to use fast Fourier transform techniques, which imp lies that the total number of operations is a multiple of n log n, where n is the number of interpolation points. Finally, we use some of our methods to study the behaviour of the multiquadric when its shape parameter increases to infinity. We find a surprising link with the sinus cardinalis or sinc function of Whittaker. Consequently, it can be highly useful to use a large shape parameter when approximating band-limited functions.

Large Subduction Earthquake Simulations using Finite Source Modeling and the Offshore-Onshore Ambient Seismic Field

NASA Astrophysics Data System (ADS)

Viens, L.; Miyake, H.; Koketsu, K.

2016-12-01

Large subduction earthquakes have the potential to generate strong long-period ground motions. The ambient seismic field, also called seismic noise, contains information about the elastic response of the Earth between two seismic stations that can be retrieved using seismic interferometry. The DONET1 network, which is composed of 20 offshore stations, has been deployed atop the Nankai subduction zone, Japan, to continuously monitor the seismotectonic activity in this highly seismically active region. The surrounding onshore area is covered by hundreds of seismic stations, which are operated the National Research Institute for Earth Science and Disaster Prevention (NIED) and the Japan Meteorological Agency (JMA), with a spacing of 15-20 km. We retrieve offshore-onshore Green's functions from the ambient seismic field using the deconvolution technique and use them to simulate the long-period ground motions of moderate subduction earthquakes that occurred at shallow depth. We extend the point source method, which is appropriate for moderate events, to finite source modeling to simulate the long-period ground motions of large Mw 7 class earthquake scenarios. The source models are constructed using scaling relations between moderate and large earthquakes to discretize the fault plane of the large hypothetical events into subfaults. Offshore-onshore Green's functions are spatially interpolated over the fault plane to obtain one Green's function for each subfault. The interpolated Green's functions are finally summed up considering different rupture velocities. Results show that this technique can provide additional information about earthquake ground motions that can be used with the existing physics-based simulations to improve seismic hazard assessment.

New impressive capabilities of SE-workbench for EO/IR real-time rendering of animated scenarios including flares

NASA Astrophysics Data System (ADS)

Le Goff, Alain; Cathala, Thierry; Latger, Jean

2015-10-01

To provide technical assessments of EO/IR flares and self-protection systems for aircraft, DGA Information superiority resorts to synthetic image generation to model the operational battlefield of an aircraft, as viewed by EO/IR threats. For this purpose, it completed the SE-Workbench suite from OKTAL-SE with functionalities to predict a realistic aircraft IR signature and is yet integrating the real-time EO/IR rendering engine of SE-Workbench called SE-FAST-IR. This engine is a set of physics-based software and libraries that allows preparing and visualizing a 3D scene for the EO/IR domain. It takes advantage of recent advances in GPU computing techniques. The recent past evolutions that have been performed concern mainly the realistic and physical rendering of reflections, the rendering of both radiative and thermal shadows, the use of procedural techniques for the managing and the rendering of very large terrains, the implementation of Image- Based Rendering for dynamic interpolation of plume static signatures and lastly for aircraft the dynamic interpolation of thermal states. The next step is the representation of the spectral, directional, spatial and temporal signature of flares by Lacroix Defense using OKTAL-SE technology. This representation is prepared from experimental data acquired during windblast tests and high speed track tests. It is based on particle system mechanisms to model the different components of a flare. The validation of a flare model will comprise a simulation of real trials and a comparison of simulation outputs to experimental results concerning the flare signature and above all the behavior of the stimulated threat.

Spatial interpolation methods and geostatistics for mapping groundwater contamination in a coastal area.

PubMed

Elumalai, Vetrimurugan; Brindha, K; Sithole, Bongani; Lakshmanan, Elango

2017-04-01

Mapping groundwater contaminants and identifying the sources are the initial steps in pollution control and mitigation. Due to the availability of different mapping methods and the large number of emerging pollutants, these methods need to be used together in decision making. The present study aims to map the contaminated areas in Richards Bay, South Africa and compare the results of ordinary kriging (OK) and inverse distance weighted (IDW) interpolation techniques. Statistical methods were also used for identifying contamination sources. Na-Cl groundwater type was dominant followed by Ca-Mg-Cl. Data analysis indicate that silicate weathering, ion exchange and fresh water-seawater mixing are the major geochemical processes controlling the presence of major ions in groundwater. Factor analysis also helped to confirm the results. Overlay analysis by OK and IDW gave different results. Areas where groundwater was unsuitable as a drinking source were 419 and 116 km 2 for OK and IDW, respectively. Such diverse results make decision making difficult, if only one method was to be used. Three highly contaminated zones within the study area were more accurately identified by OK. If large areas are identified as being contaminated such as by IDW in this study, the mitigation measures will be expensive. If these areas were underestimated, then even though management measures are taken, it will not be effective for a longer time. Use of multiple techniques like this study will help to avoid taking harsh decisions. Overall, the groundwater quality in this area was poor, and it is essential to identify alternate drinking water source or treat the groundwater before ingestion.

Advances in satellite remote sensing of environmental variables for epidemiological applications.

PubMed

Goetz, S J; Prince, S D; Small, J

2000-01-01

Earth-observing satellites have provided an unprecedented view of the land surface but have been exploited relatively little for the measurement of environmental variables of particular relevance to epidemiology. Recent advances in techniques to recover continuous fields of air temperature, humidity, and vapour pressure deficit from remotely sensed observations have significant potential for disease vector monitoring and related epidemiological applications. We report on the development of techniques to map environmental variables with relevance to the prediction of the relative abundance of disease vectors and intermediate hosts. Improvements to current methods of obtaining information on vegetation properties, canopy and surface temperature and soil moisture over large areas are also discussed. Algorithms used to measure these variables incorporate visible, near-infrared and thermal infrared radiation observations derived from time series of satellite-based sensors, focused here primarily but not exclusively on the Advanced Very High Resolution Radiometer (AVHRR) instruments. The variables compare favourably with surface measurements over a broad array of conditions at several study sites, and maps of retrieved variables captured patterns of spatial variability comparable to, and locally more accurate than, spatially interpolated meteorological observations. Application of multi-temporal maps of these variables are discussed in relation to current epidemiological research on the distribution and abundance of some common disease vectors.

Spatial diffusion of influenza outbreak-related climate factors in Chiang Mai Province, Thailand.

PubMed

Nakapan, Supachai; Tripathi, Nitin Kumar; Tipdecho, Taravudh; Souris, Marc

2012-10-24

Influenza is one of the most important leading causes of respiratory illness in the countries located in the tropical areas of South East Asia and Thailand. In this study the climate factors associated with influenza incidence in Chiang Mai Province, Northern Thailand, were investigated. Identification of factors responsible for influenza outbreaks and the mapping of potential risk areas in Chiang Mai are long overdue. This work examines the association between yearly climate patterns between 2001 and 2008 and influenza outbreaks in the Chiang Mai Province. The climatic factors included the amount of rainfall, percent of rainy days, relative humidity, maximum, minimum temperatures and temperature difference. The study develops a statistical analysis to quantitatively assess the relationship between climate and influenza outbreaks and then evaluate its suitability for predicting influenza outbreaks. A multiple linear regression technique was used to fit the statistical model. The Inverse Distance Weighted (IDW) interpolation and Geographic Information System (GIS) techniques were used in mapping the spatial diffusion of influenza risk zones. The results show that there is a significance correlation between influenza outbreaks and climate factors for the majority of the studied area. A statistical analysis was conducted to assess the validity of the model comparing model outputs and actual outbreaks.

Improving spatial resolution in skin-contact thermography: comparison between a spline based and linear interpolation.

PubMed

Giansanti, Daniele

2008-07-01

A wearable device for skin-contact thermography [Giansanti D, Maccioni G. Development and testing of a wearable integrated thermometer sensor for skin contact thermography. Med Eng Phys 2006 [ahead of print

An interpolated activity during the knowledge-of-results delay interval eliminates the learning advantages of self-controlled feedback schedules.

PubMed

Carter, Michael J; Ste-Marie, Diane M

2017-03-01

The learning advantages of self-controlled knowledge-of-results (KR) schedules compared to yoked schedules have been linked to the optimization of the informational value of the KR received for the enhancement of one's error-detection capabilities. This suggests that information-processing activities that occur after motor execution, but prior to receiving KR (i.e., the KR-delay interval) may underlie self-controlled KR learning advantages. The present experiment investigated whether self-controlled KR learning benefits would be eliminated if an interpolated activity was performed during the KR-delay interval. Participants practiced a waveform matching task that required two rapid elbow extension-flexion reversals in one of four groups using a factorial combination of choice (self-controlled, yoked) and KR-delay interval (empty, interpolated). The waveform had specific spatial and temporal constraints, and an overall movement time goal. The results indicated that the self-controlled + empty group had superior retention and transfer scores compared to all other groups. Moreover, the self-controlled + interpolated and yoked + interpolated groups did not differ significantly in retention and transfer; thus, the interpolated activity eliminated the typically found learning benefits of self-controlled KR. No significant differences were found between the two yoked groups. We suggest the interpolated activity interfered with information-processing activities specific to self-controlled KR conditions that occur during the KR-delay interval and that these activities are vital for reaping the associated learning benefits. These findings add to the growing evidence that challenge the motivational account of self-controlled KR learning advantages and instead highlights informational factors associated with the KR-delay interval as an important variable for motor learning under self-controlled KR schedules.

Evaluating interception of larval pallid sturgeon on the Lower Missouri River- data acquisition, interpolation, and visualization

NASA Astrophysics Data System (ADS)

Bulliner, E. A., IV; Erwin, S. O.; Anderson, B. J.; Wilson, H.; Jacobson, R. B.

2016-12-01

The transition from endogenous to exogenous feeding is an important life-stage transition for many riverine fish larvae. On the Missouri River, U.S., riverine alteration has decreased connectivity between the navigation channel and complex, food-producing and foraging areas on the channel margins, namely shallow side channels and sandbar complexes. A favored hypothesis, the interception hypothesis, for recruitment failure of pallid sturgeon is that drifting larvae are not able to exit the highly engineered navigation channel, and therefore starve. We present work exploring measures of hydraulic connectivity between the navigation channel and channel margins using multiple data-collection protocols with acoustic Doppler current profilers (ADCPs). As ADCP datasets alone often do not have high enough spatial resolution to characterize interception and connectivity sufficiently at the scale of drifting sturgeon larvae, they are often supplemented with physical and empirical models. Using boat-mounted ADCPs, we collected 3-dimensional current velocities with a variety of driving techniques (specifically, regularly spaced transects, reciprocal transects, and irregular patterns) around areas of potential larval interception. We then used toolkits based in Python to interpolate 3-dimensional velocity fields at spatial scales finer than the original measurements, and visualized resultant velocity vectors and flowlines in the software package Paraview. Using these visualizations, we investigated the necessary resolution of field measurements required to model connectivity with channel margin areas on large, highly engineered river ecosystems such as the Missouri River. We anticipate that results from this work will be used to help inform models of larval interception under current conditions. Furthermore, results from this work will be useful in developing monitoring strategies to evaluate the restoration of channel complexity to support ecological functions.

Geospatial Predictive Modelling for Climate Mapping of Selected Severe Weather Phenomena Over Poland: A Methodological Approach

NASA Astrophysics Data System (ADS)

Walawender, Ewelina; Walawender, Jakub P.; Ustrnul, Zbigniew

2017-02-01

The main purpose of the study is to introduce methods for mapping the spatial distribution of the occurrence of selected atmospheric phenomena (thunderstorms, fog, glaze and rime) over Poland from 1966 to 2010 (45 years). Limited in situ observations as well the discontinuous and location-dependent nature of these phenomena make traditional interpolation inappropriate. Spatially continuous maps were created with the use of geospatial predictive modelling techniques. For each given phenomenon, an algorithm identifying its favourable meteorological and environmental conditions was created on the basis of observations recorded at 61 weather stations in Poland. Annual frequency maps presenting the probability of a day with a thunderstorm, fog, glaze or rime were created with the use of a modelled, gridded dataset by implementing predefined algorithms. Relevant explanatory variables were derived from NCEP/NCAR reanalysis and downscaled with the use of a Regional Climate Model. The resulting maps of favourable meteorological conditions were found to be valuable and representative on the country scale but at different correlation ( r) strength against in situ data (from r = 0.84 for thunderstorms to r = 0.15 for fog). A weak correlation between gridded estimates of fog occurrence and observations data indicated the very local nature of this phenomenon. For this reason, additional environmental predictors of fog occurrence were also examined. Topographic parameters derived from the SRTM elevation model and reclassified CORINE Land Cover data were used as the external, explanatory variables for the multiple linear regression kriging used to obtain the final map. The regression model explained 89 % of annual frequency of fog variability in the study area. Regression residuals were interpolated via simple kriging.

The OceanFlux Greenhouse Gases methodology for deriving a sea surface climatology of CO2 fugacity in support of air-sea gas flux studies

NASA Astrophysics Data System (ADS)

Goddijn-Murphy, L. M.; Woolf, D. K.; Land, P. E.; Shutler, J. D.; Donlon, C.

2015-07-01

Climatologies, or long-term averages, of essential climate variables are useful for evaluating models and providing a baseline for studying anomalies. The Surface Ocean CO2 Atlas (SOCAT) has made millions of global underway sea surface measurements of CO2 publicly available, all in a uniform format and presented as fugacity, fCO2. As fCO2 is highly sensitive to temperature, the measurements are only valid for the instantaneous sea surface temperature (SST) that is measured concurrently with the in-water CO2 measurement. To create a climatology of fCO2 data suitable for calculating air-sea CO2 fluxes, it is therefore desirable to calculate fCO2 valid for a more consistent and averaged SST. This paper presents the OceanFlux Greenhouse Gases methodology for creating such a climatology. We recomputed SOCAT's fCO2 values for their respective measurement month and year using monthly composite SST data on a 1° × 1° grid from satellite Earth observation and then extrapolated the resulting fCO2 values to reference year 2010. The data were then spatially interpolated onto a 1° × 1° grid of the global oceans to produce 12 monthly fCO2 distributions for 2010, including the prediction errors of fCO2 produced by the spatial interpolation technique. The partial pressure of CO2 (pCO2) is also provided for those who prefer to use pCO2. The CO2 concentration difference between ocean and atmosphere is the thermodynamic driving force of the air-sea CO2 flux, and hence the presented fCO2 distributions can be used in air-sea gas flux calculations together with climatologies of other climate variables.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schreiner, S.; Paschal, C.B.; Galloway, R.L.

Four methods of producing maximum intensity projection (MIP) images were studied and compared. Three of the projection methods differ in the interpolation kernel used for ray tracing. The interpolation kernels include nearest neighbor interpolation, linear interpolation, and cubic convolution interpolation. The fourth projection method is a voxel projection method that is not explicitly a ray-tracing technique. The four algorithms` performance was evaluated using a computer-generated model of a vessel and using real MR angiography data. The evaluation centered around how well an algorithm transferred an object`s width to the projection plane. The voxel projection algorithm does not suffer from artifactsmore » associated with the nearest neighbor algorithm. Also, a speed-up in the calculation of the projection is seen with the voxel projection method. Linear interpolation dramatically improves the transfer of width information from the 3D MRA data set over both nearest neighbor and voxel projection methods. Even though the cubic convolution interpolation kernel is theoretically superior to the linear kernel, it did not project widths more accurately than linear interpolation. A possible advantage to the nearest neighbor interpolation is that the size of small vessels tends to be exaggerated in the projection plane, thereby increasing their visibility. The results confirm that the way in which an MIP image is constructed has a dramatic effect on information contained in the projection. The construction method must be chosen with the knowledge that the clinical information in the 2D projections in general will be different from that contained in the original 3D data volume. 27 refs., 16 figs., 2 tabs.« less

Virtual Seismic Observation (VSO) with Sparsity-Promotion Inversion

NASA Astrophysics Data System (ADS)

Tiezhao, B.; Ning, J.; Jianwei, M.

2017-12-01

Large station interval leads to low resolution images, sometimes prevents people from obtaining images in concerned regions. Sparsity-promotion inversion, a useful method to recover missing data in industrial field acquisition, can be lent to interpolate seismic data on none-sampled sites, forming Virtual Seismic Observation (VSO). Traditional sparsity-promotion inversion suffers when coming up with large time difference in adjacent sites, which we concern most and use shift method to improve it. The procedure of the interpolation is that we first employ low-pass filter to get long wavelength waveform data and shift the waveforms of the same wave in different seismograms to nearly same arrival time. Then we use wavelet-transform-based sparsity-promotion inversion to interpolate waveform data on none-sampled sites and filling a phase in each missing trace. Finally, we shift back the waveforms to their original arrival times. We call our method FSIS (Filtering, Shift, Interpolation, Shift) interpolation. By this way, we can insert different virtually observed seismic phases into none-sampled sites and get dense seismic observation data. For testing our method, we randomly hide the real data in a site and use the rest to interpolate the observation on that site, using direct interpolation or FSIS method. Compared with directly interpolated data, interpolated data with FSIS can keep amplitude better. Results also show that the arrival times and waveforms of those VSOs well express the real data, which convince us that our method to form VSOs are applicable. In this way, we can provide needed data for some advanced seismic technique like RTM to illuminate shallow structures.

Spatial models to predict ash pH and Electrical Conductivity distribution after a grassland fire in Lithuania

NASA Astrophysics Data System (ADS)

Pereira, Paulo; Cerda, Artemi; Misiūnė, Ieva

2015-04-01

Fire mineralizes the organic matter, increasing the pH level and the amount of dissolved ions (Pereira et al., 2014). The degree of mineralization depends among other factors on fire temperature, burned specie, moisture content, and contact time. The impact of wildland fires it is assessed using the fire severity, an index used in the absence of direct measures (e.g temperature), important to estimate the fire effects in the ecosystems. This impact is observed through the loss of soil organic matter, crown volume, twig diameter, ash colour, among others (Keeley et al., 2009). The effects of fire are highly variable, especially at short spatial scales (Pereira et al., in press), due the different fuel conditions (e.g. moisture, specie distribution, flammability, connectivity, arrangement, etc). This variability poses important challenges to identify the best spatial predictor and have the most accurate spatial visualization of the data. Considering this, the test of several interpolation methods it is assumed to be relevant to have the most reliable map. The aims of this work are I) study the ash pH and Electrical Conductivity (EC) after a grassland fire according to ash colour and II) test several interpolation methods in order to identify the best spatial predictor of pH and EC distribution. The study area is located near Vilnius at 54.42° N and 25.26°E and 154 ma.s.l. After the fire it was designed a plot with a 27 x 9 m space grid. Samples were taken every 3 meters for a total of 40 (Pereira et al., 2013). Ash color was classified according to Úbeda et al. (2009). Ash pH and EC laboratory analysis were carried out according to Pereira et al. (2014). Previous to data comparison and modelling, normality and homogeneity were assessed with the Shapiro-wilk and Levene test. pH data respected the normality and homogeneity, while EC only followed the Gaussian distribution and the homogeneity criteria after a logarithmic transformation. Data spatial correlation was calculated with the Global Moran's I Index. In order to identify the best interpolator, we tested several well known techniques as inverse distance to a power (IDP), with the power of 1, 2, 3, 4 and 5, local polynomial (LP) with the power of 1 (LP1), 2 (LP2) and 3 (LP3), spline with tension (SPT), completely regularized spline (CRS), multiquadratic (MTQ), inverse multiquadratic (IMTQ) thin plate spline (TPS) and ordinary kriging. The best interpolator was the one with the lowest Root mean square error (RMSE). The results shown that on average ash pH was 8.01 (±0.20) and EC (1408± 513.51µm cm3). The coefficient of correlation between both variables was 0.34, p<0.05. Black ash had a significantly higher pH (F=6.29, p<0.05) and EC (F=5.25, p<0.05) than dark grey ash. According to Moran's I index, pH data was significantly (p<0.05) dispersed, while EC had a random pattern. The best spatial predictor for pH was IDW1 (RMSE=0.210), and for EC IMTQ (RMSE=0.141). In both cases the least accurate technique was TPS. pH data did not showed a specific spatial pattern and some high values are very close to high values which shows a great local spatial variability, mainly observed in the northern part of the plot. In relation to EC, the high values were identified in the central part of the plot. In conclusion it was observed that ash pH and EC were different according to fire severity (ash color) and data distribution has a different spatial pattern, despite the significant correlation. pH and EC had different spatial impacts on soil properties in the immediate period after the fire. Acknowledgments POSTFIRE (Soil quality, erosion control and plant cover recovery under different post-fire management scenarios, CGL2013-47862-C2-1-R), funded by the Spanish Ministry of Economy and Competitiveness; Fuegored; RECARE (Preventing and Remediating Degradation of Soils in Europe Through Land Care, FP7-ENV-2013-TWO STAGE), funded by the European Commission; and for the COST action ES1306 (Connecting European connectivity research). References Keeley, J.E. (2009) Fire intensity, fire severity and burn severity: a brief review and suggested usage. International Journal of Wildland Fire. 18, 116-126. Pereira, P., Úbeda, X., Martin, D., Mataix-Solera, J., Cerdà, A., Burguet, M. (2014) Wildfire effects on extractable elements in ash from a Pinus pinaster forest in Portugal. Hydrological Processes, 28, 3681-3690. Pereira, P., Cerdà, A., Úbeda, X., Mataix-Solera, J. Arcenegui, V., Zavala, L. Modelling the impacts of wildfire on ash thickness in a short-term period. Land Degradation and Development, (In Press), DOI: 10.1002/ldr.2195 Pereira, P., Cerdà, A., Úbeda, X., Mataix-Solera, J., Jordan, A. Burguet, M. (2013) Spatial models for monitoring the spatio-temporal evolution of ashes after fire - a case study of a burnt grassland in Lithuania, Solid Earth, 4, 153-165. Úbeda, X., Pereira, P., Outeiro, L., Martin, D. (2009) Effects of fire temperature on the physical and chemical characteristics of the ash from two plots of cork oak (Quercus suber). Land Degradation and Development, 20(6), 589-608.

