Department of Forest Resources and Environmental Conservation

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The 1972 Virginia Outdoor Recreation Inventory
Leuschner, William A.; Groves, David L.; Bolger, William T.; Stokes, Gerald L. (Virginia Tech. Division of Forestry and Wildlife Resources, 1974)
The Virginia Commission of Outdoor Recreation coordinated the inventorying of outdoor recreation facilities in the state between June and December, 1972. The inventory is an integral part of the Virginia Outdoors Plan Information System. Its primary purpose was to provide data for the Commission to formulate and write the statewide comprehensive outdoor recreation plan. However, the intended use of these data was much broader. It was envisaged that they would be useful for other planning activities, such as those conducted by federal and state agencies or the 22 Planning District Commissions in Virginia, as well as for various research activities, special studies, and teaching. The purpose of this publication is threefold. The first is to encourage further use of the data by informing the public of its existence and the specific variables contained therein. The second is to present a limited but comprehensive set of data which can be used to answer general inquiries and which will save interested parties the trouble of writing to obtain it. Finally, we wish to inform the public of the availability of the data in other forms which may better suit individual needs but which would be too numerous to publish in this bulletin.
The 1977 Virginia Outdoor Recreation Demand Survey
Roggenbuck, Joseph W. (Virginia Tech. Division of Forestry and Wildlife Resources, 1978)
Knowledge of the present and projected public demand for outdoor recreation is a key element in the planning of a comprehensive system of outdoor recreation opportunities throughout Virginia. Public preferences for outdoor recreation experiences have changed dramatically in recent years, and formal measures of demand at any point in time remain only approximate. Nevertheless, demand analyses that are based upon the premise of satisfying public needs--as the public defines them--have a solid basis in the traditions and policies of governmental service agencies, and do provide a general guide for the planning, acquisition and development of outdoor recreation lands and facilities. This outdoor recreation demand booklet, published by the School of Forestry and Wildlife Resources, Virginia Polytechnic Institute and State University, in cooperation with the Virginia Commission of Outdoor Recreation, has three general purposes. The first is to provide federal, state and local agencies and organizations with responsibilities for the provision of outdoor recreation services with guidelines on current and projected demand for recreation activities by state, region, and locality. The second is to make the demand estimates easily available to agencies and organizations whose responsibilities do not include outdoor recreation but whose activities may impinge upon that system. Finally, the data contained here should be useful in various research activities, special studies, and teaching regarding the Virginia outdoor recreation system.
The 1977 Virginia Outdoor Recreation Needs Assessment
Roggenbuck, Joseph W. (Virginia Tech. Division of Forestry and Wildlife Resources, 1978)
This outdoor recreation needs assessment booklet, published by the School of Forestry and Wildlife Resources, Virginia Polytechnic Institute and State University, in cooperation with the Virginia Commission of Outdoor Recreation, has three general purposes. Its primary purpose is to provide guidelines on the amount and location of current and projected needs for additional outdoor recreational opportunities to federal, state, and local agencies and organizations with responsibilities for the provision of outdoor recreation services. The second is to make the needs estimates easily available to agencies and organizations whose responsibilities do not include outdoor recreation, but whose activities may impinge upon that system. Finally, the data contained here should be useful in various research activities, special studies, and teaching regarding the Virginia outdoor recreation system. Need for outdoor recreation land and facilities, as defined in this booklet, represents the difference between demand for and supply of outdoor recreation opportunities. As such, a needs assessment requires the previous calculation of present and projected recreation demand and a thorough inventory of existing recreation supply. These analyses were accomplished in 1977 and have been published as the 1977 Virginia Outdoor Recreation Demand Survey and the 1977 Virginia Outdoor Recreation Inventory. Copies of these booklets are available from the Virginia Commission of Outdoor Recreation. Since the needs estimates are dependent upon the ever-changing demand for and supply of outdoor recreation lands and facilities, the figures contained in this booklet are only approximate. The estimates should be viewed as providing general guidelines for decision-making, and not as precise measures of current deficiencies in the state's outdoor recreation system.
