TY - JOUR

T1 - Remote sensing estimates and measures of uncertainty for forest variables at different aggregation levels

AU - Mauro, F.

AU - Molina, I.

AU - García-Abril, A.

AU - Valbuena, R.

AU - Ayuga-Téllez, E.

PY - 2016

Y1 - 2016

N2 - 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. A case study illustrates the applicability of the obtained results in the context of a Light Detection and Ranging‐based forest inventory, where estimates are provided for areas of interest sizing from single pixels or population units to thousands of hectares. Based on our results, we recommend to consider the additional component in the MSE estimator when estimating in pixels and domains of interest sizing few hectares.

AB - 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. A case study illustrates the applicability of the obtained results in the context of a Light Detection and Ranging‐based forest inventory, where estimates are provided for areas of interest sizing from single pixels or population units to thousands of hectares. Based on our results, we recommend to consider the additional component in the MSE estimator when estimating in pixels and domains of interest sizing few hectares.

KW - small area estimation

KW - EBLUP

KW - MSE estimator

KW - LiDAR

KW - estimation of natural resources

U2 - 10.1002/env.2387

DO - 10.1002/env.2387

M3 - Erthygl

VL - 27

SP - 225

EP - 238

JO - EnvironMetrics

JF - EnvironMetrics

IS - 4

ER -