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The shuttle radar topography mission (SRTM), was flow on the space shuttle Endeavour in February 2000, with the objective of acquiring a digital elevation model of all land between 60° north latitude and 56° south latitude, using interferometric synthetic aperture radar (InSAR) techniques. The SRTM data are distributed at horizontal resolution of 1 arc-second (∼30 m) for areas within the USA and at 3 arc-second (∼90 m) resolution for the rest of the world. A resolution of 90 m can be considered suitable for the small or medium-scale analysis, but it is too coarse for more detailed purposes. One alternative is to interpolate the SRTM data at a finer resolution; it will not increase the level of detail of the original digital elevation model (DEM), but it will lead to a surface where there is the coherence of angular properties (i.e. slope, aspect) between neighbouring pixels, which is an important characteristic when dealing with terrain analysis. This work intents to show how the proper adjustment of variogram and kriging parameters, namely the nugget effect and the maximum distance within which values are used in interpolation, can be set to achieve quality results on resampling SRTM data from 3” to 1”. We present for a test area in western USA, which includes different adjustment schemes (changes in nugget effect value and in the interpolation radius) and comparisons with the original 1” model of the area, with the national elevation dataset (NED) DEMs, and with other interpolation methods (splines and inverse distance weighted (IDW)). The basic concepts for using kriging to resample terrain data are: (i) working only with the immediate neighbourhood of the predicted point, due to the high spatial correlation of the topographic surface and omnidirectional behaviour of variogram in short distances; (ii) adding a very small random variation to the coordinates of the points prior to interpolation, to avoid punctual artifacts generated by predicted points with the same location than original data points and; (iii) using a small value of nugget effect, to avoid smoothing that can obliterate terrain features. Drainages derived from the surfaces interpolated by kriging and by splines have a good agreement with streams derived from the 1” NED, with correct identification of watersheds, even though a few differences occur in the positions of some rivers in flat areas. Although the 1” surfaces resampled by kriging and splines are very similar, we consider the results produced by kriging as superior, since the spline-interpolated surface still presented some noise and linear artifacts, which were removed by kriging. [ABSTRACT FROM AUTHOR]

Pacific sardine ( Sardinops sagax) and northern anchovy ( Engraulis mordax) eggs exhibited different spatial structure on the scale of 0.75–2.5 km in two egg patches sampled in the Southern California Bight in April 2000. Plankton samples were collected at 4-min intervals with a Continuous Underway Fish Egg Sampler (CUFES) on 5 × 5 km grids centered on surface drifters. Variograms were calculated for sardine and anchovy eggs in Lagrangian coordinates, using abundances of individual developmental stages grouped into daily cohorts. Model variograms for sardine eggs have a low nugget effect, about 10% of the total variance, indicating high autocorrelation between adjacent samples. In contrast, model variograms for anchovy eggs have a high nugget effect of 50–100%, indicating that most of the variance at the scales sampled is spatially unstructured. The difference between observed spatial patterns of sardine and anchovy eggs on this scale may reflect the behavior of the spawning adults: larger, faster, more abundant fish may organize into larger schools with greater structure and mobility that create smoother egg distributions. Size and mobility vary with population size in clupeoids. The current high abundance of sardines and low abundance of anchovy off California agree with the greater autocorrelation of sardine egg samples and the observed tendency for locations of anchovy spawning to be more persistent on the temporal scale of days to weeks. Thus the spatial pattern of eggs and the persistence of spawning areas are suggested to depend on species, population size and age structure, spawning intensity and characteristic physical scales of the spawning habitat. [ABSTRACT FROM AUTHOR]

Modeling hyper-dimensional spatial variability is a complex task from both practical and theoretical standpoints. In this paper we develop a method for modeling hyper-dimensional covariance (variogram) structures using the product-sum covariance model initially developed to model spatio-temporal variability. We show that the product-sum model can be used recursively up to an arbitrarily large number of dimensions while preserving relative modeling simplicity and yielding valid covariance models. The method can be used to model variability in anisotropic conditions with multiple axes of anisotropy or when temporal evolution is involved, and thus is applicable to "full anisotropic 3D+time" situations often encountered in environmental sciences. It requires fewer assumptions than the traditional product-sum modeling approach. The new method also presents an alternative to classical approaches to modeling zonal anisotropy and requires fewer parameters to be estimated from data. We present an example by applying the method in conjunction with ordinary kriging to map photosynthetically-active radiation (PAR) for 2006, in Oklahoma, CA, USA and to explore effects of spatio-temporal variability in PAR on gross primary productivity (GPP). [ABSTRACT FROM AUTHOR]