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Browsing by Author "Thomas, Valerie A."

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    2014 OGIS Symposium Program
    McGee, John; Wynne, Randolph H.; Thomas, Valerie A. (2014-04-04)
    List of all proceedings from the Virginia Tech GIS Symposium, held on April 4, 2014.
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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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    Analysis of Dryland Forest Phenology using Fused Landsat and MODIS Satellite Imagery
    Walker, Jessica (Virginia Tech, 2012-09-07)
    This dissertation investigated the practicality and expediency of applying remote sensing data fusion products to the analysis of dryland vegetation phenology. The objective of the first study was to verify the quality of the output products of the spatial and temporal adaptive reflectance fusion method (STARFM) over the dryland Arizona study site. Synthetic 30 m resolution images were generated from Landsat-5 Thematic Mapper (TM) data and a range of 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance datasets and assessed via correlation analysis with temporally coincident Landsat-5 imagery. The accuracy of the results (0.61 < R2 < 0.94) justified subsequent use of STARFM data in this environment, particularly when the imagery were generated from Nadir Bi-directional Reflectance Factor (BRDF)-Adjusted Reflectance (NBAR) MODIS datasets. The primary objective of the second study was to assess whether synthetic Landsat data could contribute meaningful information to the phenological analyses of a range of dryland vegetation classes. Start-of-season (SOS) and date of peak greenness phenology metrics were calculated for each STARFM and MODIS pixel on the basis of enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) time series over a single growing season. The variability of each metric was calculated for all STARFM pixels within 500 m MODIS extents. Colorado Plateau Pinyon Juniper displayed high amounts of temporal and spatial variability that justified the use of STARFM data, while the benefit to the remaining classes depended on the specific vegetation index and phenology metric. The third study expanded the STARFM time series to five years (2005-2009) to examine the influence of site characteristics and climatic conditions on dryland ponderosa pine (Pinus ponderosa) forest phenological patterns. The results showed that elevation and slope controlled the variability of peak timing across years, with lower elevations and shallower slopes linked to higher levels of variability. During drought conditions, the number of site variables that controlled the timing and variability of vegetation peak increased.
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    Analysis of Viewshed Accuracy with Variable Resolution LIDAR Digital Surface Models and Photogrammetrically-Derived Digital Elevation Models
    Miller, Matthew Lowell (Virginia Tech, 2011-10-28)
    The analysis of visibility between two points on the earth's terrain is a common use of GIS software. Most commercial GIS software packages include the ability to generate a viewshed, or a map of terrain surrounding a particular location that would be visible to an observer. Viewsheds are often generated using "bare-earth" Digital Elevation Models (DEMs) derived from the process of photogrammetry. More detailed models, known as Digital Surface Models (DSMs), are often generated using Light Detection and Ranging (LIDAR) which uses an airborne laser to scan the terrain. In addition to having greater accuracy than photogrammetric DEMs, LIDAR DSMs include surface features such as buildings and trees. This project used a visibility algorithm to predict visibility between observer and target locations using both photogrammetric DEMs and LIDAR DSMs of varying resolution. A field survey of the locations was conducted to determine the accuracy of the visibility predictions and to gauge the extent to which the presence of surface features in the DSMs affected the accuracy. The use of different resolution terrain models allowed for the analysis of the relationship between accuracy and optimal grid size. Additionally, a series of visibility predictions were made using Monte Carlo methods to add random error to the terrain elevation to estimate the probability of a target's being visible. Finally, the LIDAR DSMs were used to determine the linear distance of terrain along the lines-of-sight between the observer and targets that were obscured by trees or bushes. A logistic regression was performed between that distance and the visibility of the target to determine the extent to which a greater amount of vegetation along the line-of-sight impacted the target's visibility.
