Small Area Estimation in Diverse Timber Types Using Multiple Sources of Auxiliary Data

Abstract

Abstract Informed forest management requires reliable information. As the demand for finer scale estimates has increased, so has the cost for obtaining them from design-based ground sampling. Small area estimation (SAE) is an estimation technique that leverages ancillary information to augment design-based samples with the goal of increasing estimate precision without increasing ground-based sample intensities. This work presents three case studies spanning an industrial timberland ownership in the United States making use of SAE techniques in operational forest inventories. Case studies include an inventory of pre-thin plantation loblolly pine (Pinus taeda L.) stands that had achieved crown closure in Alabama and Mississippi, a mixed pine–hardwood inventory in Alabama, and pre-thinning plantation Douglas-fir (Pseudotsuga menziesii [Mirb.] Franco) in Washington State. Using area-level SAE techniques, vegetation indices derived from 10 m Sentinel imagery were shown to reduce estimate uncertainty for common stand parameters. Additionally, when available, lidar and age were shown to offer additional improvements in estimate precision. The results of this study indicate the operational potential for using commonly available auxiliary data for producing forest parameter estimates with enhanced precision. The implications of these findings span multiple inventory objectives including, for example, commercial forest management, carbon accounting, and wildfire fuel assessments. Study Implications: Forest management requires reliable quantitative information for informed decisions. Data from ground-based forest inventories are commonly used to construct design-unbiased direct estimates. Due to logistical and cost constraints, samples often do not provide estimates with sufficient precision for making confident decisions. The statistical estimation procedure, small area estimation, is able to leverage linearly related ancillary data across areas of interest to form composite estimates that have less uncertainty than direct estimates alone. This study shows how combining ground-based data with auxiliary data from remote sensing and stand records produced more precise estimates of forest stand parameters in three distinct timber types spanning a large ownership in the United States. Results indicate that significant inventory efficiency and confidence can be realized by incorporating commonly available auxiliary data into the estimation of forest characteristics.