The 1977 Virginia Outdoor Recreation Survey
Spittle, Gerald D.; Buhyoff, Gregory J.; Davy, John R. Jr.; McElwee, Robert L. (Virginia Tech. Division of Forestry and Wildlife Resources, 1978)
The Virginia Commission of Outdoor Recreation coordinated through the Planning District Commissions an inventory of statewide recreation resources between April, 1977 and September, 1977. The School of Forestry and Wildlife Resources, Virginia Polytechnic Institute and State University, transferred the data into a computerized retrieval system from which this summary booklet was derived. This inventory is an integral part of the Virginia Outdoors Plan Information System. Its primary purpose is to provide information enabling the Commission to formulate and write the statewide comprehensive outdoor recreation plan. It is also envisaged that this data be used for other planning activities, such as those conducted by federal and state agencies or the 22 Planning District Commissions in Virginia, as well as for various research activities, special studies, and teaching. The purpose of this publication is threefold. The first is to encourage further use of the data it contains by informing the public of the specific type of information available. The second is to present a comprehensive set of data which can be used to answer general inquiries about the nature and distribution of recreation resources in the Commonwealth of Virginia. Finally, it is to advise that this data is available in other forms which may better suit individual needs but which would be too numerous to publish in this bulletin.
2018 Annual Report: Virginia Big Tree Program
Wiseman, P. Eric (Virginia Tech. Department of Forest Resources and Environmental Conservation, 2018)
The Virginia Big Tree Program is a public outreach program coordinated by Virginia Cooperative Extension and the Department of Forest Resources and Environmental Conservation at Virginia Tech. The program maintains a register of the 3 largest specimens of over 300 native, non-native, and naturalized tree species. This annual report details program accomplishments in 2018, including Big Tree reports, national rankings, and student intern contributions.
2019 FREC Annual Report
(Virginia Tech, 2019)
This is the 2019 annual report for the Department of Forest Resources and Environmental Conservation.
A modular curriculum to teach undergraduates ecological forecasting improves student and instructor confidence in their data science skills
Lofton, Mary E.; Moore, Tadhg N.; Woelmer, Whitney M.; Thomas, R. Quinn; Carey, Cayelan C. (Oxford University Press, 2024-10-10)
Data science skills (e.g., analyzing, modeling, and visualizing large data sets) are increasingly needed by undergraduates in the life sciences. However, a lack of both student and instructor confidence in data science skills presents a barrier to their inclusion in undergraduate curricula. To reduce this barrier, we developed four teaching modules in the Macrosystems EDDIE (for environmental data-driven inquiry and exploration) program to introduce undergraduate students and instructors to ecological forecasting, an emerging subdiscipline that integrates multiple data science skills. Ecological forecasting aims to improve natural resource management by providing future predictions of ecosystems with uncertainty. We assessed module efficacy with 596 students and 26 instructors over 3 years and found that module completion increased students’ confidence in their understanding of ecological forecasting and instructors’ likelihood to work with long-term, high-frequency sensor network data. Our modules constitute one of the first formalized data science curricula on ecological forecasting for undergraduates.
