The benefits of an integrated geographical information system (GIS) and a geostatistics approach to accurately model the spatial distribution pattern of precipitation are known. However, the determination of the most appropriate geostatistical algorithm for each case is usually neglected, i.e. it is important to select the best interpolation technique for each study area to obtain accurate results. In this work, the ordinary kriging (OK), simple kriging (SK) and universal kriging (universal kriging) methods are compared with three multivariate algorithms which take into account the altitude: collocated ordinary cokriging (OCK), simple kriging with varying local means (SKV) and regression‐kriging (RK). The different techniques are applied to monthly and annual precipitation data measured at 136 meteorological stations in a region of southwestern Spain (Extremadura). After carrying out cross‐validation, the smallest prediction errors are obtained for the three multivariate algorithms but, particularly, SKV and RK outperform collocated OCK, which needs a more demanding variogram analysis. These algorithms are easily implemented in a GIS, requiring the residual estimates and map algebra capability to generate the final maps. Results evidence the necessity of accounting for spatially dependent precipitation data and the collocated altitude, to accurately define monthly and annual precipitation maps. Copyright © 2009 Royal Meteorological Society

Fire severity is a key factor for management of post-fire vegetation regeneration strategies because it quantifies the impact of fire, describing the amount of damage. Several indices have been developed for estimation of fire severity based on terrestrial observation by satellite imagery. In order to avoid the implicit limitations of this kind of data, this work employed an Unmanned Aerial Vehicle (UAV) carrying a high-resolution multispectral sensor including green, red, near-infrared, and red edge bands. Flights were carried out pre- and post-controlled fire in a Mediterranean forest. The products obtained from the UAV-photogrammetric projects based on the Structure from Motion (SfM) algorithm were a Digital Surface Model (DSM) and multispectral images orthorectified in both periods and co-registered in the same absolute coordinate system to find the temporal differences (d) between pre- and post-fire values of the Excess Green Index (EGI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Red Edge (NDRE) index. The differences of indices (dEGI, dNDVI, and dNDRE) were reclassified into fire severity classes, which were compared with the reference data identified through the in situ fire damage location and Artificial Neural Network classification. Applying an error matrix analysis to the three difference of indices, the overall Kappa accuracies of the severity maps were 0.411, 0.563, and 0.211 and the Cramer’s Value statistics were 0.411, 0.582, and 0.269 for dEGI, dNDVI, and dNDRE, respectively. The chi-square test, used to compare the average of each severity class, determined that there were no significant differences between the three severity maps, with a 95% confidence level. It was concluded that dNDVI was the index that best estimated the fire severity according to the UAV flight conditions and sensor specifications.