Design-based approach to k-nearest neighbours technique for coupling field and remotely sensed data in forest surveys
Author(s)
Baffetta, Federica
Fattorini, Lorenzo
Franceschi, Sara
Corona, Piermaria
Date Issued
2009
Type
Article
Abstract
The statistical properties of the k-NN estimators are investigated in a design-based framework, avoiding any assumption about the population under study. The issue of coupling remotely sensed digital imagery with data arising from forest inventories conducted using probabilistic sampling schemes is considered. General results are obtained for the k-NN estimator at the pixel level. When averages (or totals) of forest attributes for the whole study area or sub-areas are of interest, the use of the empirical difference estimator is proposed. The estimator is shown to be approximately unbiased with a variance admitting unbiased or conservative estimators. The performance of the empirical difference estimator is evaluated by an extensive simulation study performed on several populations whose dimensions and covariate values are taken from a real case study. Samples are selected from the populations by means of simple random sampling without replacement. Comparisons with the generalized regression estimator and Horvitz–Thompson estimators are also performed. An application to a local forest inventory on a test area of central Italy is considered.
Additional information
L'articolo è disponibile sul sito dell'editore www.sciencedirect.com
Citation
Baffetta, F. et al. 2009. Design-based approach to k-nearest neighbours technique for coupling field and remotely sensed data in forest surveys. "Remote Sensing of Environment" 113 (3): 463-475
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