An unmanned aircraft system was evaluated for its potential to capture imagery for use in plantation loblolly pine (Pinus taeda L.) regeneration surveys. Five stands located in the Virginia Piedmont were evaluated. Imagery was collected using a recreational grade unmanned aerial vehicle at three flight heights above ground with a camera capable of capturing red–green–blue imagery. Two computer vision approaches were evaluated for their potential to automatically detect seedlings. The results of the study indicated that the proposed methods were limited in capability of generating reliable counts of seedlings in the locations evaluated. In conditions with low numbers of natural seedlings and sufficiently large planted seedlings, the detection methods performed with higher levels of accuracy. Challenges including global positioning system errors and image distortion made comparisons between ground samples and imagery difficult. In summary, unmanned aircraft systems have potential for use in plantation pine regeneration surveys if the challenges encountered can be addressed. Study Implications: Following the establishment of a pine plantation, it is important to estimate survival and possible recruitment of natural conifers. As the popularity of unmanned aircraft systems (UAS) has increased, forest managers have begun to explore their use for resource assessment. This study investigated using imagery captured with a recreational grade UAS, in conjunction with automated computer vision counting techniques, for use in regeneration surveys. The results of this research indicate that significant challenges must be addressed before UAS can become an integral component of survival assessments. Aircraft constraints, legal restrictions, low image quality, and high levels of natural pine regeneration limited the success of the proposed methods. In selected cases, however, favorable conditions led to accurate detection. Additionally, UAS imagery has the potential for assessing other stand characteristics such as competing vegetation and drainage patterns. Going forward, UAS imagery and automated counting approaches have the potential to supplement, but not fully replace, ground regeneration surveys if the challenges encountered in this study can be addressed.

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.

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.

Diameter distributions are fundamental characteristics of stand structure. It is widely assumed that unthinned plantation loblolly pine (Pinus taeda L.) diameter distributions are unimodal and slightly skewed. In this work, the assumption of unimodality is formally tested and confirmed using 413 long-term permanent plots representing three generations of genetics and silviculture across the native range of loblolly pine in the southeastern United States. Approximately 96% of plot measurements had no significant evidence to reject the hypothesis of unimodality. While levels often significantly differed, similar developmental trends of skewness, kurtosis, and estimated Weibull parameters were observed despite the advances of genetics and silviculture. The results of the study indicate the continued need for a flexible distribution for characterizing diameter distributions in plantation loblolly pine. Study Implications Knowledge of diameter distribution helps inform management activities. Further, assessing monetary or ecological value requires an understanding of a stand’s diameter structure. Using three long-term research studies established across the native range of the loblolly pine, this investigation confirms the assumption of slightly skewed, unimodal distributions. Additionally, long-term trends in skewness, kurtosis, and fitted Weibull parameters across the three generations of genetics and silviculture represented in the studies are presented. The results of this work confirm the need for a flexible distribution model form and indicate that managers can expect similar trends in diameter distribution structure in both vintage and contemporary stands at least until first thinning.