Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/2081
Title: Design-based approach to k-nearest neighbours technique for coupling field and remotely sensed data in forest surveys
Authors: Baffetta, Federica
Fattorini, Lorenzo
Franceschi, Sara
Corona, Piermaria
Keywords: Remotely sensed digital imagery;Forest inventories;k-NN method;Design-based inference;Simulation;Case study
Issue Date: 2009
Publisher: Elsevier
Source: 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
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.
Description: 
L'articolo è disponibile sul sito dell'editore www.sciencedirect.com
URI: http://hdl.handle.net/2067/2081
ISSN: 0034-4257
DOI: 10.1016/j.rse.2008.06.014
Appears in Collections:DiSAFRi - Archivio della produzione scientifica

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