Above- and Below-Ground Carbon Sequestration in Shelterbelt Trees in Canada: A Review
Mayrinck, Rafaella C.; Laroque, Colin P.; Amichev, Beyhan Y.; Van Rees, Ken (MDPI, 2019-10-19)
Shelterbelts have been planted around the world for many reasons. Recently, due to increasing awareness of climate change risks, shelterbelt agroforestry systems have received special attention because of the environmental services they provide, including their greenhouse gas (GHG) mitigation potential. This paper aims to discuss shelterbelt history in Canada, and the environmental benefits they provide, focusing on carbon sequestration potential, above- and below-ground. Shelterbelt establishment in Canada dates back to more than a century ago, when their main use was protecting the soil, farm infrastructure and livestock from the elements. As minimal-and no-till systems have become more prevalent among agricultural producers, soil has been less exposed and less vulnerable to wind erosion, so the practice of planting and maintaining shelterbelts has declined in recent decades. In addition, as farm equipment has grown in size to meet the demands of larger landowners, shelterbelts are being removed to increase efficiency and machine maneuverability in the field. This trend of shelterbelt removal prevents shelterbelt’s climate change mitigation potential to be fully achieved. For example, in the last century, shelterbelts have sequestered 4.85 Tg C in Saskatchewan. To increase our understanding of carbon sequestration by shelterbelts, in 2013, the Government of Canada launched the Agricultural Greenhouse Gases Program (AGGP). In five years, 27 million dollars were spent supporting technologies and practices to mitigate GHG release on agricultural land, including understanding shelterbelt carbon sequestration and to encourage planting on farms. All these topics are further explained in this paper as an attempt to inform and promote shelterbelts as a climate change mitigation tool on agricultural lands.
Above-ground tree carbon storage in response to nitrogen deposition in the US is heterogeneous and may have weakened
Clark, Christopher M.; Thomas, R. Quinn; Horn, Kevin J. (Springer Nature, 2023-02-14)
Long-term nitrogen deposition may not provide sustained stimulation of tree carbon storage, suggest analyses of a tree inventory and growth for the contiguous US between 2000 and 2016, compared to data for the 1980s and 1990s. Changes in nitrogen (N) availability affect the ability for forest ecosystems to store carbon (C). Here we extend an analysis of the growth and survival of 94 tree species and 1.2 million trees, to estimate the incremental effects of N deposition on changes in aboveground C (dC/dN) across the contiguous U.S. (CONUS). We find that although the average effect of N deposition on aboveground C is positive for the CONUS (dC/dN = +9 kg C per kg N), there is wide variation among species and regions. Furthermore, in the Northeastern U.S. where we may compare responses from 2000-2016 with those from the 1980s-90s, we find the recent estimate of dC/dN is weaker than from the 1980s-90s due to species-level changes in responses to N deposition. This suggests that the U.S. forest C-sink varies widely across forests and may be weakening overall, possibly necessitating more aggressive climate policies than originally thought.
Accuracy of Visible and Ultraviolet Light for Estimating Live Root Proportions with Minirhizotrons
Wang, Z. Q.; Burch, W. H.; Mou, P.; Jones, R. H.; Mitchell, R. J. (Ecological Society of America, 1995-10)
Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting
Carey, Cayelan C.; Woelmer, Whitney M.; Lofton, Mary E.; Figueiredo, Renato J.; Bookout, Bethany J.; Corrigan, Rachel S.; Daneshmand, Vahid; Hounshell, Alexandria G.; Howard, Dexter W.; Lewis, Abigail S. L.; McClure, Ryan P.; Wander, Heather L.; Ward, Nicole K.; Thomas, R. Quinn (2021-01-18)
Near-term, iterative ecological forecasts with quantified uncertainty have great potential for improving lake and reservoir management. For example, if managers received a forecast indicating a high likelihood of impending impairment, they could make decisions today to prevent or mitigate poor water quality in the future. Increasing the number of automated, real-time freshwater forecasts used for management requires integrating interdisciplinary expertise to develop a framework that seamlessly links data, models, and cyberinfrastructure, as well as collaborations with managers to ensure that forecasts are embedded into decision-making workflows. The goal of this study is to advance the implementation of near-term, iterative ecological forecasts for freshwater management. We first provide an overview of FLARE (Forecasting Lake And Reservoir Ecosystems), a forecasting framework we developed and applied to a drinking water reservoir to assist water quality management, as a potential open-source option for interested users. We used FLARE to develop scenario forecasts simulating different water quality interventions to inform manager decision-making. Second, we share lessons learned from our experience developing and running FLARE over 2 years to inform other forecasting projects. We specifically focus on how to develop, implement, and maintain a forecasting system used for active management. Our goal is to break down the barriers to forecasting for freshwater researchers, with the aim of improving lake and reservoir management globally.
