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Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventoriesAuthor(s): Steen Magnussen; Ronald E. McRoberts; Erkki O. Tomppo
Source: Remote Sensing of Environment. 113: 476-488.
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
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DescriptionNew model-based estimators of the uncertainty of pixel-level and areal k-nearest neighbour (knn) predictions of attribute Y from remotely-sensed ancillary data X are presented. Non-parametric functions predict Y from scalar 'Single Index Model' transformations of X. Variance functions generated estimates of the variance of Y. Three case studies, with data from the Forest Inventory and Analysis program of the U.S. Forest Service, the Finnish National Forest Inventory, and Landsat ETM+ ancillary data, demonstrate applications of the proposed estimators. Nearly unbiased knn predictions of three forest attributes were obtained. Estimates of mean square error indicate that knn is an attractive technique for integrating remotely-sensed and ground data for the provision of forest attribute maps and areal predictions.
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CitationMagnussen, Steen; McRoberts, Ronald E.; Tomppo, Erkki O. 2009. Model-based mean square error estimators for k-nearest neighbour predictions and applications using remotely sensed data for forest inventories. Remote Sensing of Environment. 113: 476-488.
Keywordssmall area estimation, spatial prediction, non-parametric, single index model, variance function, spatial correlation function, forest inventory
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