This research approach focused on 2x8 and 2x10 lumber sizes, in lengths ranging from 12- to 16t. Lumber sample data collection included: growth rings per inch (RPI), specific gravity (SG) and presence of pith. The lumber was tested in static bending per ASTM D 198. Small clear samples were then cut and used for compression testing using ASTM D 143. Lumber for this project was collected from retail lumber stores across the Southeastern United States. The larger 'host' study focused on generating current material property data from over 2000 southern pine boards to help establish more accurate and up to date lumber standards. The sampling, dubbed the 'production-weighted sampling' procedure, utilized in this study mimics the requirements of ASTM 1990. Moreover, the findings from this study are beneficial for industry and research communities alike as several smaller projects, including this one, highlight advances ranging from new lumber design values to non-destructive testing technique innovations evaluating wood. All data were analyzed using IBM SPSS version 25 software. Bivariate correlations were run in conjunction with linear regressions. Compression parallel to grain (C?) and compression perpendicular to grain (C?) were statistically tested with RPI, SG and the presence/absence of pith as independent variables. Initially, the independent variables were tested across all data for each lumber size. Then, a series of data segmentation techniques were employed among each of the independent variables: RPI, SG and pith presence. For RPI, the segments were categorized: less than 3 RPI, 3 to 5 RPI, and more than 5 RPI. Secondly, SG segments were developed in groups: less than 0.4, 0.4 to 0.5, more than 0.5. Lastly, the pith data were segmented: no pith, pith one end and pith both ends. Compression parallel and perpendicular to grain were not predictable by RPI, although the SG values by RPI segments did exhibit a stronger statistical relationship. The SG segments themselves did not reveal statistical significance; however, the pith segments yielded higher r2 values. For compression parallel to grain, r2 values were almost double than perpendicular to grain r2 values, across all pith segments and both lumber sizes.

The USDA Forest Service, Forest Health Protection, is responsible for maintaining an ORACLE database named the Southern Pine Beetle Information System (SPBIS). SPBIS was designed to store survey data for Southern Pine Beetle (SPB) infestations on federal land across the southern United States. The main purpose of this project is to improve the SPBIS database to aid in management of SPB infestations to reduce losses, to harvest compromised timber while it still has value, and to assist resource managers in preventing further infestations from becoming established. The SPBIS mobile mapping system addresses current problems with the database and offers viable solutions to each. Mobile mapping technology is a versatile tool, which is used to collect field data, with unique geospatial time tags and attributes, for integrating into or updating a GIS (Rasher 2001). The ability to efficiently collect data is essential to developing a useable database. The time required to manually enter data into the database is substantial. Ranger district personnel often do not have the time or the desire to enter data. Currently, the database is lacking years of survey and infestation data due to these negligent data entry practices, limiting the usefulness database. Currently, SPBIS data is recorded on a paper survey form. This system introduces a digital version of this form. A time study conducted to define the efficiency of each survey showed that the digital form to be more efficient with a Mann-Whitney p- value of 0.004. A comparative study was conducted to define the difference between currently estimated SPB infestation acreage and those measured using GPS. A Mann-Whitney pvalue of 0.000 shows the significant difference between the two acreage values. GPS measured acreage proved to be more accurate thus promoting the use of GPS for measuring acreage. Navigation us ing GPS coordinates was successful and will greatly decrease the time required to locate a SPB infestation on the ground. This thesis describes a mobile mapping system designed specifically to remedy the problems associated with SPBIS. This system eliminates the need for manual entry of field-collected data, while improving field data collection in terms of efficiency and accuracy.

Thesis--Virginia Polytechnic Institute and State University. / Vita. Abstract. Includes bibliographical references (leaves 49-53). Also available via the Internet.

The overall goal of this study was to develop a framework for using airborne lidar to derive inputs for the SPB infestation growth model TAMBEETLE. The specific objectives were (1) to estimate individual tree characteristics of XY location, individual bole height (IBH), diameter at breast height (DBH), length of crown (CrHT), and age for use in TAMBEETLE; (2) to estimate individual tree age using lidar-estimated height and site index provided by the United States Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) Soil Survey Geographic Database (SSURGO); and (3) to compare TAMBEETLE simulation results using field measurements and lidarderived measurements as inputs. Diameter at breast height, individual bole height, and crown length were estimated using lidar with an error for mean measurements at plot level of 0.16cm, 0.19m, and 1.07m, respectively. These errors were within root mean square error (RMSE) for other studies at the study site. Age was estimated using the site index provided by SSURGO and the site index curves created for the study area with an RMSE of 4.8 years for mean plot age. Underestimation of tree height by lidar and error in the site index curve explained 91% of the error in mean plot age. TAMBEETLE was used to compare spot growth between a lidar-derived forest map and a forest map generated by TAMBEETLE, based on sample plot characteristics. The lidar-derived forest performed comparably to the TAMBEETLE generated forest. Using lidar to map forests can provide the large spatial extents of the TAMBEETLE generated forest while maintaining the spatially explicit forest characteristics, which were previously only available through field measurements.

