Decision Support for Operational Plantation Forest Inventories through Auxiliary Information and Simulation
| dc.contributor.author | Green, Patrick Corey | en |
| dc.contributor.committeechair | Burkhart, Harold E. | en |
| dc.contributor.committeemember | Radtke, Philip J. | en |
| dc.contributor.committeemember | Thomas, Valerie A. | en |
| dc.contributor.committeemember | Wynne, Randolph H. | en |
| dc.contributor.committeemember | Coulston, John W. | en |
| dc.contributor.department | Forest Resources and Environmental Conservation | en |
| dc.date.accessioned | 2021-04-18T06:00:24Z | en |
| dc.date.available | 2021-04-18T06:00:24Z | en |
| dc.date.issued | 2019-10-25 | en |
| dc.description.abstract | Informed forest management requires accurate, up-to-date information. Ground-based forest inventory is commonly conducted to generate estimates of forest characteristics with a predetermined level of statistical confidence. As the importance of monitoring forest resources has increased, budgetary and logistical constraints often limit the resources needed for precise estimates. In this research, the incorporation of ancillary information in planted loblolly pine (Pinus taeda L.) forest inventory was investigated. Additionally, a simulation study using synthetic populations provided the basis for investigating the effects of plot and stand-level inventory aggregations on predictions and projections of future forest conditions. Forest regeneration surveys are important for assessing conditions immediately after plantation establishment. An unmanned aircraft system was evaluated for its ability to capture imagery that could be used to automate seedling counting using two computer vision approaches. The imagery was found to be unreliable for consistent detection in the conditions evaluated. Following establishment, conditions are assessed throughout the lifespan of forest plantations. Using small area estimation (SAE) methods, the incorporation of light detection and ranging (lidar) and thinning status improved the precision of inventory estimates compared with ground data alone. Further investigation found that reduced density lidar point clouds and lower resolution elevation models could be used to generate estimates with similar increases in precision. Individual tree detection estimates of stand density were found to provide minimal improvements in estimation precision when incorporated into the SAE models. Plot and stand level inventory aggregations were found to provide similar estimates of future conditions in simulated stands without high levels of spatial heterogeneity. Significant differences were noted when spatial heterogeneity was high. Model form was found to have a more significant effect on the observed differences than plot size or thinning status. The results of this research are of interest to forest managers who regularly conduct forest inventories and generate estimates of future stand conditions. The incorporation of auxiliary data in mid-rotation stands using SAE techniques improved estimate precision in most cases. Further, guidance on strategies for using this information for predicting future conditions is provided. | en |
| dc.description.abstractgeneral | Informed forest management requires accurate, up-to-date information. Groundbased sampling (inventory) is commonly used to generate estimates of forest characteristics such as total wood volume, stem density per unit area, heights, and regeneration survival. As the importance of assessing forest resources has increased, resources are often not available to conduct proper assessments. In this research, the incorporation of ancillary information in planted loblolly pine (Pinus taeda L.) forest inventory was investigated. Additionally, a simulation study investigated the effects of two forest inventory data aggregation methods on predictions and projections of future forest conditions. Forest regeneration surveys are important for assessing conditions immediately after tree planting. An unmanned aircraft system was evaluated for its ability to capture imagery that could be used to automate seedling counting. The imagery was found to be unreliable for use in accurately detecting seedlings in the conditions evaluated. Following establishment, forest conditions are assessed at additional points in forest development. Using a class of statistical estimators known as small-area estimation, a combination of ground and light detection and ranging data generated more confident estimates of forest conditions. Further investigation found that more coarse ancillary information can be used with similar confidence in the conditions evaluated. Forest inventory data are used to generate estimates of future conditions needed for management decisions. The final component of this research found that there are significant differences between two inventory data aggregation strategies when forest conditions are highly spatially variable. The results of this research are of interest to forest managers who regularly assess forest resources with inventories and models. The incorporation of ancillary information has potential to enhance forest resource assessments. Further, managers have guidance on strategies for using this information for estimating future conditions. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:22755 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/103054 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Loblolly pine | en |
| dc.subject | UAS | en |
| dc.subject | Small area estimation | en |
| dc.subject | auxiliary data | en |
| dc.subject | projection | en |
| dc.title | Decision Support for Operational Plantation Forest Inventories through Auxiliary Information and Simulation | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Forestry | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |