Comparison of small area estimation methods applied to biopower feedstock supply in the Northern U.S. region

Goerndt, Michael E.; Wilson, Barry T.; Aguilar, Francisco X.

doi:10.1016/j.biombioe.2018.12.008

Cited by 16 publications

(11 citation statements)

References 31 publications

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“…Despite finding modest gains when n d was large, area-level EBLUPS were demonstrably superior-in terms of RE and lack of apparent biases-to two synthetic estimators and two composite James-Stein type estimators tested (James and Stein, 1961;Goerndt et al, 2011). In testing area-level SAE with counties as small-area domains, a composite estimator similar to F-H showed 0.43 ≤ RE ≤ 0.91 over a 20-state region of the northeastern U.S. (Goerndt et al, 2019). In the same study a composite estimator based on a non-parametric nearestneighbor (NN) synthetic model showed slightly less gain in efficiency than the F-H type approach.…”

Section: Area-level Saementioning

confidence: 90%

“…For example, the Norwegian NFI has employed national canopy height maps from aerial remote sensing as auxiliary data sources since about 2010 to address needs for better local information in producing municipal forest statistics and forest-management related inventories (Astrup et al, 2019;Breidenbach et al, 2020). SAE has been used with forest inventory data from the U.S. Department of Agriculture Forest Service Forest Inventory and Analysis (FIA) program to generate estimates of forest attributes in small areas such as biofuel supply areas around co-firing power plants (Goerndt et al, 2019). In forests where field plots can be precisely referenced to high-quality geospatial auxiliary data (e.g., ALS), SAE can provide increased precision of estimates for arbitrarily small spatial areas-accounting for spatial correlations in sample data when warranted-even where no direct sample data lie within some DOI (Babcock et al, 2018;Pascual et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Review and Synthesis of Estimation Strategies to Meet Small Area Needs in Forest Inventory

Dettmann¹,

Radtke²,

Coulston³

et al. 2022

Front. For. Glob. Change

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Small area estimation is a growing area of research for making inferences over geographic, demographic, or temporal domains smaller than those in which a particular survey data set was originally intended to be used. We aimed to review a body of literature to summarize the breadth and depth of small area estimation and related estimation strategies in forest inventory and management to-date, as well as the current state of terminology, methods, concerns, data sources, research findings, challenges, and opportunities for future work relevant to forestry and forest inventory research. Estimation methodologies explored include direct, indirect, and composite estimation within design-based and model-based inference bases. A variety of estimation methods in forestry have been applied to extensive multi-resource inventory systems like national forest inventories to increase the precision of estimates on small domains or subsets of the overall populations of interest. To avoid instability and large variances associated with small sample sizes when working with small area domains, forest inventory data are often supplemented with information from auxiliary sources, especially from remote sensing platforms and other geospatial, map-based products. Results from many studies show gains in precision compared to direct estimates based only on field inventory data. Gains in precision have been demonstrated in both project-level applications and national forest inventory systems. Potential gains are possible over varying geographic and temporal scales, with the degree of success in reducing variance also dependent on the types of auxiliary information, scale, strength of model relationships, and methodological alternatives, leaving considerable opportunity for future research and growth in small area applications for forest inventory.

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Section: Area-level Saementioning

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Review and Synthesis of Estimation Strategies to Meet Small Area Needs in Forest Inventory

Dettmann¹,

Radtke²,

Coulston³

et al. 2022

Front. For. Glob. Change

Self Cite

View full text Add to dashboard Cite

show abstract

“…Mauro et al [54] compared unit and area-level EBLUPs for constructing small area estimates of stand density, volume, basal area, quadratic mean diameter, and height in a western coastal area. Goerndt et al [55] applied composite estimators to estimate values of FIA attributes relevant to bioenergy production over parts of a 20-state region in the northern US. These are just a few examples, and the field is growing rapidly.…”

