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Valerie Thomas


Dr. Valerie Thomas

Dr. Valerie Thomas
307A Cheatham Hall
  • B.S., University of Guelph, Canada, 1996 
  • M.S., Queen’s University, Canada, 2001 
  • Ph.D., Queen’s University, Canada, 2006 

My research program contributes to two major science questions that have emerged to be critical areas of investigation in the fields of remote sensing and global monitoring of forest ecosystems.

**Graduate student opportunities are available to investigate these topics.**

  1. How does forest structure relate to canopy physiology, and what drives changes in these relationships across geographic and environmental gradients? In other words, what controls forest productivity and how will forests respond to existing and future pressures?

    I have been approaching these questions through the synergistic use of field data, multitemporal Landsat data, and two cutting-edge remote sensing technologies: light detection and ranging (lidar) and hyperspectral sensors. My work in this area contributes to a better understanding of forest structure and function under varying environmental and geographic conditions, and is important for questions regarding global carbon, disturbance, and ecosystem function.  Examples of ongoing and future work in this area includes: A) a regional assessment of ecosystem nitrogen retention and the drivers of nitrogen leakage across the southeast to determine the susceptibility of ecosystems to future scenarios of increased atmospheric nitrogen deposition, and B) assessment of forest status and change through multitemporal images.

  2. How do we handle “Big Data” in the field of remote sensing to gain new insights into ecosystem function and disturbance?

    This question parallels similar issues in many other fields that have emerged due to a number of converging factors including rapidly developing national and global environmental challenges, changes in technology, the compilation of many long-term sensor networks, and the general trend toward freely accessible massive multitemporal remote sensing datasets, particularly the Landsat series. I have ongoing projects examining landscape change using multitemporal satellite data for wetlands and forests. This involves the development of new computational approaches and techniques to better integrate remote sensing with field data. To further secure our competitive edge in this area, we are investigation high performance computing approaches, including Hadoop clusters for distributed processing of our massively parallel algorithms and remote sensing images.
  • FREC 1004 Digital Planet
  • FREC 2124 Forests, Society & Climate
  • FREC 5154 Hyperspectral Remote Sensing of Natural Resources
  • FREC 6214 Forestry Lidar Applications 
  • At the undergraduate level, I have been heavily involved in the development and implementation of a new undergraduate Environmental Informatics major, which started in Fall of 2013.  The goal is to train students in the application of information science to environmental management.  Students will learn to address “Big Data” questions that are emerging in this area. Our emphasis on forest applications and geospatial technologies makes our program unique in the United States.
  • At the Graduate Level, I am an advisor for the new “Interdisciplinary Remote Sensing” PhD Program, the College of Natural Resources and Environment “Geospatial and Environmental Analysis” PhD Program, and the Department of Forest Resources and Environmental Conservation Masters of Science and Ph.D. programs.

Interdisciplinary Remote Sensing Program:
GEA Program:
Center for Environmental Applications in Remote Sensing (CEARS):