Precipitation data in a mountainous catchment in Honduras: quality assessment and spatiotemporal characteristics

NASA Astrophysics Data System (ADS)

Westerberg, I.; Walther, A.; Guerrero, J.-L.; Coello, Z.; Halldin, S.; Xu, C.-Y.; Chen, D.; Lundin, L.-C.

2010-08-01

An accurate description of temporal and spatial precipitation variability in Central America is important for local farming, water supply and flood management. Data quality problems and lack of consistent precipitation data impede hydrometeorological analysis in the 7,500 km2 Choluteca River basin in central Honduras, encompassing the capital Tegucigalpa. We used precipitation data from 60 daily and 13 monthly stations in 1913-2006 from five local authorities and NOAA's Global Historical Climatology Network. Quality control routines were developed to tackle the specific data quality problems. The quality-controlled data were characterised spatially and temporally, and compared with regional and larger-scale studies. Two gap-filling methods for daily data and three interpolation methods for monthly and mean annual precipitation were compared. The coefficient-of-correlation-weighting method provided the best results for gap-filling and the universal kriging method for spatial interpolation. In-homogeneity in the time series was the main quality problem, and 22% of the daily precipitation data were too poor to be used. Spatial autocorrelation for monthly precipitation was low during the dry season, and correlation increased markedly when data were temporally aggregated from a daily time scale to 4-5 days. The analysis manifested the high spatial and temporal variability caused by the diverse precipitation-generating mechanisms and the need for an improved monitoring network.

Assessment of Potential Location of High Arsenic Contamination Using Fuzzy Overlay and Spatial Anisotropy Approach in Iron Mine Surrounding Area

PubMed Central

Wirojanagud, Wanpen; Srisatit, Thares

2014-01-01

Fuzzy overlay approach on three raster maps including land slope, soil type, and distance to stream can be used to identify the most potential locations of high arsenic contamination in soils. Verification of high arsenic contamination was made by collection samples and analysis of arsenic content and interpolation surface by spatial anisotropic method. A total of 51 soil samples were collected at the potential contaminated location clarified by fuzzy overlay approach. At each location, soil samples were taken at the depth of 0.00-1.00 m from the surface ground level. Interpolation surface of the analysed arsenic content using spatial anisotropic would verify the potential arsenic contamination location obtained from fuzzy overlay outputs. Both outputs of the spatial surface anisotropic and the fuzzy overlay mapping were significantly spatially conformed. Three contaminated areas with arsenic concentrations of 7.19 ± 2.86, 6.60 ± 3.04, and 4.90 ± 2.67 mg/kg exceeded the arsenic content of 3.9 mg/kg, the maximum concentration level (MCL) for agricultural soils as designated by Office of National Environment Board of Thailand. It is concluded that fuzzy overlay mapping could be employed for identification of potential contamination area with the verification by surface anisotropic approach including intensive sampling and analysis of the substances of interest. PMID:25110751

Assimilation of remote sensing observations into a sediment transport model of China's largest freshwater lake: spatial and temporal effects.

PubMed

Zhang, Peng; Chen, Xiaoling; Lu, Jianzhong; Zhang, Wei

2015-12-01

Numerical models are important tools that are used in studies of sediment dynamics in inland and coastal waters, and these models can now benefit from the use of integrated remote sensing observations. This study explores a scheme for assimilating remotely sensed suspended sediment (from charge-coupled device (CCD) images obtained from the Huanjing (HJ) satellite) into a two-dimensional sediment transport model of Poyang Lake, the largest freshwater lake in China. Optimal interpolation is used as the assimilation method, and model predictions are obtained by combining four remote sensing images. The parameters for optimal interpolation are determined through a series of assimilation experiments evaluating the sediment predictions based on field measurements. The model with assimilation of remotely sensed sediment reduces the root-mean-square error of the predicted sediment concentrations by 39.4% relative to the model without assimilation, demonstrating the effectiveness of the assimilation scheme. The spatial effect of assimilation is explored by comparing model predictions with remotely sensed sediment, revealing that the model with assimilation generates reasonable spatial distribution patterns of suspended sediment. The temporal effect of assimilation on the model's predictive capabilities varies spatially, with an average temporal effect of approximately 10.8 days. The current velocities which dominate the rate and direction of sediment transport most likely result in spatial differences in the temporal effect of assimilation on model predictions.

Spatial distribution of tropospheric ozone in national parks of California: interpretation of passive-sampler data.

PubMed

Ray, J D

2001-09-28

The National Park Service (NPS) has tested and used passive ozone samplers for several years to get baseline values for parks and to determine the spatial variability within parks. Experience has shown that the Ogawa passive samplers can provide +/-10% accuracy when used with a quality assurance program consisting of blanks, duplicates, collocated instrumentation, and a standard operating procedure that carefully guides site operators. Although the passive device does not meet EPA criteria as a certified method (mainly, that hourly values be measured), it does provide seasonal summed values of ozone. The seasonal ozone concentrations from the passive devices can be compared to other monitoring to determine baseline values, trends, and spatial variations. This point is illustrated with some kriged interpolation maps of ozone statistics. Passive ozone samplers were used to get elevational gradients and spatial distributions of ozone within a park. This was done in varying degrees at Mount Rainier, Olympic, Sequoia-Kings Canyon, Yosemite, Joshua Tree, Rocky Mountain, and Great Smoky Mountains national parks. The ozone has been found to vary by factors of 2 and 3 within a park when average ozone is compared between locations. Specific examples of the spatial distributions of ozone in three parks within California are given using interpolation maps. Positive aspects and limitations of the passive sampling approach are presented.

Micromorphological characterization of zinc/silver particle composite coatings.

PubMed

Méndez, Alia; Reyes, Yolanda; Trejo, Gabriel; StĘpień, Krzysztof; Ţălu, Ştefan

2015-12-01

The aim of this study was to evaluate the three-dimensional (3D) surface micromorphology of zinc/silver particles (Zn/AgPs) composite coatings with antibacterial activity prepared using an electrodeposition technique. These 3D nanostructures were investigated over square areas of 5 μm × 5 μm by atomic force microscopy (AFM), fractal, and wavelet analysis. The fractal analysis of 3D surface roughness revealed that (Zn/AgPs) composite coatings have fractal geometry. Triangulation method, based on the linear interpolation type, applied for AFM data was employed in order to characterise the surfaces topographically (in amplitude, spatial distribution and pattern of surface characteristics). The surface fractal dimension Df , as well as height values distribution have been determined for the 3D nanostructure surfaces. © 2015 The Authors published by Wiley Periodicals, Inc.

Spherical-earth Gravity and Magnetic Anomaly Modeling by Gauss-legendre Quadrature Integration

NASA Technical Reports Server (NTRS)

Vonfrese, R. R. B.; Hinze, W. J.; Braile, L. W.; Luca, A. J. (Principal Investigator)

1981-01-01

The anomalous potential of gravity and magnetic fields and their spatial derivatives on a spherical Earth for an arbitrary body represented by an equivalent point source distribution of gravity poles or magnetic dipoles were calculated. The distribution of equivalent point sources was determined directly from the coordinate limits of the source volume. Variable integration limits for an arbitrarily shaped body are derived from interpolation of points which approximate the body's surface envelope. The versatility of the method is enhanced by the ability to treat physical property variations within the source volume and to consider variable magnetic fields over the source and observation surface. A number of examples verify and illustrate the capabilities of the technique, including preliminary modeling of potential field signatures for Mississippi embayment crustal structure at satellite elevations.

Definition and verification of a complex aircraft for aerodynamic calculations

NASA Technical Reports Server (NTRS)

Edwards, T. A.

1986-01-01

Techniques are reviewed which are of value in CAD/CAM CFD studies of the geometries of new fighter aircraft. In order to refine the computations of the flows to take advantage of the computing power available from supercomputers, it is often necessary to interpolate the geometry of the mesh selected for the numerical analysis of the aircraft shape. Interpolating the geometry permits a higher level of detail in calculations of the flow past specific regions of a design. A microprocessor-based mathematics engine is described for fast image manipulation and rotation to verify that the interpolated geometry will correspond to the design geometry in order to ensure that the flow calculations will remain valid through the interpolation. Applications of the image manipulation system to verify geometrical representations with wire-frame and shaded-surface images are described.

Improved pattern scaling approaches for the use in climate impact studies

NASA Astrophysics Data System (ADS)

Herger, Nadja; Sanderson, Benjamin M.; Knutti, Reto

2015-05-01

Pattern scaling is a simple way to produce climate projections beyond the scenarios run with expensive global climate models (GCMs). The simplest technique has known limitations and assumes that a spatial climate anomaly pattern obtained from a GCM can be scaled by the global mean temperature (GMT) anomaly. We propose alternatives and assess their skills and limitations. One approach which avoids scaling is to consider a period in a different scenario with the same GMT change. It is attractive as it provides patterns of any temporal resolution that are consistent across variables, and it does not distort variability. Second, we extend the traditional approach with a land-sea contrast term, which provides the largest improvements over the traditional technique. When interpolating between known bounding scenarios, the proposed methods significantly improve the accuracy of the pattern scaled scenario with little computational cost. The remaining errors are much smaller than the Coupled Model Intercomparison Project Phase 5 model spread.

Regularization techniques on least squares non-uniform fast Fourier transform.

PubMed

Gibiino, Fabio; Positano, Vincenzo; Landini, Luigi; Santarelli, Maria Filomena

2013-05-01

Non-Cartesian acquisition strategies are widely used in MRI to dramatically reduce the acquisition time while at the same time preserving the image quality. Among non-Cartesian reconstruction methods, the least squares non-uniform fast Fourier transform (LS_NUFFT) is a gridding method based on a local data interpolation kernel that minimizes the worst-case approximation error. The interpolator is chosen using a pseudoinverse matrix. As the size of the interpolation kernel increases, the inversion problem may become ill-conditioned. Regularization methods can be adopted to solve this issue. In this study, we compared three regularization methods applied to LS_NUFFT. We used truncated singular value decomposition (TSVD), Tikhonov regularization and L₁-regularization. Reconstruction performance was evaluated using the direct summation method as reference on both simulated and experimental data. We also evaluated the processing time required to calculate the interpolator. First, we defined the value of the interpolator size after which regularization is needed. Above this value, TSVD obtained the best reconstruction. However, for large interpolator size, the processing time becomes an important constraint, so an appropriate compromise between processing time and reconstruction quality should be adopted. Copyright © 2013 John Wiley & Sons, Ltd.

Modeling of Aerosol Optical Depth Variability during the 1998 Canadian Forest Fire Smoke Event

NASA Astrophysics Data System (ADS)

Aubé, M.; O`Neill, N. T.; Royer, A.; Lavoué, D.

2003-04-01

Monitoring of aerosol optical depth (AOD) is of particular importance due to the significant role of aerosols in the atmospheric radiative budget. Up to now the two standard techniques used for retrieving AOD are; (i) sun photometry which provides measurements of high temporal frequency and sparse spatial frequency, and (ii) satellite based approaches such as based DDV (Dense Dark Vegetation) inversion algorithms which extract AOD over dark targets in remotely sensed imagery. Although the latter techniques allow AOD retrieval over appreciable spatial domains, the irregular spatial pattern of dark targets and the typically low repeat frequencies of imaging satellites exclude the acquisition of AOD databases on a continuous spatio-temporal basis. We attempt to fill gaps in spatio-temporal AOD measurements using a new methodology that links AOD measurements and particulate matter Transport Model using a data assimilation approach. This modelling package (AODSEM for Aerosol Optical Depth Spatio-temporal Evolution Model) uses a size and aerosol type segregated semi-Lagrangian-Eulerian trajectory algorithm driven by analysed meteorological data. Its novelty resides in the fact that the model evolution is tied to both ground based and satellite level AOD measurement and all physical processes have been optimized to track this important but crude parameter. We applied this methodology to a significant smoke event that occurred over Canada in august 1998. The results show the potential of this approach inasmuch as residuals between AODSEM assimilated analysis and measurements are smaller than typical errors associated to remotely sensed AOD (satellite or ground based). The AODSEM assimilation approach also gives better results than classical interpolation techniques. This improvement is especially evident when the available number of AOD measurements is small.

Clustering P-Wave Receiver Functions To Constrain Subsurface Seismic Structure

NASA Astrophysics Data System (ADS)

Chai, C.; Larmat, C. S.; Maceira, M.; Ammon, C. J.; He, R.; Zhang, H.

2017-12-01

The acquisition of high-quality data from permanent and temporary dense seismic networks provides the opportunity to apply statistical and machine learning techniques to a broad range of geophysical observations. Lekic and Romanowicz (2011) used clustering analysis on tomographic velocity models of the western United States to perform tectonic regionalization and the velocity-profile clusters agree well with known geomorphic provinces. A complementary and somewhat less restrictive approach is to apply cluster analysis directly to geophysical observations. In this presentation, we apply clustering analysis to teleseismic P-wave receiver functions (RFs) continuing efforts of Larmat et al. (2015) and Maceira et al. (2015). These earlier studies validated the approach with surface waves and stacked EARS RFs from the USArray stations. In this study, we experiment with both the K-means and hierarchical clustering algorithms. We also test different distance metrics defined in the vector space of RFs following Lekic and Romanowicz (2011). We cluster data from two distinct data sets. The first, corresponding to the western US, was by smoothing/interpolation of receiver-function wavefield (Chai et al. 2015). Spatial coherence and agreement with geologic region increase with this simpler, spatially smoothed set of observations. The second data set is composed of RFs for more than 800 stations of the China Digital Seismic Network (CSN). Preliminary results show a first order agreement between clusters and tectonic region and each region cluster includes a distinct Ps arrival, which probably reflects differences in crustal thickness. Regionalization remains an important step to characterize a model prior to application of full waveform and/or stochastic imaging techniques because of the computational expense of these types of studies. Machine learning techniques can provide valuable information that can be used to design and characterize formal geophysical inversion, providing information on spatial variability in the subsurface geology.

Two Methods to Derive Ground-level Concentrations of PM2.5 with Improved Accuracy in the North China, Calibrating MODIS AOD and CMAQ Model Predictions

NASA Astrophysics Data System (ADS)

Lyu, Baolei; Hu, Yongtao; Chang, Howard; Russell, Armistead; Bai, Yuqi

2016-04-01

Reliable and accurate characterizations of ground-level PM2.5 concentrations are essential to understand pollution sources and evaluate human exposures etc. Monitoring network could only provide direct point-level observations at limited locations. At the locations without monitors, there are generally two ways to estimate the pollution levels of PM2.5. One is observations of aerosol properties from the satellite-based remote sensing, such as Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD). The other one is from deterministic atmospheric chemistry models, such as the Community Multi-Scale Air Quality Model (CMAQ). In this study, we used a statistical spatio-temporal downscaler to calibrate the two datasets to monitor observations to derive fine-scale ground-level concentrations of PM2.5 with improved accuracy. We treated both MODIS AOD and CMAQ model predictions as biased proxy estimations of PM2.5 pollution levels. The downscaler proposed a Bayesian framework to model the spatially and temporally varying coefficients of the two types of estimations in the linear regression setting, in order to correct biases. Especially for calibrating MODIS AOD, a city-specific linear model was established to fill the missing AOD values, and a novel interpolation-based variable, i.e. PM2.5 Spatial Interpolator, was introduced to account for the spatial dependence among grid cells. We selected the heavy polluted and populated North China as our study area, in a grid setting of 81×81 12-km cells. For the evaluation of calibration performance for retrieved MODIS AOD, the R2 was 0.61 by the full model with PM2.5 Spatial Interpolator being presented, and was 0.48 with PM2.5 Spatial Interpolator not being presented. The constructed AOD values effectively predicted PM2.5 concentrations under our model structure, with R2=0.78. For the evaluation of calibrated CMAQ predictions, the R2 was 0.51, a little less than that of calibrated AOD. Finally we obtained two sets of calibrated estimations of ground-level PM2.5 concentrations with complete spatial coverage. By comparing the two datasets, we found that the prediction from AOD have a little smoother texture than that from CMAQ. The former also predicted larger heavy pollution area in the southern Hebei province than the latter, but in a small margin. In general, they have pretty similar spatial patterns, indicating the reliability of our data fusion method. In summary, the statistical spatio-temporal downscaler could provide improvements on MODIS AOD and CMAQ's predictions on PM2.5 pollution levels. Future work would focus on fusing three datasets, as aforementioned monitor observations, MODIS AOD and CMAQ predictions, to derive predictions of ground-level PM2.5 pollution levels with even increased accuracy.

Data Descriptor: TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015

Treesearch

John T. Abatzoglou; Solomon Z. Dobrowski; Sean A. Parks; Katherine C. Hegewisch

2018-01-01

We present TerraClimate, a dataset of high-spatial resolution (1/24°, ~4-km) monthly climate and climatic water balance for global terrestrial surfaces from 1958–2015. TerraClimate uses climatically aided interpolation, combining high-spatial resolution climatological normals from the WorldClim dataset, with coarser resolution time varying (i.e., monthly) data from...

Accurate Estimation of Solvation Free Energy Using Polynomial Fitting Techniques

PubMed Central

Shyu, Conrad; Ytreberg, F. Marty

2010-01-01

This report details an approach to improve the accuracy of free energy difference estimates using thermodynamic integration data (slope of the free energy with respect to the switching variable λ) and its application to calculating solvation free energy. The central idea is to utilize polynomial fitting schemes to approximate the thermodynamic integration data to improve the accuracy of the free energy difference estimates. Previously, we introduced the use of polynomial regression technique to fit thermodynamic integration data (Shyu and Ytreberg, J Comput Chem 30: 2297–2304, 2009). In this report we introduce polynomial and spline interpolation techniques. Two systems with analytically solvable relative free energies are used to test the accuracy of the interpolation approach. We also use both interpolation and regression methods to determine a small molecule solvation free energy. Our simulations show that, using such polynomial techniques and non-equidistant λ values, the solvation free energy can be estimated with high accuracy without using soft-core scaling and separate simulations for Lennard-Jones and partial charges. The results from our study suggest these polynomial techniques, especially with use of non-equidistant λ values, improve the accuracy for ΔF estimates without demanding additional simulations. We also provide general guidelines for use of polynomial fitting to estimate free energy. To allow researchers to immediately utilize these methods, free software and documentation is provided via http://www.phys.uidaho.edu/ytreberg/software. PMID:20623657

Estimating Small-area Populations by Age and Sex Using Spatial Interpolation and Statistical Inference Methods

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qai, Qiang; Rushton, Gerald; Bhaduri, Budhendra L

The objective of this research is to compute population estimates by age and sex for small areas whose boundaries are different from those for which the population counts were made. In our approach, population surfaces and age-sex proportion surfaces are separately estimated. Age-sex population estimates for small areas and their confidence intervals are then computed using a binomial model with the two surfaces as inputs. The approach was implemented for Iowa using a 90 m resolution population grid (LandScan USA) and U.S. Census 2000 population. Three spatial interpolation methods, the areal weighting (AW) method, the ordinary kriging (OK) method, andmore » a modification of the pycnophylactic method, were used on Census Tract populations to estimate the age-sex proportion surfaces. To verify the model, age-sex population estimates were computed for paired Block Groups that straddled Census Tracts and therefore were spatially misaligned with them. The pycnophylactic method and the OK method were more accurate than the AW method. The approach is general and can be used to estimate subgroup-count types of variables from information in existing administrative areas for custom-defined areas used as the spatial basis of support in other applications.« less

[Spatial pattern of land surface dead combustible fuel load in Huzhong forest area in Great Xing'an Mountains].

PubMed

Liu, Zhi-Hua; Chang, Yu; Chen, Hong-Wei; Zhou, Rui; Jing, Guo-Zhi; Zhang, Hong-Xin; Zhang, Chang-Meng

2008-03-01

By using geo-statistics and based on time-lag classification standard, a comparative study was made on the land surface dead combustible fuels in Huzhong forest area in Great Xing'an Mountains. The results indicated that the first level land surface dead combustible fuel, i. e., 1 h time-lag dead fuel, presented stronger spatial auto-correlation, with an average of 762.35 g x m(-2) and contributing to 55.54% of the total load. Its determining factors were species composition and stand age. The second and third levels land surface dead combustible fuel, i. e., 10 h and 100 h time-lag dead fuels, had a sum of 610.26 g x m(-2), and presented weaker spatial auto-correlation than 1 h time-lag dead fuel. Their determining factor was the disturbance history of forest stand. The complexity and heterogeneity of the factors determining the quality and quantity of forest land surface dead combustible fuels were the main reasons for the relatively inaccurate interpolation. However, the utilization of field survey data coupled with geo-statistics could easily and accurately interpolate the spatial pattern of forest land surface dead combustible fuel loads, and indirectly provide a practical basis for forest management.

