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Estimating areal means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imageryAuthor(s): Ronald E. McRoberts; Erkki O. Tomppo; Andrew O. Finley; Heikkinen Juha
Source: Remote Sensing of Environment. 111:466-480.
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
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DescriptionThe k-Nearest Neighbor (k-NN) technique has become extremely popular for a variety of forest inventory mapping and estimation applications. Much of this popularity may be attributed to the non-parametric, multivariate features of the technique, its intuitiveness, and its ease of use. When used with satellite imagery and forest inventory plot data, the technique has been shown to produce useful estimates of many forest attributes including forest/non-forest, volume, and basal area. However, variance estimators for quantifying the uncertainty of means or sums of k-NN pixel-level predictions for areas of interest (AOI) consisting of multiple pixels have not been reported. The primary objectives of the study were to derive variance estimators for AOI estimates obtained from k-NN predictions and to compare precision estimates resulting from different approaches to k-NN prediction and different interpretations of those predictions.
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CitationMcRoberts, Ronald E.; Tomppo, Erkki O.; Finley, Andrew O.; Juha, Heikkinen. 2007. Estimating areal means and variances of forest attributes using the k-Nearest Neighbors technique and satellite imagery. Remote Sensing of Environment. 111:466-480.
KeywordsLandsat, probability-based inference, model-based inference, variance estimator
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