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    Application on Lidar and Time Series Landsat Data for Mapping and Monitoring Wetlands
    Kayastha, Nilam (Virginia Tech, 2014-01-09)
    To successfully protect and manage wetlands, efficient and accurate tools are needed to identify where wetlands are located, the wetland type, what condition they are in, what are the stressors present, and the trend in their condition. Wetland mapping and monitoring are useful to accomplish these tasks. Wetland mapping and monitoring with optical remote sensing data has mainly focused on using a single image or using image acquired over two seasons within the same year. Now that Landsat data are available freely, a multi-temporal approach utilizing images that span multiple seasons and multiple years can potentially be used to characterize wetland dynamics in more detail. In addition, newer remote sensing techniques such as Light Detection and Ranging (lidar) can provide highly detailed and accurate topographic information, which can improve our ability to discriminate wetlands. Thus, the overall objective of this study was to investigate the utility of lidar and multi-temporal Landsat data for mapping and monitoring of wetlands. My research is presented as three independent studies related to wetland mapping and monitoring. In the first study, inter-annual time series of Landsat data from 1985 to 2009 was used to map changes in wetland ecosystems in northern Virginia. Z-scores calculated on tasseled cap images were used to develop temporal profile for wetlands delineated by the National Wetland Inventory. A change threshold was derived based on the Chi-square distribution of the Z-scores. The accuracy of a change/no change map produced was 89% with a kappa value of 0.79. Assessment of the change map showed that the method used was able to detect complete wetland loss together with other subtle changes resulting from development, harvesting, thinning and farming practices. The objective of the second study was to characterize differences in spectro-temporal profile of forested upland and wetland using intra and inter annual time series of Landsat data (1999-2012). The results show that the spector-temporal metrics derived from Landsat can accurately discriminate between forested upland and wetland (accuracy of 88.5%). The objective of the third study was to investigate the ability of topographic variables derived from lidar to map wetlands. Different topographic variables were derived from a high resolution lidar digital elevation model. Random forest model was used to assess the ability of these variables in mapping wetlands and uplands area. The result shows that lidar data can discriminate between wetlands and uplands with an accuracy of 72%. In summary, because of its spatial, spectral, temporal resolution, availability and cost Landsat data will be a primary data source for mapping and monitoring wetlands. The multi-temporal approach presented in this study has great potential for significantly improving our ability to detect and monitor wetlands. In addition, synergistic use of multi-temporal analysis of Landsat data combined with lidar data may be superior to using either data alone for future wetland mapping and monitoring approaches.
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    Applications of Imaging Spectroscopy in Forest Ecosystems at Multiple Scales
    Stein, Beth R. (Virginia Tech, 2015-10-19)
    Forests provide a number of ecosystem services which sustain and enrich the wildlife, human societies, and the environment. However, many disturbances threaten forest ecosystems, making it necessary to monitor their health for optimal management and conservation. Although there are many indicators of forest health, changes in biogeochemical cycling, loss of species diversity, and invasive plants are particularly useful due to their vulnerability to the effects of climate change and intensive agricultural land use. Thus, this work evaluates the use of imaging spectroscopy to monitor forest nutrient status, species diversity, and plant invasions in the Mid-Atlantic region. The research is divided into four separate studies, each of which evaluated a unique application for imaging spectroscopy data at a different scale within the forest. The first two studies examined loblolly pine nutrient status at the leaf and canopy scales, respectively. The first study determined that loblolly pine foliar macronutrient concentrations can be successfully modeled across the Southeastern US (R2=0.39-0.74). Following on these results, the second study focused on the relationship between physical characteristics, reflectance, and nutrients. Reflectance values and W scattering coefficients produced successful nitrogen models across loblolly pine plots at the canopy scale. Regression models showed similar explanatory power for nitrogen, although W scattering coefficients were significantly correlated with nitrogen at multiple wavelengths and reflectance variables were not. However, the direction of some of the correlations with W and the unusually high directional area scattering factor values indicate a need for further experimentation. The third study found that several imaging spectroscopy algorithms were moderately successful in identifying wavyleaf basketgrass invasions in mixed deciduous forests (overall accuracy=0.35-0.78; kappa=0.41-0.53). Lastly, the fourth study used a novel imaging spectroscopy/lidar fusion to identify canopy gaps and measure species diversity of understory vegetation. The lidar algorithm identified 29 of 34 canopy gaps, and regression models explained 49 percent of the variance in gap species diversity. In conclusion, imaging spectroscopy can be used to evaluate ecosystem health through forest nutrient status, nitrogen models, species diversity estimates, and identification of invasive plant species.
