scholarly journals United States Forest Service Use of Forest Inventory Data: Examples and Needs for Small Area Estimation

2021 ◽  
Vol 4 ◽  
Author(s):  
Sarah S. Wiener ◽  
Renate Bush ◽  
Amy Nathanson ◽  
Kristen Pelz ◽  
Marin Palmer ◽  
...  
Keyword(s):  
United States ◽  
Forest Inventory ◽  
Small Area ◽  
Service Use ◽  
Small Area Estimation ◽  
Simulation Models ◽  
The United States ◽  
Northern Region ◽  
Data Set ◽  
Area Estimation

Forest Inventory and Analysis (FIA) data provides robust information for the United States Forest Service’s (USFS) mid-to-broad-scale planning and assessments, but ecological challenges (i.e., climate change, wildfire) necessitate increasingly strategic information without significantly increasing field sampling. Small area estimation (SAE) techniques could provide more precision supported by a rapidly growing suite of landscape-scale datasets. We present three Regional case studies demonstrating current FIA uses, how SAE techniques could enhance existing uses, and steps FIA could take to enable SAE applications that are user-friendly, comprehensive, and statistically appropriate. The Northern Region uses FIA data for planning and assessments, but SAE techniques could provide more specificity to guide vegetation management activities. State and transition simulation models (STSM) are run with FIA data in the Southwestern Region to predict effects of treatments and disturbances, but SAE could support model validation and more precision to identify treatable areas. The Southern Region used FIA to identify existing longleaf pine stands and evaluate condition, but SAE techniques within FIA tools would streamline analyses. Each case study demonstrates a desire to have FIA data on non-forested conditions and non-tree variables. Additional tools to measure statistical confidence would help maximize utility. FIA’s SAE techniques could add value to a widely used data set, if FIA can support key supplements to basic data and functionality.

scholarly journals Multilevel Small-Area Estimation of Colorectal Cancer Screening in the United States

2018 ◽  
Vol 27 (3) ◽  
pp. 245-253 ◽  
Author(s):  
Zahava Berkowitz ◽  
Xingyou Zhang ◽  
Thomas B. Richards ◽  
Marion Nadel ◽  
Lucy A. Peipins ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
United States ◽  
Cancer Screening ◽  
Colorectal Cancer Screening ◽  
Small Area ◽  
Small Area Estimation ◽  
The United States ◽  
Area Estimation

scholarly journals Multilevel Regression for Small-Area Estimation of Mammography Use in the United States, 2014

2018 ◽  
Vol 28 (1) ◽  
pp. 32-40 ◽  
Author(s):  
Zahava Berkowitz ◽  
Xingyou Zhang ◽  
Thomas B. Richards ◽  
Susan A. Sabatino ◽  
Lucy A. Peipins ◽  
...  
Keyword(s):  
United States ◽  
Small Area ◽  
Small Area Estimation ◽  
The United States ◽  
Multilevel Regression ◽  
Area Estimation ◽  
Mammography Use

Health-related quality of life among adults 65 years and older in the United States, 2011–2012: a multilevel small area estimation approach

2017 ◽  
Vol 27 (1) ◽  
pp. 52-58 ◽  
Author(s):  
Yu-Hsiu Lin ◽  
Alexander C. McLain ◽  
Janice C. Probst ◽  
Kevin J. Bennett ◽  
Zaina P. Qureshi ◽  
...  
Keyword(s):  
Quality Of Life ◽  
United States ◽  
Small Area ◽  
Small Area Estimation ◽  
The United States ◽  
Health Related Quality ◽  
Area Estimation ◽  
Related Quality ◽  
Health Related

scholarly journals A Systematic Review of Small Domain Estimation Research in Forestry During the Twenty-First Century From Outside the United States

2021 ◽  
Vol 4 ◽  
Author(s):  
Richard W. Guldin
Keyword(s):  
United States ◽  
Small Area ◽  
Small Area Estimation ◽  
National Level ◽  
The United States ◽  
Area Estimation ◽  
National Statistics ◽  
Forest Inventories ◽  
Small Domain ◽  
Forest Managers

