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Diagnostic tools for nearest neighbors techniques when used with satellite imageryAuthor(s): Ronald E. McRoberts
Source: Remote Sensing of Environment. 113: 489-499.
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
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DescriptionNearest neighbors techniques are non-parametric approaches to multivariate prediction that are useful for predicting both continuous and categorical forest attribute variables. Although some assumptions underlying nearest neighbor techniques are common to other prediction techniques such as regression, other assumptions are unique to nearest neighbor techniques. Graphical diagnostic tools are proposed to evaluate the assumptions and to address issues of bias, homoscedasticity, influential observations, outliers, and extrapolations. The tools are illustrated using results obtained from applying the k-Nearest Neighbors technique with Landsat imagery and forest inventory ground observations.
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CitationMcRoberts, Ronald E. 2009. Diagnostic tools for nearest neighbors techniques when used with satellite imagery. Remote Sensing of Environment. 113: 489-499.
Keywordsbias, homoscedasticity, outliers, influential observations, extrapolations, forest inventory
- Estimating forest attribute parameters for small areas using nearest neighbors techniques
- Optimizing the k-Nearest Neighbors technique for estimating forest aboveground biomass using airborne laser scanning data
- A two-step nearest neighbors algorithm using satellite imagery for predicting forest structure within species composition classes
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