A completely data-driven, design-based sampling strategy is proposed for mapping a forest attribute within the spatial units tessellating a survey region. Based on sample data, a model is selected, and model parameters are estimated using least-squares criteria for predicting the attribute of interest within units as a linear function of a set of auxiliary variables. The spatial interpolation of residuals arising from model predictions is performed by inverse distance weighting. The leave-one-out cross validation procedure is adopted for selecting the smoothing parameter used for interpolation. The densities of the attributes of interest within units are estimated by summing predictions and interpolated residuals. Finally, density estimates are rescaled to match the total estimate over the survey region obtained by the traditional regression estimator with the total estimate obtained from the map as the sum of the density estimates within units. A bootstrap procedure accounts for the uncertainty. The consistency of the strategy is proven by incorporating previous results. A simulation study is performed and an application for mapping wood volume densities in the forest estate of Rincine (Central Italy) is described. © 2022 John Wiley & Sons Ltd.

Biodiversity will probably be threatened by climate change effects and the Mediterranean area is a well know hotspot of genetic diversity. Climatic data are a very important source of information for those studies and the aim of this work was to study and compare eight methods for spatial interpolation of climatic data and indices including parametric and non-parametric methods, deterministic, regressive and geostatistical. The Root Mean Square Error (RMSE), relative RMSE (rRMSE) and relative BIAS (rBIAS) were calculated to assess algorithm’s performances in a Mediterranean region. None of the eight methods performed much better than others with a very complex physiographic environment. The range of errors was very high and rRMSE varied from 3.8% to 295%. Anyway, even in case of low differences among methods and despite the necessity of the assumption of normality of data, the interpolation at local scale with parametric and geostatistical methods (e.g. kriging or cokriging) should be preferred to globally-interpolated climatic data due to the possibility to obtain the distribution of prediction’s error. © 2017, Patron Editore S.r.l. All rights reserved.