Using Digital Elevation Model to Improve Soil pH Prediction in an Alpine Doline.

Among spatial interpolation techniques, geostatistics is generally preferred because it takes into account the spatial correlation between neighbouring observations in order to predict attribute values at unsampled locations. A doline of approximately 15 000 m(2) at 1 900 m above sea level (North Italy) was selected as the study area to estimate a digital elevation model (DEM) using geostatistics, to provide a realistic distribution of the errors and to demonstrate whether using widely available secondary data provided more accurate estimates of soil pH than those obtained by univariate kriging. Elevation was measured at 467 randomly distributed points that were converted into a regular DEM using ordinary kriging. Further, 110 pits were located using spatial simulated annealing (SSA) method. The interpolation techniques were multi-linear regression analysis (MLR), ordinary kriging (OK), regression kriging (RK), kriging with external drift (KED) and multi-collocated ordinary cokriging (CKmc). A cross-validation test was used to assess the prediction performances of the different algorithms and then evaluate which methods performed best. RK and KED yielded better results than the more complex CKmc and OK. The choice of the most appropriate interpolation method accounting for redundant auxiliary information was strongly conditioned by site specific situations.