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    Approximating Prediction Uncertainty for Random Forest Regression Models
    Coulston, John W.; Blinn, Christine E.; Thomas, Valerie A.; Wynne, Randolph H. (2016-03)
    Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as inputs to other modeling applications such as fire modeling. Here we use a Monte Carlo approach to quantify prediction uncertainty for random forest regression models. We test the approach by simulating maps of dependent and independent variables with known characteristics and comparing actual errors with prediction errors. Our approach produced conservative prediction intervals across most of the range of predicted values. However, because the Monte Carlo approach was data driven, prediction intervals were either too wide or too narrow in sparse parts of the prediction distribution. Overall, our approach provides reasonable estimates of prediction uncertainty for random forest regression models.
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    ArcGIS Pro, Python, and R-Bridge Support Small Area Estimation for Forests
    Bell, David M.; Blinn, Christine E.; Peery, Stephen S.; Wynne, Randolph H.; Radtke, Philip J.; Thomas, Valerie A.; Oswalt, Christopher M.; Wilson, B. Ty (2023-07-12)
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    Assessing age-height relationship using ICESat-2 and Landsat time series products of southern pines in southeastern region
    Sharma Banjade, Sonia (Virginia Tech, 2023-12-01)
    This study investigates pine heights by age for actively managed stands in the southeastern U.S. using ICESat-2 ATL08-derived height data and maps derived from the Landsat time series. We intersected ICESat-2 ground tracks with locations of pine plantations and the Landscape Change Monitoring System (LCMS) Fast Loss product to identify previously clear-cut pine plantations. We subtracted the LCMS Fast Loss year from the date of the ICESat-2 acquisition to determine plantation age at the time of the height measurement. We stratified the data for management intensity, where stands that experience both thinning and harvesting were considered actively managed. The goal was to develop age-height relationships across the region to characterize better the impact of management on productivity and site index. This research involved the analysis of over 137,998 ICESat-2 ATL08 segments in actively managed pine stands in the U.S. Southeast. We compared a subset of ICESat-2 heights with heights derived from airborne laser scanning acquisitions (ALS) available through the USGS 3D Elevation Program. The resulting R2 was 0.82, giving us confidence in the ICESat-2 ATL08-derived forest heights. Then, through data processing and analysis, we successfully stratified the spatial patterns of ICESat-2 ATL08 heights in the southeastern region. These patterns provided insights into the distribution and variability of forest heights across the region, contributing to informed decisions in forest management. We identified some challenges in predicting pine stand age through Landsat-derived disturbance products. We found that LCMS Fast Loss labels some heavy thins as a ‘Fast Loss,’ in addition to stand-clearing disturbances like clear-cuts, adding noise to our estimation of stand age. To overcome this issue, we employed a robust model of the logarithm of heights with a reciprocal of age using a random sample consensus (RANSAC) model to calculate site indices at base age 25 (years). Our results showed the site index for the region at a base age of 25 years is 20.1 m with a model R2 of 0.91. We compared the ICESat-2-derived site index with the FIA-derived site index to see the robustness of our results. Then, the modeled site index values were used to produce a map at a base age of 25 years for the U.S. Southeast, offering insights into spatial differences in regional forest productivity. The results of this study have important implications for ecological research, forest management, and well-informed decision-making. Insights into the distribution and trends of actively managed forest heights in the Southeast are gained from studying the vast dataset, allowing for more efficient land management and conservation initiatives. In actively manage stands, our site index equation improves the ability to anticipate site productivity and estimate future timber outputs. The difficulties with age estimation that have been observed highlight the need for better methods for mapping disturbances using remote sensing in forests that use thinning as a silvicultural prescription.
