Browsing by Author "Peduzzi, Alicia"

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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 characteristics
    Sumnall, 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.
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    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 USA
    Sumnall, 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.
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    Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests
    Peduzzi, Alicia; Wynne, Randolph H.; Thomas, Valerie A.; Nelson, Ross F.; Reis, James J.; Sanford, Mark (MDPI, 2012-06-13)
    The objective of this study was to determine whether leaf area index (LAI) in temperate mixed forests is best estimated using multiple-return airborne laser scanning (lidar) data or dual-band, single-pass interferometric synthetic aperture radar data (from GeoSAR) alone, or both in combination. In situ measurements of LAI were made using the LiCor LAI-2000 Plant Canopy Analyzer on 61 plots (21 hardwood, 36 pine, 4 mixed pine hardwood; stand age ranging from 12-164 years; mean height ranging from 0.4 to 41.2 m) in the Appomattox-Buckingham State Forest, Virginia, USA. Lidar distributional metrics were calculated for all returns and for ten one meter deep crown density slices (a new metric), five above and five below the mode of the vegetation returns for each plot. GeoSAR metrics were calculated from the X-band backscatter coefficients (four looks) as well as both X- and P-band interferometric heights and magnitudes for each plot. Lidar metrics alone explained 69% of the variability in LAI, while GeoSAR metrics alone explained 52%. However, combining the lidar and GeoSAR metrics increased the R2 to 0.77 with a CV-RMSE of 0.42. This study indicates the clear potential for X-band backscatter and interferometric height (both now available from spaceborne sensors), when combined with small-footprint lidar data, to improve LAI estimation in temperate mixed forests.
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    Estimating forest attributes using laser scanning data and dual-band, single-pass interferometric aperture radar to improve forest management
    Peduzzi, Alicia (Virginia Tech, 2011-09-08)
    The overall objectives of this dissertation were to (1) determine whether leaf area index (LAI) (Chapter 2), as well as stem density and height to live crown (Chapter 3) can be estimated accurately in intensively managed pine plantations using small-footprint, multiple-return airborne laser scanner (lidar) data, and (2) ascertain whether leaf area index in temperate mixed forests is best estimated using multiple-return airborne laser scanning (lidar) data or dual-band, single-pass interferometric synthetic aperture radar data (from GeoSAR) alone or both in combination (Chapter 4). In situ measurements of LAI, mean height, height to live crown, and stem density were made on 109 (LAI) or 110 plots (all other variables) under a variety of stand conditions. Lidar distributional metrics were calculated for each plot as a whole as well as for crown density slices (newly introduced in this dissertation). These metrics were used as independent variables in best subsets regressions with LAI, number of trees, mean height to live crown, and mean height (measured in situ) as the dependent variables. The best resulting model for LAI in pine plantations had an R2 of 0.83 and a cross-validation (CV) RMSE of 0.5. The CV-RMSE for estimating number of trees on all 110 plots was 11.8 with an R2 of 0.92. Mean height to live crown was also well-predicted (R2 = 0.96, CV-RMSE = 0.8 m) with a one-variable model. In situ measurements of temperate mixed forest LAI were made on 61 plots (21 hardwood, 36 pine, 4 mixed pine hardwood). GeoSAR metrics were calculated from the X-band backscatter coefficients (four looks) as well as both X- and P-band interferometric heights and magnitudes. Both lidar and GeoSAR metrics were used as independent variables in best subsets regressions with LAI (measured in situ) as the dependent variable. Lidar metrics alone explained 69% of the variability in temperate mixed forest LAI, while GeoSAR metrics alone explained 52%. However, combining the LAI and GeoSAR metrics increased the R2 to 0.77 with a CV-RMSE of 0.42. Analysis of data from active sensors shows strong potential for eventual operational estimation of biophysical parameters essential to silviculture.