Application of nearest–neighbour regression for generalizing sample tree information

  • Korhonen K
  • Kangas A
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Abstract

Nearest?neighbour regression was tested for generalizing sample tree information in data from the national forest inventory of Finland. The following variables were found to be good regressors: stem diameter, mean diameter, density and age of growing stock, and plot location. The nearest?neighbour estimator appears to maintain the natural variation of the variables to be estimated well. Reliable volume and height estimates can be obtained even when using only one nearest neighbour. Increasing the number of neighbours improves the accuracy of estimates.
Nearest?neighbour regression was tested for generalizing sample tree information in data from the national forest inventory of Finland. The following variables were found to be good regressors: stem diameter, mean diameter, density and age of growing stock, and plot location. The nearest?neighbour estimator appears to maintain the natural variation of the variables to be estimated well. Reliable volume and height estimates can be obtained even when using only one nearest neighbour. Increasing the number of neighbours improves the accuracy of estimates.

Author-supplied keywords

  • Inventory
  • Models
  • Nearest–neighbour regression
  • Non–parametric regression
  • Volume

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