Temporal-Like Bivariate Fay-Herriot Model: Leveraging Past Responses and Advanced Preprocessing for Enhanced Small Area Estimation of Growing Stock Volume

Forest inventories are crucial for effective ecosystem management but often lack precision for smaller geographical units due to limited sample sizes. This study introduces an enhanced temporal-like bivariate Fay-Herriot model, improving upon its univariate counterpart. The model incorporates field...

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Published inOperations Research Forum Vol. 5; no. 1; p. 9
Main Authors Georgakis, Aristeidis, Papageorgiou, Vasileios E., Gatziolis, Demetrios, Stamatellos, Georgios
Format Journal Article
LanguageEnglish
Published Cham Springer International Publishing 01.03.2024
Springer Nature B.V
Subjects
Accuracy
Applications of Mathematics
Bivariate analysis
Business and Management
Cluster analysis
Data analysis
Dependent variables
Estimates
Forest management
Math Applications in Computer Science
Mathematical and Computational Engineering
Operations Research/Decision Theory
Optimization
Outliers (statistics)
Preprocessing
Remote sensing
Sample size
Topical Collection on Mathematical Models and Optimization for Environmental Engineering and Sustainable Technologies
Variables
Variance analysis
Outliers
EBLUP
62P12
Repeated forest inventories
62H30
Remote sensing data
Clustering
62J20
62H11
Multivariate area-level model
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Abstract Forest inventories are crucial for effective ecosystem management but often lack precision for smaller geographical units due to limited sample sizes. This study introduces an enhanced temporal-like bivariate Fay-Herriot model, improving upon its univariate counterpart. The model incorporates field data and auxiliary data, including canopy height metrics from WorldView stereo-imagery and past census data, sourced from the University Forest of Pertouli in Central Greece. The model aims to estimate the growing stock volume for 2008 and 2018, focusing on enhancing the precision of the 2018 estimates. The 2008 dependent variable is used as auxiliary information by the model for more reliable 2018 small area estimates. A novel preprocessing pipeline is also introduced, which includes outlier identification, cluster analysis, and variance smoothing. Compared to direct estimates and the standard univariate Fay-Herriot model, our bivariate approach shows a percentage variance reduction of 96.58% and 13.52%, respectively. The methodology not only offers more reliable estimates with reduced variance and bias but also contributes to more accurate decision-making for sustainable forest management.
AbstractList Forest inventories are crucial for effective ecosystem management but often lack precision for smaller geographical units due to limited sample sizes. This study introduces an enhanced temporal-like bivariate Fay-Herriot model, improving upon its univariate counterpart. The model incorporates field data and auxiliary data, including canopy height metrics from WorldView stereo-imagery and past census data, sourced from the University Forest of Pertouli in Central Greece. The model aims to estimate the growing stock volume for 2008 and 2018, focusing on enhancing the precision of the 2018 estimates. The 2008 dependent variable is used as auxiliary information by the model for more reliable 2018 small area estimates. A novel preprocessing pipeline is also introduced, which includes outlier identification, cluster analysis, and variance smoothing. Compared to direct estimates and the standard univariate Fay-Herriot model, our bivariate approach shows a percentage variance reduction of 96.58% and 13.52%, respectively. The methodology not only offers more reliable estimates with reduced variance and bias but also contributes to more accurate decision-making for sustainable forest management.
Abstract Forest inventories are crucial for effective ecosystem management but often lack precision for smaller geographical units due to limited sample sizes. This study introduces an enhanced temporal-like bivariate Fay-Herriot model, improving upon its univariate counterpart. The model incorporates field data and auxiliary data, including canopy height metrics from WorldView stereo-imagery and past census data, sourced from the University Forest of Pertouli in Central Greece. The model aims to estimate the growing stock volume for 2008 and 2018, focusing on enhancing the precision of the 2018 estimates. The 2008 dependent variable is used as auxiliary information by the model for more reliable 2018 small area estimates. A novel preprocessing pipeline is also introduced, which includes outlier identification, cluster analysis, and variance smoothing. Compared to direct estimates and the standard univariate Fay-Herriot model, our bivariate approach shows a percentage variance reduction of 96.58% and 13.52%, respectively. The methodology not only offers more reliable estimates with reduced variance and bias but also contributes to more accurate decision-making for sustainable forest management.
ArticleNumber 9
Author Papageorgiou, Vasileios E.
Georgakis, Aristeidis
Stamatellos, Georgios
Gatziolis, Demetrios
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Issue 1
Keywords Outliers
EBLUP
62P12
Repeated forest inventories
62H30
Remote sensing data
Clustering
62J20
62H11
Multivariate area-level model
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Snippet Forest inventories are crucial for effective ecosystem management but often lack precision for smaller geographical units due to limited sample sizes. This...
Abstract Forest inventories are crucial for effective ecosystem management but often lack precision for smaller geographical units due to limited sample sizes....
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StartPage 9
SubjectTerms Accuracy
Applications of Mathematics
Bivariate analysis
Business and Management
Cluster analysis
Data analysis
Dependent variables
Estimates
Forest management
Math Applications in Computer Science
Mathematical and Computational Engineering
Operations Research/Decision Theory
Optimization
Outliers (statistics)
Preprocessing
Remote sensing
Sample size
Topical Collection on Mathematical Models and Optimization for Environmental Engineering and Sustainable Technologies
Variables
Variance analysis
Title Temporal-Like Bivariate Fay-Herriot Model: Leveraging Past Responses and Advanced Preprocessing for Enhanced Small Area Estimation of Growing Stock Volume
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https://www.proquest.com/docview/2932844152
Volume 5
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