Browsing by Author "Sumnall, Matthew"
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- Analysis of a lidar voxel-derived vertical profile at the plot and individual tree scales for the estimation of forest canopy layer characteristicsSumnall, Matthew; Peduzzi, Alicia; Fox, Thomas R.; Wynne, Randolph H.; Thomas, Valerie A. (2016)The goal of the current study was to develop methods of estimating the height of vertical components within plantation coniferous forest using airborne discrete multiple return lidar. In the summer of 2008, airborne lidar and field data were acquired for Loblolly pine forest locations in North Carolina and Virginia, USA, which comprised a variety of stand conditions (e.g. stand age, nutrient regime, and stem density). The methods here implement both field plot-scale analysis and an automated approach for the delineation of individual tree crown (ITC) locations and horizontal extents through a marker-based region growing process applied to a lidar derived canopy height model. The estimation of vertical features was accomplished through aggregating lidar return height measurements into vertical height bins, of a given horizontal extent (plot or ITC), creating a vertical 'stack' of bins describing the frequency of returns by height. Once height bins were created the resulting vertical distributions were smoothed with a regression curve-line function and canopy layers were identified through the detection of local maxima and minima. Estimates from Lorey's mean canopy height was estimated from plot-level curve-fitting with an overall accuracy of 5.9% coefficient of variation (CV) and the coefficient of determination (R-2) value of 0.93. Estimates of height to the living canopy produced an overall R-2 value of 0.91 (11.0% CV). The presence of vertical features within the sub-canopy component of the fitted vertical function also corresponded to areas of known understory presence and absence. Estimates from ITC data were averaged to the plot level. Estimates of field Lorey's mean canopy top height from average ITC data produced an R-2 value of 0.96 (7.9% CV). Average ITC estimates of height to the living canopy produced the closest correspondence to the field data, producing an R-2 value of 0.97 (6.2% CV). These results were similar to estimates produced by a statistical regression method, where R-2 values were 0.99 (2.4% CV) and 0.98 (4.9% CV) for plot average top canopy height and height to the living canopy, respectively. These results indicate that the characteristics of the dominant canopy can be estimated accurately using airborne lidar without the development of regression models, in a variety of intensively managed coniferous stand conditions.
- Assessing the transferability of statistical predictive models for leaf area index between two airborne discrete return LiDAR sensor designs within multiple intensely managed Loblolly pine forest locations in the south-eastern USASumnall, Matthew; Peduzzi, Alicia; Fox, Thomas R.; Wynne, Randolph H.; Thomas, Valerie A.; Cook, Bruce (2016-04)Leaf area is an important forest structural variable which serves as the primary means of mass and energy exchange within vegetated ecosystems. The objective of the current study was to determine if leaf area index (LAI) could be estimated accurately and consistently in five intensively managed pine plantation forests using two multiple-return airborne LiDAR datasets. Field measurements of LAI were made using the LiCOR LAI2000 and LAI2200 instruments within 116 plots were established of varying size and within a variety of stand conditions (i.e. stand age, nutrient regime and stem density) in North Carolina and Virginia in 2008 and 2013. A number of common LiDAR return height and intensity distribution metrics were calculated (e.g. average return height), in addition to ten indices, with two additional variants, utilized in the surrounding literature which have been used to estimate LAI and fractional cover, were calculated from return heights and intensity, for each plot extent. Each of the indices was assessed for correlation with each other, and was used as independent variables in linear regression analysis with field LAI as the dependent variable. All LiDAR derived metrics were also entered into a forward stepwise linear regression. The results from each of the indices varied from an R-2 of 0.33 (S.E. 0.87) to 0.89 (S.E. 0.36). Those indices calculated using ratios of all returns produced the strongest correlations, such as the Above and Below Ratio Index (ABRI) and Laser Penetration Index 1 ( LPI1). The regression model produced from a combination of three metrics did not improve correlations greatly (R-2 0.90; S.E. 0.35). The results indicate that LAI can be predicted over a range of intensively managed pine plantation forest environments accurately when using different LiDAR sensor designs. Those indices which incorporated counts of specific return numbers (e.g. first returns) or return intensity correlated poorly with field measurements. There were disparities between the number of different types of returns and intensity values when comparing the results from two LiDAR sensors, indicating that predictive models developed using such metrics are not transferable between datasets with different acquisition parameters. Each of the indices were significantly correlated with one another, with one exception (LAI proxy), in particular those indices calculated from all returns, which indicates similarities in information content for those indices. It can then be argued that LiDAR indices have reached a similar stage in development to those calculated from optical-spectral sensors, but which offer a number of advantages, such as the reduction or removal of saturation issues in areas of high biomass.
