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

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.

2018 ◽  
Vol 27 (3) ◽  
pp. 245-253 ◽  
Author(s):  
Zahava Berkowitz ◽  
Xingyou Zhang ◽  
Thomas B. Richards ◽  
Marion Nadel ◽  
Lucy A. Peipins ◽  
...  

2021 ◽  
Vol 4 ◽  
Author(s):  
Sarah S. Wiener ◽  
Renate Bush ◽  
Amy Nathanson ◽  
Kristen Pelz ◽  
Marin Palmer ◽  
...  

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.


2018 ◽  
Vol 28 (1) ◽  
pp. 32-40 ◽  
Author(s):  
Zahava Berkowitz ◽  
Xingyou Zhang ◽  
Thomas B. Richards ◽  
Susan A. Sabatino ◽  
Lucy A. Peipins ◽  
...  

2017 ◽  
Vol 27 (1) ◽  
pp. 52-58 ◽  
Author(s):  
Yu-Hsiu Lin ◽  
Alexander C. McLain ◽  
Janice C. Probst ◽  
Kevin J. Bennett ◽  
Zaina P. Qureshi ◽  
...  

Author(s):  
John W Coulston ◽  
P Corey Green ◽  
Philip J Radtke ◽  
Stephen P Prisley ◽  
Evan B Brooks ◽  
...  

Abstract National Forest Inventories (NFI) are designed to produce unbiased estimates of forest parameters at a variety of scales. These parameters include means and totals of current forest area and volume, as well as components of change such as means and totals of growth and harvest removals. Over the last several decades, there has been a steadily increasing demand for estimates for smaller geographic areas and/or for finer temporal resolutions. However, the current sampling intensities of many NFI and the reliance on design-based estimators often leads to inadequate precision of estimates at these scales. This research focuses on improving the precision of forest removal estimates both in terms of spatial and temporal resolution through the use of small area estimation techniques (SAE). In this application, a Landsat-derived tree cover loss product and the information from mill surveys were used as auxiliary data for area-level SAE. Results from the southeastern US suggest improvements in precision can be realized when using NFI data to make estimates at relatively fine spatial and temporal scales. Specifically, the estimated precision of removal volume estimates by species group and size class was improved when SAE methods were employed over post-stratified, design-based estimates alone. The findings of this research have broad implications for NFI analysts or users interested in providing estimates with increased precision at finer scales than those generally supported by post-stratified estimators.


PLoS ONE ◽  
2017 ◽  
Vol 12 (12) ◽  
pp. e0189401 ◽  
Author(s):  
Francisco Mauro ◽  
Vicente J. Monleon ◽  
Hailemariam Temesgen ◽  
Kevin R. Ford

2019 ◽  
Vol 56 (2) ◽  
pp. 298-302
Author(s):  
Justin T. McDaniel ◽  
Minjee Lee ◽  
David L. Albright ◽  
Hee Y. Lee ◽  
Jay Maddock

2014 ◽  
Vol 44 (9) ◽  
pp. 1079-1090 ◽  
Author(s):  
Steen Magnussen ◽  
Daniel Mandallaz ◽  
Johannes Breidenbach ◽  
Adrian Lanz ◽  
Christian Ginzler

This study introduces five facets that can improve inference in small area estimation (SAE) problems: (1) model groups, (2) test of area effects, (3) conditional EBLUPs, (4) model selection, and (5) model averaging. Two contrasting case studies with data from the Swiss and Norwegian national forest inventories demonstrate the five facets. The target variable of interest was mean stem volume per hectare on forested land in 108 Swiss forest districts (FD) and in 14 Norwegian municipalities (KOM) in the County of Vestfold. Auxiliary variables from airborne laser scanning (Switzerland) and photogrammetric point clouds (Vestfold) with full coverage and a resolution of 25 m × 25 m (Switzerland) and 16 m × 16 m (Vestfold) were available. Only the data metric mean canopy height was statistically significant. Ten linear fixed-effects models and three mixed linear models were assessed. Area effects were statistically significant in the Swiss case but not in Vestfold case. A model selection based on AIC favored separate linear regression models for each FD and a single common regression model in Vestfold. Model averaging increased, on average, an estimated variance by 15%. Reported estimates of uncertainty were consistently larger than corresponding unconditional EBLUPs.


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