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A Separable Bootstrap Variance Estimation Algorithm for Hierarchical Model‐Based Inference of Forest Aboveground Biomass Using Data From NASA's GEDI and Landsat Missions

Author

Listed:
  • Svetlana Saarela
  • Sean P. Healey
  • Zhiqiang Yang
  • Bjørn‐Eirik Roald
  • Paul L. Patterson
  • Terje Gobakken
  • Erik Næsset
  • Zhengyang Hou
  • Ronald E. McRoberts
  • Göran Ståhl

Abstract

The hierarchical model‐based (HMB) statistical method is currently applied in connection with NASA's Global Ecosystem Dynamics Investigation (GEDI) mission for assessing forest aboveground biomass (AGB) in areas lacking a sufficiently large number of GEDI footprints for employing hybrid inference. This study focuses on variance estimation using a bootstrap procedure that separates the computations into parts, thus considerably reducing the computational time required and making bootstrapping a viable option in this context. The procedure we propose uses a theoretical decomposition of the HMB variance into two parts. Through this decomposition, each variance component can be estimated separately and simultaneously. For demonstrating the proposed procedure, we applied a square‐root‐transformed ordinary least squares (OLS) model, and parametric bootstrapping, in the first modeling step of HMB. In the second step, we applied a random forest model and pairwise bootstrapping. Monte Carlo simulations showed that the proposed variance estimator is approximately unbiased. The study was performed on an artificial copula‐generated population that mimics forest conditions in Oregon, USA, using a dataset comprising AGB, GEDI, and Landsat variables.

Suggested Citation

  • Svetlana Saarela & Sean P. Healey & Zhiqiang Yang & Bjørn‐Eirik Roald & Paul L. Patterson & Terje Gobakken & Erik Næsset & Zhengyang Hou & Ronald E. McRoberts & Göran Ståhl, 2025. "A Separable Bootstrap Variance Estimation Algorithm for Hierarchical Model‐Based Inference of Forest Aboveground Biomass Using Data From NASA's GEDI and Landsat Missions," Environmetrics, John Wiley & Sons, Ltd., vol. 36(1), January.
    • Handle: RePEc:wly:envmet:v:36:y:2025:i:1:n:e2883
    DOI: 10.1002/env.2883
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    1. Jae Kwang Kim & J. Michael Brick & Wayne A. Fuller & Graham Kalton, 2006. "On the bias of the multiple‐imputation variance estimator in survey sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(3), pages 509-521, June.
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