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.

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 Small Area Estimation for Crime Analysis

2020 ◽  
Author(s):  
David Buil-Gil
Keyword(s):  
Small Area ◽  
Spatial Scales ◽  
Small Area Estimation ◽  
European Social Survey ◽  
Sample Sizes ◽  
Perceived Safety ◽  
Area Estimation ◽  
Weighted Means ◽  
The Us ◽  
Victimization Surveys

Victimization surveys provide key information about crimes known and unknown to the police, and are the main source of data to analyze perceived safety and trust in the police. These surveys, however, are only designed to allow the aggregation of responses and production of reliable direct estimates (i.e., weighted means or totals) at very large spatial scales, such as countries or states. Sample sizes are generally too small to produce direct estimates of adequate precision at the increasingly refined spatial scales of the criminology of place. Model-based small area estimation may be used to increase the reliability of small area estimates produced from victimization surveys. Small area estimation techniques are designed to produce reliable estimates of parameters of interest (and their associated measures of error) for areas for which only small or zero sample sizes are available. In 2008, the US Panel to Review the Programs of the Bureau of Justice Statistics recommended the use of small area estimation to produce subnational estimates of crime. Since then, these techniques have been applied to study many variables of interest in criminology. This chapter introduces theory and a step-by-step exemplar study in R to show the utility of small area estimation to analyze crime and place. Small area estimates of trust in the police are produced from European Social Survey data.

scholarly journals Uncertainty in photo-interpreted forest inventory variables and effects on estimates of error in Canada’s National Forest Inventory

2012 ◽  
Vol 88 (04) ◽  
pp. 439-447 ◽  
Author(s):  
Steen Magnussen ◽  
Glenda Russo
Keyword(s):  
Forest Inventory ◽  
Measurement Errors ◽  
Age Determination ◽  
National Forest Inventory ◽  
National Forest ◽  
Sampling Errors ◽  
Source Data ◽  
Maturity Class ◽  
Resource Data ◽  
Crown Closure

Canada’s National Forest Inventory (NFI) relies on photo-interpreted forest resource data provided by provincial and territorial agencies. NFI data are collected at regular intervals in time from a nominal 20 × 20 km network of 2 × 2 km photoplots. Attribute-specific NFI estimates of precision include contributions from sampling errors and uncertainty in the source data. We assessed this uncertainty in NFI photo-interpreted forest attribute data from New Brunswick and Nova Scotia. Attributes examined were: cover type, age, maturity (class), crown closure, height, volume, and area associated with an attribute. Monte-Carlo simulations, with measurement errors superimposed on NFI data assumed to be error-free, showed that estimates of precision were inflated by an average of 7% (range 0%–36%) due to the uncertainty in the source data. Species misclassification and age determination were the largest sources of uncertainty.

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.

National forest inventories in the service of small area estimation of stem volume

2014 ◽  
Vol 44 (9) ◽  
pp. 1079-1090 ◽  
Author(s):  
Steen Magnussen ◽  
Daniel Mandallaz ◽  
Johannes Breidenbach ◽  
Adrian Lanz ◽  
Christian Ginzler
Keyword(s):  
Model Selection ◽  
Small Area ◽  
Small Area Estimation ◽  
Model Averaging ◽  
Stem Volume ◽  
National Forest ◽  
Area Estimation ◽  
Forest Inventories ◽  
Area Effects ◽  
National Forest Inventories

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.

Updating plots to improve the precision of small-area estimates: the example of the Lorraine region, France

2020 ◽  
Vol 50 (7) ◽  
pp. 648-658
Author(s):  
Mathieu Fortin
Keyword(s):  
Forest Inventory ◽  
Forest Dynamics ◽  
Small Area ◽  
Large Data ◽  
National Forest Inventory ◽  
National Forest ◽  
Dynamics Model ◽  
Data Set ◽  
Dynamics Models

The sampling intensity of a national forest inventory is usually low. Forest dynamics models can be used to update plots from past inventory campaigns to enhance the precision of the estimate on smaller areas. By doing this, however, the inference relies not only on the sampling design, but also on the model. In this study, the contribution of model predictions to the variance of enhanced small-area estimates was assessed through a case study. The French national forest inventory provided different annual campaigns for a particular region and department of France. Three past campaigns were updated using a forest dynamics model, and estimates of the standing volumes were obtained through two methods: a modified multiple imputation and the Bayesian method. The update greatly increased the precision of the estimate, and the gain was similar between the two methods. The sampling-related variance represented the largest share of the total variance in all cases. This study suggests that plot updating provides more precise estimates as long as (i) the forest dynamics model exhibits no systematic lack of fit and was fitted to a large data set and (ii) the sampling-related variance clearly outweighs the model-related variance.

scholarly journals Precision of exogenous post-stratification in small-area estimation based on a continuous national forest inventory

2020 ◽  
Vol 50 (4) ◽  
pp. 359-370
Author(s):  
Helena Haakana ◽  
Juha Heikkinen ◽  
Matti Katila ◽  
Annika Kangas
Keyword(s):  
Small Area ◽  
Variance Estimation ◽  
Spatial Scales ◽  
Small Area Estimation ◽  
Forest Resources ◽  
National Forest ◽  
Area Estimation ◽  
Model Based ◽  
Relative Standard ◽  
Post Stratification

National forest inventories (NFIs) are designed to provide accurate information on forest resources at the national and regional levels, but there is also a demand for such information at smaller spatial scales. Auxiliary data such as satellite imagery have been used to facilitate small-area estimation. The commonly used method, k-nearest neighbour (k-NN), provides a model-based estimator for small areas, but a design-unbiased estimator for the mean square error is not available. Post-stratification (PS) is an alternative approach to using auxiliary information that allows for design-based variance estimation. In a case study using real inventory data of the Finnish NFI, we applied this method to the municipality level to explore the lower limit to the area for which the key forest parameters, forest area and growing stock volumes, can be estimated with sufficient precision. For PS, we employed exogenous forest resources maps based on the previous NFI round. In the municipalities of the two study provinces, the relative standard errors of total volume estimates ranged from 2.3% to 26.9%. They were smaller than 10% for municipalities with an area of 390 km2 or larger, corresponding to approximately 100 or more sample plots on forestland. We also demonstrated the usefulness of design-unbiased variance estimation in showing discrepancies between design-based PS and model-based k-NN estimates.

Past and present individual-tree damage assessments of the US national forest inventory

2021 ◽  
Vol 193 (3) ◽  
Author(s):  
KaDonna C. Randolph ◽  
Kerry Dooley ◽  
John D. Shaw ◽  
Randall S. Morin ◽  
Christopher Asaro ◽  
...  
Keyword(s):  
Forest Inventory ◽  
National Forest Inventory ◽  
National Forest ◽  
Individual Tree ◽  
Tree Damage ◽  
The Us
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