Global Navigational Satellite System (GNSS) technologies are actively being developed to address the demand for enhanced positional accuracy. Smartphones are the most prevalent GNSS receiver today and have garnered attention thanks to improved positional accuracy and usability that can be accessed at an affordable price. In a forested environment, multipath error can deteriorate the positional accuracy, depending on the state of nearby vegetation. Therefore, this study was conducted to investigate the impacts of the size and location of vegetation on positional accuracy of GNSS receivers to determine whether the errors observed are systematic. Twenty-six control points within the Whitehall Forest GPS Test site in Athens, Georgia were used to evaluate positional accuracy of three different GNSS receivers (two traditional handheld GNSS receivers (including Garmin and Trimble receivers) and a smartphone). Thirty-five forest variables were developed from information around each control point to conduct a correlation analysis with observed horizontal position error in the positions determined by each device. In this study, we confirmed that the positional error of the smartphone was significantly lower than the Garmin receiver, and similar, but significantly different than the positional error observed by the Trimble receiver. It was confirmed that correlations between forest variables and horizontal position error regardless of the GNSS receiver employed were significant, yet trends were not consistent. The effect of the size of nearby trees on horizontal position error could not be generalized; however, the location of nearby trees on horizontal position error could.

Due to developments in global navigation satellite systems (GNSS) and the miniaturization of their components, the usage of Global Positioning System (GPS) is no longer restricted to professional applications, but has become available in various consumer type devices, such as wristwatches. These commercial devices, however, were primarily designed for tracking activities in predominately urban settings and their accuracy has not been tested in forested areas. In this study, we present an assessment of the positional accuracy of a GPS watch (Ambit Peak 3, Suunto, Finland) under different forest cover types, seasons and meteorological conditions within the Whitehall Forest GPS Test Site located in Athens, Georgia, USA. As a standard of comparison, the performance of the GPS watch measurements was juxtaposed to that of a mapping-grade receiver (Juno T41, Trimble Inc., USA). In this study, we analyzed the differences between the determined and control positions using root-mean-square-error (RMSE), along with the distribution of observed positions through the standard deviational ellipse. The results suggest that the seasonal variations contributed to a statistically significant impact on the RMSE values for the GPS watch. However, there were no statistically significant differences in horizontal position accuracy by forest cover-type when using the GPS watch. Furthermore, no significant differences were found in horizontal position accuracy during the leaf-off period between the RMSE values for the GPS watch and those of the mapping-grade receiver. Lastly, the positional accuracies for both types of receivers were found to be weakly, but significantly correlated with fluctuations in air temperature and absolute humidity.

Accurately assessing forest structure and maintaining up-to-date information about forest structure is crucial for various forest planning efforts, including the development of reliable forest plans and assessments of the sustainable management of natural resources. Field measurements traditionally applied to acquire forest inventory information (e.g., basal area, tree volume, and aboveground biomass) are labor intensive and time consuming. To address this limitation, remote sensing technology has been widely applied in modeling efforts to help estimate forest inventory information. Among various remotely sensed data, LiDAR can potentially help describe forest structure. This study was conducted to estimate and map forest inventory information across the Shoal Creek and Talladega Ranger Districts of the Talladega National Forest by employing ALS-derived data and aerial photography. The quality of the predictive models was evaluated to determine whether additional remotely sensed data can help improve forest structure estimates. Additionally, the quality of general predictive models was compared to that of species group models. This study confirms that quality level 2 LiDAR data were sufficient for developing adequate predictive models (R²_adj. ranging between 0.71 and 0.82), when compared to the predictive models based on LiDAR and aerial imagery. Additionally, this study suggests that species group predictive models were of higher quality than general predictive models. Lastly, landscape level maps were created from the predictive models and these may be helpful to planners, forest managers, and landowners in their management efforts.

Forest management plans often suggest that economic, ecological, and/or social sustainability will be achieved if the proposed management actions are followed. Using forest plans developed by the US Department of Agriculture Forest Service and the Republic of Turkey, the purposes of this study were to (i) extract those statements that suggest sustainability will be achieved and (ii) assess whether there exist statistical differences between the two countries’ plans and the emphases of the findings. A content analysis and non-parametric statistical tests were employed to measure the frequency of a set of terms related to sustainability and to estimate significant differences in the use of sustainability terms in the plans sampled. Results suggest that ecological aspects are dominant in forest plans from both countries. While silviculture, sustained yield, and multiple use were the most frequently used terms in Turkish plans, the occurrences of conservation and recreation were significantly higher in US plans (p < 0.001). These findings suggest that the differences in the plans’ emphases could be attributable to the importance of Turkey’s forests for the wood production-related needs of Turkish society, whereas US national forests might no longer be seen as an important wood supply base but instead have been given a more “passive” forest use.

Accurately assessing forest structure and maintaining up-to-date information about forest structure is crucial for various forest planning efforts, including the development of reliable forest plans and assessments of the sustainable management of natural resources. Field measurements traditionally applied to acquire forest inventory information (e.g., basal area, tree volume, and aboveground biomass) are labor-intensive and time-consuming. To address this limitation, remote sensing tech-nology has been widely applied in modeling efforts to help estimate forest inventory information. Among various remotely sensed data, LiDAR can potentially help describe forest structure. This study was conducted to estimate and map forest inventory information across the Talladega Na-tional Forest by employing ALS-derived data and aerial photography. The quality of predictive models was evaluated to determine whether additional remotely sensed data can help improve forest structure estimates. Additionally, the quality of general predictive models was compared to that of species-group models. This study confirms that quality level 2 LiDAR data was sufficient for developing adequate predictive models (R2adj. ranging between 0.71 and 0.82) when compared to the predictive models based on LiDAR and aerial imagery. Additionally, this study suggests that species-group predictive models were of higher quality than general predictive models. Lastly, landscape-level maps were created from the predictive models, and these may be helpful to plan-ners, forest managers, and landowners in their management efforts.

Surveys of forestry professionals who actively manage, or advise upon the management of, forest lands were conducted to determine their opinions of the usefulness of a forest management decision support model. The surveys were aimed at evaluating attitudes and concerns about the eYield model, which was developed to assist in the examination of management options for eastern United States forests. The coronavirus issue that began in 2020 necessitated a virtual workshop environment to illustrate the potential usefulness of the eYield model. Pre- and post-workshop assessment surveys suggested that there was an interest by land managers in tools like eYield that are straightforward to use. The results suggested that the instructions associated with eYield were generally clearly presented, and the outcomes produced by eYield were generally representative of real-world conditions. The surveys also indicated that people represented by the sample frame were willing to consider new technology that may be used to address complex forest land management issues. Improvements suggested by survey participants may result in greater user interaction with Internet-based decision support systems that focus on the management of land.