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

Georgakis, Aristeidis; Gatziolis, Demetrios; Stamatellos, Georgios

doi:10.3390/f14101994

Cited by 6 publications

(1 citation statement)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To determine the optimal number of clusters (k), the elbow method was employed through visual analysis of the graph. Utilizing the elbow method with k-means clustering in the context of forest analysis is a conventional approach in data science and ecology [60][61][62][63][64]. An evaluation of the dissimilarity of the variables (SSEs) among each cluster was conducted to identify potential heterogeneities in forest exploitation.…”

Section: Determining the Number Of Clusters And Analyzing The Spatial...mentioning

confidence: 99%

Spatio-Temporal Assessment of Heterogeneity by Logging Intensity in a Federal Concession Area in the Brazilian Amazon

Oliveira,

Freitas,

Magliano

et al. 2024

Forests

View full text Add to dashboard Cite

The logging intensity often does not take into account the spatial heterogeneity of the forest volume of commercial native species in the Brazilian Amazon. This study aims to evaluate the spatio-temporal heterogeneity distribution by assessing logging intensity and its effects on the volumetric stock and abundance of commercial species, with a focus on sustainable management practices. This study was conducted in the Saracá-Taquera National Forest in the Brazilian Amazon. Forest inventory data, elevation, and PlanetScope satellite images were integrated into a geographic information system. The information was aggregated into regular 1-hectare cells for the times before, during, and after logging (t0, t1, and t2). The unsupervised classification algorithm k-means with four clusters was used to analyze heterogeneity. Before logging, areas with higher commercial volumes were distant from water bodies, while areas with lower elevation had lower wood stocks. Logging intensity was generally low, concentrating on a few trees per hectare. Logging in the study area revealed a heterogeneous spatial distribution by intensifying in areas with the highest wood stocks. These results suggest that, in addition to the recommended logging intensity according to legislation, forest heterogeneity should be considered by the manager, promoting adaptive strategies to ensure the conservation of forest resources.

show abstract

Section: Determining the Number Of Clusters And Analyzing The Spatial...mentioning

confidence: 99%

Spatio-Temporal Assessment of Heterogeneity by Logging Intensity in a Federal Concession Area in the Brazilian Amazon

Oliveira,

Freitas,

Magliano

et al. 2024

Forests

View full text Add to dashboard Cite

show abstract

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

View full text Add to dashboard Cite

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.

show abstract

A new approach to small area estimation: improving forest management unit estimates with advanced preprocessing in a multivariate Fay–Herriot model

Georgakis,

Papageorgiou,

Stamatellos

2024

Forestry: An International Journal of Forest Research

View full text Add to dashboard Cite

Forest inventories are essential for informing sustainable forest management decisions, and small area estimation (SAE) techniques aim to enhance the precision of these inventories, particularly when sample sizes are limited. This study presents a novel approach to SAE by integrating trivariate empirical best linear unbiased prediction Fay–Herriot (FH) models with advanced preprocessing techniques. By employing multivariate Fay–Herriot (MFH) models, the methodology utilizes clustering analysis, variable selection, and outlier treatment to improve the precision of estimates for small areas. A comparative analysis with traditional univariate Fay–Herriot (UFH) models demonstrates that MFH outperforms UFH in estimating key forest attributes such as forest growing stock volume, basal area, and Lorey’s mean tree Height, even in areas with limited sample sizes. The use of auxiliary variables derived from remote sensing data and past censuses proved critical, with remote sensing playing a dual role: aiding in clustering forest management units into larger small areas of interest and serving as covariates in the FH models. The results highlight the effectiveness of MFH1 (assuming independent and identically distributed random effects), which consistently produced estimates with <5% coefficient of variation, indicating high precision. Across all response variables, MFH1 led to reductions in standard errors compared to UFH, with median percentage gains in precision of 17.22% for volume, 13.91% for basal area, and 3.95% for mean height. Mean precision gains were even higher, at 18.27%, 16.51%, and 10.87%, respectively. This study advances SAE methodologies by providing a robust framework for accurately estimating critical forest attributes in challenging scenarios, including geolocation errors, limited sample sizes, and the smallest applicable small areas for area-level models. It highlights the contribution of the correlation between multiple response variables to improving the precision of estimates. The proposed methodology has significant implications for enhancing the accuracy of forest inventories and supporting informed forest management decisions.

show abstract

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

Cited by 6 publications

References 56 publications

Spatio-Temporal Assessment of Heterogeneity by Logging Intensity in a Federal Concession Area in the Brazilian Amazon

Spatio-Temporal Assessment of Heterogeneity by Logging Intensity in a Federal Concession Area in the Brazilian Amazon

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

A new approach to small area estimation: improving forest management unit estimates with advanced preprocessing in a multivariate Fay–Herriot model

Contact Info

Product

Resources

About