%0 Research article
%T Segmentation of model localization sub-areas by Getis statistics
%A Räty, Minna
%A Kangas, Annika
%D 2010
%J Silva Fennica
%V 44
%N 2
%R doi:10.14214/sf.155
%U https://silvafennica.fi/article/155
%X Models for large areas (global models) are often biased in smaller  sub-areas, even when the model is unbiased for the whole area.  Localization of the global model removes the local bias, but the problem  is to find homogenous sub-areas in which to localize the function. In  this study, we used the eCognition Professional 4.0 (later versions  called Definies Pro) segmentation process to segment the study area into  homogeneous sub-areas with respect to residuals of the global model of  the form height and/or local Getis statistics calculated for the  residuals, i.e., G<sub>i</sub><sup>*</sup>-indices. The segmentation resulted in four different rasters: 1) residuals of the global model, 2) the local G<sub>i</sub><sup>*</sup>-index, and 3) residuals and the local G<sub>i</sub><sup>*</sup>-index  weighted by the inverse of the variance, and 4) without weighting. The  global model was then localized (re-fitted) for these sub-areas. The  number of resulting sub-areas varied from 4 to 366. On average, the root  mean squared errors (RMSEs) were 3.6% lower after localization than the  global model RMSEs in sub-areas before localization. However, the  localization actually increased the RMSE in some sub-areas, indicating  the sub-area were not appropriate for local fitting. For 56% of the  sub-areas, coordinates and distance from coastline were not  statistically significant variables, in other words these areas were  spatially homogenous. To compare the segmentations, we calculated an  aggregate standard error of the RMSEs of the single sub-areas in the  segmentation. The segmentations in which the local index was present had  slightly lower standard errors than segmentations based on residuals.