Aids for Unit Planning on the Appalachian National Forests
Burkhart, Harold E.; Leuschner, William A.; Stuck, R. Dean; Porter, John R.; Reynolds, Marion R. Jr. (Virginia Tech. Division of Forestry and Wildlife Resources, 1976)
This report summarizes the results of studies conducted in response to a cooperative agreement between the Southern Region, U.S. Forest Service and the Department of Forestry and Forest Products, Virginia Polytechnic Institute and State University. The objective of the agreement was to improve National Forest management planning techniques. The agreement covered the period July 1, 1973 to June 30, 1975. Literature citations are given for those who desire additional detail.
Alternate Trait-Based Leaf Respiration Schemes Evaluated at Ecosystem-Scale Through Carbon Optimization Modeling and Canopy Property Data
Thomas, R. Quinn; Williams, M.; Cavaleri, M. A.; Exbrayat, J. -F.; Smallman, T. L.; Street, L. E. (American Geophysical Union, 2019-12-25)
Leaf maintenance respiration (Rleaf,m) is a major but poorly understood component of the terrestrial carbon cycle (C). Earth systems models (ESMs) use simple sub-models relating Rleaf,m to leaf traits, applied at canopy scale. Rleaf,m models vary depending on which leaf N traits they incorporate (e.g., mass or area based) and the form of relationship (linear or nonlinear). To simulate vegetation responses to global change, some ESMs include ecological optimization to identify canopy structures that maximize net C accumulation. However, the implications for optimization of using alternate leaf-scale empirical Rleaf,m models are undetermined. Here we combine alternate well-known empirical models of Rleaf,m with a process model of canopy photosynthesis. We quantify how net canopy exports of C vary with leaf area index (LAI) and total canopy N (TCN). Using data from tropical and arctic canopies, we show that estimates of canopy Rleaf,m vary widely among the three models. Using an optimization framework, we show that the LAI and TCN values maximizing C export depends strongly on the Rleaf,m model used. No single model could match observed arctic and tropical LAI-TCN patterns with predictions of optimal LAI-TCN. We recommend caution in using leaf-scale empirical models for components of ESMs at canopy-scale. Rleaf,m models may produce reasonable results for a specified LAI, but, due to their varied representations of Rleaf,mfoliar N sensitivity, are associated with different and potentially unrealistic optimization dynamics at canopy scale. We recommend ESMs to be evaluated using response surfaces of canopy C export in LAI-TCN space to understand and mitigate these risks.
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.
An Analysis of Several Alternatives to Oil As an Industrial Fuel
Kluender, Richard A.; Reisinger, Thomas W.; Farrar, Kenneth D.; Stuart, William B. (Virginia Tech. Division of Forestry and Wildlife Resources, 1981-10)
This paper presents an analysis of some significant factors that should be evaluated when considering alternatives to oil burning boilers. Managers contemplating using or increasing the use of wood for energy should find the analysis particularly pertinent. A fundamental assumption of the analysis is that additional boiler capacity is to be added to an existing power generating facility. The method of analysis provides a yearly cash flow stream that tells how much better off the installation would be with an alternative to oil. A logical extension of this is the ran king of alternatives from most to least attractive.
