About

Our faculty are engaged and dedicated educators, advisors, and mentors and have been honored with numerous university-wide and national teaching awards. Our classes emphasize the latest research coupled with cutting-edge technology and practices making our graduates among the most competitive candidates in the country for natural resource professions. Our curricula include everything from protected lands management and urban forestry, to industrial forestry operations and ecology. Small class sizes and faculty dedicated to teaching afford FREC students the chance to get to know their professors personally. Wide varieties of academic and professional opportunities are available through research, student organizations, and public outreach programs organized by the faculty.

Research topics

• Geography
• Computer Science
• Environmental science
• Remote sensing
• Forestry
• Sociology
• Cartography
• Geology
• Social Science
• Machine Learning
• Ecology
• Natural resource economics
• Archaeology
• Biology
• Mathematics
• Psychology
• Economics
• Business
• Agroforestry
• Engineering
• Botany
• Agronomy
• Social psychology
• Agricultural economics

Selected publications

• Estimating Pinus taeda (L.) Timber Volume with Multi-Temporal SAR-Optical Fusion and Supervised Learning Techniques

  SSRN Electronic Journal · 2026-01-01

  preprintOpen access
  PublisherDOI

• Factors Influencing the Consistency in Crowdsourced Interpretations of Aerial Photographs to Measure Tree Canopy Cover

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Factors influencing the consistency in crowdsourced interpretations of aerial photographs to measure tree canopy cover

  Ecological Informatics · 2025-07-15

  articleOpen accessCorresponding

  Machine learning models are typically data-hungry algorithms that require large data inputs for training. When they produce wall-to-wall remote sensing products, model validation also requires large sets of temporally harmonized field observations. Crowdsourcing may offer a potential solution for the collection of photointerpretations for the training and validation of spatial models of tree canopy cover (TCC), as it harnesses the power of a large anonymous crowd in the completion of repetitive discrete analyses or human intelligence tasks (HITs). This study explores the factors that determine the consistency of TCC interpretations collected by an anonymous crowd to those collected by a control group. The crowd interpretations were obtained through an anonymous platform with a task-reward framework, while those collected by the control group were collected by known interpreters in a more traditional setting. Both groups carried out this task using an interface developed for Amazon’s Mechanical Turk platform. We collected multiple interpretations at sample plot locations from both crowd and control interpreters, and sampled these data in a Monte Carlo framework to estimate a classification model predicting the consistency of each crowd interpretation with control interpretations. Using this model, we identified the most important variables in estimating the relationship between a location’s characteristics and interpretation behaviors which affect consistency in interpretations between crowd workers our control group. Overall, we show low agreement between crowdsourced and control interpretations, as well as interpretations from individual control group members. This warrants caution in considering the crowdsourced photointerpretation of TCC as a data source for model training and validation without adequate interpreter training as well as significant quality control measures and consistency standards. We show that the number of plots interpreted was the strongest indicator of the reliability of an individual’s interpretations, further evidenced by apparent fatigue effects in crowd interpretations. The second most important variable related to the use of the false color display during interpretation followed by a variable related to the use of the natural color display during interpretation, reflecting the differences in interpretation methodologies used by crowd workers and control group interpreters and the impact display has on the interpretation of tree canopy cover. Finally, we discuss recommendations for further study and future implementations of crowdsourced photointerpretation. These include the enhanced use of existing mechanisms within Mechanical Turk such as worker qualifications to identify and reward more attentive workers, as well as enhanced attention to quality control measures throughout the data collection process and measures to increase intrinsic motivation. For our study we also recommend a minimum time on task or other measures to reduce the punishment of access to HITs for workers who took their time providing detailed interpretations. We also recommend using optimized default interface settings instead of providing a variety of options to the interpreter. • We evaluated the photointerpretation of tree canopy cover (a continuous variable) based on application utilization and various worker behaviors. • Photointerpretation of tree canopy cover can result in variability among control interpreters as well as crowd workers, and so should be used with caution. • Statistical tests show that reliability of crowd photointerpretation of tree canopy cover is related most significantly to interpreter fatigue, followed by interface utilization and time-money motivations. • For studies with similar applications and methods, we recommend robust quality control measures, evaluation of fair wages, mechanisms to identify and reward attentive workers, and interface design for optimal interpreter performance.

