Spatially consistent nearest neighbor imputation of forest stand data

This study suggests a method for improving spatial consistency in the estimation of forest stand data. Traditional nearest neighbor imputation can preserve between-variable consistency within a unit, but not between geographically nearby units. The lack of spatial consistency may cause problems when data are used for purposes of forestry planning or scenario analysis. In spatially consistent nearest neighbor imputation, adjacent units are considered in the estimation. The first step of the method is a k-Nearest Neighbor imputation. Secondly, based on the initial imputation an optimization algorithm, Simulated Annealing, is applied in order to reach certain spatial variation targets. The proposed method was tested in a case study where tree stem volume data were imputed to each unit (pixel) of forest stands, using satellite digital numbers as carrier data. The spatial variation measures used were between-pixel correlation and short-range variance. In addition, accuracy of the estimated stand level mean volume was used as a target in order to avoid drifts in mean volume during the optimization. The method was successful in three out of four stands where it resulted in imputations corresponding exactly to the target spatial variation measures. In the fourth stand it was not possible to find an exact solution. However, in this case the two spatial variation targets were reached whereas the mean stem volume was slightly overestimated (stem volume of 375 m3 ha− 1 rather than 336 m3 ha− 1).