Section: Model-based Inferencementioning

confidence: 99%

Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory

Lister

Andersen

Frescino

et al. 2020

Forests

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Globally, forests are a crucial natural resource, and their sound management is critical for human and ecosystem health and well-being. Efforts to manage forests depend upon reliable data on the status of and trends in forest resources. When these data come from well-designed natural resource monitoring (NRM) systems, decision makers can make science-informed decisions. National forest inventories (NFIs) are a cornerstone of NRM systems, but require capacity and skills to implement. Efficiencies can be gained by incorporating auxiliary information derived from remote sensing (RS) into ground-based forest inventories. However, it can be difficult for countries embarking on NFI development to choose among the various RS integration options, and to develop a harmonized vision of how NFI and RS data can work together to meet monitoring needs. The NFI of the United States, which has been conducted by the USDA Forest Service’s (USFS) Forest Inventory and Analysis (FIA) program for nearly a century, uses RS technology extensively. Here we review the history of the use of RS in FIA, beginning with general background on NFI, FIA, and sampling statistics, followed by a description of the evolution of RS technology usage, beginning with paper aerial photography and ending with present day applications and future directions. The goal of this review is to offer FIA’s experience with NFI-RS integration as a case study for other countries wishing to improve the efficiency of their NFI programs.

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“…Small area estimation is an increasingly important tool for forest inventory analyses (Breidenbach et al, 2020). To date, however, most efforts have focused on estimation of biophysical variables (Breidenbach and Astrup, 2012;Goerndt et al, 2019;Green et al, 2020). An equal need, however, exists for precise estimates of ownership attributes within small domains, especially small (i.e., sub-state) spatial domains.…”

Section: Introductionmentioning

confidence: 99%

Small-Area Estimation for the USDA Forest Service, National Woodland Owner Survey: Creating a Fine-Scale Land Cover and Ownership Layer to Support County-Level Population Estimates

Harris

Caputo

Butler

et al. 2021

Front. For. Glob. Change

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Small area estimation is a powerful modeling technique in which ancillary data can be utilized to “borrow” additional information, effectively increasing sample sizes in small spatial, temporal, or categorical domains. Though more commonly applied to biophysical variables within the study of forest inventory analyses, small area estimation can also be implemented in the context of understanding social values, behaviors, and trends among types of forest landowners within small domains. Here, we demonstrate a method for deriving a continuous fine-scale land cover and ownership layer for the state of Delaware, United States, and an application of that ancillary layer to facilitate small-area estimation of several variables from the USDA Forest Service’s National Woodland Owner Survey. Utilizing a proprietary parcel layer alongside the National Land Cover Database, we constructed a continuous layer with 10-meter resolution depicting land cover and land ownership classes. We found that the National Woodland Owner Survey state-level estimations of total acreage and total ownerships by ownership class were generally within one standard error of the population values calculated from the raster layer, which supported the direct calculation of several population-level summary variables at the county levels. Subsequently, we compare design-based and model-based methods of predicting commercial harvesting by family forest ownerships in Delaware in which forest ownership acreage, taken from the parcel map, was utilized to inform the model-based approach. Results show general agreement between the two modes, indicating that a small area estimation approach can be utilized successfully in this context and shows promise for other variables, especially if additional variables, e.g., United States Census Bureau data, are also incorporated.

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Comparison of small area estimation methods applied to biopower feedstock supply in the Northern U.S. region

Cited by 16 publications

References 31 publications

Review and Synthesis of Estimation Strategies to Meet Small Area Needs in Forest Inventory

Review and Synthesis of Estimation Strategies to Meet Small Area Needs in Forest Inventory

Use of Remote Sensing Data to Improve the Efficiency of National Forest Inventories: A Case Study from the United States National Forest Inventory

Small-Area Estimation for the USDA Forest Service, National Woodland Owner Survey: Creating a Fine-Scale Land Cover and Ownership Layer to Support County-Level Population Estimates

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