Analyzing Trade-Offs, Synergies, and Drivers among Timber Production, Carbon Sequestration, and Water Yield in Pinus elliotii Forests in Southeastern USA
Cademus, Ronald; Escobedo, Francisco J.; McLaughlin, Daniel L.; Abd-Elrahman, Amr (MDPI, 2014-06-20)
Managing Pinus elliotii forests for timber production and/or carbon sequestration is a common management objective, but can negatively affect water yield due to high losses from evapotranspiration. Thus, understanding the trade-offs and potential synergies among multiple ecosystem goods services, as well as the drivers influencing these interactions, can help identify effective forest management practices. We used available data from 377 permanent plots from the USDA Forest Service Forest Inventory and Analysis Program for 2002–2011, and a forest water yield model to quantify provision levels and spatial distribution and patterns of carbon sequestration, timber volume and water yield for Pinus elliotii ecosystems in North Florida, USA. A ranking-classification framework and statistical analyses were used to better understand the interactions among ecosystem services and the effect of biophysical drivers on ecosystem service bundles. Results indicate that increased biomass reduced water yield but this trade-off varied across space. Specific synergies, or acceptable provision levels, among carbon sequestration, timber volume and water yield were identified and mapped. Additionally, stand age, silvicultural treatment, and site quality significantly affected the provision level of, and interactions among, the three ecosystem goods and services. The framework developed in this study can be used to assess, map, and manage subtropical forests for optimal provision of ecosystem services.
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.
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)
Assessing Biotic and Abiotic Effects on Biodiversity Index Using Machine Learning
Bayat, Mahmoud; Burkhart, Harold E.; Namiranian, Manouchehr; Hamidi, Seyedeh Kosar; Heidari, Sahar; Hassani, Majid (MDPI, 2021-04-10)
Forest ecosystems play multiple important roles in meeting the habitat needs of different organisms and providing a variety of services to humans. Biodiversity is one of the structural features in dynamic and complex forest ecosystems. One of the most challenging issues in assessing forest ecosystems is understanding the relationship between biodiversity and environmental factors. The aim of this study was to investigate the effect of biotic and abiotic factors on tree diversity of Hyrcanian forests in northern Iran. For this purpose, we analyzed tree diversity in 8 forest sites in different locations from east to west of the Caspian Sea. 15,988 trees were measured in 655 circular permanent sample plots (0.1 ha). A combination of machine learning methods was used for modeling and investigating the relationship between tree diversity and biotic and abiotic factors. Machine learning models included generalized additive models (GAMs), support vector machine (SVM), random forest (RF) and K-nearest–neighbor (KNN). To determine the most important factors related to tree diversity we used from variables such as the average diameter at breast height (DBH) in the plot, basal area in largest trees (BAL), basal area (BA), number of trees per hectare, tree species, slope, aspect and elevation. A comparison of RMSEs, relative RMSEs, and the coefficients of determination of the different methods, showed that the random forest (RF) method resulted in the best models among all those tested. Based on the results of the RF method, elevation, BA and BAL were recognized as the most influential factors defining variation of tree diversity.
Assessing Ecosystem State Space Models: Identifiability and Estimation
Smith, John W.; Johnson, Leah R.; Thomas, R. Quinn (Springer, 2023-03)
Hierarchical probability models are being used more often than non-hierarchical deterministic process models in environmental prediction and forecasting, and Bayesian approaches to fitting such models are becoming increasingly popular. In particular, models describing ecosystem dynamics with multiple states that are autoregressive at each step in time can be treated as statistical state space models (SSMs). In this paper, we examine this subset of ecosystem models, embed a process-based ecosystem model into an SSM, and give closed form Gibbs sampling updates for latent states and process precision parameters when process and observation errors are normally distributed. Here, we use simulated data from an example model (DALECev) and study the effects changing the temporal resolution of observations on the states (observation data gaps), the temporal resolution of the state process (model time step), and the level of aggregation of observations on fluxes (measurements of transfer rates on the state process). We show that parameter estimates become unreliable as temporal gaps between observed state data increase. To improve parameter estimates, we introduce a method of tuning the time resolution of the latent states while still using higher-frequency driver information and show that this helps to improve estimates. Further, we show that data cloning is a suitable method for assessing parameter identifiability in this class of models. Overall, our study helps inform the application of state space models to ecological forecasting applications where (1) data are not available for all states and transfers at the operational time step for the ecosystem model and (2) process uncertainty estimation is desired.

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