A panoramic imaging system based on fish-eye lens

NASA Astrophysics Data System (ADS)

Wang, Ye; Hao, Chenyang

2017-10-01

Panoramic imaging has been closely watched as one of the major technologies of AR and VR. Mainstream panoramic imaging techniques lenses include fish-eye lenses, image splicing, and catadioptric imaging system. Meanwhile, fish-eyes are widely used in the big picture video surveillance. The advantage of fish-eye lenses is that they are easy to operate and cost less, but how to solve the image distortion of fish-eye lenses has always been a very important topic. In this paper, the image calibration algorithm of fish-eye lens is studied by comparing the method of interpolation, bilinear interpolation and double three interpolation, which are used to optimize the images.

Using multi-dimensional Smolyak interpolation to make a sum-of-products potential

DOE Office of Scientific and Technical Information (OSTI.GOV)

Avila, Gustavo, E-mail: Gustavo-Avila@telefonica.net; Carrington, Tucker, E-mail: Tucker.Carrington@queensu.ca

2015-07-28

We propose a new method for obtaining potential energy surfaces in sum-of-products (SOP) form. If the number of terms is small enough, a SOP potential surface significantly reduces the cost of quantum dynamics calculations by obviating the need to do multidimensional integrals by quadrature. The method is based on a Smolyak interpolation technique and uses polynomial-like or spectral basis functions and 1D Lagrange-type functions. When written in terms of the basis functions from which the Lagrange-type functions are built, the Smolyak interpolant has only a modest number of terms. The ideas are tested for HONO (nitrous acid)

Estimating monthly temperature using point based interpolation techniques

NASA Astrophysics Data System (ADS)

Saaban, Azizan; Mah Hashim, Noridayu; Murat, Rusdi Indra Zuhdi

2013-04-01

This paper discusses the use of point based interpolation to estimate the value of temperature at an unallocated meteorology stations in Peninsular Malaysia using data of year 2010 collected from the Malaysian Meteorology Department. Two point based interpolation methods which are Inverse Distance Weighted (IDW) and Radial Basis Function (RBF) are considered. The accuracy of the methods is evaluated using Root Mean Square Error (RMSE). The results show that RBF with thin plate spline model is suitable to be used as temperature estimator for the months of January and December, while RBF with multiquadric model is suitable to estimate the temperature for the rest of the months.

Turning Avatar into Realistic Human Expression Using Linear and Bilinear Interpolations

NASA Astrophysics Data System (ADS)

Hazim Alkawaz, Mohammed; Mohamad, Dzulkifli; Rehman, Amjad; Basori, Ahmad Hoirul

2014-06-01

The facial animation in term of 3D facial data has accurate research support of the laser scan and advance 3D tools for complex facial model production. However, the approach still lacks facial expression based on emotional condition. Though, facial skin colour is required to offers an effect of facial expression improvement, closely related to the human emotion. This paper presents innovative techniques for facial animation transformation using the facial skin colour based on linear interpolation and bilinear interpolation. The generated expressions are almost same to the genuine human expression and also enhance the facial expression of the virtual human.

Spatial variability of soil carbon, pH, available phosphorous and potassium in organic farm located in Mediterranean Croatia

NASA Astrophysics Data System (ADS)

Bogunović, Igor; Pereira, Paulo; Šeput, Miranda

2016-04-01

Soil organic carbon (SOC), pH, available phosphorus (P), and potassium (K) are some of the most important factors to soil fertility. These soil parameters are highly variable in space and time, with implications to crop production. The aim of this work is study the spatial variability of SOC, pH, P and K in an organic farm located in river Rasa valley (Croatia). A regular grid (100 x 100 m) was designed and 182 samples were collected on Silty Clay Loam soil. P, K and SOC showed moderate heterogeneity with coefficient of variation (CV) of 21.6%, 32.8% and 51.9%, respectively. Soil pH record low spatial variability with CV of 1.5%. Soil pH, P and SOC did not follow normal distribution. Only after a Box-Cox transformation, data respected the normality requirements. Directional exponential models were the best fitted and used to describe spatial autocorrelation. Soil pH, P and SOC showed strong spatial dependence with nugget to sill ratio with 13.78%, 0.00% and 20.29%, respectively. Only K recorded moderate spatial dependence. Semivariogram ranges indicate that future sampling interval could be 150 - 200 m in order to reduce sampling costs. Fourteen different interpolation models for mapping soil properties were tested. The method with lowest Root Mean Square Error was the most appropriated to map the variable. The results showed that radial basis function models (Spline with Tension and Completely Regularized Spline) for P and K were the best predictors, while Thin Plate Spline and inverse distance weighting models were the least accurate. The best interpolator for pH and SOC was the local polynomial with the power of 1, while the least accurate were Thin Plate Spline. According to soil nutrient maps investigated area record very rich supply with K while P supply was insufficient on largest part of area. Soil pH maps showed mostly neutral reaction while individual parts of alkaline soil indicate the possibility of penetration of seawater and salt accumulation in the soil profile. Future research should focus on spatial patterns on soil pH, electrical conductivity and sodium adsorption ratio. Keywords: geostatistics, semivariogram, interpolation models, soil chemical properties

Development of Spatiotemporal Bias-Correction Techniques for Downscaling GCM Predictions

NASA Astrophysics Data System (ADS)

Hwang, S.; Graham, W. D.; Geurink, J.; Adams, A.; Martinez, C. J.

2010-12-01

Accurately representing the spatial variability of precipitation is an important factor for predicting watershed response to climatic forcing, particularly in small, low-relief watersheds affected by convective storm systems. Although Global Circulation Models (GCMs) generally preserve spatial relationships between large-scale and local-scale mean precipitation trends, most GCM downscaling techniques focus on preserving only observed temporal variability on point by point basis, not spatial patterns of events. Downscaled GCM results (e.g., CMIP3 ensembles) have been widely used to predict hydrologic implications of climate variability and climate change in large snow-dominated river basins in the western United States (Diffenbaugh et al., 2008; Adam et al., 2009). However fewer applications to smaller rain-driven river basins in the southeastern US (where preserving spatial variability of rainfall patterns may be more important) have been reported. In this study a new method was developed to bias-correct GCMs to preserve both the long term temporal mean and variance of the precipitation data, and the spatial structure of daily precipitation fields. Forty-year retrospective simulations (1960-1999) from 16 GCMs were collected (IPCC, 2007; WCRP CMIP3 multi-model database: https://esg.llnl.gov:8443/), and the daily precipitation data at coarse resolution (i.e., 280km) were interpolated to 12km spatial resolution and bias corrected using gridded observations over the state of Florida (Maurer et al., 2002; Wood et al, 2002; Wood et al, 2004). In this method spatial random fields which preserved the observed spatial correlation structure of the historic gridded observations and the spatial mean corresponding to the coarse scale GCM daily rainfall were generated. The spatiotemporal variability of the spatio-temporally bias-corrected GCMs were evaluated against gridded observations, and compared to the original temporally bias-corrected and downscaled CMIP3 data for the central Florida. The hydrologic response of two southwest Florida watersheds to the gridded observation data, the original bias corrected CMIP3 data, and the new spatiotemporally corrected CMIP3 predictions was compared using an integrated surface-subsurface hydrologic model developed by Tampa Bay Water.

[Assessment on ecological security spatial differences of west areas of Liaohe River based on GIS].

PubMed

Wang, Geng; Wu, Wei

2005-09-01

Ecological security assessment and early warning research have spatiality; non-linearity; randomicity, it is needed to deal with much spatial information. Spatial analysis and data management are advantages of GIS, it can define distribution trend and spatial relations of environmental factors, and show ecological security pattern graphically. The paper discusses the method of ecological security spatial differences of west areas of Liaohe River based on GIS and ecosystem non-health. First, studying on pressure-state-response (P-S-R) assessment indicators system, investigating in person and gathering information; Second, digitizing the river, applying fuzzy AHP to put weight, quantizing and calculating by fuzzy comparing; Last, establishing grid data-base; expounding spatial differences of ecological security by GIS Interpolate and Assembly.

Impact of Geocoding Methods on Associations between Long-term Exposure to Urban Air Pollution and Lung Function

PubMed Central

Jacquemin, Bénédicte; Lepeule, Johanna; Boudier, Anne; Arnould, Caroline; Benmerad, Meriem; Chappaz, Claire; Ferran, Joane; Kauffmann, Francine; Morelli, Xavier; Pin, Isabelle; Pison, Christophe; Rios, Isabelle; Temam, Sofia; Künzli, Nino; Slama, Rémy

2013-01-01

Background: Errors in address geocodes may affect estimates of the effects of air pollution on health. Objective: We investigated the impact of four geocoding techniques on the association between urban air pollution estimated with a fine-scale (10 m × 10 m) dispersion model and lung function in adults. Methods: We measured forced expiratory volume in 1 sec (FEV1) and forced vital capacity (FVC) in 354 adult residents of Grenoble, France, who were participants in two well-characterized studies, the Epidemiological Study on the Genetics and Environment on Asthma (EGEA) and the European Community Respiratory Health Survey (ECRHS). Home addresses were geocoded using individual building matching as the reference approach and three spatial interpolation approaches. We used a dispersion model to estimate mean PM10 and nitrogen dioxide concentrations at each participant’s address during the 12 months preceding their lung function measurements. Associations between exposures and lung function parameters were adjusted for individual confounders and same-day exposure to air pollutants. The geocoding techniques were compared with regard to geographical distances between coordinates, exposure estimates, and associations between the estimated exposures and health effects. Results: Median distances between coordinates estimated using the building matching and the three interpolation techniques were 26.4, 27.9, and 35.6 m. Compared with exposure estimates based on building matching, PM10 concentrations based on the three interpolation techniques tended to be overestimated. When building matching was used to estimate exposures, a one-interquartile range increase in PM10 (3.0 μg/m3) was associated with a 3.72-point decrease in FVC% predicted (95% CI: –0.56, –6.88) and a 3.86-point decrease in FEV1% predicted (95% CI: –0.14, –3.24). The magnitude of associations decreased when other geocoding approaches were used [e.g., for FVC% predicted –2.81 (95% CI: –0.26, –5.35) using NavTEQ, or 2.08 (95% CI –4.63, 0.47, p = 0.11) using Google Maps]. Conclusions: Our findings suggest that the choice of geocoding technique may influence estimated health effects when air pollution exposures are estimated using a fine-scale exposure model. Citation: Jacquemin B, Lepeule J, Boudier A, Arnould C, Benmerad M, Chappaz C, Ferran J, Kauffmann F, Morelli X, Pin I, Pison C, Rios I, Temam S, Künzli N, Slama R, Siroux V. 2013. Impact of geocoding methods on associations between long-term exposure to urban air pollution and lung function. Environ Health Perspect 121:1054–1060; http://dx.doi.org/10.1289/ehp.1206016 PMID:23823697

Simple, Fast and Effective Correction for Irradiance Spatial Nonuniformity in Measurement of IVs of Large Area Cells at NREL

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moriarty, Tom

The NREL cell measurement lab measures the IV parameters of cells of multiple sizes and configurations. A large contributing factor to errors and uncertainty in Jsc, Imax, Pmax and efficiency can be the irradiance spatial nonuniformity. Correcting for this nonuniformity through its precise and frequent measurement can be very time consuming. This paper explains a simple, fast and effective method based on bicubic interpolation for determining and correcting for spatial nonuniformity and verification of the method's efficacy.

Improving the spatial and temporal resolution with quantification of uncertainty and errors in earth observation data sets using Data Interpolating Empirical Orthogonal Functions methodology

NASA Astrophysics Data System (ADS)

El Serafy, Ghada; Gaytan Aguilar, Sandra; Ziemba, Alexander

2016-04-01

There is an increasing use of process-based models in the investigation of ecological systems and scenario predictions. The accuracy and quality of these models are improved when run with high spatial and temporal resolution data sets. However, ecological data can often be difficult to collect which manifests itself through irregularities in the spatial and temporal domain of these data sets. Through the use of Data INterpolating Empirical Orthogonal Functions(DINEOF) methodology, earth observation products can be improved to have full spatial coverage within the desired domain as well as increased temporal resolution to daily and weekly time step, those frequently required by process-based models[1]. The DINEOF methodology results in a degree of error being affixed to the refined data product. In order to determine the degree of error introduced through this process, the suspended particulate matter and chlorophyll-a data from MERIS is used with DINEOF to produce high resolution products for the Wadden Sea. These new data sets are then compared with in-situ and other data sources to determine the error. Also, artificial cloud cover scenarios are conducted in order to substantiate the findings from MERIS data experiments. Secondly, the accuracy of DINEOF is explored to evaluate the variance of the methodology. The degree of accuracy is combined with the overall error produced by the methodology and reported in an assessment of the quality of DINEOF when applied to resolution refinement of chlorophyll-a and suspended particulate matter in the Wadden Sea. References [1] Sirjacobs, D.; Alvera-Azcárate, A.; Barth, A.; Lacroix, G.; Park, Y.; Nechad, B.; Ruddick, K.G.; Beckers, J.-M. (2011). Cloud filling of ocean colour and sea surface temperature remote sensing products over the Southern North Sea by the Data Interpolating Empirical Orthogonal Functions methodology. J. Sea Res. 65(1): 114-130. Dx.doi.org/10.1016/j.seares.2010.08.002

Mapping extreme rainfall in the Northwest Portugal region: statistical analysis and spatial modelling

NASA Astrophysics Data System (ADS)

Santos, Monica; Fragoso, Marcelo

2010-05-01

Extreme precipitation events are one of the causes of natural hazards, such as floods and landslides, making its investigation so important, and this research aims to contribute to the study of the extreme rainfall patterns in a Portuguese mountainous area. The study area is centred on the Arcos de Valdevez county, located in the northwest region of Portugal, the rainiest of the country, with more than 3000 mm of annual rainfall at the Peneda-Gerês mountain system. This work focus on two main subjects related with the precipitation variability on the study area. First, a statistical analysis of several precipitation parameters is carried out, using daily data from 17 rain-gauges with a complete record for the 1960-1995 period. This approach aims to evaluate the main spatial contrasts regarding different aspects of the rainfall regime, described by ten parameters and indices of precipitation extremes (e.g. mean annual precipitation, the annual frequency of precipitation days, wet spells durations, maximum daily precipitation, maximum of precipitation in 30 days, number of days with rainfall exceeding 100 mm and estimated maximum daily rainfall for a return period of 100 years). The results show that the highest precipitation amounts (from annual to daily scales) and the higher frequency of very abundant rainfall events occur in the Serra da Peneda and Gerês mountains, opposing to the valleys of the Lima, Minho and Vez rivers, with lower precipitation amounts and less frequent heavy storms. The second purpose of this work is to find a method of mapping extreme rainfall in this mountainous region, investigating the complex influence of the relief (e.g. elevation, topography) on the precipitation patterns, as well others geographical variables (e.g. distance from coast, latitude), applying tested geo-statistical techniques (Goovaerts, 2000; Diodato, 2005). Models of linear regression were applied to evaluate the influence of different geographical variables (altitude, latitude, distance from sea and distance to the highest orographic barrier) on the rainfall behaviours described by the studied variables. The techniques of spatial interpolation evaluated include univariate and multivariate methods: cokriging, kriging, IDW (inverse distance weighted) and multiple linear regression. Validation procedures were used, assessing the estimated errors in the analysis of descriptive statistics of the models. Multiple linear regression models produced satisfactory results in relation to 70% of the rainfall parameters, suggested by lower average percentage of error. However, the results also demonstrates that there is no an unique and ideal model, depending on the rainfall parameter in consideration. Probably, the unsatisfactory results obtained in relation to some rainfall parameters was motivated by constraints as the spatial complexity of the precipitation patterns, as well as to the deficient spatial coverage of the territory by the rain-gauges network. References Diodato, N. (2005). The influence of topographic co-variables on the spatial variability of precipitation over small regions of complex terrain. Internacional Journal of Climatology, 25(3), 351-363. Goovaerts, P. (2000). Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228, 113 - 129.

An Approach to Unbiased Subsample Interpolation for Motion Tracking

PubMed Central

McCormick, Matthew M.; Varghese, Tomy

2013-01-01

Accurate subsample displacement estimation is necessary for ultrasound elastography because of the small deformations that occur and the subsequent application of a derivative operation on local displacements. Many of the commonly used subsample estimation techniques introduce significant bias errors. This article addresses a reduced bias approach to subsample displacement estimations that consists of a two-dimensional windowed-sinc interpolation with numerical optimization. It is shown that a Welch or Lanczos window with a Nelder–Mead simplex or regular-step gradient-descent optimization is well suited for this purpose. Little improvement results from a sinc window radius greater than four data samples. The strain signal-to-noise ratio (SNR) obtained in a uniformly elastic phantom is compared with other parabolic and cosine interpolation methods; it is found that the strain SNR ratio is improved over parabolic interpolation from 11.0 to 13.6 in the axial direction and 0.7 to 1.1 in the lateral direction for an applied 1% axial deformation. The improvement was most significant for small strains and displacement tracking in the lateral direction. This approach does not rely on special properties of the image or similarity function, which is demonstrated by its effectiveness with the application of a previously described regularization technique. PMID:23493609

Subcellular localization for Gram positive and Gram negative bacterial proteins using linear interpolation smoothing model.

PubMed

Saini, Harsh; Raicar, Gaurav; Dehzangi, Abdollah; Lal, Sunil; Sharma, Alok

2015-12-07

Protein subcellular localization is an important topic in proteomics since it is related to a protein׳s overall function, helps in the understanding of metabolic pathways, and in drug design and discovery. In this paper, a basic approximation technique from natural language processing called the linear interpolation smoothing model is applied for predicting protein subcellular localizations. The proposed approach extracts features from syntactical information in protein sequences to build probabilistic profiles using dependency models, which are used in linear interpolation to determine how likely is a sequence to belong to a particular subcellular location. This technique builds a statistical model based on maximum likelihood. It is able to deal effectively with high dimensionality that hinders other traditional classifiers such as Support Vector Machines or k-Nearest Neighbours without sacrificing performance. This approach has been evaluated by predicting subcellular localizations of Gram positive and Gram negative bacterial proteins. Copyright © 2015 Elsevier Ltd. All rights reserved.

Sandia Higher Order Elements (SHOE) v 0.5 alpha

DOE Office of Scientific and Technical Information (OSTI.GOV)

2013-09-24

SHOE is research code for characterizing and visualizing higher-order finite elements; it contains a framework for defining classes of interpolation techniques and element shapes; methods for interpolating triangular, quadrilateral, tetrahedral, and hexahedral cells using Lagrange and Legendre polynomial bases of arbitrary order; methods to decompose each element into domains of constant gradient flow (using a polynomial solver to identify critical points); and an isocontouring technique that uses this decomposition to guarantee topological correctness. Please note that this is an alpha release of research software and that some time has passed since it was actively developed; build- and run-time issues likelymore » exist.« less

Spectral analysis of views interpolated by chroma subpixel downsampling for 3D autosteroscopic displays

NASA Astrophysics Data System (ADS)

Marson, Avishai; Stern, Adrian

2015-05-01

One of the main limitations of horizontal parallax autostereoscopic displays is the horizontal resolution loss due the need to repartition the pixels of the display panel among the multiple views. Recently we have shown that this problem can be alleviated by applying a color sub-pixel rendering technique1. Interpolated views are generated by down-sampling the panel pixels at sub-pixel level, thus increasing the number of views. The method takes advantage of lower acuity of the human eye to chromatic resolution. Here we supply further support of the technique by analyzing the spectra of the subsampled images.

Kriging - a challenge in geochemical mapping

NASA Astrophysics Data System (ADS)

Stojdl, Jiri; Matys Grygar, Tomas; Elznicova, Jitka; Popelka, Jan; Vachova, Tatina; Hosek, Michal

2017-04-01

Geochemists can easily provide datasets for contamination mapping thanks to recent advances in geographical information systems (GIS) and portable chemical-analytical instrumentation. Kriging is commonly used to visualise the results of such mapping. It is understandable, as kriging is a well-established method of spatial interpolation. It was created in 1950's for geochemical data processing to estimate the most likely distribution of gold based on samples from a few boreholes. However, kriging is based on the assumption of continuous spatial distribution of numeric data that is not realistic in environmental geochemistry. The use of kriging is correct when the data density is sufficient with respect to heterogeneity of the spatial distribution of the geochemical parameters. However, if anomalous geochemical values are focused in hotspots of which boundaries are insufficiently densely sampled, kriging could provide misleading maps with the real contours of hotspots blurred by data smoothing and levelling out individual (isolated) but relevant anomalous values. The data smoothing can thus it results in underestimation of geochemical extremes, which may in fact be of the greatest importance in mapping projects. In our study we characterised hotspots of contamination by uranium and zinc in the floodplain of the Ploučnice River. The first objective of our study was to compare three methods of sampling: random (based on stochastic generation of sampling points), systematic (square grid) and judgemental sampling (based on judgement stemming from principles of fluvial deposition) as the basis for pollution maps. The first detected problem in production of the maps was the reduction of the smoothing effect of kriging using appropriate function of empirical semivariogram and setting the variation of at microscales smaller than the sampling distances to minimum (the "nugget" parameter of semivariogram). Exact interpolators such as Inverse Distance Weighting (IDW) or Radial Basis Functions (RBF) provides better solutions in this respect. The second detected problem was heterogeneous structure of the floodplain: it consists of distinct sedimentary bodies (e.g., natural levees, meander scars, point bars), which have been formed by different process (erosion or deposition on flooding, channel shifts by meandering, channel abandonment). Interpolation through these sedimentary bodies has thus not much sense. Solution is to identify boundaries between sedimentary bodies and interpolation of data with this additional information using exact interpolators with barriers (IDW, RBF or stratified kriging) or regression kriging. Those boundaries can be identified using, e.g., digital elevation model (DEM), dipole electromagnetic profiling (DEMP), gamma spectrometry, or an expertise by a geomorphologist.

