Spatial dependence pattern of topographical data at hillslope and small catchments scale

[Abstract] Landscapes are characterized by both, random and non-random variability components. Random variability of topographical data, summarized by the semivariance, may be used to elaborate DEMs. The main objective of this work was the study of the spatial dependence pattern of topographical data, using geostatistical techniques. The experimental data sets were directly measured by means of an Abney level in six relief units, hill slopes or elementary first-order small catchments ranging from about 0,62 to 5,72 ha. Forall the six landscape units, the spatial variation of elevation could be expressed as the sum of a deterministic term given by a lineal function and a stochastic component given by spatially correlated height residuals. For the residual elevation data sets, the experimental semivariograms were best fitted by gaussian isotropic models with a small nugget effect. Scaled semivariograms, using the sample variance as scaling factor, allow the comparison of the variability pattern for different landscape units. Cross-validation was used to determine the number of data points for DEM elaboration by block kriging

Using geostatistics and G.I.S., for DTM’s assessment

[Abstract] The main objective of this study was to examine topographical information by means of geostatistical techniques. Moreover, the spatial dependence of point measurements was used in order to assist in making digital elevation models (DEM’s). The survey was conducted in cultivated land. Topographical data were measured for two different fields which size is 2.24 Ha and 0.62 Ha using an Abney level. The continuity of the spatial distribution of point measurements has been evaluated using geostatistics. The analysed data sets showed a lineal trend. After removing the trend experimental semivariograms were calculated and scaled by dividing each of them by the value of their respective variances. Variogram models with small nugget effect and a spatial component described well the residual data resulting from trend removing. The curve fitting technique used to adjust models was jack-knifing. Effects of sampling density during data collection was critically evaluated. Once topographical data were estimated on a fine grid through kriging, contour maps were obtained and subsequently transported to the GIS. The methodology used to expand information from point to landscape with the geostatistical techniques has proved itself very useful

Assessment of the spatial variability of soil chemical properties along a transect using multifractal analysis

[Abstract]The spatial variability of soil properties can be assessed through concepts of scale invariance, fractals and multifractals. The aim of this study was to characterize the scaling patterns and structural heterogeneity properties of general soil chemical properties along a short (i.e. 52 m large) transect. Field measurements were carried out at the experimental farm of CIAM located in Mabegondo, A Coruña, Spain. The studied transect was marked following land slope, and 66 soil samples were collected at the 0-20 cm depth every 0.8 m. The soil properties analyzed were: pH (H2O ), organic carbon content, exchangeable Ca, Mg and K, exchangeable acidity (H + Al), exchangeable bases (SB), cation exchange capacity (CEC), percent base saturation (V) and extractable P. The soil properties studied showed various degrees of multifractality. The spatial distribution of pH was characterized by quasi-monofractal behaviour; CEC, (H+Al) and OM, presented a relatively low degree of multifractality, and the other soil properties studied showed stronger degrees of multifractality, being the highest one for Olsen extractable P. In general, the scaling features of the properties studied implied a multifractal nature, where the low and high density regions scaled differently