Small domain estimation (SDE) research outside of the United States has been centered in Canada and Europe—both in transnational organizations, such as the European Union, and in the national statistics offices of individual countries. Support for SDE research is driven by government policy-makers responsible for core national statistics across domains. Examples include demographic information about provision of health care or education (a social domain) or business data for a manufacturing sector (economic domain). Small area estimation (SAE) research on forest statistics has typically studied a subset of core environmental statistics for a limited geographic domain. The statistical design and sampling intensity of national forest inventories (NFIs) provide population estimates of acceptable precision at the national level and sometimes for broad sub-national regions. But forest managers responsible for smaller areas—states/provinces, districts, counties—are facing changing market conditions, such as emerging forest carbon markets, and budgetary pressures that limit local forest inventories. They need better estimates of conditions and trends for small sub-sets of a national-scale domain than can be provided at acceptable levels of precision from NFIs. Small area estimation research is how forest biometricians at the science-policy interface build bridges to inform decisions by forest managers, landowners, and investors.

scholarly journals Needs for Small Area Estimation: Perspectives From the US Private Forest Sector

2021 ◽  
Vol 4 ◽  
Author(s):  
Steve Prisley ◽  
Jeff Bradley ◽  
Mike Clutter ◽  
Suzy Friedman ◽  
Dick Kempka ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Small Area ◽  
Small Area Estimation ◽  
Forest Products ◽  
National Forest Inventory ◽  
National Forest ◽  
Forest Sector ◽  
Area Estimation ◽  
The Us ◽  
Resource Data

The commercial forest sector in the US includes forest landowners and forest products manufacturers, as well as numerous service providers along the supply chain. Landowners (and contractors working for them) manage forestland in part for roundwood production, and manufacturers purchase roundwood as raw material for forest products including building products, paper products, wood pellets, and others. Both types of organizations need forest resource data for applications such as strategic planning, support for certification of sustainable forestry, analysis of timber supply, and assessment of forest carbon, biodiversity, or other ecosystem services. The geographic areas of interest vary widely but typically focus upon ownership blocks or manufacturing facilities and are frequently small enough that estimates from national forest inventory data have insufficient precision. Small area estimation (SAE) has proven potential to combine field data from the national forest inventory with abundant sources of remotely sensed or other resource data to provide needed information with improved precision. Successful implementation of SAE by this sector will require cooperation and collaboration among federal and state government agencies and academic institutions and will require increased funding to improve data collection, data accessibility, and further develop and implement the needed technologies.

Small Area Estimation of Mental Distress Among United States Military Veterans in Illinois

2019 ◽  
Vol 56 (2) ◽  
pp. 298-302
Author(s):  
Justin T. McDaniel ◽  
Minjee Lee ◽  
David L. Albright ◽  
Hee Y. Lee ◽  
Jay Maddock
Keyword(s):  
United States ◽  
Small Area ◽  
Mental Distress ◽  
Small Area Estimation ◽  
Military Veterans ◽  
United States Military ◽  
Area Estimation

scholarly journals Auxiliary information resolution effects on small area estimation in plantation forest inventory

2020 ◽  
Vol 93 (5) ◽  
pp. 685-693
Author(s):  
P Corey Green ◽  
Harold E Burkhart ◽  
John W Coulston ◽  
Philip J Radtke ◽  
Valerie A Thomas
Keyword(s):  
Loblolly Pine ◽  
Forest Inventory ◽  
Small Area ◽  
Point Cloud ◽  
Small Area Estimation ◽  
Auxiliary Information ◽  
Plantation Forest ◽  
Area Estimation ◽  
Improve Estimate ◽  
Elevation Model

Abstract In forest inventory, traditional ground-based resource assessments are often expensive and time-consuming forcing managers to reduce sample sizes to meet budgetary and logistical constraints. Small area estimation (SAE) is a class of statistical estimators that uses a combination of traditional survey data and linearly related auxiliary information to improve estimate precision. These techniques have been shown to improve the precision of stand-level inventory estimates in loblolly pine plantations using lidar height percentiles and thinning status as covariates. In this study, the effects of reduced lidar point-cloud densities and lower digital elevation model (DEM) spatial resolutions were investigated for total planted volume estimates using area-level SAE models. In the managed Piedmont pine plantation conditions evaluated, lower lidar point-cloud densities and DEM spatial resolutions were found to have minimal effects on estimates and precision. The results of this study are promising to those interested in incorporating SAE methods into forest inventory programs.

scholarly journals 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

2021 ◽  
Vol 4 ◽  
Author(s):  
Vance Harris ◽  
Jesse Caputo ◽  
Andrew Finley ◽  
Brett J. Butler ◽  
Forrest Bowlick ◽  
...  
Keyword(s):  
United States ◽  
Land Cover ◽  
Small Area ◽  
Small Area Estimation ◽  
Fine Scale ◽  
Ancillary Data ◽  
United States Census Bureau ◽  
Area Estimation ◽  
Model Based ◽  
National Woodland Owner Survey

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.

Sign in / Sign up

Export Citation Format

Share Document