Improving the accuracy of livestock distribution estimates through spatial interpolation.

PubMed

Bryssinckx, Ward; Ducheyne, Els; Muhwezi, Bernard; Godfrey, Sunday; Mintiens, Koen; Leirs, Herwig; Hendrickx, Guy

2012-11-01

Animal distribution maps serve many purposes such as estimating transmission risk of zoonotic pathogens to both animals and humans. The reliability and usability of such maps is highly dependent on the quality of the input data. However, decisions on how to perform livestock surveys are often based on previous work without considering possible consequences. A better understanding of the impact of using different sample designs and processing steps on the accuracy of livestock distribution estimates was acquired through iterative experiments using detailed survey. The importance of sample size, sample design and aggregation is demonstrated and spatial interpolation is presented as a potential way to improve cattle number estimates. As expected, results show that an increasing sample size increased the precision of cattle number estimates but these improvements were mainly seen when the initial sample size was relatively low (e.g. a median relative error decrease of 0.04% per sampled parish for sample sizes below 500 parishes). For higher sample sizes, the added value of further increasing the number of samples declined rapidly (e.g. a median relative error decrease of 0.01% per sampled parish for sample sizes above 500 parishes. When a two-stage stratified sample design was applied to yield more evenly distributed samples, accuracy levels were higher for low sample densities and stabilised at lower sample sizes compared to one-stage stratified sampling. Aggregating the resulting cattle number estimates yielded significantly more accurate results because of averaging under- and over-estimates (e.g. when aggregating cattle number estimates from subcounty to district level, P <0.009 based on a sample of 2,077 parishes using one-stage stratified samples). During aggregation, area-weighted mean values were assigned to higher administrative unit levels. However, when this step is preceded by a spatial interpolation to fill in missing values in non-sampled areas, accuracy is improved remarkably. This counts especially for low sample sizes and spatially even distributed samples (e.g. P <0.001 for a sample of 170 parishes using one-stage stratified sampling and aggregation on district level). Whether the same observations apply on a lower spatial scale should be further investigated.

A New Method for Computed Tomography Angiography (CTA) Imaging via Wavelet Decomposition-Dependented Edge Matching Interpolation.

PubMed

Li, Zeyu; Chen, Yimin; Zhao, Yan; Zhu, Lifeng; Lv, Shengqing; Lu, Jiahui

2016-08-01

The interpolation technique of computed tomography angiography (CTA) image provides the ability for 3D reconstruction, as well as reduces the detect cost and the amount of radiation. However, most of the image interpolation algorithms cannot take the automation and accuracy into account. This study provides a new edge matching interpolation algorithm based on wavelet decomposition of CTA. It includes mark, scale and calculation (MSC). Combining the real clinical image data, this study mainly introduces how to search for proportional factor and use the root mean square operator to find a mean value. Furthermore, we re- synthesize the high frequency and low frequency parts of the processed image by wavelet inverse operation, and get the final interpolation image. MSC can make up for the shortage of the conventional Computed Tomography (CT) and Magnetic Resonance Imaging(MRI) examination. The radiation absorption and the time to check through the proposed synthesized image were significantly reduced. In clinical application, it can help doctor to find hidden lesions in time. Simultaneously, the patients get less economic burden as well as less radiation exposure absorbed.

Novel true-motion estimation algorithm and its application to motion-compensated temporal frame interpolation.

PubMed

Dikbas, Salih; Altunbasak, Yucel

2013-08-01

In this paper, a new low-complexity true-motion estimation (TME) algorithm is proposed for video processing applications, such as motion-compensated temporal frame interpolation (MCTFI) or motion-compensated frame rate up-conversion (MCFRUC). Regular motion estimation, which is often used in video coding, aims to find the motion vectors (MVs) to reduce the temporal redundancy, whereas TME aims to track the projected object motion as closely as possible. TME is obtained by imposing implicit and/or explicit smoothness constraints on the block-matching algorithm. To produce better quality-interpolated frames, the dense motion field at interpolation time is obtained for both forward and backward MVs; then, bidirectional motion compensation using forward and backward MVs is applied by mixing both elegantly. Finally, the performance of the proposed algorithm for MCTFI is demonstrated against recently proposed methods and smoothness constraint optical flow employed by a professional video production suite. Experimental results show that the quality of the interpolated frames using the proposed method is better when compared with the MCFRUC techniques.

Adaptive color demosaicing and false color removal

NASA Astrophysics Data System (ADS)

Guarnera, Mirko; Messina, Giuseppe; Tomaselli, Valeria

2010-04-01

Color interpolation solutions drastically influence the quality of the whole image generation pipeline, so they must guarantee the rendering of high quality pictures by avoiding typical artifacts such as blurring, zipper effects, and false colors. Moreover, demosaicing should avoid emphasizing typical artifacts of real sensors data, such as noise and green imbalance effect, which would be further accentuated by the subsequent steps of the processing pipeline. We propose a new adaptive algorithm that decides the interpolation technique to apply to each pixel, according to its neighborhood analysis. Edges are effectively interpolated through a directional filtering approach that interpolates the missing colors, selecting the suitable filter depending on edge orientation. Regions close to edges are interpolated through a simpler demosaicing approach. Thus flat regions are identified and low-pass filtered to eliminate some residual noise and to minimize the annoying green imbalance effect. Finally, an effective false color removal algorithm is used as a postprocessing step to eliminate residual color errors. The experimental results show how sharp edges are preserved, whereas undesired zipper effects are reduced, improving the edge resolution itself and obtaining superior image quality.

Application of cokriging techniques for the estimation of hail size

NASA Astrophysics Data System (ADS)

Farnell, Carme; Rigo, Tomeu; Martin-Vide, Javier

2018-01-01

There are primarily two ways of estimating hail size: the first is the direct interpolation of point observations, and the second is the transformation of remote sensing fields into measurements of hail properties. Both techniques have advantages and limitations as regards generating the resultant map of hail damage. This paper presents a new methodology that combines the above mentioned techniques in an attempt to minimise the limitations and take advantage of the benefits of interpolation and the use of remote sensing data. The methodology was tested for several episodes with good results being obtained for the estimation of hail size at practically all the points analysed. The study area presents a large database of hail episodes, and for this reason, it constitutes an optimal test bench.

Using R to implement spatial analysis in open source environment

NASA Astrophysics Data System (ADS)

Shao, Yixi; Chen, Dong; Zhao, Bo

2007-06-01

R is an open source (GPL) language and environment for spatial analysis, statistical computing and graphics which provides a wide variety of statistical and graphical techniques, and is highly extensible. In the Open Source environment it plays an important role in doing spatial analysis. So, to implement spatial analysis in the Open Source environment which we called the Open Source geocomputation is using the R data analysis language integrated with GRASS GIS and MySQL or PostgreSQL. This paper explains the architecture of the Open Source GIS environment and emphasizes the role R plays in the aspect of spatial analysis. Furthermore, one apt illustration of the functions of R is given in this paper through the project of constructing CZPGIS (Cheng Zhou Population GIS) supported by Changzhou Government, China. In this project we use R to implement the geostatistics in the Open Source GIS environment to evaluate the spatial correlation of land price and estimate it by Kriging Interpolation. We also use R integrated with MapServer and php to show how R and other Open Source software cooperate with each other in WebGIS environment, which represents the advantages of using R to implement spatial analysis in Open Source GIS environment. And in the end, we points out that the packages for spatial analysis in R is still scattered and the limited memory is still a bottleneck when large sum of clients connect at the same time. Therefore further work is to group the extensive packages in order or design normative packages and make R cooperate better with other commercial software such as ArcIMS. Also we look forward to developing packages for land price evaluation.

Polynomial-interpolation algorithm for van der Pauw Hall measurement in a metal hydride film

NASA Astrophysics Data System (ADS)

Koon, D. W.; Ares, J. R.; Leardini, F.; Fernández, J. F.; Ferrer, I. J.

2008-10-01

We apply a four-term polynomial-interpolation extension of the van der Pauw Hall measurement technique to a 330 nm Mg-Pd bilayer during both absorption and desorption of hydrogen at room temperature. We show that standard versions of the van der Pauw DC Hall measurement technique produce an error of over 100% due to a drifting offset signal and can lead to unphysical interpretations of the physical processes occurring in this film. The four-term technique effectively removes this source of error, even when the offset signal is drifting by an amount larger than the Hall signal in the time interval between successive measurements. This technique can be used to increase the resolution of transport studies of any material in which the resistivity is rapidly changing, particularly when the material is changing from metallic to insulating behavior.

Comparison of two interpolation methods for empirical mode decomposition based evaluation of radiographic femur bone images.

PubMed

Udhayakumar, Ganesan; Sujatha, Chinnaswamy Manoharan; Ramakrishnan, Swaminathan

2013-01-01

Analysis of bone strength in radiographic images is an important component of estimation of bone quality in diseases such as osteoporosis. Conventional radiographic femur bone images are used to analyze its architecture using bi-dimensional empirical mode decomposition method. Surface interpolation of local maxima and minima points of an image is a crucial part of bi-dimensional empirical mode decomposition method and the choice of appropriate interpolation depends on specific structure of the problem. In this work, two interpolation methods of bi-dimensional empirical mode decomposition are analyzed to characterize the trabecular femur bone architecture of radiographic images. The trabecular bone regions of normal and osteoporotic femur bone images (N = 40) recorded under standard condition are used for this study. The compressive and tensile strength regions of the images are delineated using pre-processing procedures. The delineated images are decomposed into their corresponding intrinsic mode functions using interpolation methods such as Radial basis function multiquadratic and hierarchical b-spline techniques. Results show that bi-dimensional empirical mode decomposition analyses using both interpolations are able to represent architectural variations of femur bone radiographic images. As the strength of the bone depends on architectural variation in addition to bone mass, this study seems to be clinically useful.

INTERPOLATING VANCOUVER'S DAILY AMBIENT PM 10 FIELD

EPA Science Inventory

In this article we develop a spatial predictive distribution for the ambient space- time response field of daily ambient PM10 in Vancouver, Canada. Observed responses have a consistent temporal pattern from one monitoring site to the next. We exploit this feature of the field b...

Experiences with digital processing of images at INPE

NASA Technical Reports Server (NTRS)

Mascarenhas, N. D. A. (Principal Investigator)

1984-01-01

Four different research experiments with digital image processing at INPE will be described: (1) edge detection by hypothesis testing; (2) image interpolation by finite impulse response filters; (3) spatial feature extraction methods in multispectral classification; and (4) translational image registration by sequential tests of hypotheses.

Spatial interpolation of daily reference evapotranspiration in the Texas High Plains

USDA-ARS?s Scientific Manuscript database

The Texas High Plains Evapotranspiration (ET) Network collects meteorological data from 18 grass reference weather stations at hourly intervals and estimates hourly and daily reference ET using the American Society of Civil Engineers (ASCE) Standardized Reference ET equation. Producers in the Texas ...

Spatial interpolation of daily evapotranspiration data in the Texas High Plains

USDA-ARS?s Scientific Manuscript database

The Texas High Plains Evapotranspiration (ET) Network collects meteorological data from grass-referenced weather stations at hourly intervals and estimates hourly and daily reference ET using the American Society of Civil Engineers (ASCE) Standardized Reference ET equation. Producers in the Texas Hi...

Hand skin reconstruction from skeletal landmarks.

PubMed

Lefèvre, P; Van Sint Jan, S; Beauthier, J P; Rooze, M

2007-11-01

Many studies related to three-dimensional facial reconstruction have been previously reported. On the other hand, no extensive work has been found in the literature about hand reconstruction as an identification method. In this paper, the feasibility of virtual reconstruction of hand skin based on (1) its skeleton and (2) another hand skin and skeleton used as template was assessed. One cadaver hand and one volunteer's hand have been used. For the two hands, computer models of the bones and skin were obtained from computerized tomography. A customized software allowed locating spatial coordinates of bony anatomical landmarks on the models. From these landmarks, the spatial relationships between the models were determined and used to interpolate the missing hand skin. The volume of the interpolated skin was compared to the real skin obtained from medical imaging for validation. Results seem to indicate that such a method is of interest to give forensic investigators morphological clues related to an individual hand skin based on its skeleton. Further work is in progress to finalize the method.

Use of geostatistics for remediation planning to transcend urban political boundaries.

PubMed

Milillo, Tammy M; Sinha, Gaurav; Gardella, Joseph A

2012-11-01

Soil remediation plans are often dictated by areas of jurisdiction or property lines instead of scientific information. This study exemplifies how geostatistically interpolated surfaces can substantially improve remediation planning. Ordinary kriging, ordinary co-kriging, and inverse distance weighting spatial interpolation methods were compared for analyzing surface and sub-surface soil sample data originally collected by the US EPA and researchers at the University at Buffalo in Hickory Woods, an industrial-residential neighborhood in Buffalo, NY, where both lead and arsenic contamination is present. Past clean-up efforts estimated contamination levels from point samples, but parcel and agency jurisdiction boundaries were used to define remediation sites, rather than geostatistical models estimating the spatial behavior of the contaminants in the soil. Residents were understandably dissatisfied with the arbitrariness of the remediation plan. In this study we show how geostatistical mapping and participatory assessment can make soil remediation scientifically defensible, socially acceptable, and economically feasible. Copyright © 2012 Elsevier Ltd. All rights reserved.

Automated Approach to Very High-Order Aeroacoustic Computations. Revision

NASA Technical Reports Server (NTRS)

Dyson, Rodger W.; Goodrich, John W.

2001-01-01

Computational aeroacoustics requires efficient, high-resolution simulation tools. For smooth problems, this is best accomplished with very high-order in space and time methods on small stencils. However, the complexity of highly accurate numerical methods can inhibit their practical application, especially in irregular geometries. This complexity is reduced by using a special form of Hermite divided-difference spatial interpolation on Cartesian grids, and a Cauchy-Kowalewski recursion procedure for time advancement. In addition, a stencil constraint tree reduces the complexity of interpolating grid points that am located near wall boundaries. These procedures are used to develop automatically and to implement very high-order methods (> 15) for solving the linearized Euler equations that can achieve less than one grid point per wavelength resolution away from boundaries by including spatial derivatives of the primitive variables at each grid point. The accuracy of stable surface treatments is currently limited to 11th order for grid aligned boundaries and to 2nd order for irregular boundaries.

An Automated Approach to Very High Order Aeroacoustic Computations in Complex Geometries

NASA Technical Reports Server (NTRS)

Dyson, Rodger W.; Goodrich, John W.

2000-01-01

Computational aeroacoustics requires efficient, high-resolution simulation tools. And for smooth problems, this is best accomplished with very high order in space and time methods on small stencils. But the complexity of highly accurate numerical methods can inhibit their practical application, especially in irregular geometries. This complexity is reduced by using a special form of Hermite divided-difference spatial interpolation on Cartesian grids, and a Cauchy-Kowalewslci recursion procedure for time advancement. In addition, a stencil constraint tree reduces the complexity of interpolating grid points that are located near wall boundaries. These procedures are used to automatically develop and implement very high order methods (>15) for solving the linearized Euler equations that can achieve less than one grid point per wavelength resolution away from boundaries by including spatial derivatives of the primitive variables at each grid point. The accuracy of stable surface treatments is currently limited to 11th order for grid aligned boundaries and to 2nd order for irregular boundaries.

Space-time light field rendering.

PubMed

Wang, Huamin; Sun, Mingxuan; Yang, Ruigang

2007-01-01

In this paper, we propose a novel framework called space-time light field rendering, which allows continuous exploration of a dynamic scene in both space and time. Compared to existing light field capture/rendering systems, it offers the capability of using unsynchronized video inputs and the added freedom of controlling the visualization in the temporal domain, such as smooth slow motion and temporal integration. In order to synthesize novel views from any viewpoint at any time instant, we develop a two-stage rendering algorithm. We first interpolate in the temporal domain to generate globally synchronized images using a robust spatial-temporal image registration algorithm followed by edge-preserving image morphing. We then interpolate these software-synchronized images in the spatial domain to synthesize the final view. In addition, we introduce a very accurate and robust algorithm to estimate subframe temporal offsets among input video sequences. Experimental results from unsynchronized videos with or without time stamps show that our approach is capable of maintaining photorealistic quality from a variety of real scenes.

Employing a portable X-Ray fluorescence (P-XRF) analyser and GIS to identify and map heavy metal pollution in soils of a traditional bonfire site

NASA Astrophysics Data System (ADS)

Dao, Ligang; Zhang, Chaosheng; Morrison, Liam

2010-05-01

Soils in the vicinity of bonfires are recipients of metal contaminants from burning of metal-containing materials. In order to better understand the impacts of bonfires on soils, a total of 218 surface soil samples were collected from a traditional bonfire site in Galway City, Ireland. Concentrations of Cu, Pb and Zn were determined using a portable X-ray Fluorescence (P-XRF) analyser. Strong variations were observed for these metals, and several samples contained elevated Zn concentrations which exceeded the intervention threshold of the Dutch criteria (720 mg kg-1). Spatial clusters and spatial outliers were detected using the local Moran's I index and were mapped using GIS. Two clear high value spatial clusters could be observed on the upper left side and centre part of the study area for Cu, Pb and Zn. Results of variogram analyses showed high nugget-sill-ratios for Cu, Pb and Zn, indicating strong spatial variation over short distances which could be resulted from anthropogenic activities. The spatial interpolation method of ordinary kriging was applied to produce the spatial interpolation maps for Cu, Pb and Zn, and the areas with elevated concentrations were in line with historical locations of the bonfires. The hazard maps showed small parts of the study area with Zn concentrations exceeding the Dutch intervention values. In order to prevent further contamination from bonfires, it is advised that tyres and other metal-containing wastes should not be burnt. The results in this study provide useful information for management of bonfires.

Creating a monthly time series of the potentiometric surface in the Upper Floridan aquifer, Northern Tampa Bay area, Florida, January 2000-December 2009

USGS Publications Warehouse

Lee, Terrie M.; Fouad, Geoffrey G.

2014-01-01

In Florida’s karst terrain, where groundwater and surface waters interact, a mapping time series of the potentiometric surface in the Upper Floridan aquifer offers a versatile metric for assessing the hydrologic condition of both the aquifer and overlying streams and wetlands. Long-term groundwater monitoring data were used to generate a monthly time series of potentiometric surfaces in the Upper Floridan aquifer over a 573-square-mile area of west-central Florida between January 2000 and December 2009. Recorded groundwater elevations were collated for 260 groundwater monitoring wells in the Northern Tampa Bay area, and a continuous time series of daily observations was created for 197 of the wells by estimating missing daily values through regression relations with other monitoring wells. Kriging was used to interpolate the monthly average potentiometric-surface elevation in the Upper Floridan aquifer over a decade. The mapping time series gives spatial and temporal coherence to groundwater monitoring data collected continuously over the decade by three different organizations, but at various frequencies. Further, the mapping time series describes the potentiometric surface beneath parts of six regionally important stream watersheds and 11 municipal well fields that collectively withdraw about 90 million gallons per day from the Upper Floridan aquifer. Monthly semivariogram models were developed using monthly average groundwater levels at wells. Kriging was used to interpolate the monthly average potentiometric-surface elevations and to quantify the uncertainty in the interpolated elevations. Drawdown of the potentiometric surface within well fields was likely the cause of a characteristic decrease and then increase in the observed semivariance with increasing lag distance. This characteristic made use of the hole effect model appropriate for describing the monthly semivariograms and the interpolated surfaces. Spatial variance reflected in the monthly semivariograms decreased markedly between 2002 and 2003, timing that coincided with decreases in well-field pumping. Cross-validation results suggest that the kriging interpolation may smooth over the drawdown of the potentiometric surface near production wells. The groundwater monitoring network of 197 wells yielded an average kriging error in the potentiometric-surface elevations of 2 feet or less over approximately 70 percent of the map area. Additional data collection within the existing monitoring network of 260 wells and near selected well fields could reduce the error in individual months. Reducing the kriging error in other areas would require adding new monitoring wells. Potentiometric-surface elevations fluctuated by as much as 30 feet over the study period, and the spatially averaged elevation for the entire surface rose by about 2 feet over the decade. Monthly potentiometric-surface elevations describe the lateral groundwater flow patterns in the aquifer and are usable at a variety of spatial scales to describe vertical groundwater recharge and discharge conditions for overlying surface-water features.

