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Optimizing the k-Nearest Neighbors technique for estimating forest aboveground biomass using airborne laser scanning dataAuthor(s): Ronald E. McRoberts; Erik Næsset; Terje Gobakken
Source: Remote Sensing of Environment
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
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DescriptionNearest neighbors techniques calculate predictions as linear combinations of observations for a selected number of population units in a sample that aremost similar, or nearest, in a space of auxiliary variables to the population unit requiring the prediction. Nearest neighbors techniques have been shown to be particularly effective when used with forest inventory and remotely sensed data. Recent attention has focused on developing an underlying foundation consisting of diagnostic tools, inferential extensions, and techniques for optimization. For a study area in Norway, forest inventory and airborne laser scanning data were used with the k-Nearest Neighbors technique to estimate mean aboveground biomass per unit area. Optimization entailed reduction of the dimension of feature space, deletion of influential outliers, and selection of optimalweights for the weighted Euclidean distance metric. These optimization steps increased the proportion of variability explained in the reference set by as much as 20%, reduced confidence interval widths by asmuch as 35%, and produced standard errors that were as small as 3% of the estimate of the mean.
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CitationMcRoberts, Ronald E.; Næsset, Erik; Gobakken, Terje. 2015. Optimizing the k-Nearest Neighbors technique for estimating forest aboveground biomass using airborne laser scanning data. Remote Sensing of Environment. 163: 13-22. https://doi.org/10.1016/j.rse.2015.02.026.
KeywordsDistance metric, Precision
- Estimating forest attribute parameters for small areas using nearest neighbors techniques
- Using genetic algorithms to optimize k-Nearest Neighbors configurations for use with airborne laser scanning data
- Optimizing nearest neighbour configurations for airborne laser scanning-assisted estimation of forest volume and biomass
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