Exploring a valid model for the variogram of an isotropic spatial process

Maglione, Dora Silvia; Diblasi, Ángela Magdalena

doi:10.1007/s00477-003-0143-7

Cited by 17 publications

(10 citation statements)

References 12 publications

(16 reference statements)

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“…We compared these scales by fuel variable and then by sample site to evaluate if fuel properties are constant across study sites. We also evaluated whether the spatial variation in fuel variables is isotropic (same in all directions) or anisotropic (directional) (Maglione and Diblasi 2004), and we addressed whether the variation is stationary (homogeneous in space). Two spatial statistics were computed to describe the spatial structure of fuel variables: Moran's I and Geary's C. Spatial analysis was performed using the geoR spatial package (Ribeiro and Diggle 2001) in R statistical computing software.…”

Section: Calculating Fuel Variabilitymentioning

confidence: 99%

Spatial variability of wildland fuel characteristics in northern Rocky Mountain ecosystems

Keane¹,

Gray²,

Bacciu³

2012

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We investigated the spatial variability of a number of wildland fuel characteristics for the major fuel components found in six common northern Rocky Mountain ecosystems. Surface fuel characteristics of loading, particle density, bulk density, and mineral content were measured for eight fuel components-four downed dead woody fuel size classes (1, 10, 100, 1000 hr), duff, litter, shrub, and herb-on nested plots located within sampling grids to describe their variability across spatial scales. We also sampled canopy bulk density, biomass, and cover for each plot in the grid. The spatial distribution and variability of surface and canopy fuel characteristics are described using the variance, spatial autocorrelation, semi-variograms, and Moran's I. We found that all fuels had high variability in loading (two to three times the mean), and this variability increased with the size of fuel particle. We also found that fuel components varied at different scales, with fine fuels varying at scales of 1 to 5 m, coarse fuels at 10 to 150 m, and canopy fuels at 100 to 500 m. Findings and data from this study can be used to sample, describe, and map fuel characteristics, such as loading, at the appropriate spatial scales to accommodate the next generation of fire behavior prediction models.Keywords: wildland fire, biomass, landscape ecology, woody debris, spatial scale, mineral content, particle density, bulk density, loading AuthorsRobert E. Keane is a Research Ecologist with the U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station at the Missoula Fire Sciences Laboratory in Montana. Since 1985, Keane has developed various ecological computer models for research and management applications. His recent research includes (1) developing ecological computer models for exploring landscape, fire, and climate dynamics; (2) describing, classifying, and mapping fuel characteristics; (3) investigating the ecology and restoration of whitebark pine; and (4) conducting fundamental wildland fuel science. He

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Section: Calculating Fuel Variabilitymentioning

confidence: 99%

Spatial variability of wildland fuel characteristics in northern Rocky Mountain ecosystems

Keane¹,

Gray²,

Bacciu³

2012

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“…Various approaches, such as computational fluid dynamics model (CFD) (Lu et al 1997;Cheng and Hu 2005) and statistical model (Kalpasanov and Kurchatova 1976;Kottegoda and Rosso 1998), have been applied to study particulate emission phenomena. Among these, the statistical model, particularly the statistical distribution model, has been proven to be a sophisticated tool for representing the useful information of pollutant data (Kumar and Jain 2010; Kim 2010; Romano et al 2004;Cassidy et al 2007;Gokhale and Khare 2007;Maglione and Diblasi 2004;Ocana-Peinado et al 2008;Babak et al 2010;Spöck and Pilz 2010). Gokhale and Khare (2007) developed a statistical distribution model to fit carbon monoxide concentrations in Delhi and the results showed that the log-logistic distribution model best fits the data at intersections, as well as roadsides.…”

Section: Introductionmentioning

confidence: 97%

Urban aerosol particulates on Hong Kong roadsides: size distribution and concentration levels with time

2011

Stoch Environ Res Risk Assess

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A detailed study on particulate matter from vehicle emission was carried out at roadsides in Hong Kong downtown area. The study aims to explore the variations of particulate matter at roadsides in morning and afternoon. Data of concentrations of different size groups of particulate matter were collected and analyzed. It was found that the particulate levels generally vary periodically with traffic signal changes. During the green traffic light period, concentrations of the particulate matter increase to peak point and then diffuse to a relatively stable level in the red traffic light period. Such stable level is regarded as background level, to which pedestrians are exposed when they walk-by and cross the zebra zones. To analyze and further explore the collected data, a statistical distribution model, i.e., goodness-of-fit test, was employed. It was noticed that the lognormal distribution best fits the particulate matter data in both morning and afternoon. In addition, the non-parametric test was also used to assess the differences between morning and afternoon data. The results show that both data sets statistically differ from each other at 5% significance level. It can be deduced that the change of traffic volume, humidity and wind speed between morning and afternoon may cause this difference.

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“…However, there are slightly few references devoted to the testing problem. Diblasi and Bowman (2001) propose a goodness-of-fit technique to analyze spatial independence and Maglione and Diblasi (2004) extend this result in order to test if the spatial dependence structure of a data set can be explained by a certain parametric family. For multidimensional lattice data (see Besag 1974), there are several classical references on estimation and modelling.…”

Section: Introductionmentioning

confidence: 99%

Goodness-of-fit tests for the spatial spectral density

Crujeiras

Fernández-Casal

González–Manteiga

2009

Stoch Environ Res Risk Assess

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Detection and modeling the spatial correlation is an important issue in spatial data analysis. We extend in this work two different goodness-of-fit testing techniques for the spatial spectral density. The first approach is based on a smoothed version of the ratio between the periodogram and a parametric estimator of the spectral density. The second one is a generalized likelihood ratio test statistic, based on the log-periodogram representation as the response variable in a regression model. As a particular case, we provide tests for independence. Asymptotic normal distribution of both statistics is obtained, under the null hypothesis. For the application in practice, a resampling procedure for calibrating these tests is also given. The performance of the method is checked by a simulation study. Application to real data is also provided.

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Exploring a valid model for the variogram of an isotropic spatial process

Cited by 17 publications

References 12 publications

Spatial variability of wildland fuel characteristics in northern Rocky Mountain ecosystems

Spatial variability of wildland fuel characteristics in northern Rocky Mountain ecosystems

Urban aerosol particulates on Hong Kong roadsides: size distribution and concentration levels with time

Goodness-of-fit tests for the spatial spectral density

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