A Stochastic Mixed Finite Element Heterogeneous Multiscale Method for Flow in Porous Media

DTIC Science & Technology

2010-08-01

applicable for flow in porous media has drawn significant interest in the last few years. Several techniques like generalized polynomial chaos expansions (gPC...represents the stochastic solution as a polynomial approxima- tion. This interpolant is constructed via independent function calls to the de- terministic...of orthogonal polynomials [34,38] or sparse grid approximations [39–41]. It is well known that the global polynomial interpolation cannot resolve lo

Method for Pre-Conditioning a Measured Surface Height Map for Model Validation

NASA Technical Reports Server (NTRS)

Sidick, Erkin

2012-01-01

This software allows one to up-sample or down-sample a measured surface map for model validation, not only without introducing any re-sampling errors, but also eliminating the existing measurement noise and measurement errors. Because the re-sampling of a surface map is accomplished based on the analytical expressions of Zernike-polynomials and a power spectral density model, such re-sampling does not introduce any aliasing and interpolation errors as is done by the conventional interpolation and FFT-based (fast-Fourier-transform-based) spatial-filtering method. Also, this new method automatically eliminates the measurement noise and other measurement errors such as artificial discontinuity. The developmental cycle of an optical system, such as a space telescope, includes, but is not limited to, the following two steps: (1) deriving requirements or specs on the optical quality of individual optics before they are fabricated through optical modeling and simulations, and (2) validating the optical model using the measured surface height maps after all optics are fabricated. There are a number of computational issues related to model validation, one of which is the "pre-conditioning" or pre-processing of the measured surface maps before using them in a model validation software tool. This software addresses the following issues: (1) up- or down-sampling a measured surface map to match it with the gridded data format of a model validation tool, and (2) eliminating the surface measurement noise or measurement errors such that the resulted surface height map is continuous or smoothly-varying. So far, the preferred method used for re-sampling a surface map is two-dimensional interpolation. The main problem of this method is that the same pixel can take different values when the method of interpolation is changed among the different methods such as the "nearest," "linear," "cubic," and "spline" fitting in Matlab. The conventional, FFT-based spatial filtering method used to eliminate the surface measurement noise or measurement errors can also suffer from aliasing effects. During re-sampling of a surface map, this software preserves the low spatial-frequency characteristic of a given surface map through the use of Zernike-polynomial fit coefficients, and maintains mid- and high-spatial-frequency characteristics of the given surface map by the use of a PSD model derived from the two-dimensional PSD data of the mid- and high-spatial-frequency components of the original surface map. Because this new method creates the new surface map in the desired sampling format from analytical expressions only, it does not encounter any aliasing effects and does not cause any discontinuity in the resultant surface map.

Evaluation of Reconstructed Remote Sensing Time Series Data

NASA Astrophysics Data System (ADS)

Rivera-Camacho, J.; Didan, K.; Barreto-munoz, A.; Yitayew, M.

2011-12-01

Vegetation phenology is the study of vegetation state, function and change over time and is directly linked to the carbon cycle and an integrative measure of climate change impacts. Field observations of phenology can address some questions associated with phenology and climate change, but they are not effective at estimating and understanding large scale change in biome seasonality. Synoptic remote sensing has emerged as a practical tool for studying the land surface vegetation over large spatial and temporal scales. However, the presence of clouds, noise, inadequate processing algorithms result in poor quality data that needs to be discarded. Discarded data is so prevalent sometimes that up to 80% of the spatial and temporal coverage is missing which inhibits the proper study of vegetation phenology. To improve these data records gap filling techniques are employed. The purpose is to accurately reconstruct the VI time series profile, while preserving as much of the original data to support accurate land surface vegetation characterization. Some methods use complex Fourier Transform (FT) functions, Gaussian fitting models, or Piecewise techniques, while others are based on simpler linear interpolation. The impact of these gap filling methods on the resulting record is yet to be fully explored and characterized. In this project, we devised a new hybrid gap filling technique based on finding the seasonally variable per-pixel optimum composite period and then filling the remaining gaps with a simple local interpolation using the Inverse Distance Weighting (IDW) approach. The method is further constrained by a moving window long term average to minimize the biases that may result from over- or under-fitting. This method was applied to a 30-year sensor independent Vegetation Index ESDR from AHRR and MODIS records. To understand the impact of this gap filling technique, we performed statistical analyses to determine the error and uncertainty associated with estimating the start of season, length of season and integrated VI signal over the growing season (proxy of Gross Primary Productivity, or Carbon). Our preliminary results indicate that the time series is sensitive to the gap filling technique, particularly over areas prone to residual cloud noise and/or areas subject to long period of snow cover. This has a direct impact on the growing season characterization by making the season shorter (up to 4 weeks) and the start of the season later (up to 2 weeks). The seasonal summation of VI becomes then smaller (13%) with a direct impact on the carbon budget estimation. Another important finding is that special attention must be paid to data filtering, since this will impact the residual noise/signal in the input data and will subsequently impact the gap filling outcome.

Analysis of the spatial distribution of prostate cancer obtained from histopathological images

NASA Astrophysics Data System (ADS)

Diaz, Kristians; Castaneda, Benjamin; Montero, Maria Luisa; Yao, Jorge; Joseph, Jean; Rubens, Deborah; Parker, Kevin J.

2013-03-01

Understanding the spatial distribution of prostate cancer and how it changes according to prostate specific antigen (PSA) values, Gleason score, and other clinical parameters may help comprehend the disease and increase the overall success rate of biopsies. This work aims to build 3D spatial distributions of prostate cancer and examine the extent and location of cancer as a function of independent clinical parameters. The border of the gland and cancerous regions from wholemount histopathological images are used to reconstruct 3D models showing the localization of tumor. This process utilizes color segmentation and interpolation based on mathematical morphological distance. 58 glands are deformed into one prostate atlas using a combination of rigid, affine, and b-spline deformable registration techniques. Spatial distribution is developed by counting the number of occurrences in a given position in 3D space from each registered prostate cancer. Finally a difference between proportions is used to compare different spatial distributions. Results show that prostate cancer has a significant difference (SD) in the right zone of the prostate between populations with PSA greater and less than 5ng/ml. Age does not have any impact in the spatial distribution of the disease. Positive and negative capsule-penetrated cases show a SD in the right posterior zone. There is SD in almost all the glands between cases with tumors larger and smaller than 10% of the whole prostate. A larger database is needed to improve the statistical validity of the test. Finally, information from whole-mount histopathological images may provide better insight into prostate cancer.

Spatial distribution of grape root borer (Lepidoptera: Sesiidae) infestations in Virginia vineyards and implications for sampling.

PubMed

Rijal, J P; Brewster, C C; Bergh, J C

2014-06-01

Grape root borer, Vitacea polistiformis (Harris) (Lepidoptera: Sesiidae) is a potentially destructive pest of grape vines, Vitis spp. in the eastern United States. After feeding on grape roots for ≍2 yr in Virginia, larvae pupate beneath the soil surface around the vine base. Adults emerge during July and August, leaving empty pupal exuviae on or protruding from the soil. Weekly collections of pupal exuviae from an ≍1-m-diameter weed-free zone around the base of a grid of sample vines in Virginia vineyards were conducted in July and August, 2008-2012, and their distribution was characterized using both nonspatial (dispersion) and spatial techniques. Taylor's power law showed a significant aggregation of pupal exuviae, based on data from 19 vineyard blocks. Combined use of geostatistical and Spatial Analysis by Distance IndicEs methods indicated evidence of an aggregated pupal exuviae distribution pattern in seven of the nine blocks used for those analyses. Grape root borer pupal exuviae exhibited spatial dependency within a mean distance of 8.8 m, based on the range values of best-fitted variograms. Interpolated and clustering index-based infestation distribution maps were developed to show the spatial pattern of the insect within the vineyard blocks. The temporal distribution of pupal exuviae showed that the majority of moths emerged during the 3-wk period spanning the third week of July and the first week of August. The spatial distribution of grape root borer pupal exuviae was used in combination with temporal moth emergence patterns to develop a quantitative and efficient sampling scheme to assess infestations.

Accessing groundwater quality in lower part of Nagapattinam district, Southern India: using hydrogeochemistry and GIS interpolation techniques

NASA Astrophysics Data System (ADS)

Gnanachandrasamy, G.; Ramkumar, T.; Venkatramanan, S.; Vasudevan, S.; Chung, S. Y.; Bagyaraj, M.

2015-03-01

The aim of this present study was to evaluate groundwater quality in the lower part of Nagapattinam district, Tamil Nadu, Southern India. A detailed geochemical study of groundwater region is described, and the origin of the chemical composition of groundwater has been qualitatively evaluated, using observations over a period of two seasons premonsoon (June) and monsoon (November) in the year of 2010. To attempt this goal, samples were analysed for various physico-chemical parameters such as temperature, pH, salinity, Na+, Ca2+, K+, Mg2+, Cl-, HCO3 - and SO4 2-. The abundance of major cations concentration in groundwater is as Na > Ca > Mg > K, while that of anions is Cl > SO4 > HCO3. The Piper trilinear diagram indicates Ca-Cl2 facies, and according to USSL diagram, most of the sample exhibits high salinity hazard (C3S1) type in both seasons. It indicates that high salinity (C3) and low sodium (S1) are moderately suitable for irrigation purposes. Gibbs boomerang exhibits most of the samples mainly controlled by evaporation and weathering process sector in both seasons. Irrigation status of the groundwater samples indicates that it was moderately suitable for agricultural purpose. ArcGIS 9.3 software was used for the generation of various thematic maps and the final groundwater quality map. An interpolation technique inverse distance weighting was used to obtain the spatial distribution of groundwater quality parameters. The final map classified the ground quality in the study area. The results of this research show that the development of the management strategies for the aquifer system is vitally necessary.

Using a dynamical advection to reconstruct a part of the SSH evolution in the context of SWOT, application to the Mediterranean Sea

NASA Astrophysics Data System (ADS)

Rogé, Marine; Morrow, Rosemary; Ubelmann, Clément; Dibarboure, Gérald

2017-08-01

The main oceanographic objective of the future SWOT mission is to better characterize the ocean mesoscale and sub-mesoscale circulation, by observing a finer range of ocean topography dynamics down to 20 km wavelength. Despite the very high spatial resolution of the future satellite, it will not capture the time evolution of the shorter mesoscale signals, such as the formation and evolution of small eddies. SWOT will have an exact repeat cycle of 21 days, with near repeats around 5-10 days, depending on the latitude. Here, we investigate a technique to reconstruct the missing 2D SSH signal in the time between two satellite revisits. We use the dynamical interpolation (DI) technique developed by Ubelmann et al. (2015). Based on potential vorticity (hereafter PV) conservation using a one and a half layer quasi-geostrophic model, it features an active advection of the SSH field. This model has been tested in energetic open ocean regions such as the Gulf Stream and the Californian Current, and has given promising results. Here, we test this model in the Western Mediterranean Sea, a lower energy region with complex small scale physics, and compare the SSH reconstruction with the high-resolution Symphonie model. We investigate an extension of the simple dynamical model including a separated mean circulation. We find that the DI gives a 16-18% improvement in the reconstruction of the surface height and eddy kinetic energy fields, compared with a simple linear interpolation, and a 37% improvement in the Northern Current subregion. Reconstruction errors are higher during winter and autumn but statistically, the improvement from the DI is also better for these seasons.

Analysis of spatial-temporal patterns of water table change as a tool for conjunctive water management in the Upper Central Plain of the Chao Phraya River Basin, Thailand

NASA Astrophysics Data System (ADS)

Vasconcelos, Vitor Vieira; Koontanakulvong, Sucharit; Suthidhummajit, Chokchai; Junior, Paulo Pereira Martins; Hadad, Renato Moreira

2017-03-01

A sustainable strategy for conjunctive water management must include information on the temporal and spatial availability of this natural resource. Because of water shortages in the dry seasons, farmers on the Upper Plain of the Chao Phraya River basin, Thailand, are increasingly using groundwater to meet their irrigation needs. To evaluate the possibilities of conjunctive water management in the area, the spatial-temporal changes in the water table of the Younger Terrace Aquifer were investigated. First, a regional geomorphological map based on field surveys, remote sensing and previous environmental studies was developed. Then, the well data were analyzed in relation to rainfall, streamflow, yield and pumpage, and the data were interpolated using geostatistical techniques. The results were analyzed via integrated zoning based on color theory as applied to multivariate visualization. The analysis results indicate areas that would be more suitable for groundwater extraction in a conjunctive management framework with regard to the natural hydrogeological processes and the effects of human interaction. The kriging results were compared with the geomorphological map, and the geomorphological areas exhibit distinct hydrogeological patterns. The western fans exhibit the best potential for the expansion of conjunctive use, whereas the borders of the northern fans exhibit the lowest potential.

Use of artificial neural network for spatial rainfall analysis

NASA Astrophysics Data System (ADS)

Paraskevas, Tsangaratos; Dimitrios, Rozos; Andreas, Benardos

2014-04-01

In the present study, the precipitation data measured at 23 rain gauge stations over the Achaia County, Greece, were used to estimate the spatial distribution of the mean annual precipitation values over a specific catchment area. The objective of this work was achieved by programming an Artificial Neural Network (ANN) that uses the feed-forward back-propagation algorithm as an alternative interpolating technique. A Geographic Information System (GIS) was utilized to process the data derived by the ANN and to create a continuous surface that represented the spatial mean annual precipitation distribution. The ANN introduced an optimization procedure that was implemented during training, adjusting the hidden number of neurons and the convergence of the ANN in order to select the best network architecture. The performance of the ANN was evaluated using three standard statistical evaluation criteria applied to the study area and showed good performance. The outcomes were also compared with the results obtained from a previous study in the area of research which used a linear regression analysis for the estimation of the mean annual precipitation values giving more accurate results. The information and knowledge gained from the present study could improve the accuracy of analysis concerning hydrology and hydrogeological models, ground water studies, flood related applications and climate analysis studies.

A collocation--Galerkin finite element model of cardiac action potential propagation.

PubMed

Rogers, J M; McCulloch, A D

1994-08-01

A new computational method was developed for modeling the effects of the geometric complexity, nonuniform muscle fiber orientation, and material inhomogeneity of the ventricular wall on cardiac impulse propagation. The method was used to solve a modification to the FitzHugh-Nagumo system of equations. The geometry, local muscle fiber orientation, and material parameters of the domain were defined using linear Lagrange or cubic Hermite finite element interpolation. Spatial variations of time-dependent excitation and recovery variables were approximated using cubic Hermite finite element interpolation, and the governing finite element equations were assembled using the collocation method. To overcome the deficiencies of conventional collocation methods on irregular domains, Galerkin equations for the no-flux boundary conditions were used instead of collocation equations for the boundary degrees-of-freedom. The resulting system was evolved using an adaptive Runge-Kutta method. Converged two-dimensional simulations of normal propagation showed that this method requires less CPU time than a traditional finite difference discretization. The model also reproduced several other physiologic phenomena known to be important in arrhythmogenesis including: Wenckebach periodicity, slowed propagation and unidirectional block due to wavefront curvature, reentry around a fixed obstacle, and spiral wave reentry. In a new result, we observed wavespeed variations and block due to nonuniform muscle fiber orientation. The findings suggest that the finite element method is suitable for studying normal and pathological cardiac activation and has significant advantages over existing techniques.

Ground-state energy of an exciton-(LO) phonon system in a parabolic quantum well

NASA Astrophysics Data System (ADS)

Gerlach, B.; Wüsthoff, J.; Smondyrev, M. A.

1999-12-01

This paper presents a variational study of the ground-state energy of an exciton-(LO) phonon system, which is spatially confined to a quantum well. The exciton-phonon interaction is of Fröhlich type, the confinement potentials are assumed to be parabolic functions of the coordinates. Making use of functional integral techniques, the phonon part of the problem can be eliminated exactly, leading us to an effective two-particle system, which has the same spectral properties as the original one. Subsequently, Jensen's inequality is applied to obtain an upper bound on the ground-state energy. The main intention of this paper is to analyze the influence of the quantum-well-induced localization of the exciton on its ground-state energy (or its binding energy, respectively). To do so, we neglect any mismatch of the masses or the dielectric constants, but admit an arbitrary strength of the confinement potentials. Our approach allows for a smooth interpolation of the ultimate limits of vanishing and infinite confinement, corresponding to the cases of a free three-dimensional and a free two-dimensional exciton-phonon system. The interpolation formula for the ground-state energy bound corresponds to similar formulas for the free polaron or the free exciton-phonon system. These bounds in turn are known to compare favorably with all previous ones, which we are aware of.

Assessment and modeling of the groundwater hydrogeochemical quality parameters via geostatistical approaches

NASA Astrophysics Data System (ADS)

Karami, Shawgar; Madani, Hassan; Katibeh, Homayoon; Fatehi Marj, Ahmad

2018-03-01

Geostatistical methods are one of the advanced techniques used for interpolation of groundwater quality data. The results obtained from geostatistics will be useful for decision makers to adopt suitable remedial measures to protect the quality of groundwater sources. Data used in this study were collected from 78 wells in Varamin plain aquifer located in southeast of Tehran, Iran, in 2013. Ordinary kriging method was used in this study to evaluate groundwater quality parameters. According to what has been mentioned in this paper, seven main quality parameters (i.e. total dissolved solids (TDS), sodium adsorption ratio (SAR), electrical conductivity (EC), sodium (Na+), total hardness (TH), chloride (Cl-) and sulfate (SO4 2-)), have been analyzed and interpreted by statistical and geostatistical methods. After data normalization by Nscore method in WinGslib software, variography as a geostatistical tool to define spatial regression was compiled and experimental variograms were plotted by GS+ software. Then, the best theoretical model was fitted to each variogram based on the minimum RSS. Cross validation method was used to determine the accuracy of the estimated data. Eventually, estimation maps of groundwater quality were prepared in WinGslib software and estimation variance map and estimation error map were presented to evaluate the quality of estimation in each estimated point. Results showed that kriging method is more accurate than the traditional interpolation methods.

Validation study of an interpolation method for calculating whole lung volumes and masses from reduced numbers of CT-images in ponies.

PubMed

Reich, H; Moens, Y; Braun, C; Kneissl, S; Noreikat, K; Reske, A

2014-12-01

Quantitative computer tomographic analysis (qCTA) is an accurate but time intensive method used to quantify volume, mass and aeration of the lungs. The aim of this study was to validate a time efficient interpolation technique for application of qCTA in ponies. Forty-one thoracic computer tomographic (CT) scans obtained from eight anaesthetised ponies positioned in dorsal recumbency were included. Total lung volume and mass and their distribution into four compartments (non-aerated, poorly aerated, normally aerated and hyperaerated; defined based on the attenuation in Hounsfield Units) were determined for the entire lung from all 5 mm thick CT-images, 59 (55-66) per animal. An interpolation technique validated for use in humans was then applied to calculate qCTA results for lung volumes and masses from only 10, 12, and 14 selected CT-images per scan. The time required for both procedures was recorded. Results were compared statistically using the Bland-Altman approach. The bias ± 2 SD for total lung volume calculated from interpolation of 10, 12, and 14 CT-images was -1.2 ± 5.8%, 0.1 ± 3.5%, and 0.0 ± 2.5%, respectively. The corresponding results for total lung mass were -1.1 ± 5.9%, 0.0 ± 3.5%, and 0.0 ± 3.0%. The average time for analysis of one thoracic CT-scan using the interpolation method was 1.5-2 h compared to 8 h for analysis of all images of one complete thoracic CT-scan. The calculation of pulmonary qCTA data by interpolation from 12 CT-images was applicable for equine lung CT-scans and reduced the time required for analysis by 75%. Copyright © 2014 Elsevier Ltd. All rights reserved.

Autoregressive linear least square single scanning electron microscope image signal-to-noise ratio estimation.

PubMed

Sim, Kok Swee; NorHisham, Syafiq

2016-11-01

A technique based on linear Least Squares Regression (LSR) model is applied to estimate signal-to-noise ratio (SNR) of scanning electron microscope (SEM) images. In order to test the accuracy of this technique on SNR estimation, a number of SEM images are initially corrupted with white noise. The autocorrelation function (ACF) of the original and the corrupted SEM images are formed to serve as the reference point to estimate the SNR value of the corrupted image. The LSR technique is then compared with the previous three existing techniques known as nearest neighbourhood, first-order interpolation, and the combination of both nearest neighborhood and first-order interpolation. The actual and the estimated SNR values of all these techniques are then calculated for comparison purpose. It is shown that the LSR technique is able to attain the highest accuracy compared to the other three existing techniques as the absolute difference between the actual and the estimated SNR value is relatively small. SCANNING 38:771-782, 2016. © 2016 Wiley Periodicals, Inc. © Wiley Periodicals, Inc.