Southern pine (SP) lumber, the primary softwood lumber in the United States, has been popular since early colonization of the U.S.A. and remains a preferred choice for today’s lumber construction professionals. SP trees, as the major commercial softwood forest in South region, are largely grown, managed, and harvested on privately owned plantation forests. In the United States, softwood lumber consumption as a percentage of total lumber consumption has remained around 83% since the 1960s. Most softwood lumber is used for housing construction. SP comprises the primary softwood lumber products in the United States due to its superior mechanical properties, rapid drying capability, versatility, and availability. SP lumber also has long been the preferred species for pressure treatment because of its unique cellular structure that permits deep, uniform penetration of preservatives. However, as a natural material, the mechanical and physical performance of SP lumber has changed over the past decades according to the intensive lumber plantation management and dynamic market consumption. As a result, visually graded lumber products contain a large portion of juvenile wood and a lack of large cross section became two main issues in the most recent SP lumber marketing activities. According to the recent United States timber production statistic report, the South has given up its position as the largest softwood lumber-producing position in 2011. The total U.S. softwood lumber production in 2011 was about 26.8 × 109 bf, with 13.3 × 109 bf produced from West region and 12.0 × 109 bf produced from South region (Howard and Westby 2013). In this study, research that attempts to develop better and higher value SP lumber has been conducted for lumber construction marketing purpose. Five chapters are included in this study to fulfill the research purposes. Two lumber composite products made with low grade SP lumber were fabricated and evaluated for market purpose. A prototype in-grade testing plan to assess the actual design values of visually graded southern pine lumber from the gulf south region in the USA was developed. For comparison purpose, nondestructive tests (NDT) with four commercial techniques on on-grade No.2 SP lumber were conducted.

M. S.

SPBMODEL, a legacy southern pine beetle (SPB) simulation model, was translated into a new JavaTM model called Javahog. The Javahog output was verified to be essentially identical to SPBMODEL output by means of standard and paired t-tests. Javahog was placed online and is currently accessible via a servlet. Genetic algorithms (GAs) were applied to the Javahog model. GAs are a type of optimization heuristic that operate as an analog to evolution. GAs "evolve" a very good solution to a complex problem. In this case, GAs were intended to evolve a very good version of SPBMODEL. GAs were applied in part to improve upon the SPBMODEL design, and in part to demonstrate that GAs are effective tools for recalibrating legacy simulation models. Beyond simply recalibrating model parameters, the GA was used to select optimal functional forms for the development rates of each SPB life stage. The GA evolved a model that performed better than SPBMODEL at predicting observed field data, according to a balanced fitness function and according to sums of squared errors. However, from a visual comparison of the output of both models versus observed field data, neither model achieved satisfactory performance. / Master of Science

The southern pine beetle (SPB) (Dendroctonus frontalis Zimm.) is one of the most destructive forest insect pests in the southeastern United States and has historically had a large impact on the forests of North Carolina. Many characteristics of a forest can contribute to SPB susceptibility including stand density, growth rate, age, soil type, and position on the landscape. This work was undertaken in an effort to assist and improve on the current federal SPB hazard modeling being conducted for North Carolina by the USDA Forest Service – Forest Health Protection's Forest Health Technology Enterprise Team (FHTET). In our study, predictive SPB susceptibility models were developed for each physiographic region in North Carolina using two variables not currently included in the FHTET modeling, mean stand age and the in-stand percentage of sawtimber-sized pines. These variables were obtained from USDA Forest Service – Forest Inventory and Analysis (FIA) data and North Carolina Forest Service historical SPB records creating a dataset of both infested and non-infested stands and the models were developed using the CART® classification tree approach. Two model-derived age classes (older than and younger than 22 years) were identified on the landscape using current Landsat 5 Thematic Mapper (TM) imagery chronosequences of disturbance index (DI) â transformed scenes to identify stand-replacing disturbances, resulting in a kappa statistic of 0.6364 for the younger than 22 year age class and 0.7778 for the older than 22 years age class. A kappa value of 1 is ideal. The CART® modeling effort produced valid models in all three physiographic regions of North Carolina, though the complexity of the piedmont model makes it impractical for use in the field. The dependent variable in the classification tree was presence or absence of SPB outbreak and the test sample error percentages were similar across regions, with errors ranging between 23.76 - 34.95 percent. Overall prediction success, based on the software's internal cross-validation procedure, was likewise comparable across the regions with 72.28 - 89.56 percent correctly predicted. Based on our modeling, stand age and percent sawtimber should be included in future FHTET SPB hazard modeling efforts for the coastal plain and mountains, respectively. Age classes can be reasonably estimated using Landsat or other multispectral imagery. / Master of Science

M. S.

Master of Science