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Analysis of lidar fields using local polynomial regression
Author(s) -
Lindström Torgny,
Holst Ulla,
Weibring Petter
Publication year - 2005
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.726
Subject(s) - bivariate analysis , polynomial regression , lidar , ranging , statistics , inference , regression , mathematics , bandwidth (computing) , polynomial , computer science , environmental science , remote sensing , geology , mathematical analysis , artificial intelligence , telecommunications , computer network
Lidar (light detection and ranging) is a laser based tool for remote measurement of several atmospheric species of importance. We consider the analysis of a field, consisting of several consecutive measurements, in which the concentrations are proportional to the derivatives in the directions of the light paths. Inference is based on local polynomial kernel regression, both for estimation of the derivatives of the mean‐function and for estimation of the variance‐function. Bivariate bandwidth matrices are selected using the empirical‐bias bandwidth selector (EBBS) adapted to allow for dependent data and to support selection of bivariate bandwidths. The estimation procedure is demonstrated on measurements of atomic mercury from the Solvay industries mercury cell chlor‐alkali plant in Rosignano Solvay, Italy. Copyright © 2005 John Wiley & Sons, Ltd.