A Novel Stimulus Artifact Removal Technique for High-Rate Electrical Stimulation

PubMed Central

Heffer, Leon F; Fallon, James B

2008-01-01

Electrical stimulus artifact corrupting electrophysiological recordings often make the subsequent analysis of the underlying neural response difficult. This is particularly evident when investigating short-latency neural activity in response to high-rate electrical stimulation. We developed and evaluated an off-line technique for the removal of stimulus artifact from electrophysiological recordings. Pulsatile electrical stimulation was presented at rates of up to 5000 pulses/s during extracellular recordings of guinea pig auditory nerve fibers. Stimulus artifact was removed by replacing the sample points at each stimulus artifact event with values interpolated along a straight line, computed from neighbouring sample points. This technique required only that artifact events be identifiable and that the artifact duration remained less than both the inter-stimulus interval and the time course of the action potential. We have demonstrated that this computationally efficient sample-and-interpolate technique removes the stimulus artifact with minimal distortion of the action potential waveform. We suggest that this technique may have potential applications in a range of electrophysiological recording systems. PMID:18339428

Spatial Scale Variability of NH3 and Impacts to interpolated Concentration Grids

EPA Science Inventory

Over the past decade, reduced nitrogen (NH3, NH4) has become an important component of atmospheric nitrogen deposition due to increases in agricultural activities and reductions in oxidized sulfur and nitrogen emissions from the power sector and mobile sources. Reduced nitrogen i...

Rtop - an R package for interpolation along the stream network

NASA Astrophysics Data System (ADS)

Skøien, J. O.

2009-04-01

Rtop - an R package for interpolation along the stream network Geostatistical methods have been used to a limited extent for estimation along stream networks, with a few exceptions(Gottschalk, 1993; Gottschalk, et al., 2006; Sauquet, et al., 2000; Skøien, et al., 2006). Interpolation of runoff characteristics are more complicated than the traditional random variables estimated by geostatistical methods, as the measurements have a more complicated support, and many catchments are nested. Skøien et al. (2006) presented the model Top-kriging which takes these effects into account for interpolation of stream flow characteristics (exemplified by the 100 year flood). The method has here been implemented as a package in the statistical environment R (R Development Core Team, 2004). Taking advantage of the existing methods in R for working with spatial objects, and the extensive possibilities for visualizing the result, this makes it considerably easier to apply the method on new data sets, in comparison to earlier implementation of the method. Gottschalk, L. 1993. Interpolation of runoff applying objective methods. Stochastic Hydrology and Hydraulics, 7, 269-281. Gottschalk, L., I. Krasovskaia, E. Leblois, and E. Sauquet. 2006. Mapping mean and variance of runoff in a river basin. Hydrology and Earth System Sciences, 10, 469-484. R Development Core Team. 2004. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. Sauquet, E., L. Gottschalk, and E. Leblois. 2000. Mapping average annual runoff: a hierarchical approach applying a stochastic interpolation scheme. Hydrological Sciences Journal, 45 (6), 799-815. Skøien, J. O., R. Merz, and G. Blöschl. 2006. Top-kriging - geostatistics on stream networks. Hydrology and Earth System Sciences, 10, 277-287.

An Immersed Boundary method with divergence-free velocity interpolation and force spreading

NASA Astrophysics Data System (ADS)

Bao, Yuanxun; Donev, Aleksandar; Griffith, Boyce E.; McQueen, David M.; Peskin, Charles S.

2017-10-01

The Immersed Boundary (IB) method is a mathematical framework for constructing robust numerical methods to study fluid-structure interaction in problems involving an elastic structure immersed in a viscous fluid. The IB formulation uses an Eulerian representation of the fluid and a Lagrangian representation of the structure. The Lagrangian and Eulerian frames are coupled by integral transforms with delta function kernels. The discretized IB equations use approximations to these transforms with regularized delta function kernels to interpolate the fluid velocity to the structure, and to spread structural forces to the fluid. It is well-known that the conventional IB method can suffer from poor volume conservation since the interpolated Lagrangian velocity field is not generally divergence-free, and so this can cause spurious volume changes. In practice, the lack of volume conservation is especially pronounced for cases where there are large pressure differences across thin structural boundaries. The aim of this paper is to greatly reduce the volume error of the IB method by introducing velocity-interpolation and force-spreading schemes with the properties that the interpolated velocity field in which the structure moves is at least C1 and satisfies a continuous divergence-free condition, and that the force-spreading operator is the adjoint of the velocity-interpolation operator. We confirm through numerical experiments in two and three spatial dimensions that this new IB method is able to achieve substantial improvement in volume conservation compared to other existing IB methods, at the expense of a modest increase in the computational cost. Further, the new method provides smoother Lagrangian forces (tractions) than traditional IB methods. The method presented here is restricted to periodic computational domains. Its generalization to non-periodic domains is important future work.

Geostatistical analysis of the flood risk perception queries in the village of Navaluenga (Central Spain)

NASA Astrophysics Data System (ADS)

Guardiola-Albert, Carolina; Díez-Herrero, Andrés; Amérigo, María; García, Juan Antonio; María Bodoque, José; Fernández-Naranjo, Nuria

2017-04-01

Flash floods provoke a high average mortality as they are usually unexpected events which evolve rapidly and affect relatively small areas. The short time available for minimizing risks requires preparedness and response actions to be put into practice. Therefore, it is necessary the development of emergency response plans to evacuate and rescue people in the context of a flash-flood hazard. In this framework, risk management has to integrate the social dimension of flash-flooding and its spatial distribution by understanding the characteristics of local communities in order to enhance community resilience during a flash-flood. In this regard, the flash-flood social risk perception of the village of Navaluenga (Central Spain) has been recently assessed, as well as the level of awareness of civil protection and emergency management strategies (Bodoque et al., 2016). This has been done interviewing 254 adults, representing roughly 12% of the population census. The present study wants to go further in the analysis of the resulting questionnaires, incorporating in the analysis the location of home spatial coordinates in order to characterize the spatial distribution and possible geographical interpretation of flood risk perception. We apply geostatistical methods to analyze spatial relations of social risk perception and level of awareness with distance to the rivers (Alberche and Chorrerón) or to the flood-prone areas (50-year, 100-year and 500-year flood plains). We want to discover spatial patterns, if any, using correlation functions (variograms). Geostatistical analyses results can help to either confirm the logical pattern (i.e., less awareness further to the rivers or high return period of flooding) or reveal departures from expected. It can also be possible to identify hot spots, cold spots, and spatial outliers. The interpretation of these spatial patterns can give valuable information to define strategies to improve the awareness regarding preparedness and response actions, such as designing optimal evacuation routes during flood emergencies. Geostatistical tools also provide a set of interpolation techniques for the prediction of the variable value at unstudied similar locations, basing on the sample point values and other variables related with the measured variable. We attempt different geostatistical interpolation methods to obtain continuous surfaces of the risk perception and level of awareness in the study area. The use of these maps for future extensions and actualizations of the Civil Protection Plan is evaluated. References Bodoque, J. M., Amérigo, M., Díez-Herrero, A., García, J. A., Cortés, B., Ballesteros-Cánovas, J. A., & Olcina, J. (2016). Improvement of resilience of urban areas by integrating social perception in flash-flood risk management.Journal of Hydrology.

An evaluation of HEMT potential for millimeter-wave signal sources using interpolation and harmonic balance techniques

NASA Technical Reports Server (NTRS)

Kwon, Youngwoo; Pavlidis, Dimitris; Tutt, Marcel N.

1991-01-01

A large-signal analysis method based on an harmonic balance technique and a 2-D cubic spline interpolation function has been developed and applied to the prediction of InP-based HEMT oscillator performance for frequencies extending up to the submillimeter-wave range. The large-signal analysis method uses a limited number of DC and small-signal S-parameter data and allows the accurate characterization of HEMT large-signal behavior. The method has been validated experimentally using load-pull measurement. Oscillation frequency, power performance, and load requirements are discussed, with an operation capability of 300 GHz predicted using state-of-the-art devices (fmax is approximately equal to 450 GHz).

Analysis of rainfall distribution in Kelantan river basin, Malaysia

NASA Astrophysics Data System (ADS)

Che Ros, Faizah; Tosaka, Hiroyuki

2018-03-01

Using rainfall gauge on its own as input carries great uncertainties regarding runoff estimation, especially when the area is large and the rainfall is measured and recorded at irregular spaced gauging stations. Hence spatial interpolation is the key to obtain continuous and orderly rainfall distribution at unknown points to be the input to the rainfall runoff processes for distributed and semi-distributed numerical modelling. It is crucial to study and predict the behaviour of rainfall and river runoff to reduce flood damages of the affected area along the Kelantan river. Thus, a good knowledge on rainfall distribution is essential in early flood prediction studies. Forty six rainfall stations and their daily time-series were used to interpolate gridded rainfall surfaces using inverse-distance weighting (IDW), inverse-distance and elevation weighting (IDEW) methods and average rainfall distribution. Sensitivity analysis for distance and elevation parameters were conducted to see the variation produced. The accuracy of these interpolated datasets was examined using cross-validation assessment.

Spatiotemporal Interpolation Methods for the Application of Estimating Population Exposure to Fine Particulate Matter in the Contiguous U.S. and a Real-Time Web Application.

PubMed

Li, Lixin; Zhou, Xiaolu; Kalo, Marc; Piltner, Reinhard

2016-07-25

Appropriate spatiotemporal interpolation is critical to the assessment of relationships between environmental exposures and health outcomes. A powerful assessment of human exposure to environmental agents would incorporate spatial and temporal dimensions simultaneously. This paper compares shape function (SF)-based and inverse distance weighting (IDW)-based spatiotemporal interpolation methods on a data set of PM2.5 data in the contiguous U.S. Particle pollution, also known as particulate matter (PM), is composed of microscopic solids or liquid droplets that are so small that they can get deep into the lungs and cause serious health problems. PM2.5 refers to particles with a mean aerodynamic diameter less than or equal to 2.5 micrometers. Based on the error statistics results of k-fold cross validation, the SF-based method performed better overall than the IDW-based method. The interpolation results generated by the SF-based method are combined with population data to estimate the population exposure to PM2.5 in the contiguous U.S. We investigated the seasonal variations, identified areas where annual and daily PM2.5 were above the standards, and calculated the population size in these areas. Finally, a web application is developed to interpolate and visualize in real time the spatiotemporal variation of ambient air pollution across the contiguous U.S. using air pollution data from the U.S. Environmental Protection Agency (EPA)'s AirNow program.

Spatiotemporal Interpolation Methods for the Application of Estimating Population Exposure to Fine Particulate Matter in the Contiguous U.S. and a Real-Time Web Application

PubMed Central

Li, Lixin; Zhou, Xiaolu; Kalo, Marc; Piltner, Reinhard

2016-01-01

Appropriate spatiotemporal interpolation is critical to the assessment of relationships between environmental exposures and health outcomes. A powerful assessment of human exposure to environmental agents would incorporate spatial and temporal dimensions simultaneously. This paper compares shape function (SF)-based and inverse distance weighting (IDW)-based spatiotemporal interpolation methods on a data set of PM2.5 data in the contiguous U.S. Particle pollution, also known as particulate matter (PM), is composed of microscopic solids or liquid droplets that are so small that they can get deep into the lungs and cause serious health problems. PM2.5 refers to particles with a mean aerodynamic diameter less than or equal to 2.5 micrometers. Based on the error statistics results of k-fold cross validation, the SF-based method performed better overall than the IDW-based method. The interpolation results generated by the SF-based method are combined with population data to estimate the population exposure to PM2.5 in the contiguous U.S. We investigated the seasonal variations, identified areas where annual and daily PM2.5 were above the standards, and calculated the population size in these areas. Finally, a web application is developed to interpolate and visualize in real time the spatiotemporal variation of ambient air pollution across the contiguous U.S. using air pollution data from the U.S. Environmental Protection Agency (EPA)’s AirNow program. PMID:27463722

Wavelet-based adaptation methodology combined with finite difference WENO to solve ideal magnetohydrodynamics

NASA Astrophysics Data System (ADS)

Do, Seongju; Li, Haojun; Kang, Myungjoo

2017-06-01

In this paper, we present an accurate and efficient wavelet-based adaptive weighted essentially non-oscillatory (WENO) scheme for hydrodynamics and ideal magnetohydrodynamics (MHD) equations arising from the hyperbolic conservation systems. The proposed method works with the finite difference weighted essentially non-oscillatory (FD-WENO) method in space and the third order total variation diminishing (TVD) Runge-Kutta (RK) method in time. The philosophy of this work is to use the lifted interpolating wavelets as not only detector for singularities but also interpolator. Especially, flexible interpolations can be performed by an inverse wavelet transformation. When the divergence cleaning method introducing auxiliary scalar field ψ is applied to the base numerical schemes for imposing divergence-free condition to the magnetic field in a MHD equation, the approximations to derivatives of ψ require the neighboring points. Moreover, the fifth order WENO interpolation requires large stencil to reconstruct high order polynomial. In such cases, an efficient interpolation method is necessary. The adaptive spatial differentiation method is considered as well as the adaptation of grid resolutions. In order to avoid the heavy computation of FD-WENO, in the smooth regions fixed stencil approximation without computing the non-linear WENO weights is used, and the characteristic decomposition method is replaced by a component-wise approach. Numerical results demonstrate that with the adaptive method we are able to resolve the solutions that agree well with the solution of the corresponding fine grid.

Real-Time Fourier Synthesis of Ensembles with Timbral Interpolation

NASA Astrophysics Data System (ADS)

Haken, Lippold

1990-01-01

In Fourier synthesis, natural musical sounds are produced by summing time-varying sinusoids. Sounds are analyzed to find the amplitude and frequency characteristics for their sinusoids; interpolation between the characteristics of several sounds is used to produce intermediate timbres. An ensemble can be synthesized by summing all the sinusoids for several sounds, but in practice it is difficult to perform such computations in real time. To solve this problem on inexpensive hardware, it is useful to take advantage of the masking effects of the auditory system. By avoiding the computations for perceptually unimportant sinusoids, and by employing other computation reduction techniques, a large ensemble may be synthesized in real time on the Platypus signal processor. Unlike existing computation reduction techniques, the techniques described in this thesis do not sacrifice independent fine control over the amplitude and frequency characteristics of each sinusoid.

Visualization of AMR data with multi-level dual-mesh interpolation.

PubMed

Moran, Patrick J; Ellsworth, David

2011-12-01

We present a new technique for providing interpolation within cell-centered Adaptive Mesh Refinement (AMR) data that achieves C(0) continuity throughout the 3D domain. Our technique improves on earlier work in that it does not require that adjacent patches differ by at most one refinement level. Our approach takes the dual of each mesh patch and generates "stitching cells" on the fly to fill the gaps between dual meshes. We demonstrate applications of our technique with data from Enzo, an AMR cosmological structure formation simulation code. We show ray-cast visualizations that include contributions from particle data (dark matter and stars, also output by Enzo) and gridded hydrodynamic data. We also show results from isosurface studies, including surfaces in regions where adjacent patches differ by more than one refinement level. © 2011 IEEE

Method of Characteristics Calculations and Computer Code for Materials with Arbitrary Equations of State and Using Orthogonal Polynomial Least Square Surface Fits

NASA Technical Reports Server (NTRS)

Chang, T. S.

1974-01-01

A numerical scheme using the method of characteristics to calculate the flow properties and pressures behind decaying shock waves for materials under hypervelocity impact is developed. Time-consuming double interpolation subroutines are replaced by a technique based on orthogonal polynomial least square surface fits. Typical calculated results are given and compared with the double interpolation results. The complete computer program is included.

Sample-interpolation timing: an optimized technique for the digital measurement of time of flight for γ rays and neutrons at relatively low sampling rates

NASA Astrophysics Data System (ADS)

Aspinall, M. D.; Joyce, M. J.; Mackin, R. O.; Jarrah, Z.; Boston, A. J.; Nolan, P. J.; Peyton, A. J.; Hawkes, N. P.

2009-01-01

A unique, digital time pick-off method, known as sample-interpolation timing (SIT) is described. This method demonstrates the possibility of improved timing resolution for the digital measurement of time of flight compared with digital replica-analogue time pick-off methods for signals sampled at relatively low rates. Three analogue timing methods have been replicated in the digital domain (leading-edge, crossover and constant-fraction timing) for pulse data sampled at 8 GSa s-1. Events arising from the 7Li(p, n)7Be reaction have been detected with an EJ-301 organic liquid scintillator and recorded with a fast digital sampling oscilloscope. Sample-interpolation timing was developed solely for the digital domain and thus performs more efficiently on digital signals compared with analogue time pick-off methods replicated digitally, especially for fast signals that are sampled at rates that current affordable and portable devices can achieve. Sample interpolation can be applied to any analogue timing method replicated digitally and thus also has the potential to exploit the generic capabilities of analogue techniques with the benefits of operating in the digital domain. A threshold in sampling rate with respect to the signal pulse width is observed beyond which further improvements in timing resolution are not attained. This advance is relevant to many applications in which time-of-flight measurement is essential.

A Spatial Analysis of the Potato Cyst Nematode Globodera pallida in Idaho.

PubMed

Dandurand, Louise-Marie; Contina, Jean Bertrand; Knudsen, Guy R

2018-03-13

The potato cyst nematode (PCN), Globodera pallida, is a globally regulated and quarantine potato pest. It was detected for the first time in the U.S. in the state of Idaho in 2006. A spatial analysis was performed to: (i) understand the spatial arrangement of PCN infested fields in southern Idaho using spatial point pattern analysis; and (ii) evaluate the potential threat of PCN for entry to new areas using spatial interpolation techniques. Data point locations, cyst numbers and egg viability values for each infested field were collected by USDA-APHIS during 2006-2014. Results showed the presence of spatially clustered PCN infested fields (P = 0.003). We determined that the spread of PCN grew in diameter from the original center of infestation toward the southwest as an ellipsoidal-shaped cluster. Based on the aggregated spatial pattern of distribution and the low extent level of PCN infested fields in southern Idaho, we determined that PCN spread followed a contagion effect scenario, where nearby infested fields contributed to the infestation of new fields, probably through soil contaminated agricultural equipment or tubers. We determined that the recent PCN presence in southern Idaho is unlikely to be associated with new PCN entry from outside the state of Idaho. The relative aggregation of PCN infested fields, the low number of cysts recovered, and the low values in egg viability facilitate quarantine activities and confine this pest to a small area, which, in 2017, is estimated to be 1,233 hectares. The tools and methods provided in this study should facilitate comprehensive approaches to improve PCN control and eradication programs as well as to raise public awareness about this economically important potato pest.

SWB Groundwater Recharge Analysis, Catalina Island, California: Assessing Spatial and Temporal Recharge Patterns Within a Mediterranean Climate Zone

NASA Astrophysics Data System (ADS)

Harlow, J.

2017-12-01

Groundwater recharge quantification is a key parameter for sustainable groundwater management. Many recharge quantification techniques have been devised, each with advantages and disadvantages. A free, GIS based recharge quantification tool - the Soil Water Balance (SWB) model - was developed by the USGS to produce fine-tuned recharge constraints in watersheds and illuminate spatial and temporal dynamics of recharge. The subject of this research is to examine SWB within a Mediterranean climate zone, focusing on the Catalina Island, California. This project relied on publicly available online resources with the exception the geospatial processing software, ArcGIS. Daily climate station precipitation and temperature data was obtained from the Desert Research Institute for the years 2008-2014. Precipitation interpolations were performed with ArcGIS using the Natural Neighbor method. The USGS-National Map Viewer (NMV) website provided a 30-meter DEM - to interpolate high and low temperature ASCII grids using the Temperature Lapse Rate (TLR) method, to construct a D-8 flow direction grid for downhill redirection of soil-moisture saturated runoff toward non-saturated cells, and for aesthetic map creation. NMV also provided a modified Anderson land cover classification raster. The US Department of Agriculture-National Resource Conservation Service (NRCS) Web Soil Survey website provided shapefiles of soil water capacity and hydrologic soil groups. The Hargreaves and Samani method was implemented to determine evapotranspiration rates. The resulting SWB output data, in the form of ASCII grids are easily added to ArcGIS for quick visualization and data analysis (Figure 1). Calculated average recharge for 2008-2014 was 3537 inches/year, or 0.0174 acre feet/year. Recharge was 10.2% of the islands gross precipitation. The spatial distribution of the most significant recharge is in hotspots which dominate the residential hills above Avalon, followed by grassy/unvegetated areas associated with dirt roads, and then higher elevation southeast-eastern facing slopes. The greatest large-scale concentration of recharge is centered in the area from Two Harbors to Blackjack Mountain. Further examination within this project will determine parameter significance to recharge and runoff.

Analytical Incorporation of Velocity Parameters into Ice Sheet Elevation Change Rate Computations

NASA Astrophysics Data System (ADS)

Nagarajan, S.; Ahn, Y.; Teegavarapu, R. S. V.

2014-12-01

NASA, ESA and various other agencies have been collecting laser, optical and RADAR altimetry data through various missions to study the elevation changes of the Cryosphere. The laser altimetry collected by various airborne and spaceborne missions provides multi-temporal coverage of Greenland and Antarctica since 1993 to now. Though these missions have increased the data coverage, considering the dynamic nature of the ice surface, it is still sparse both spatially and temporally for accurate elevation change detection studies. The temporal and spatial gaps are usually filled by interpolation techniques. This presentation will demonstrate a method to improve the temporal interpolation. Considering the accuracy, repeat coverage and spatial distribution, the laser scanning data has been widely used to compute elevation change rate of Greenland and Antarctica ice sheets. A major problem with these approaches is non-consideration of ice sheet velocity dynamics into change rate computations. Though the correlation between velocity and elevation change rate have been noticed by Hurkmans et al., 2012, the corrections for velocity changes were applied after computing elevation change rates by assuming linear or higher polynomial relationship. This research will discuss the possibilities of parameterizing ice sheet dynamics as unknowns (dX and dY) in the adjustment mathematical model that computes elevation change (dZ) rates. It is a simultaneous computation of changes in all three directions of the ice surface. Also, the laser points between two time epochs in a crossover area have different distribution and count. Therefore, a registration method that does not require point-to-point correspondence is required to recover the unknown elevation and velocity parameters. This research will experiment the possibilities of registering multi-temporal datasets using volume minimization algorithm, which determines the unknown dX, dY and dZ that minimizes the volume between two or more time-epoch point clouds. In order to make use of other existing data as well as to constrain the adjustment, InSAR velocity will be used as initial values for the parameters dX and dY. The presentation will discuss the results of analytical incorporation of parameters and the volume based registration method for a test site in Greenland.

Interpolating seismic data via the POCS method based on shearlet transform

NASA Astrophysics Data System (ADS)

Jicheng, Liu; Yongxin, Chou; Jianjiang, Zhu

2018-06-01

A method based on shearlet transform and the projection onto convex sets with L0-norm constraint is proposed to interpolate irregularly sampled 2D and 3D seismic data. The 2D directional filter of shearlet transform is constructed by modulating a low-pass diamond filter pair to minimize the effect of additional edges introduced by the missing traces. In order to abate the spatial aliasing and control the maximal gap between missing traces for a 3D data cube, a 2D separable jittered sampling strategy is discussed. Finally, numerical experiments on 2D and 3D synthetic and real data with different under-sampling rates prove the validity of the proposed method.

Comparing Approaches to Deal With Non-Gaussianity of Rainfall Data in Kriging-Based Radar-Gauge Rainfall Merging

NASA Astrophysics Data System (ADS)

Cecinati, F.; Wani, O.; Rico-Ramirez, M. A.

2017-11-01

Merging radar and rain gauge rainfall data is a technique used to improve the quality of spatial rainfall estimates and in particular the use of Kriging with External Drift (KED) is a very effective radar-rain gauge rainfall merging technique. However, kriging interpolations assume Gaussianity of the process. Rainfall has a strongly skewed, positive, probability distribution, characterized by a discontinuity due to intermittency. In KED rainfall residuals are used, implicitly calculated as the difference between rain gauge data and a linear function of the radar estimates. Rainfall residuals are non-Gaussian as well. The aim of this work is to evaluate the impact of applying KED to non-Gaussian rainfall residuals, and to assess the best techniques to improve Gaussianity. We compare Box-Cox transformations with λ parameters equal to 0.5, 0.25, and 0.1, Box-Cox with time-variant optimization of λ, normal score transformation, and a singularity analysis technique. The results suggest that Box-Cox with λ = 0.1 and the singularity analysis is not suitable for KED. Normal score transformation and Box-Cox with optimized λ, or λ = 0.25 produce satisfactory results in terms of Gaussianity of the residuals, probability distribution of the merged rainfall products, and rainfall estimate quality, when validated through cross-validation. However, it is observed that Box-Cox transformations are strongly dependent on the temporal and spatial variability of rainfall and on the units used for the rainfall intensity. Overall, applying transformations results in a quantitative improvement of the rainfall estimates only if the correct transformations for the specific data set are used.

Scaling Properties and Spatial Interpolation of Soil Moisture

DTIC Science & Technology

2004-08-24

the sensitivities is useful not only for characterizing soil moisture but also for forecasting the vulnerability of a region’s water cycle to climate...regional water balance was presented that can be used to assess the impact of climatic fluctuations and changes on the water cycle of a region. In

A JACKNIFE APPROACH TO EXAMINE UNCERTAINTY AND TEMPORAL CHANGES IN THE SPATIL CORRELATION OF A VOC PLUME

EPA Science Inventory

ABSTRACT: The application of geostatistics to spatial interpolation of time-invariant properties in ground-water studies (such as transmissivity or aquifer thickness) is well documented. The use of geostatistics on time-variant conditions such as ground-water quality is also be...

Improved computer-aided detection of small polyps in CT colonography using interpolation for curvature estimationa

PubMed Central

Liu, Jiamin; Kabadi, Suraj; Van Uitert, Robert; Petrick, Nicholas; Deriche, Rachid; Summers, Ronald M.

2011-01-01

Purpose: Surface curvatures are important geometric features for the computer-aided analysis and detection of polyps in CT colonography (CTC). However, the general kernel approach for curvature computation can yield erroneous results for small polyps and for polyps that lie on haustral folds. Those erroneous curvatures will reduce the performance of polyp detection. This paper presents an analysis of interpolation’s effect on curvature estimation for thin structures and its application on computer-aided detection of small polyps in CTC. Methods: The authors demonstrated that a simple technique, image interpolation, can improve the accuracy of curvature estimation for thin structures and thus significantly improve the sensitivity of small polyp detection in CTC. Results: Our experiments showed that the merits of interpolating included more accurate curvature values for simulated data, and isolation of polyps near folds for clinical data. After testing on a large clinical data set, it was observed that sensitivities with linear, quadratic B-spline and cubic B-spline interpolations significantly improved the sensitivity for small polyp detection. Conclusions: The image interpolation can improve the accuracy of curvature estimation for thin structures and thus improve the computer-aided detection of small polyps in CTC. PMID:21859029

Comparison of volatility function technique for risk-neutral densities estimation

NASA Astrophysics Data System (ADS)

Bahaludin, Hafizah; Abdullah, Mimi Hafizah

2017-08-01

Volatility function technique by using interpolation approach plays an important role in extracting the risk-neutral density (RND) of options. The aim of this study is to compare the performances of two interpolation approaches namely smoothing spline and fourth order polynomial in extracting the RND. The implied volatility of options with respect to strike prices/delta are interpolated to obtain a well behaved density. The statistical analysis and forecast accuracy are tested using moments of distribution. The difference between the first moment of distribution and the price of underlying asset at maturity is used as an input to analyze forecast accuracy. RNDs are extracted from the Dow Jones Industrial Average (DJIA) index options with a one month constant maturity for the period from January 2011 until December 2015. The empirical results suggest that the estimation of RND using a fourth order polynomial is more appropriate to be used compared to a smoothing spline in which the fourth order polynomial gives the lowest mean square error (MSE). The results can be used to help market participants capture market expectations of the future developments of the underlying asset.

Numerical simulation of supersonic and hypersonic inlet flow fields

NASA Technical Reports Server (NTRS)

Mcrae, D. Scott; Kontinos, Dean A.

1995-01-01

This report summarizes the research performed by North Carolina State University and NASA Ames Research Center under Cooperative Agreement NCA2-719, 'Numerical Simulation of Supersonic and Hypersonic Inlet Flow Fields". Four distinct rotated upwind schemes were developed and investigated to determine accuracy and practicality. The scheme found to have the best combination of attributes, including reduction to grid alignment with no rotation, was the cell centered non-orthogonal (CCNO) scheme. In 2D, the CCNO scheme improved rotation when flux interpolation was extended to second order. In 3D, improvements were less dramatic in all cases, with second order flux interpolation showing the least improvement over grid aligned upwinding. The reduction in improvement is attributed to uncertainty in determining optimum rotation angle and difficulty in performing accurate and efficient interpolation of the angle in 3D. The CCNO rotational technique will prove very useful for increasing accuracy when second order interpolation is not appropriate and will materially improve inlet flow solutions.

Colorization-Based RGB-White Color Interpolation using Color Filter Array with Randomly Sampled Pattern

PubMed Central

Oh, Paul; Lee, Sukho; Kang, Moon Gi

2017-01-01

Recently, several RGB-White (RGBW) color filter arrays (CFAs) have been proposed, which have extra white (W) pixels in the filter array that are highly sensitive. Due to the high sensitivity, the W pixels have better SNR (Signal to Noise Ratio) characteristics than other color pixels in the filter array, especially, in low light conditions. However, most of the RGBW CFAs are designed so that the acquired RGBW pattern image can be converted into the conventional Bayer pattern image, which is then again converted into the final color image by using conventional demosaicing methods, i.e., color interpolation techniques. In this paper, we propose a new RGBW color filter array based on a totally different color interpolation technique, the colorization algorithm. The colorization algorithm was initially proposed for colorizing a gray image into a color image using a small number of color seeds. Here, we adopt this algorithm as a color interpolation technique, so that the RGBW color filter array can be designed with a very large number of W pixels to make the most of the highly sensitive characteristics of the W channel. The resulting RGBW color filter array has a pattern with a large proportion of W pixels, while the small-numbered RGB pixels are randomly distributed over the array. The colorization algorithm makes it possible to reconstruct the colors from such a small number of RGB values. Due to the large proportion of W pixels, the reconstructed color image has a high SNR value, especially higher than those of conventional CFAs in low light condition. Experimental results show that many important information which are not perceived in color images reconstructed with conventional CFAs are perceived in the images reconstructed with the proposed method. PMID:28657602

Colorization-Based RGB-White Color Interpolation using Color Filter Array with Randomly Sampled Pattern.

PubMed

Oh, Paul; Lee, Sukho; Kang, Moon Gi

2017-06-28

Recently, several RGB-White (RGBW) color filter arrays (CFAs) have been proposed, which have extra white (W) pixels in the filter array that are highly sensitive. Due to the high sensitivity, the W pixels have better SNR (Signal to Noise Ratio) characteristics than other color pixels in the filter array, especially, in low light conditions. However, most of the RGBW CFAs are designed so that the acquired RGBW pattern image can be converted into the conventional Bayer pattern image, which is then again converted into the final color image by using conventional demosaicing methods, i.e., color interpolation techniques. In this paper, we propose a new RGBW color filter array based on a totally different color interpolation technique, the colorization algorithm. The colorization algorithm was initially proposed for colorizing a gray image into a color image using a small number of color seeds. Here, we adopt this algorithm as a color interpolation technique, so that the RGBW color filter array can be designed with a very large number of W pixels to make the most of the highly sensitive characteristics of the W channel. The resulting RGBW color filter array has a pattern with a large proportion of W pixels, while the small-numbered RGB pixels are randomly distributed over the array. The colorization algorithm makes it possible to reconstruct the colors from such a small number of RGB values. Due to the large proportion of W pixels, the reconstructed color image has a high SNR value, especially higher than those of conventional CFAs in low light condition. Experimental results show that many important information which are not perceived in color images reconstructed with conventional CFAs are perceived in the images reconstructed with the proposed method.

Performance comparison of LUR and OK in PM2.5 concentration mapping: a multidimensional perspective

PubMed Central

Zou, Bin; Luo, Yanqing; Wan, Neng; Zheng, Zhong; Sternberg, Troy; Liao, Yilan

2015-01-01

Methods of Land Use Regression (LUR) modeling and Ordinary Kriging (OK) interpolation have been widely used to offset the shortcomings of PM2.5 data observed at sparse monitoring sites. However, traditional point-based performance evaluation strategy for these methods remains stagnant, which could cause unreasonable mapping results. To address this challenge, this study employs ‘information entropy’, an area-based statistic, along with traditional point-based statistics (e.g. error rate, RMSE) to evaluate the performance of LUR model and OK interpolation in mapping PM2.5 concentrations in Houston from a multidimensional perspective. The point-based validation reveals significant differences between LUR and OK at different test sites despite the similar end-result accuracy (e.g. error rate 6.13% vs. 7.01%). Meanwhile, the area-based validation demonstrates that the PM2.5 concentrations simulated by the LUR model exhibits more detailed variations than those interpolated by the OK method (i.e. information entropy, 7.79 vs. 3.63). Results suggest that LUR modeling could better refine the spatial distribution scenario of PM2.5 concentrations compared to OK interpolation. The significance of this study primarily lies in promoting the integration of point- and area-based statistics for model performance evaluation in air pollution mapping. PMID:25731103

Optimizing weather radar observations using an adaptive multiquadric surface fitting algorithm

NASA Astrophysics Data System (ADS)

Martens, Brecht; Cabus, Pieter; De Jongh, Inge; Verhoest, Niko

2013-04-01

Real time forecasting of river flow is an essential tool in operational water management. Such real time modelling systems require well calibrated models which can make use of spatially distributed rainfall observations. Weather radars provide spatial data, however, since radar measurements are sensitive to a large range of error sources, often a discrepancy between radar observations and ground-based measurements, which are mostly considered as ground truth, can be observed. Through merging ground observations with the radar product, often referred to as data merging, one may force the radar observations to better correspond to the ground-based measurements, without losing the spatial information. In this paper, radar images and ground-based measurements of rainfall are merged based on interpolated gauge-adjustment factors (Moore et al., 1998; Cole and Moore, 2008) or scaling factors. Using the following equation, scaling factors (C(xα)) are calculated at each position xα where a gauge measurement (Ig(xα)) is available: Ig(xα)+-? C (xα) = Ir(xα)+ ? (1) where Ir(xα) is the radar-based observation in the pixel overlapping the rain gauge and ? is a constant making sure the scaling factor can be calculated when Ir(xα) is zero. These scaling factors are interpolated on the radar grid, resulting in a unique scaling factor for each pixel. Multiquadric surface fitting is used as an interpolation algorithm (Hardy, 1971): C*(x0) = aTv + a0 (2) where C*(x0) is the prediction at location x0, the vector a (Nx1, with N the number of ground-based measurements used) and the constant a0 parameters describing the surface and v an Nx1 vector containing the (Euclidian) distance between each point xα used in the interpolation and the point x0. The parameters describing the surface are derived by forcing the surface to be an exact interpolator and impose that the sum of the parameters in a should be zero. However, often, the surface is allowed to pass near the observations (i.e. the observed scaling factors C(xα)) on a distance aαK by introducing an offset parameter K, which results in slightly different equations to calculate a and a0. The described technique is currently being used by the Flemish Environmental Agency in an online forecasting system of river discharges within Flanders (Belgium). However, rescaling the radar data using the described algorithm is not always giving rise to an improved weather radar product. Probably one of the main reasons is the parameters K and ? which are implemented as constants. It can be expected that, among others, depending on the characteristics of the rainfall, different values for the parameters should be used. Adaptation of the parameter values is achieved by an online calibration of K and ? at each time step (every 15 minutes), using validated rain gauge measurements as ground truth. Results demonstrate that rescaling radar images using optimized values for K and ? at each time step lead to a significant improvement of the rainfall estimation, which in turn will result in higher quality discharge predictions. Moreover, it is shown that calibrated values for K and ? can be obtained in near-real time. References Cole, S. J., and Moore, R. J. (2008). Hydrological modelling using raingauge- and radar-based estimators of areal rainfall. Journal of Hydrology, 358(3-4), 159-181. Hardy, R.L., (1971) Multiquadric equations of topography and other irregular surfaces, Journal of Geophysical Research, 76(8): 1905-1915. Moore, R. J., Watson, B. C., Jones, D. A. and Black, K. B. (1989). London weather radar local calibration study. Technical report, Institute of Hydrology.

Signal-to-noise ratio enhancement on SEM images using a cubic spline interpolation with Savitzky-Golay filters and weighted least squares error.

PubMed

Kiani, M A; Sim, K S; Nia, M E; Tso, C P

2015-05-01

A new technique based on cubic spline interpolation with Savitzky-Golay smoothing using weighted least squares error filter is enhanced for scanning electron microscope (SEM) images. A diversity of sample images is captured and the performance is found to be better when compared with the moving average and the standard median filters, with respect to eliminating noise. This technique can be implemented efficiently on real-time SEM images, with all mandatory data for processing obtained from a single image. Noise in images, and particularly in SEM images, are undesirable. A new noise reduction technique, based on cubic spline interpolation with Savitzky-Golay and weighted least squares error method, is developed. We apply the combined technique to single image signal-to-noise ratio estimation and noise reduction for SEM imaging system. This autocorrelation-based technique requires image details to be correlated over a few pixels, whereas the noise is assumed to be uncorrelated from pixel to pixel. The noise component is derived from the difference between the image autocorrelation at zero offset, and the estimation of the corresponding original autocorrelation. In the few test cases involving different images, the efficiency of the developed noise reduction filter is proved to be significantly better than those obtained from the other methods. Noise can be reduced efficiently with appropriate choice of scan rate from real-time SEM images, without generating corruption or increasing scanning time. © 2015 The Authors Journal of Microscopy © 2015 Royal Microscopical Society.

Wavelet-Smoothed Interpolation of Masked Scientific Data for JPEG 2000 Compression

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brislawn, Christopher M.

2012-08-13

How should we manage scientific data with 'holes'? Some applications, like JPEG 2000, expect logically rectangular data, but some sources, like the Parallel Ocean Program (POP), generate data that isn't defined on certain subsets. We refer to grid points that lack well-defined, scientifically meaningful sample values as 'masked' samples. Wavelet-smoothing is a highly scalable interpolation scheme for regions with complex boundaries on logically rectangular grids. Computation is based on forward/inverse discrete wavelet transforms, so runtime complexity and memory scale linearly with respect to sample count. Efficient state-of-the-art minimal realizations yield small constants (O(10)) for arithmetic complexity scaling, and in-situ implementationmore » techniques make optimal use of memory. Implementation in two dimensions using tensor product filter banks is straighsorward and should generalize routinely to higher dimensions. No hand-tuning required when the interpolation mask changes, making the method aeractive for problems with time-varying masks. Well-suited for interpolating undefined samples prior to JPEG 2000 encoding. The method outperforms global mean interpolation, as judged by both SNR rate-distortion performance and low-rate artifact mitigation, for data distributions whose histograms do not take the form of sharply peaked, symmetric, unimodal probability density functions. These performance advantages can hold even for data whose distribution differs only moderately from the peaked unimodal case, as demonstrated by POP salinity data. The interpolation method is very general and is not tied to any particular class of applications, could be used for more generic smooth interpolation.« less

Spatially Detailed Porosity Prediction From Airborne Electromagnetics and Sparse Borehole Fluid Sampling

NASA Astrophysics Data System (ADS)

Macnae, J.; Ley-Cooper, Y.

2009-05-01

Sub-surface porosity is of importance in estimating fluid contant and salt-load parameters for hydrological modelling. While sparse boreholes may adequately sample the depth to a sub-horizontal water-table and usually also adequately sample ground-water salinity, they do not provide adequate sampling of the spatial variations in porosity or hydraulic permeability caused by spatial variations in sedimentary and other geological processes.. We show in this presentation that spatially detailed porosity can be estimated by applying Archie's law to conductivity estimates from airborne electromagnetic surveys with interpolated ground-water conductivity values. The prediction was tested on data from the Chowilla flood plain in the Murray-Darling Basin of South Australia. A frequency domain, helicopter-borne electromagnetic system collected data at 6 frequencies and 3 to 4 m spacings on lines spaced 100 m apart. This data was transformed into conductivity-depth sections, from which a 3D bulk-conductivity map could be created with about 30 m spatial resolution and 2 to 5 m vertical depth resolution. For that portion of the volume below the interpolated water-table, we predicted porosity in each cell using Archie's law. Generally, predicted porosities were in the 30 to 50 % range, consistent with expectations for the partially consolidated sediments in the floodplain. Porosities were directly measured on core from eight boreholes in the area, and compared quite well with the predictions. The predicted porosity map was spatially consistent, and when combined with measured salinities in the ground water, was able to provide a detailed 3D map of salt-loads in the saturated zone, and as such contribute to a hazard assessment of the saline threat to the river.

Py-SPHViewer: Cosmological simulations using Smoothed Particle Hydrodynamics

NASA Astrophysics Data System (ADS)

Benítez-Llambay, Alejandro

2017-12-01

Py-SPHViewer visualizes and explores N-body + Hydrodynamics simulations. The code interpolates the underlying density field (or any other property) traced by a set of particles, using the Smoothed Particle Hydrodynamics (SPH) interpolation scheme, thus producing not only beautiful but also useful scientific images. Py-SPHViewer enables the user to explore simulated volumes using different projections. Py-SPHViewer also provides a natural way to visualize (in a self-consistent fashion) gas dynamical simulations, which use the same technique to compute the interactions between particles.

