Sampling Design Contribution to Small Area Estimation Procedure in Forest Inventories

Georgakis, Aristeidis

doi:10.31031/mcda.2020.07.000654

Cited by 2 publications

(1 citation statement)

References 33 publications

(54 reference statements)

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“…Different estimators can be considered when area-level auxiliary data are aggregated, and they show correlation with the variables of interest. Additionally, the widely used model-based Fay-Herriot model has been incorporated in recent studies to explore the application of model-assisted SAE within different sampling schemes [59][60][61][62][63][64].…”

Section: Discussionmentioning

confidence: 99%

A Primer on Clustering of Forest Management Units for Reliable Design-Based Direct Estimates and Model-Based Small Area Estimation

Georgakis,

Gatziolis,

Stamatellos

2023

Forests

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This study employs clustering analysis to group forest management units using auxiliary, satellite imagery-derived height metrics and past wall-to-wall tree census data from a natural, uneven-aged forest. Initially, we conducted an exhaustive exploration to determine the optimal number of clusters k, considering a wide range of clustering schemes, indices, and two specific k ranges. The optimal k is influenced by various factors, including the minimum k considered, the selected clustering algorithm, the clustering indices used, and the auxiliary variables. Specifically, the minimum k, the Euclidean distance metric, and the clustering index were instrumental in determining the optimal cluster numbers, with algorithms exerting minimal influence. Unlike traditional validation indices, we assessed the performance of these optimally defined clusters based on direct estimates and additional criteria. Subsequently, our research introduces a twofold methodology for Small Area Estimation (SAE). The first approach focuses on aggregating forest management units at the cluster level to increase the sample size, thereby yielding reliable design-based direct estimates for key forest attributes, including growing stock volume, basal area, tree density, and mean tree height. The second approach prepares area-level data for the future application of model-based estimators, contingent on establishing a strong correlation between target and auxiliary variables. Our methodology has the potential to enhance forest inventory practices across a wide range of forests where area-level auxiliary covariates are available.

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Section: Discussionmentioning

confidence: 99%

A Primer on Clustering of Forest Management Units for Reliable Design-Based Direct Estimates and Model-Based Small Area Estimation

Georgakis,

Gatziolis,

Stamatellos

2023

Forests

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

Georgakis,

Papageorgiou,

Gatziolis

et al. 2024

Oper. Res. Forum

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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.

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Sampling Design Contribution to Small Area Estimation Procedure in Forest Inventories

Cited by 2 publications

References 33 publications

A Primer on Clustering of Forest Management Units for Reliable Design-Based Direct Estimates and Model-Based Small Area Estimation

A Primer on Clustering of Forest Management Units for Reliable Design-Based Direct Estimates and Model-Based Small Area Estimation

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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