VTechWorks staff will be away for the Thanksgiving holiday beginning at noon on Wednesday, November 27, through Friday, November 29. We will resume normal operations on Monday, December 2. Thank you for your patience.

Browsing by Author "Green, P. Corey"

Now showing 1 - 7 of 7
  • Thumbnail Image
    Assessing the utility of NAIP digital aerial photogrammetric point clouds for estimating canopy height of managed loblolly pine plantations in the southeastern United States
    Ritz, Alison L.; Thomas, Valerie A.; Wynne, Randolph H.; Green, P. Corey; Schroeder, Todd A.; Albaugh, Timothy J.; Burkhart, Harold E.; Carter, David R.; Cook, Rachel L.; Campoe, Otavio C.; Rubilar, Rafael A.; Rakestraw, Jim (Elsevier, 2022-09)
    Remote sensing offers many advantages to supplement traditional, ground-based forest measurements, such as limiting time in the field and fast spatial coverage. Data from airborne laser scanning (lidar) have provided accurate estimates of forest height, where, and when available. However, lidar is expensive to collect, and wall-to-wall coverage in the United States is lacking. Recent studies have investigated whether point clouds derived from digital aerial photogrammetry (DAP) can supplement lidar data for estimating forest height due to DAP's lower costs and more frequent acquisitions. We estimated forest heights using point clouds derived from the National Agricultural Imagery Program (NAIP) DAP program in the United States to create a predicted height map for managed loblolly pine stands. For 534 plots in Virginia and North Carolina, with stand age ranging from 1 year to 42 years old, field-collected canopy heights were regressed against the 90th percentile of heights derived from NAIP point clouds. Model performance was good, with an R2 of 0.93 and an RMSE of 1.44 m. However, heights in recent heavily thinned stands were consistently underestimated, likely due to between-row shadowing leading to a poor photogrammetric solution. The model was applied to non-thinned evergreen areas in Virginia, North Carolina, and Tennessee to produce a multi-state 5 m x 5 m canopy height map. NAIP-derived point clouds are a viable means of predicting canopy height in southern pine stands that have not been thinned recently.
  • Thumbnail Image
    Auxiliary information resolution effects on small area estimation in plantation forest inventory
    Green, P. Corey; Burkhart, Harold E.; Coulston, John W.; Radtke, Philip J.; Thomas, Valerie A. (2020-10)
    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.
  • Thumbnail Image
    Comparison of Data Grouping Strategies on Prediction Accuracy of Tree-Stem Taper for Six Common Species in the Southeastern US
    Yang, Sheng-I; Green, P. Corey (MDPI, 2022-01-20)
    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.
  • Thumbnail Image
    Culture and Density Effects on Tree Quality in Midrotation Non-Thinned Loblolly Pine Plantations
    Green, P. Corey; Bullock, Bronson P.; Kane, Michael B. (MDPI, 2018-02-09)
    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.
  • Thumbnail Image
    Enhancing the precision of broad-scale forestland removals estimates with small area estimation techniques
    Coulston, John W.; Green, P. Corey; Radtke, Philip J.; Prisley, Stephen P.; Brooks, Evan B.; Thomas, Valerie A.; Wynne, Randolph H.; Burkhart, Harold E. (2021-07)
    Naional Forest Inventories (NFI) are designed to produce unbiased estimates of forest parameters at a variety of scales. These parameters include means and totals of current forest area and volume, as well as components of change such as means and totals of growth and harvest removals. Over the last several decades, there has been a steadily increasing demand for estimates for smaller geographic areas and/or for finer temporal resolutions. However, the current sampling intensities of many NFI and the reliance on design-based estimators often leads to inadequate precision of estimates at these scales. This research focuses on improving the precision of forest removal estimates both in terms of spatial and temporal resolution through the use of small area estimation techniques (SAE). In this application, a Landsat-derived tree cover loss product and the information from mill surveys were used as auxiliary data for area-level SAE. Results from the southeastern US suggest improvements in precision can be realized when using NFI data to make estimates at relatively fine spatial and temporal scales. Specifically, the estimated precision of removal volume estimates by species group and size class was improved when SAE methods were employed over post-stratified, design-based estimates alone. The findings of this research have broad implications for NFI analysts or users interested in providing estimates with increased precision at finer scales than those generally supported by post-stratified estimators.
  • Thumbnail Image
    A novel application of small area estimation in loblolly pine forest inventory
    Green, P. Corey; Burkhart, Harold E.; Coulston, John W.; Radtke, Philip J. (2020-04)
    Loblolly pine (Pinus taeda L.) is one of the most widely planted tree species globally. As the reliability of estimating forest characteristics such as volume, biomass and carbon becomes more important, the necessary resources available for assessment are often insufficient to meet desired confidence levels. Small area estimation (SAE) methods were investigated for their potential to improve the precision of volume estimates in loblolly pine plantations aged 9-43. Area-level SAE models that included lidar height percentiles and stand thinning status as auxiliary information were developed to test whether precision gains could be achieved. Models that utilized both forms of auxiliary data provided larger gains in precision compared to using lidar alone. Unit-level SAE models were found to offer additional gains compared with area-level models in some cases; however, area-level models that incorporated both lidar and thinning status performed nearly as well or better. Despite their potential gains in precision, unit-level models are more difficult to apply in practice due to the need for highly accurate, spatially defined sample units and the inability to incorporate certain area-level covariates. The results of this study are of interest to those looking to reduce the uncertainty of stand parameter estimates. With improved estimate precision, managers, stakeholders and policy makers can have more confidence in resource assessments for informed decisions.
  • Thumbnail Image
    Review and Synthesis of Estimation Strategies to Meet Small Area Needs in Forest Inventory
    Dettmann, Garret T.; Radtke, Philip J.; Coulston, John W.; Green, P. Corey; Wilson, Barry T.; Moisen, Gretchen G. (Frontiers, 2022-03-16)
    Small area estimation is a growing area of research for making inferences over geographic, demographic, or temporal domains smaller than those in which a particular survey data set was originally intended to be used. We aimed to review a body of literature to summarize the breadth and depth of small area estimation and related estimation strategies in forest inventory and management to-date, as well as the current state of terminology, methods, concerns, data sources, research findings, challenges, and opportunities for future work relevant to forestry and forest inventory research. Estimation methodologies explored include direct, indirect, and composite estimation within design-based and model-based inference bases. A variety of estimation methods in forestry have been applied to extensive multi-resource inventory systems like national forest inventories to increase the precision of estimates on small domains or subsets of the overall populations of interest. To avoid instability and large variances associated with small sample sizes when working with small area domains, forest inventory data are often supplemented with information from auxiliary sources, especially from remote sensing platforms and other geospatial, map-based products. Results from many studies show gains in precision compared to direct estimates based only on field inventory data. Gains in precision have been demonstrated in both project-level applications and national forest inventory systems. Potential gains are possible over varying geographic and temporal scales, with the degree of success in reducing variance also dependent on the types of auxiliary information, scale, strength of model relationships, and methodological alternatives, leaving considerable opportunity for future research and growth in small area applications for forest inventory.