  • Hwang, W.H., E. Wiseman, and V.A. Thomas.  2016 (in press). Enhancing the energy conservation benefits of shade trees in dense residential developments using an alternative tree placement strategy.  Landscape and Urban Planning. Accepted Sept. 26, 2016.
  • Brooks, E.B, J.W. Coulston, R.H. Wynne, and V.A. Thomas, 2016. Improving the precision of dynamic forest parameter estimates using Landsat. Remote Sensing of Environment, 179:162-169.
  • Hwang, W.H., E. Wiseman, and V.A. Thomas, 2016. Simulation of shade tree effects on residential energy consumption for four U.S. cities. Cities and the Environment, 9(1).
  • Sumnall, M., A. Peduzzi, T.R. Fox, R.H. Wynne, and V.A. Thomas, 2016. Analysis of lidar voxel-derived vertical profile at the plot and individual tree scales for the estimation of forest canopy layer characteristics. International Journal of Remote Sensing. 37 (11): 2653-2681.
  • Sumnall, M.J., A. Peduzzi, T.R. Fox, R.H. Wynne, V.A. Thomas, and B. Cook, 2016. 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. Remote Sensing of Environment 176: 308-319.
  • Sumnall, M., T.R. Fox, R.H. Wynne, C. Blinn, and V.A. Thomas, 2016. Estimating leaf area index at multiple heights within the understory component of Loblolly pine forests from airborne discrete-return lidar. International Journal of Remote Sensing. 37:78-99.
  • Gopalakrishnan, R., V.A. Thomas, J. Coulston, and R.H. Wynne. 2015. Efficacy of using heterogeneous lidar datasets in predicting canopy heights over a large region. Remote Sensing, 7: 11036-11060.
  • Gokkaya, K., V.A. Thomas, T. Noland, J.H. McCaughey, P.M. Treitz, I. Morrison, 2015. Prediction of Macronutrients at the Canopy Level Using Spaceborne Imaging Spectroscopy and Lidar Data in a Mixedwood Boreal Forest. Remote Sensing, 7(7): 9045-9069.
  • Coulston, J., C. Blinn, V.A. Thomas, and R.H. Wynne, 2016. Approximating prediction uncertainty for random forest models. Photogrammetric Engineering & Remote Sensing, 82(3): 189-197.
  • Hwang, W.H., P.E. Wiseman, and V.A. Thomas, 2015. Tree Planting Configuration Influences Shade on Residential Structures in Four U.S. Cities, in press with Arboriculture & Urban Forestry.  41(4): 208-222.
  • Yu, L. S.Ball, C. Blinn, K. Moeltner, S. Peery, V. Thomas, and R. Wynne, 2015. Cloud-Sourcing: Using an online labor force to detect clouds and cloud shadows in Landsat images.  Remote Sensing, 7: 2334-2351.
  • Banskota, A., S.P. Serbin, R.H. Wynne, V.A. Thomas, M.J. Falkowski, N. Kayastha, J.P. Gastellu-Etchegorry, and P.A. Townsend, 2015. A LUT-Based Inversion of DART model to Estimate Forest LAI from Hyperspectral Data., Journal of Selected Topics in Applied Earth Observations in Remote Sensing. 8(6):3147-3160.
  • Gokkaya, K.*, V. Thomas, T. Noland, H. McCaughey, I Morrison, and P. Treitz, 2015. Mapping continuous forest type variation by means of correlating remotely sensed metrics to canopy nitrogen to phosphorus ratio in a boreal mixedwood forest. Applied Vegetation Science, 18: 143-157.
  • Oliver, R. and V. Thomas, 2014. Micropolitan land conversion to development in Appalachia and the Black Belt. Southeastern Geographer, 54(4): 366-383.
  • Wu, Y-J., V. Thomas, and R.D. Oliver. 2014. Forest Change Dynamics Across Levels of Urbanization in the Eastern United States. Southeastern Geographer 54(4): 406-420.
  • Stein, B.*, V.A. Thomas, L.J. Lorentz, and B.D. Strahm, 2014. Predicting macronutrient concentrations from loblolly pine leaf reflectance across local and regional scales, GIScience & Remote Sensing, 51:3, 269-287.
  • Brooks, E.B.*, R.H. Wynne, V.A. Thomas, C.E. Blinn, and J.W. Coulston, 2014. On-the-fly massively multitemporal change detection using statistical quality control charts and Landsat data. IEEE Transactions on Geoscience and Remote Sensing, 52(6): 3316-3332.
  • Oliver, R. and V. Thomas, 2014. Micropolitan areas: Exploring the linkages between demography and land-cover change in the United States.  Cities, 38: 84-94.