  • Assessing the extent and drivers of forest plantation establishment in Andhra Pradesh
    Wynne, Randolph H.; Thomas, Valerie A.; Gundimeda, Haripriya; Amacher, Gregory S.; Cobourn, Kelly M.; Köhlin, Gunnar (2017-07)
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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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    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.
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    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; Thomas, Valerie A.; Wynne, Randolph H. (Virginia Tech, 2021-04-30)
    Remote sensing offers many advantages to previous forest measurements, such as limiting costs and time in the field. Light detection and ranging (lidar) has been shown to enable accurate estimates of forest height. Lidar does produce precise measurements for ground elevation and forest height, where and when it is available. However, it is expensive to collect and does not have wall-to-wall coverage in the United States. In this study, we estimated height using the National Agricultural Imagery Program (NAIP) photogrammetric point clouds to create a predicted height map for managed loblolly pine stands in the southeastern United States. Recent studies have investigated the ability of digital aerial photogrammetry (DAP), and more specifically NAIP, as an alternative to lidar as a means of estimating forest height due to its lower costs, frequency of acquisition, and wall-to-wall coverage across the United States. Field-collected canopy height for 534 plots in Virginia and North Carolina were regressed against distributional metrics derived from NAIP and lidar point clouds. The best regression model for predicted pine height used the 90th percentile of height (P90), predicted pine height = 1.09(P90) – 0.43. The adjusted R^2 is 0.93 and the RMSE is 1.44 m. This model is being used to produce a 5 x 5 m canopy height model for all pine stands across Virginia, North Carolina, and Tennessee. NAIP-derived point clouds are thus a viable means of predicting canopy height in southern pines.
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    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 (Virginia Tech, 2021-05-10)
    Remote sensing offers many advantages to previous forest measurements, such as limiting costs and time in the field. Light detection and ranging (lidar) has been shown to enable accurate estimates of forest height. Lidar does produce precise measurements for ground elevation and forest height, where and when it is available. However, it is expensive to collect and does not have wall-to-wall coverage in the United States. In this study, we estimated height using the National Agricultural Imagery Program (NAIP) photogrammetric point clouds to create a predicted height map for managed loblolly pine stands in the southeastern United States. Recent studies have investigated the ability of digital aerial photogrammetry (DAP), and more specifically NAIP, as an alternative to lidar as a means of estimating forest height due to its lower costs, frequency of acquisition, and wall-to-wall coverage across the United States. Field-collected canopy height for 534 plots in Virginia and North Carolina were regressed against the 90th percentile derived from NAIP point clouds. The model for predicted pine height using the 90th percentile of height (P90) is predicted pine height = 1.09(P90) – 0.43. The adjusted R^2 is 0.93, and the RMSE is 1.44 m. This model is being used to produce a 5 m x 5 m canopy height model for all pine stands across Virginia, North Carolina, and Tennessee. NAIP-derived point clouds are thus a viable means of predicting canopy height in southern pines.
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    Assessment of Canopy Health with Drone-Based Orthoimagery in a Southern Appalachian Red Spruce Forest
    Harris, Ryley C.; Kennedy, Lisa M.; Pingel, Thomas J.; Thomas, Valerie A. (MDPI, 2022-03-10)
    Consumer-grade drone-produced digital orthoimagery is a valuable tool for conservation management and enables the low-cost monitoring of remote ecosystems. This study demonstrates the applicability of RGB orthoimagery for the assessment of forest health at the scale of individual trees in a 46-hectare plot of rare southern Appalachian red spruce forest on Whitetop Mountain, Virginia. We used photogrammetric Structure from Motion software Pix4Dmapper with drone-collected imagery to generate a mosaic for point cloud reconstruction and orthoimagery of the plot. Using 3-band RBG digital orthoimagery, we visually classified 9402 red spruce individuals, finding 8700 healthy (92.5%), 251 declining/dying (2.6%), and 451 dead (4.8%). We mapped individual spruce trees in each class and produced kernel density maps of health classes (live, dead, and dying). Our approach provided a nearly gap-free assessment of the red spruce canopy in our study site, versus a much more time-intensive field survey. Our maps provided useful information on stand mortality patterns and canopy gaps that could be used by managers to identify optimal locations for selective thinning to facilitate understory sapling regeneration. This approach, dependent mainly on an off-the-shelf drone system and visual interpretation of orthoimagery, could be applied by land managers to measure forest health in other spruce, or possibly spruce-fir, communities in the Appalachians. Our study highlights the usefulness of drone-produced orthoimagery for conservation monitoring, presenting a valid and accessible protocol for the monitoring and assessment of forest health in remote spruce, and possibly other conifer, populations. Adoption of drone-based monitoring may be especially useful in light of climate change and the possible displacement of southern Appalachian red spruce (and spruce-fir) ecosystems by the upslope migration of deciduous trees.
