Remote sensing estimates and measures of uncertainty for forest variables at different aggregation levels Articles uri icon

publication date

  • June 2016

start page

  • 225

end page

  • 238

issue

  • 4

volume

  • 27

international standard serial number (ISSN)

  • 1180-4009

electronic international standard serial number (EISSN)

  • 1099-095X

abstract

  • Empirical best linear unbiased predictors (EBLUPs) based on auxiliary information are efficient for estimating means or totals of environmental attributes in small domains. In small area estimation, the EBLUPs and their approximately unbiased mean square error (MSE) estimators are obtained under the premise of having a large number of population units in the target domain. In remote sensing, single pixels are regarded as population units, and EBLUPs and MSE estimators may also be required for subdomains containing a low number of pixels or even single pixels. In this study, EBLUPs, their MSE, and an unbiased estimator of the MSE are derived when predicting linear parameters for subdomains with a small number of population units that do not intersect with the training sample. In these situations, an additional MSE component should be considered to prevent MSE underestimation.

keywords

  • small area estimation; eblup; mse estimator; lidar; estimation of natural resources; mixed linear-models; mean squared error; plot size; prediction; inventory; canopies; volume