  PublisherDOI

• Characterizing the influence of varying functional traits from remotely sensed data on forest productivity acquired from selected NEON sites

  Maryland Shared Open Access Repository (USMAI Consortium) · 2025-12-01

  articleOpen access
  PublisherDOI

• Comparing Canopy Height Models from Regional-Scale Aerial Photogrammetry with Global Spaceborne Lidar-Derived Data for Estimating Forest Volume and Biomass

  Forest Science · 2025-05-27

  article
  PublisherDOI

• Predicting the Yield of Pinus Taeda (L.) Using Uav Lidar Data in Random Forest and Support Vector Machine Models

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• A regional coastal Douglas-fir index of site quality for young stands in western Washington, USA

  Forest Ecology and Management · 2025-08-12

  article
  PublisherDOI

• Predicting the Yield of Pinus Taeda (L.) Using Uav Lidar Data in Random Forest and Support Vector Machine Models

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Predicting the yield of Pinus taeda (L.) using UAV LiDAR data in random forest and support vector machine models

  Forest Ecology and Management · 2025-07-15 · 2 citations

  article
  PublisherDOI

• Characterizing the influence of varying functional traits from remotely sensed data on forest productivity acquired from selected NEON sites

  Science of Remote Sensing · 2025-07-24 · 2 citations

  articleOpen access

  Gross primary productivity (GPP) describes total photosynthesis (carbon fixation) in an ecosystem and is key to the global land carbon budget. To reduce uncertainties in carbon accounting for different forest ecosystems, it is crucial to analyze the health and productivity of forested ecosystems. Plant functional traits, which are a combination of morphological, physiological, and environmental characteristics, have been shown to be predictive of forest ecosystem carbon dynamics. This study aimed to assess how well GPP can be predicted by remotely quantified functional traits across varying forested ecosystems. Airborne remote sensing observations and in situ flux tower measurements used in this analysis were acquired from selected forested sites from the National Ecological Observatory Network (NEON) data portal. We investigated hyperspectral indices and lidar derived products as proxies of remotely sensed plant functional traits. Average midday GPP around the date of flight was calculated by developing a relationship between night respiration and temperature and removing that component from the net surface-atmosphere CO 2 exchange (NSAE). We applied multiple linear regression with a best subset approach for three trait classes: morphological and environmental traits from lidar, physiological traits from hyperspectral data, and a combined functional trait model. The best-performing model, using lidar and hyperspectral traits, included CHM mean, DSM standard deviation, PRI standard deviation, and WBI mean producing a R 2 of 0.87, an adjusted R 2 of 0.84, a PRESS R 2 of 0.75 and RMSE of 3.48 μmol CO 2 /m 2 /s. Results show that a combination of plant functional traits are important predictors of forest productivity. • Integrating multiple plant functional traits is important to predict forest productivity accurately. • All three types of remotely sensed plant functional traits were significant predictors of forest productivity — morphological (canopy structure), environmental (topography), and physiological (leaf water content, light use efficiency). • GPP generally increases with canopy height, with the exception of an old-growth site. • Dominant vegetation types describe the range of forest functional traits contributing to productivity.

  PublisherDOI

Frequent coauthors

• Randolph H. Wynne

  Virginia Tech

  74 shared

• John W. Coulston

  20 shared

• Evan B. Brooks

  16 shared

• Paul Treitz

  Queen's University

  12 shared

• Thomas L. Noland

  Ontario Forest Research Institute

  11 shared

• Christine E. Blinn

  Virginia Tech

  11 shared

• Matthew J. Sumnall

  Virginia Tech

  9 shared

• J. H. McCaughey

  9 shared