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.

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.

Six non-thinned loblolly pine (Pinus taeda L.) culture × density study sites in the Piedmont and Upper Coastal Plain of the Southeast U.S. were used to examine the effects of two cultural intensities and three planting densities on solid wood potential as well as the proportion and position of product-defining defects (forks, crooks, broken tops). A tree quality index (TQI) was used to grade stems for solid wood potential. The results show that an operational management regime exhibited a higher proportion of trees with solid wood product potential than did a very intensive management regime. Trees subject to operational management exhibited product-defining defects higher on the stem; however, the proportion of stems with defects was not significantly different from the intensive management. Planting densities of 741, 1482, and 2223 trees per hectare (TPH) exhibited a relatively narrow range of the proportion of trees with solid wood product potential that were not significantly different. Density did not have a significant effect on the heights of the product-defining defects. These results show that management intensity and less so planting density, affect the solid wood product potential indicators evaluated and should be considered when making management decisions.

Red spruce (Picea rubens Sarg.) is a commercially and ecologically important conifer species that primarily exists at northern latitudes of eastern North America and extends southward following the Appalachian Mountains into North Carolina and Tennessee. Due to a warming climate and human-caused disturbances, only fragmented, sky-island populations remain at the highest peaks of the southern Appalachians where their habitat continues to be threatened. While they have been recognized for the rare wildlife habitat they provide in the region, these populations remain understudied. This work aimed to examine differences in stem form between the northern and southern populations of red spruce and to provide additional quantitative methods for managing red spruce stands through providing regionally fitted stem taper models. First, we examined differences in stem form using two methods: a sectional rate of change in diameter and a region variable added to the Kozak (2004) Model 02 taper model. The sectional taper comparison showed significant differences (P < .05) in taper rate throughout the stem that were most pronounced below breast height and above the midpoint. The nested model comparison also showed a significant difference after performing a likelihood ratio test. These results agreed that significant differences in stem form between the two populations exist and supported the idea that localized taper models would provide the best results. Next, we evaluated four stem taper models for their ability to predict upper stem diameters and total volume in southern Appalachian red spruce: a quadratic polynomial, a segmented, a variable exponent, and a geometric model. Fitting data came from a 1992 study across three southern Appalachian spruce-fir sites. Validation data came from a new dataset collected in 2022 at Unaka Mountain, Tennessee. Based on past studies and our results, we found that the Kozak (My last words on taper equations. For Chron 2004;80:507–15. https://doi.org/10.5558/tfc80507-4.) Model 02 variable exponent and the Max and Burkhart (Segmented polynomial regression applied to taper equations. For Sci 1976;22:283–9.) segmented polynomial models performed equally well. The choice of the final model should depend on the users’ objectives and practical limitations (i.e. programming ability, availability of fitting data, error tolerance).

Eucalyptus ( Eucalyptus camaldulensis Dehnh.) is an important exotic species in northern Nigeria commonly used for poles and timber. Sustainable management of this resource would require quantifying its volume. Stem taper equations are one of the main and most efficient methods for estimating stem volume to any merchantable limit of a species. There is currently no taper equation for Eucalyptus species in Nigeria. Therefore, this study developed taper equations for E. camaldulensis in northern Nigeria. Data for this study were obtained from a private plantation in Jalingo Local Government Area, Taraba State, Nigeria. 68 trees were felled and sectioned into 1-m bolt across the stem to a merchantable limit of 5 cm, which were used as the fitting dataset. An additional 22 trees were felled and used to validate the taper equations for stem volume estimation. Seven taper equations were initially fitted to the dataset using nonlinear least squares. The best taper equation was then refitted using a nonlinear mixed-effects approach and calibrated using diameters of one to five sections from the butt end. The taper equations were numerically integrated to obtain the stem volume, which was compared with empirical volume equations. The result shows that the Kozak (Can J For Res 27(5): 619–629. 10.1139/x97-011, 1997) equation, which included eight parameters, provided the best fit for predicting section diameters for under and over bark. The mixed-effects taper equation (NLME-TE) explained most stem diameter variations in the fitting dataset (pseudo- R ² : 0.986–0.987; RMSE: 0.547–0.578 cm) without substantial residual trends. The validation showed that the prediction accuracy of the integrated NLME-TE improved as the number of sectional diameter measurements increased, with at least a 35% reduction in volume estimate error. For practical implementation, two calibration sectional diameter measurements taken from the butt end per tree are recommended. This approach would reduce measurement effort and cost while improving model performance.

Clustering data into similar characteristic groups is a commonly-used strategy in model development. However, the impact of data grouping strategies on modeling stem taper has not been well quantified. The objective of this study was to compare the prediction accuracy of different data grouping strategies. Specifically, a population-level model was compared to the models fitted with grouped data based on taxonomic rank, tree form and size. A total of 3678 trees were used in the analyses, which included six common species in upland hardwood forests of the southeastern U.S. Results showed that overall predictions are more accurate when building stem taper models at the species, species group or division level rather than at the population level. The prediction accuracy was not considerably improved between species-specific functions and models fitted with species-related groups for the four hardwood species examined. Grouping data by taxonomic rank provided more reliable predictions than height-to-diameter ratio (H–D ratio) or diameter at breast height (DBH). The form/size-related grouping methods (i.e., data grouped by H–D ratio or DBH) generally did not improve the prediction precision compared to a population-level model. In this study, the effect of sample size in model fitting showed a minimal impact on prediction accuracy. The methodology presented in this study provides a modeling strategy for mixed-species data, which will be of practical importance when data grouping is needed for developing stem taper models.