Timescape: a simple space-time interpolation geostatistical Algorithm

NASA Astrophysics Data System (ADS)

Ciolfi, Marco; Chiocchini, Francesca; Gravichkova, Olga; Pisanelli, Andrea; Portarena, Silvia; Scartazza, Andrea; Brugnoli, Enrico; Lauteri, Marco

2016-04-01

Environmental sciences include both time and space variability in their datasets. Some established tools exist for both spatial interpolation and time series analysis alone, but mixing space and time variability calls for compromise: Researchers are often forced to choose which is the main source of variation, neglecting the other. We propose a simple algorithm, which can be used in many fields of Earth and environmental sciences when both time and space variability must be considered on equal grounds. The algorithm has already been implemented in Java language and the software is currently available at https://sourceforge.net/projects/timescapeglobal/ (it is published under GNU-GPL v3.0 Free Software License). The published version of the software, Timescape Global, is focused on continent- to Earth-wide spatial domains, using global longitude-latitude coordinates for samples localization. The companion Timescape Local software is currently under development ad will be published with an open license as well; it will use projected coordinates for a local to regional space scale. The basic idea of the Timescape Algorithm consists in converting time into a sort of third spatial dimension, with the addition of some causal constraints, which drive the interpolation including or excluding observations according to some user-defined rules. The algorithm is applicable, as a matter of principle, to anything that can be represented with a continuous variable (a scalar field, technically speaking). The input dataset should contain position, time and observed value of all samples. Ancillary data can be included in the interpolation as well. After the time-space conversion, Timescape follows basically the old-fashioned IDW (Inverse Distance Weighted) interpolation Algorithm, although users have a wide choice of customization options that, at least partially, overcome some of the known issues of IDW. The three-dimensional model produced by the Timescape Algorithm can be explored in many ways, including the extraction of time series at fixed locations and GIS layers at constant times, allowing for the inclusion of the model in the users' established workflow. The software requirements are relatively modest since it has been purposely designed for potential users in various research field with a limited computing power at their disposal. Any respectful modern PC or laptop can run it. Users however need a separate database for sample data and models storage because these can be quite bulky in terms of data output: a single model can be composed of several billions of voxels (three-dimensional discrete cells, a sort of 3D pixels). Running times range from a few minutes for sketch models to some days of evaluation for a full-size model, depending on the users' hardware and model size.

A comparison of interpolation methods for predicting spatial variability of soil organic matter content in Eastern Croatia

NASA Astrophysics Data System (ADS)

Đurđević, Boris; Jug, Irena; Jug, Danijel; Vukadinović, Vesna; Bogunović, Igor; Brozović, Bojana; Stipešević, Bojan

2017-04-01

Soil organic matter (SOM) plays crucial role in soil health and productivity and represents one of the key functions for determining soil degradation and soil suitability for crop production. Nowadays, continuing decline of organic matter in soils in agroecosystems, due to inappropriate agricultural practice (burning and removal of crop residue, overgrazing, inappropriate tillage, etc.) and environmental conditions (climate change, extreme weather conditions, erosion) leads to devastating soil degradation processes and decreases soil productivity. The main objectives of this research is to compare three different interpolation methods (Inverse Distance Weighting IDW, Ordinary kriging OK and Empirical Bayesian Kriging EBK) and provide best spatial predictor in order to ensure detailed analysis of the agricultural land in Osijek-Baranja County, Croatia. A number of 9,099 soil samples have been compiled from layer 0-30 cm and analyzed in laboratory. The average value of SOM in the study area was 2.66%, while 70.7 % of samples had SOM value below 3% in Osijek-Baranja County. Among the applied methods, the lowest root mean square error was recorded under Empirical Bayesian Kriging method which had most accurately assessed soil organic matter. The main advantage of EBK is that the process of creating a valid kriging model is automated so the manual parameter adjusting is eliminated, and this resulted with reduced uncertainty of EBK model. Conducted interpolation and visualization of data showed that 85.7% of agricultural land in Osijek-Baranja County has SOM content lower than 3%, which may indicate some sort of soil degradation process. By using interpolation methods combined with visualization of data, we can detect problematic areas much easier and with additional analysis, suggest measures to repair degraded soils. This kind of approach to problem solving in agriculture can be applied on various agroecological conditions and can significantly facilitate and accelerate the decision-making process, and thus directly affect the profitability and sustainability of agricultural production.

Distributed snow modeling suitable for use with operational data for the American River watershed.

NASA Astrophysics Data System (ADS)

Shamir, E.; Georgakakos, K. P.

2004-12-01

The mountainous terrain of the American River watershed (~4300 km2) at the Western slope of the Northern Sierra Nevada is subject to significant variability in the atmospheric forcing that controls the snow accumulation and ablations processes (i.e., precipitation, surface temperature, and radiation). For a hydrologic model that attempts to predict both short- and long-term streamflow discharges, a plausible description of the seasonal and intermittent winter snow pack accumulation and ablation is crucial. At present the NWS-CNRFC operational snow model is implemented in a semi distributed manner (modeling unit of about 100-1000 km2) and therefore lump distinct spatial variability of snow processes. In this study we attempt to account for the precipitation, temperature, and radiation spatial variability by constructing a distributed snow accumulation and melting model suitable for use with commonly available sparse data. An adaptation of the NWS-Snow17 energy and mass balance that is used operationally at the NWS River Forecast Centers is implemented at 1 km2 grid cells with distributed input and model parameters. The input to the model (i.e., precipitation and surface temperature) is interpolated from observed point data. The surface temperature was interpolated over the basin based on adiabatic lapse rates using topographic information whereas the precipitation was interpolated based on maps of climatic mean annual rainfall distribution acquired from PRISM. The model parameters that control the melting rate due to radiation were interpolated based on aspect. The study was conducted for the entire American basin for the snow seasons of 1999-2000. Validation of the Snow Water Equivalent (SWE) prediction is done by comparing to observation from 12 snow Sensors. The Snow Cover Area (SCA) prediction was evaluated by comparing to remotely sensed 500m daily snow cover derived from MODIS. The results that the distribution of snow over the area is well captured and the quantity compared to the snow gauges are well estimated in the high elevation.

Effect of the precipitation interpolation method on the performance of a snowmelt runoff model

NASA Astrophysics Data System (ADS)

Jacquin, Alexandra

2014-05-01

Uncertainties on the spatial distribution of precipitation seriously affect the reliability of the discharge estimates produced by watershed models. Although there is abundant research evaluating the goodness of fit of precipitation estimates obtained with different gauge interpolation methods, few studies have focused on the influence of the interpolation strategy on the response of watershed models. The relevance of this choice may be even greater in the case of mountain catchments, because of the influence of orography on precipitation. This study evaluates the effect of the precipitation interpolation method on the performance of conceptual type snowmelt runoff models. The HBV Light model version 4.0.0.2, operating at daily time steps, is used as a case study. The model is applied in Aconcagua at Chacabuquito catchment, located in the Andes Mountains of Central Chile. The catchment's area is 2110[Km2] and elevation ranges from 950[m.a.s.l.] to 5930[m.a.s.l.] The local meteorological network is sparse, with all precipitation gauges located below 3000[m.a.s.l.] Precipitation amounts corresponding to different elevation zones are estimated through areal averaging of precipitation fields interpolated from gauge data. Interpolation methods applied include kriging with external drift (KED), optimal interpolation method (OIM), Thiessen polygons (TP), multiquadratic functions fitting (MFF) and inverse distance weighting (IDW). Both KED and OIM are able to account for the existence of a spatial trend in the expectation of precipitation. By contrast, TP, MFF and IDW, traditional methods widely used in engineering hydrology, cannot explicitly incorporate this information. Preliminary analysis confirmed that these methods notably underestimate precipitation in the study catchment, while KED and OIM are able to reduce the bias; this analysis also revealed that OIM provides more reliable estimations than KED in this region. Using input precipitation obtained by each method, HBV parameters are calibrated with respect to Nash-Sutcliffe efficiency. The performance of HBV in the study catchment is not satisfactory. Although volumetric errors are modest, efficiency values are lower than 70%. Discharge estimates resulting from the application of TP, MFF and IDW obtain similar model efficiencies and volumetric errors. These error statistics moderately improve if KED or OIM are used instead. Even though the quality of precipitation estimates of distinct interpolation methods is dissimilar, the results of this study show that these differences do not necessarily produce noticeable changes in HBV's model performance statistics. This situation arises because the calibration of the model parameters allows some degree of compensation of deficient areal precipitation estimates, mainly through the adjustment of model simulated evaporation and glacier melt, as revealed by the analysis of water balances. In general, even if there is a good agreement between model estimated and observed discharge, this information is not sufficient to assert that the internal hydrological processes of the catchment are properly simulated by a watershed model. Other calibration criteria should be incorporated if a more reliable representation of these processes is desired. Acknowledgements: This research was funded by FONDECYT, Research Project 1110279. The HBV Light software used in this study was kindly provided by J. Seibert, Department of Geography, University of Zürich.

A web-based tool for groundwater mapping and drought analysis

NASA Astrophysics Data System (ADS)

Christensen, S.; Burns, M.; Jones, N.; Strassberg, G.

2012-12-01

In 2011-2012, the state of Texas saw the worst one-year drought on record. Fluctuations in gravity measured by GRACE satellites indicate that as much as 100 cubic kilometers of water was lost during this period. Much of this came from reservoirs and shallow soil moisture, but a significant amount came from aquifers. In response to this crisis, a Texas Drought Technology Steering Committee (TDTSC) consisting of academics and water managers was formed to develop new tools and strategies to assist the state in monitoring, predicting, and responding to drought events. In this presentation, we describe one of the tools that was developed as part of this effort. When analyzing the impact of drought on groundwater levels, it is fairly common to examine time series data at selected monitoring wells. However, accurately assessing impacts and trends requires both spatial and temporal analysis involving the development of detailed water level maps at various scales. Creating such maps in a flexible and rapid fashion is critical for effective drought analysis, but can be challenging due to the massive amounts of data involved and the processing required to generate such maps. Furthermore, wells are typically not sampled at the same points in time, and so developing a water table map for a particular date requires both spatial and temporal interpolation of water elevations. To address this challenge, a Cloud-based water level mapping system was developed for the state of Texas. The system is based on the Texas Water Development Board (TWDB) groundwater database, but can be adapted to use other databases as well. The system involves a set of ArcGIS workflows running on a server with a web-based front end and a Google Earth plug-in. A temporal interpolation geoprocessing tool was developed to estimate the piezometric heads for all wells in a given region at a specific date using a regression analysis. This interpolation tool is coupled with other geoprocessing tools to filter data and interpolate point elevations spatially to produce water level, drawdown, and depth to groundwater maps. The web interface allows for users to generate these maps at locations and times of interest. A sequence of maps can be generated over a period of time and animated to visualize how water levels are changing. The time series regression analysis can also be used to do short-term predictions of future water levels.

Proceedings of the NASA Workshop on Surface Fitting

NASA Technical Reports Server (NTRS)

Guseman, L. F., Jr. (Principal Investigator)

1982-01-01

Surface fitting techniques and their utilization are addressed. Surface representation, approximation, and interpolation are discussed. Along with statistical estimation problems associated with surface fitting.

Fusion-based multi-target tracking and localization for intelligent surveillance systems

NASA Astrophysics Data System (ADS)

Rababaah, Haroun; Shirkhodaie, Amir

2008-04-01

In this paper, we have presented two approaches addressing visual target tracking and localization in complex urban environment. The two techniques presented in this paper are: fusion-based multi-target visual tracking, and multi-target localization via camera calibration. For multi-target tracking, the data fusion concepts of hypothesis generation/evaluation/selection, target-to-target registration, and association are employed. An association matrix is implemented using RGB histograms for associated tracking of multi-targets of interests. Motion segmentation of targets of interest (TOI) from the background was achieved by a Gaussian Mixture Model. Foreground segmentation, on other hand, was achieved by the Connected Components Analysis (CCA) technique. The tracking of individual targets was estimated by fusing two sources of information, the centroid with the spatial gating, and the RGB histogram association matrix. The localization problem is addressed through an effective camera calibration technique using edge modeling for grid mapping (EMGM). A two-stage image pixel to world coordinates mapping technique is introduced that performs coarse and fine location estimation of moving TOIs. In coarse estimation, an approximate neighborhood of the target position is estimated based on nearest 4-neighbor method, and in fine estimation, we use Euclidean interpolation to localize the position within the estimated four neighbors. Both techniques were tested and shown reliable results for tracking and localization of Targets of interests in complex urban environment.

Diabat Interpolation for Polymorph Free-Energy Differences.

PubMed

Kamat, Kartik; Peters, Baron

2017-02-02

Existing methods to compute free-energy differences between polymorphs use harmonic approximations, advanced non-Boltzmann bias sampling techniques, and/or multistage free-energy perturbations. This work demonstrates how Bennett's diabat interpolation method ( J. Comput. Phys. 1976, 22, 245 ) can be combined with energy gaps from lattice-switch Monte Carlo techniques ( Phys. Rev. E 2000, 61, 906 ) to swiftly estimate polymorph free-energy differences. The new method requires only two unbiased molecular dynamics simulations, one for each polymorph. To illustrate the new method, we compute the free-energy difference between face-centered cubic and body-centered cubic polymorphs for a Gaussian core solid. We discuss the justification for parabolic models of the free-energy diabats and similarities to methods that have been used in studies of electron transfer.

Algebraic grid generation with corner singularities

NASA Technical Reports Server (NTRS)

Vinokur, M.; Lombard, C. K.

1983-01-01

A simple noniterative algebraic procedure is presented for generating smooth computational meshes on a quadrilateral topology. Coordinate distribution and normal derivative are provided on all boundaries, one of which may include a slope discontinuity. The boundary conditions are sufficient to guarantee continuity of global meshes formed of joined patches generated by the procedure. The method extends to 3-D. The procedure involves a synthesis of prior techniques stretching functions, cubic blending functions, and transfinite interpolation - to which is added the functional form of the corner solution. The procedure introduces the concept of generalized blending, which is implemented as an automatic scaling of the boundary derivatives for effective interpolation. Some implications of the treatment at boundaries for techniques solving elliptic PDE's are discussed in an Appendix.

Performance of signal-to-noise ratio estimation for scanning electron microscope using autocorrelation Levinson-Durbin recursion model.

PubMed

Sim, K S; Lim, M S; Yeap, Z X

2016-07-01

A new technique to quantify signal-to-noise ratio (SNR) value of the scanning electron microscope (SEM) images is proposed. This technique is known as autocorrelation Levinson-Durbin recursion (ACLDR) model. To test the performance of this technique, the SEM image is corrupted with noise. The autocorrelation function of the original image and the noisy image are formed. The signal spectrum based on the autocorrelation function of image is formed. ACLDR is then used as an SNR estimator to quantify the signal spectrum of noisy image. The SNR values of the original image and the quantified image are calculated. The ACLDR is then compared with the three existing techniques, which are nearest neighbourhood, first-order linear interpolation and nearest neighbourhood combined with first-order linear interpolation. It is shown that ACLDR model is able to achieve higher accuracy in SNR estimation. © 2016 The Authors Journal of Microscopy © 2016 Royal Microscopical Society.

Accounting for irregular support in spatial interpolation - analysing the effect of using alternative distance measures

NASA Astrophysics Data System (ADS)

Skøien, J. O.; Gottschalk, L.; Leblois, E.

2009-04-01

Whereas geostatistical and objective methods mostly have been developed for observations with point support or a regular support, e.g. runoff related data can be assumed to have an irregular support in space, and sometimes also a temporal support. The correlations between observations and between observations and the prediction location are found through an integration of a point variogram or point correlation function, a method known as regularisation. Being a relatively simple method for observations with equal and regular support, it can be computationally demanding if the observations have irregular support. With improved speed of computers, solving such integrations has become easier, but there can still be numerical problems that are not easily solved even with high-resolution computations. This can particularly be a problem in hydrological sciences where catchments are overlapping, the correlations are high, and small numerical errors can give ill-posed covariance matrices. The problem increases with increasing number of spatial and/or temporal dimensions. Gottschalk [1993a; 1993b] suggested to replace the integration by a Taylor expansion, hence reducing the computation time considerably, and also expecting less numerical problems with the covariance matrices. In practice, the integrated correlation/semivariance between observations are replaced by correlations/semivariances using the so called Ghosh-distance. Although Gottschalk and collaborators have used the Ghosh-distance also in other papers [Sauquet, et al., 2000a; Sauquet, et al., 2000b], the properties of the simplification have not been examined in detail. Hence, we will here analyse the replacement of the integration by the use of Ghosh-distances, both in sense of the ability to reproduce regularised semivariogram and correlation values, and the influence on the final interpolated maps. Comparisons will be performed both for real observations with a support (hydrological data) and for more hypothetical observations with regular supports where analytical expressions for the regularised semivariances/correlations in some cases can be derived. The results indicate that the simplification is useful for spatial interpolation when the support of the observations has to be taken into account. The difference in semivariogram value or correlation value between the simplified method and the full integration is limited on short distances, increasing for larger distances. However, this is to some degree taken into account while fitting a model for the point process, so that the results after interpolation are less affected by the simplification. The method is of particular use if computation time is of importance, e.g. in the case of real-time mapping procedures. Gottschalk, L. (1993a) Correlation and covariance of runoff, Stochastic Hydrology and Hydraulics, 7, 85-101. Gottschalk, L. (1993b) Interpolation of runoff applying objective methods, Stochastic Hydrology and Hydraulics, 7, 269-281. Sauquet, E., L. Gottschalk, and E. Leblois (2000a) Mapping average annual runoff: a hierarchical approach applying a stochastic interpolation scheme, Hydrological Sciences Journal, 45, 799-815. Sauquet, E., I. Krasovskaia, and E. Leblois (2000b) Mapping mean monthly runoff pattern using EOF analysis, Hydrology and Earth System Sciences, 4, 79-93.

Effects of fire on ash thickness in a Lithuanian grassland and short-term spatio-temporal changes

NASA Astrophysics Data System (ADS)

Pereira, P.; Cerdà, A.; Úbeda, X.; Mataix-Solera, J.; Martin, D.; Jordán, A.; Burguet, M.

2012-12-01

Ash thickness is a key variable in the protection of soil against erosion agents after planned and unplanned fires. Thicker ash provides better protection against raindrop impact and reduces the runoff response by retaining water and promoting water infiltration although little is known about the distribution and the evolution of the ash layer after the fires. Ash thickness measurements were conducted along two transects (flat and sloping areas) following a a grid experimental design. Both transects extended from the burned area into an adjacent unburned area. We analysed ash thickness evolution according to time and fire severity. In order to interpolate data with accuracy and identify the techniques with the least bias, several interpolation methods were tested in the grid plot. Overall, the fire had a low severity. The fire significantly reduced the ground cover, especially on sloping areas owing to the higher fire severity and/or less biomass previous to the fire. Ash thickness depends on fire severity and is thin where fire severity was higher and thicker in lower fire severity sites. The ash thickness decreased with time after the fire. Between 4 and 16 days after the fire, ash was transported by wind. The major reduction took place between 16 and 34 days after the fire as a result of rainfall, and was more efficient where fire severity was higher. Between 34 and 45 days after the fire no significant differences in ash thickness were identified among ash colours and only traces of the ash layer remained. The omni-directional experimental variograms shown that variable structure did not change importantly with the time, however, the most accurate interpolation methods were different highlighting the slight different patterns of ash thickness distribution with the time. The ash spatial variability increased with the time, particularly on the slope, as a result of water erosion.

A k-Vector Approach to Sampling, Interpolation, and Approximation

NASA Astrophysics Data System (ADS)

Mortari, Daniele; Rogers, Jonathan

2013-12-01

The k-vector search technique is a method designed to perform extremely fast range searching of large databases at computational cost independent of the size of the database. k-vector search algorithms have historically found application in satellite star-tracker navigation systems which index very large star catalogues repeatedly in the process of attitude estimation. Recently, the k-vector search algorithm has been applied to numerous other problem areas including non-uniform random variate sampling, interpolation of 1-D or 2-D tables, nonlinear function inversion, and solution of systems of nonlinear equations. This paper presents algorithms in which the k-vector search technique is used to solve each of these problems in a computationally-efficient manner. In instances where these tasks must be performed repeatedly on a static (or nearly-static) data set, the proposed k-vector-based algorithms offer an extremely fast solution technique that outperforms standard methods.

Spatial Normalization of Reverse Phase Protein Array Data

PubMed Central

Kaushik, Poorvi; Molinelli, Evan J.; Miller, Martin L.; Wang, Weiqing; Korkut, Anil; Liu, Wenbin; Ju, Zhenlin; Lu, Yiling; Mills, Gordon; Sander, Chris

2014-01-01

Reverse phase protein arrays (RPPA) are an efficient, high-throughput, cost-effective method for the quantification of specific proteins in complex biological samples. The quality of RPPA data may be affected by various sources of error. One of these, spatial variation, is caused by uneven exposure of different parts of an RPPA slide to the reagents used in protein detection. We present a method for the determination and correction of systematic spatial variation in RPPA slides using positive control spots printed on each slide. The method uses a simple bi-linear interpolation technique to obtain a surface representing the spatial variation occurring across the dimensions of a slide. This surface is used to calculate correction factors that can normalize the relative protein concentrations of the samples on each slide. The adoption of the method results in increased agreement between technical and biological replicates of various tumor and cell-line derived samples. Further, in data from a study of the melanoma cell-line SKMEL-133, several slides that had previously been rejected because they had a coefficient of variation (CV) greater than 15%, are rescued by reduction of CV below this threshold in each case. The method is implemented in the R statistical programing language. It is compatible with MicroVigene and SuperCurve, packages commonly used in RPPA data analysis. The method is made available, along with suggestions for implementation, at http://bitbucket.org/rppa_preprocess/rppa_preprocess/src. PMID:25501559