Interpolación geoestadística de datos topográficos para obtener un MED de una pequeña cuenca forestal en el noroeste de España

[Abstract] This article gives an example of the elaboration of a digital elevation model (DEM) with the aid of geostatistics, using the case of a small experimental catchment near Arcos de la Condesa in Galicia, Spain. A DEM is a necessary tool in present-day erosion and landscape modelling. The geostatistical method of DEM construction involves six steps, starting with the removal of the drift and ending with the final interpolation. The drift was almost completely eliminated by a first order trend surface. After it had been confirmed that no heteroscedacity is present in the data set, the resulting experimental variogram was fitted by an anisotropic Gaussian variogram model, which is the variogram model that is generally used for DEM interpolation. Cross validation was used to determine the optimal number of data points to be used in interpolation. The interpolation results were found to be satisfactory and the interpolation standard deviations are below the data set standard deviation. It is yet noted that this uncertainty in the DEM – although small – may influence its derivatives and subsequent model results. However, when compared to other methods of DEM elaboration, the method as used here is an easy, adequate and relatively fast method, that has the major advantage of providing interpolation errors, enabling an evaluation of the interpolation result

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