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    Assessment of the diurnal relationship of photochemical reflectance index to forest light use efficiency by accounting for sunlit and shaded foliage
    Williams, Paige Tatum; Harding, David J.; Thomas, Valerie A.; Wynne, Randolph H.; Ranson, Kenneth J.; Huemmrich, Karl F.; Middleton, Elizabeth; Campbell, Petya K. (Virginia Tech, 2021-04-30)
    Gross Primary Productivity (GPP) is the amount of carbon fixed during photosynthesis by all producers in the ecosystem. GPP is dependent on light use efficiency (LUE), photosynthetically active radiation (PAR), and fraction of absorbed PAR (fPAR). To estimate light use efficiency (LUE), which is dependent on the exposure of leaves to photosynthetically active radiation (PAR), the photochemical reflectance index (PRI) is calculated using 531 nm and 570 nm wavelengths. Our team has examined the sensitivity of forest canopy PRI to canopy shadows using airborne hyperspectral data acquired in eastern North Carolina. A bounding box for this study was placed adjacent to a flux tower in a loblolly pine stand to evaluate the variability of LUE derived from the reflectance data acquired in the morning, midday and afternoon, and compare LUE estimates to the flux tower observations. We compute PRI values for the sunlit and shadowed parts of the canopy determined by thresholding a 2 m panchromatic image produced by averaging wavelength bands from 525 nm to 600 nm. We show that PRI for the sunlit canopy is substantially lower than for the shadowed components at all times of day, leading to an overestimate of LUE when using whole-canopy reflectance. Implications for estimating GPP using PRI reflectance as a surrogate for LUE is examined by comparing to the flux tower derivation of GPP. This work is being done to refine measurement requirements for a diurnal constellation concept, the Structure and Function of Ecosystems (SAFE).
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    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.
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    Beyond Finding Change: multitemporal Landsat for forest monitoring and management
    Wynne, Randolph H.; Thomas, Valerie A.; Brooks, Evan B.; Coulston, J. O.; Derwin, Jill M.; Liknes, Greg C.; Yang, Z.; Fox, Thomas R.; Ghannam, S.; Abbott, A. Lynn; House, M. N.; Saxena, R.; Watson, Layne T.; Gopalakrishnan, Ranjith (2017-07)
    Take homes
    • Tobler’s Law still in effect with time series – spatial autocorrelation in temporal coherence can help in both preprocessing and estimation
    • Continual process improvement in extant algorithms needed
    • Need additional means by which variations within (parameterization) and across algorithms addressed (the Reverend…)
    • Time series improving higher order products (example with NLCD TCC) enabling near continuous monitoring
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    Biotic and Abiotic Factors of Picea rubens (Red Spruce) Seedling Regeneration in Disturbed Heathland Barrens of the Central Appalachians
    White, Helen M. (Virginia Tech, 2019-06-20)
    During the late 19th and early 20th centuries, extensive logging reduced the forests of red spruce (Picea rubens) by nearly 99% through portions of West Virginia. In the wake of this disturbance, red spruce has begun regenerating on the ridge and mountaintop areas of Canaan Valley, West Virginia, where heath and grassland communities have both persisted in natural barrens and expanded into formerly forested areas. To understand abiotic and biotic conditions guiding the advance of the red spruce stand, I conducted a broad-scale assessment of thirty-one demographics plots spread across two sites (north Cabin Mountain and Bear Rocks/Dolly Sods), and a more focused assessment of red spruce species associations within thirty-two paired plots at Cabin Mountain. At the 15m x 15m demographics plots, I conducted a count of all P. rubens present, measured specimen height, DBH