- Complementarity increases production in genetic mixture of loblolly pine (Pinus taeda L.) throughout planted rangeCarter, David R.; Albaugh, Timothy J.; Camo, Otávio C.; Grossman, Jake J.; Rubilar, Rafael A.; Sumnall, Matthew; Maier, Christopher A.; Cook, Rachel L.; Fox, Thomas R. (ESA, 2020-09-01)Increased genotypic diversity has been associated with increased biomass production in shortrotation tree species. Increasing the genotypic diversity of loblolly pine (Pinus taeda L.) in an attempt to increase productivity has not been extensively studied nor tested operationally or over long durations (i.e., >7 yr). We used genetically mixed and pure rows of loblolly pine growing throughout its planted range— Virginia, North Carolina, and Brazil—to test the effects of genetic mixing on volume production. There were no significant effects of mixing rows compared to pure rows on uniformity or mortality. Under intensive silviculture, individual trees planted in mixed rows had approximately 7% greater volume than those in the pure rows (estimate = 0.015 m³/tree ± 0.006) in the final year of measurement—year 8 for Brazil and year 10 for North Carolina and Virginia. Scaling the increase in individual stem volume under mixed rows and intensive silviculture to 1235 stems ha⁻¹ would equate to an additional 1.85 m³∙ha⁻¹∙yr⁻¹ in mean annual increment. Measuring the net biodiversity effect, our data suggest the positive growth response is driven by complementarity and not selection, meaning both genetic entries tend to grow larger when grown together. Additional trials are necessary to test the effects of mixing rows across large plots and to assess whether this increase is sustained throughout the rotation. If this increasing trend were to hold for intensively managed plantations, strategically mixing rows to increase productivity could be a valuable addition to an intensively managed plantation requiring relatively little added operational consideration to implement.
- Growth, Removals, and Management IntensityWynne, Randolph H.; Thomas, Valerie A.; Bender, Stacie; Brooks, Evan B.; Coulston, John W.; Derwin, Jill M.; Gopalakrishnan, Ranjith; Green, Patrick; Harding, David; Sumnall, Matthew; Joshi, Pratik; Ranson, Jon; Schleeweis, Karen; Thomas, R. Quinn; Yang, Zhiqiang (2019-05-01)
- Landsat 8 Based Leaf Area Index Estimation in Loblolly Pine PlantationsBlinn, Christine E.; House, Matthew N.; Wynne, Randolph H.; Thomas, Valerie A.; Fox, Thomas R.; Sumnall, Matthew (MDPI, 2019-03-02)Leaf area index (LAI) is an important biophysical parameter used to monitor, model, and manage loblolly pine plantations across the southeastern United States. Landsat provides forest scientists and managers the ability to obtain accurate and timely LAI estimates. The objective of this study was to investigate the relationship between loblolly pine LAI measured in situ (at both leaf area minimum and maximum through two growing seasons at two geographically disparate study areas) and vegetation indices calculated using data from Landsat 7 (ETM+) and Landsat 8 (OLI). Sub-objectives included examination of the impact of georegistration accuracy, comparison of top-of-atmosphere and surface reflectance, development of a new empirical model for the species and region, and comparison of the new empirical model with the current operational standard. Permanent plots for the collection of ground LAI measurements were established at two locations near Appomattox, Virginia and Tuscaloosa, Alabama in 2013 and 2014, respectively. Each plot is thirty by thirty meters in size and is located at least thirty meters from a stand boundary. Plot LAI measurements were collected twice a year using the LI-COR LAI-2200 Plant Canopy Analyzer. Ground measurements were used as dependent variables in ordinary least squares regressions with ETM+ and OLI-derived vegetation indices. We conclude that accurately-located ground LAI estimates at minimum and maximum LAI in loblolly pine stands can be combined and modeled with Landsat-derived vegetation indices using surface reflectance, particularly simple ratio (SR) and normalized difference moisture index (NDMI), across sites and sensors. The best resulting model (LAI = −0.00212 + 0.3329SR) appears not to saturate through an LAI of 5 and is an improvement over the current operational standard for loblolly pine monitoring, modeling, and management in this ecologically and economically important region.
- Virginia Tech GIS & Remote Sensing 2014 Research Symposium - Airborne LiDAR Analysis for Forested Environments in the South-East of the USASumnall, Matthew (2014-05-13)Airborne LiDAR Analysis for Forested Environments in the South-East of the USA .The annual 2014 Virginia Tech GIS and Remote Sensing Research Symposium provides a venue to share information about recent advances in geographic information systems and remote sensing applications and research. The Symposium focuses on interaction among participants and the sharing of data, applications, and techniques. It includes both presentation and poster sessions as well as a keynote speaker.