Increased Precision in County-Level Volume Estimates in the United States National Forest Inventory With Area-Level Small Area Estimation

Abstract

Many National Forest Inventory (NFI) stakeholders would benefit from accurate estimates at finer geographic scales than most currently implemented in operational estimates using NFI sample data. In the past decade small area estimation techniques have been shown to increase precision in forest inventory estimates by combining field observations and remote-sensing. We sought to demonstrate the potential for improving the precision of forest inventory growing stock volume estimates for counties in United States of North Carolina, Tennessee, and Virginia, by pairing canopy height models from digital aerial photogrammetry (DAP) and field plot data from the United States NFI. Area-level Fay-Herriot estimators were used to avoid the need for precise (GPS) coordinates of field plots. Reductions in standard errors averaging 30% for North Carolina county estimates were observed, with 19% average reductions in standard errors in both Tennessee and Virginia. Accounting for spatial autocorrelation among adjacent counties provided further gains in precision when the three states were treated as a single forest land population; however, analyses conducted one state at a time showed that good results could be achieved without accounting for spatial autocorrelation. Apparent gains in sample sizes ranged from about 65% in Virginia to 128% in North Carolina, compared to the current number of inventory plots. Results should allow for determining whether acquisition of statewide DAP would be cost-effective as a means for increasing the accuracy of county-level forest volume estimates in the United States NFI.

Evaluating Statewide NAIP Photogrammetric Point Clouds for Operational Improvement of National Forest Inventory Estimates in Mixed Hardwood Forests of the Southeastern U.S.

“…The additional precision gained from using the NAIP height data added 175 plots per survey unit on average to the FIA sample, offering huge financial savings compared to the costs of installing more field plots. Although more testing is needed to determine the full benefit of using NAIP DAP point clouds in other forest types across the U.S., our results (and those of others, e.g., [27,76,77,80]) highlight the benefits, potential, and growing feasibility of using NAIP DAP point clouds to improve the precision of FIA's operational forest inventory estimates. Moving forward, future work will focus on using NAIP DHMs and tree canopy cover data with more sophisticated model-assisted approaches, such as the generalized regression estimator (GREG), which uses linear regression and calibration techniques such as raking and lasso to improve estimate precision.…”

“…At the high end, stratifying the survey unit total forest volume estimates with the NAIP height maps added 938 and 951 plots to the FIA sample in TN and VA, respectively, which in total nets nearly USD 2 million in potential cost savings from not having to measure 1889 additional field plots. As other studies have reported similar precision gains when using NAIP point clouds with area-level small area estimation approaches [80], there is growing evidence to suggest NAIP DAP is a highly cost-effective option for increasing the precision of NFI forest volume estimates. While these numbers are impressive, they omit the costs associated with processing and storing the NAIP point clouds, which are not insignificant.…”

Composite estimation to combine spatially overlapping environmental monitoring surveys

“…The estimator balances the unbiasedness of direct and the precision of synthetic estimates to provide improved estimates for the subset [19]. The forest inventory community over the past 20 years has researched SAE techniques to acquire more reliable estimates of forest attributes at smaller spatial scales (county level, national forests, foreststand level) than those provided by national forest inventories, such as the USDA Forest Inventory and Analysis (FIA) program, and by state forest inventories [17][18][19]. The nationwide sampling intensity of one FIA plot per 2,438 ha on multiple ownerships provides insufficient data at within-state scales for reliable estimates but at the same time provides a large regional sample for SAE applications.…”

“…The nationwide sampling intensity of one FIA plot per 2,438 ha on multiple ownerships provides insufficient data at within-state scales for reliable estimates but at the same time provides a large regional sample for SAE applications. Synthetic estimate applications using several compositing techniques (similar to Eqs (1-3)) have demonstrated the utility of SAE with FIA data for improving county and state estimates of forest attributes [17,18]. Other environmental monitoring efforts possibly could take advantage of these techniques either directly or as a template for customizing SAE applications, such as the National Park Service Inventory and Monitoring Vital Signs program (NPS I&M) [2].…”

“…Composite estimator applications have used spatially overlapping local and regional surveys to improve estimates of the smaller scale area [7] and at both scales [8], and even to composite temporally repeated observations of a survey to improve estimates of different time periods [9][10][11]. Composite estimators are commonly used in what is formally called small area estimation to bolster subpopulation estimates with low sample sizes and precisions by combining them with more precise synthetic (often modeled) estimates derived from a larger portion of the surveyed population [12][13][14][15][16][17][18][19] or a similar population [20]. Composite estimation with complementary data offers environmental monitoring efforts an analytical approach to improve assessments of resource conditions without costly modification of an ongoing monitoring design, such as increasing the number of sampling sites.…”