Skip to Main Content
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
View PDF (1008.0 KB)
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.
- Check the Northern Research Station web site to request a printed copy of this publication.
- Our on-line publications are scanned and captured using Adobe Acrobat.
- During the capture process some typographical errors may occur.
- Please contact Sharon Hobrla, firstname.lastname@example.org if you notice any errors which make this publication unusable.
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
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
- Monitoring landscape level processes using remote sensing of large plots
- Properties of Endogenous Post-Stratified Estimation using remote sensing data
- An improved strategy for regression of biophysical variables and Landsat ETM+ data.
XML: View XML