Smoothing methodology for predicting regional averages in multi-source forest inventory

The paper examines alternative non-parametric estimation methods or smoothing methods in the context of the Finnish multi-source forest inventory. It uses satellite images in addition to field data to produce forest variable predictions for regions ranging from the single pixel level up to the national level. With the help of the bias-variance decomposition, the influence of the smoothing parameters on prediction accuracy is considered when the smoother's pixel-level predictions are averaged in order to produce predictions for larger areas. A novel variation of cross-validation, called region-wise cross-validation, is proposed for selecting the smoothing parameters. Experimental results are presented using local linear ridge regression (LLRR), which is a variant of the better known local linear regression method.