or diameter at ground level (DGL) for specimens < 1.37m tall, and assessed the relative percent cover of rock, shrub, herbaceous, and tree cover. These data, along with additional abiotic components derived from a DEM, formed the basis of my assessment using a generalized linear mixed model (GLMM) to identify the most significant biophysical variables related to P. rubens count. In the paired plots, I used the relative interactions index (RII) to compare the total cover of each present non-graminoid vascular species and the grouped cover types Rock, Graminoid, Lichen, Litter, and Moss in one 45cm-radius plot with a < 1.37m P. rubens specimen, and one paired 45cm-radius plot in open heath. The significance of differences in total cover were assessed with the Wilcoxon test and Tukey HSD. The GLMM identified percent rock cover and distance from the nearest P. rubens stand to be important correlates of P. rubens count at the demographic plots. Graminoid cover was found to be higher in P. rubens 45cm-radius plots than in paired heath plots, and Vaccinium angustifolium cover was found to be concentrated in 45cm radius plots beyond the first 15cm from the P. rubens stem. These findings reinforce a complex interplay between both the biotic and abiotic characteristics of a microsite and the successful germination and regeneration of a red spruce seedling in the heathland.
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    Characterizing Impacts of and Recovery from Surface Coal Mining in Appalachian Forested Landscapes Using Landsat Imagery
    Sen, Susmita (Virginia Tech, 2011-06-23)
    This dissertation describes research investigating the potential for using Landsat data to identify and characterize woody canopy cover on reclaimed coal-mined lands through three separate studies. The objective of the first study was to assess whether surface coal mines in the forested central Appalachian regions of the US can be separated from the other prevalent forest-replacing disturbances through analysis of an interannual chronosequence of Landsat images. Disturbances were classified using descriptors of the disturbance/recovery trajectories: disturbance minimum, recovery slope and recovery maximum. Three vegetation indices (VIs) (normalized difference vegetation index, NDVI; tasseled cap greenness/brightness ratio, TC G/B; and inverse of Landsat band 3, B3I) were used to analyze multitemporal trajectories generated using both pixels and objects. Classification accuracies using objects were better than those obtained using pixels for all VIs. The highest object-based classification accuracy was achieved using TC G/B (89%), followed by NDVI (88%) and B3I (80%). The objective of the second study was to evaluate performance of a woody canopy cover (including both native and invasive species) estimation method based on the 2011 National Land Cover Database (NLCD) protocol for both mined and non-mined areas of the central Appalachians. Potential explanatory variables included raw and derived bands from leaf-on and leaf-off Landsat scenes plus terrain descriptors. Results show that the model developed to estimate canopy cover for mines (R2 = 0.78, Adj. R2 = 0.77, RMSE = 16%) is more robust than the models developed for non-mines, mixed, and all areas combined. The objective of the third study was to determine whether four disturbance/recovery parameters (recovery time, disturbance minimum, recovery slope, and recovery maximum), alone or in combination with variables identified in the second study, enable robust estimation of woody canopy cover on reclaimed surface coal mines. Of the disturbance/recovery parameters, only recovery time made a significant contribution to the model (R2 0.45, Adj. R2 0.44, RMSE 14%). Addition of leaf-on and leaf-off NDVI improved the R2 to 0.54 (Adj. R2 0.53, RMSE 13%). Analysis of Landsat data has strong potential for identifying reclaimed mines and characterizing the extent to which woody canopy has recovered post-reclamation.