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Please use this identifier to cite or link to this item: http://hdl.handle.net/2067/2634

Title: Mapping by spatial predictors exploiting remotely sensed and ground data: a comparative design-based perspective
Authors: Corona, Piermaria
Fattorini, Lorenzo
Franceschi, Sara
Chirici, Gherardo
Maselli, Fabio
Secondi, Luca
Keywords: Forest mapping
Tessellation stratified sampling
Universal kriging
Locally weighted regression
k-Nearest neighbor predictor
Issue Date: 2014
Publisher: Elsevier
Citation: Corona, P. et al. 2014. Mapping by spatial predictors exploiting remotely sensed and ground data: a comparative design-based perspective. "Remote sensing of environment" 152: 29–37
Abstract: This study was designed to compare the performance – in terms of bias and accuracy – of four different parametric,semiparametric and nonparametric methods in spatially predicting a forest response variable using auxiliary information from remote sensing. The comparison was carried out in simulated and real populations where the value of response variable was known for each pixel of the study region. Sampling was simulated through a tessellation stratified design. Universal kriging and cokriging
DOI: 10.1016/j.rse.2014.05.011
URI: http://www.sciencedirect.com/science/article/pii/S0034425714001990
ISSN: 0034-4257
Appears in Collections:DiSAFRi - Archivio della produzione scientifica

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