The United States (US) Forest Service Forest Inventory and Analysis (FIA) program operates the national forest inventory of the US. Traditionally, the FIA program has relied on sample-based approaches -- permanent plot networks and associated design-based estimators -- to estimate forest variables across large geographic areas and long periods of time. These approaches generally offer unbiased inference on large domains but fail to provide reliable estimates for small domains due to low sample sizes. Rising demand for small domain estimates will thus require the FIA program to adopt non-traditional estimation approaches that are capable of delivering defensible estimates of forest variables at increased spatial and temporal resolution, without the expense of collecting additional field data. In light of this challenge, the development of small area estimation (SAE) methods for FIA data has become an active and highly productive area of research. Yet, SAE methods remain difficult to apply to FIA data, due in part to the complex data structures and inventory design used by the FIA program. Thus, we argue that a new suite of estimation tools (i.e., software) will be required to accommodate shifts in demand for inference on large geographic areas and long time periods to inference on small spatial and/or temporal domains. Herein, we present rFIA, an open-source R package designed to increase the accessibility of FIA data, as one such tool. Specifically, we present two case studies chosen to demonstrate rFIA's potential to simplify the application of a broad suite of SAE methods to FIA data: (1) estimation of contemporary county-level forest carbon stocks across the conterminous US using a spatial Fay-Herriot model; and (2) temporally-explicit estimation of multi-decadal trends in merchantable wood volume in Washington County, Maine using a Bayesian mixed-effects model.
翻译:美国(美国)森林服务森林清单和分析(FIA)方案管理美国的国家森林清单。传统上,FIA方案依靠基于样本的方法 -- -- 永久性地块网络和相关的设计估算器 -- -- 来估计大地理区域和长时期的森林变量。这些方法一般对大域提供了公正的推论,但由于样本规模小,无法对小域进行可靠的估计。因此,对小域估计的需求增加,要求FIA方案采用非传统的估计方法,这些方法能够以更高的空间和时间分辨率提供对森林变量的可靠估计,而无需花费收集更多实地数据的费用。鉴于这一挑战,为FIA数据开发小面积估计(SAE)方法已成为一个活跃和高度有效的研究领域。 然而,SAE方法仍然难以适用于FIA数据,部分原因是由于FIA程序使用复杂的数据结构和库存设计。因此,模型需要采用新的可选择的估算工具(即:软件)来应对大规模地理区域和长期地段地区对森林变量的需求的推断变化,并使用我们所设计的实时数据流数据流流数据,在目前一个案例和长时间段上,将AFIAFIA数据流数据流数据流数据流流数据流流中,用于一个空间数据流数据流数据流数据流数据流数据流的模型的模型的模型。