  • Gokkaya, K.*, V. Thomas, H. McCaughey, and P. Treitz,. 2013. Testing the robustness of predictive models for chlorophyll generated from spaceborn imaging spectroscopy data for a mixedwood boreal forest canopy. International Journal of Remote Sensing, 35:1, 218-233.
  • Banskota, A.*, R.H. Wynne, V.A. Thomas, S.P. Servin, N. Kayastha, J.P. Gastellu-Etchegorry, and P.A. Townsend, 2013. Investigating the utility of wavelet transforms for inverting a 3-D radiative transfer model using hyperspectral data to retrieve forest LAI. Remote Sensing, 5: 2639-2659.
  • Kim, D., V. Thomas, J. Olson, M. Williams, and N. Clements, 2013. Statistical Trend and Change Point Analysis of Land Cover Change Patterns in East Africa. International Journal of Remote Sensing, 34(19): 6636-6650.
  • Banskota, A.*, R.H. Wynne, S.P. Serbin, N. Kayastha, V.A. Thomas, and P.A. Townsend, 2013. Utility of the Wavelet transform for LAI estimation using hyperspectral data. Photogrammetric Engineering & Remote Sensing, 79: 653-662.
  • Kayastha, N.*, V. Thomas, J. Galbraith, and A. Banskota, 2012. Monitoring Wetland Change Using Inter-Annual Landsat Time-Series Data. Wetlands,32(6): 1149-1162.
  • Peduzzi, A.*, R.H. Wynne, V.A. Thomas, R.F. Nelson, J.J. Reis and M. Sanford, 2012. Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests. Remote Sensing, 4(6): 1758-1780.
  • Brooks, E.B.*, V.A. Thomas, R.H. Wynne, and J.W. Coulston, 2012. Fitting the multitemporal curve: a fourier series approach to the missing data problem in remote sensing analysis. IEEE Transactions on Geoscience and Remote Sensing, 50(9): 3340-3353.
  • Peduzzi, A.*, R.H. Wynne, T.R. Fox, R.F. Nelson, and V.A. Thomas. 2012. Estimating leaf area index in intensively managed pine plantations using airborne laser scanning data. Forest Ecology and Management, 270: 54-65.
  • Thomas, V., T. Noland, J.H. McCaughey, and P. Treitz (2011). Leaf area and clumping indices for a boreal mixedwood forest: lidar and hyperspectral models. International Journal of Remote Sensing, 32(23): 8271-8297.
  • Thomas, V., J.H. McCaughey, P. Treitz, D.A. Finch, T. Noland, and L. Rich, 2009. Spatial modelling of photosynthesis for a boreal mixedwood forest by integrating micrometeorological, lidar and hyperspectral remote sensing data.  Agricultural and Forest Meteorology, 149: 639-654.
  • Thomas, V., R. Oliver, K. Lim, and M. Woods, 2008. Lidar and Weibull modeling of diameters and basal area distributions. The Forestry Chronicle, 84(6):866-875.
  • Thomas, V., P. Treitz, J.H. McCaughey, T. Noland, and L. Rich, 2008. Canopy chlorophyll concentration estimation using hyperspectral and lidar data for a boreal mixedwood forest in northern Ontario, Canada. International Journal of Remote Sensing, 29(4): 1029-1052.
  • Sun, J., C. Peng, H. McCaughey, X. Zhou, V. Thomas, F. Berninger, B. St-Onge, and D. Hua, 2008. Simulating carbon exchange of Canadian boreal forests II. Comparing the carbon budgets of a boreal mixedwood stand to a black spruce forest stand.  Ecological Modelling, 219: 276-286.
  • Thomas, V., D.A. Finch, J.H. McCaughey, T. Noland, L. Rich, and P. Treitz, 2006. Spatial modelling of the fraction of photosynthetically active radiation absorbed by a boreal mixedwood forest using a lidar-hyperspectral approach. Agricultural and Forest Meteorology, 140: 287-307.
  • Thomas, V., P. Treitz, J.H. McCaughey, and I. Morrison, 2006. Mapping stand-level forest biophysical variables for a mixedwood boreal forest using lidar: an examination of scanning density. Canadian Journal of Forest Research, 36: 34-47.
  • Thomas, V., P. Treitz, D. Jelinski, J. Miller, P. Lafleur, and J.H. McCaughey, 2002. Image classification of a northern peatland complex using spectral and plant community data, Remote Sensing of Environment, 84(1): 83-99.
  • Massam, B., B. Prenzel, V. Thomas, and P. Treitz, 2000. Quality of Life Surfaces: An Application of Two Techniques, Journal of Geographic Information and Decision Analysis, 4:12-26.