Estimating forest attribute parameters for small areas using nearest neighbors techniques

Abstract

Nearest neighbors techniques have become extremely popular, particularly for use with forest inventory data. With these techniques, a population unit prediction is calculated as a linear combination of observations for a selected number of population units in a sample that are most similar, or nearest, in a space of ancillary variables to the population unit requiring the prediction. Nearest neighbors techniques are appealing for multiple reasons: they can be used with categorical response variables for which the objective is classification and with continuous response variables for which the objective is prediction; they can be used for both univariate and multivariate prediction; they are non-parametric in the sense that no assumptions regarding the distributions of response or predictor variables are necessary; they are synthetic in the sense that they can readily use information external to the geographic area for which an estimate is sought; they are useful for map construction, small area estimation, and inference; and they can be used with a wide variety of data sets. Recent advances and emerging issues in nearest neighbors techniques are reviewed for four topic areas: (1) distance metrics, (2) optimization, (3) diagnostic tools, and (4) inference. The focus of the study is estimation of mean forest stem volume per unit area for small areas using a combination of forest inventory observations and Landsat Thematic Mapper (TM) imagery. However, the concepts and techniques are generally applicable for all nearest neighbors problems.

Citation

McRoberts, Ronald E. 2012. Estimating forest attribute parameters for small areas using nearest neighbors techniques. Forest Ecology and Management. 272: 3-12. Doi: 10.1016/j.foreco.2011.06.039.