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

IF 1.5 3区 环境科学与生态学 Q4 ENVIRONMENTAL SCIENCES
Environmetrics Pub Date : 2024-10-22 DOI:10.1002/env.2883
Svetlana Saarela, Sean P. Healey, Zhiqiang Yang, Bjørn-Eirik Roald, Paul L. Patterson, Terje Gobakken, Erik Næsset, Zhengyang Hou, Ronald E. McRoberts, Göran Ståhl
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引用次数: 0

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.

Abstract Image

基于层次模型推断森林地上生物量的可分离自举方差估计算法(基于NASA GEDI和Landsat任务数据
基于层次模型(HMB)的统计方法目前应用于美国宇航局的全球生态系统动力学调查(GEDI)任务,用于评估缺乏足够数量的GEDI足迹的地区的森林地上生物量(AGB)。本研究的重点是方差估计,使用一个自举过程,将计算分成几个部分,从而大大减少了所需的计算时间,并使自举在这种情况下成为一个可行的选择。我们提出的方法是将HMB方差的理论分解为两部分。通过这种分解,可以对各个方差分量进行单独和同时的估计。为了证明所提出的过程,我们在HMB的第一步建模中应用了平方根变换的普通最小二乘(OLS)模型和参数自举。在第二步中,我们应用了随机森林模型和两两自举。蒙特卡罗模拟表明,所提出的方差估计器是近似无偏的。该研究是在模拟美国俄勒冈州森林条件的人工交配种群上进行的,使用的数据集包括AGB、GEDI和Landsat变量。
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来源期刊
Environmetrics
Environmetrics 环境科学-环境科学
CiteScore
2.90
自引率
17.60%
发文量
67
审稿时长
18-36 weeks
期刊介绍: Environmetrics, the official journal of The International Environmetrics Society (TIES), an Association of the International Statistical Institute, is devoted to the dissemination of high-quality quantitative research in the environmental sciences. The journal welcomes pertinent and innovative submissions from quantitative disciplines developing new statistical and mathematical techniques, methods, and theories that solve modern environmental problems. Articles must proffer substantive, new statistical or mathematical advances to answer important scientific questions in the environmental sciences, or must develop novel or enhanced statistical methodology with clear applications to environmental science. New methods should be illustrated with recent environmental data.
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