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Open image in new window Fig. 2 Nonlinear distribution of ground temperature.
Open image in new window Fig. 13 Distribution of ground pressure at the working face.
The results show that climate and heterogeneous landscape compositions may influence the distribution of ground beetle communities in agricultural fields.
In our present research, a GIS based computational methodology is developed for calculating the distribution of ground movement and deformation at arbitrary surface points (Djamaluddin et al. 2012).
Finally, SIVW could be applied to high degree SV modeling by mitigating the bias due to the poor distribution of ground observatories.
Next, using empirical attenuation relationships, one may predict the distribution of ground motion intensity as a function of magnitude and distance.
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b On CT, a crazy-paving pattern was seen with a geographic distribution of ground-glass opacities with the superimposition of thickened inter- (1) and intralobular (2) septa.
Due to the uneven geographic distribution of ground-based observations these models have much higher uncertainties than models employing satellite data.
A crazy-paving pattern with a scattered distribution of ground-glass opacities and a linear pattern superimposed, with multiple small irregular lines, was visible.
To test this, we examined the distribution of ground-active beetles in a landscape of remnant Eucalyptus open woodland patches where large areas of lower contrast matrix (farmland) are being transformed to high-contrast pine plantations in south-eastern Australia.
In principle, this implies the need to assimilate a greater density of observations; however, the distribution of ground-based observing instruments is often the most sparse at the highest latitudes, putting a greater premium on the assimilation of satellite data, e.g. from Langmuir probes and radio occultation techniques, or data derived from GPS tomography.
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