I was born in Pelham, in the Niagara Region of southern Ontario. My undergraduate training in Biology and Geography was obtained at Brock University (B.Sc. (Hons) –1983, BEd – 1986) and my graduate degrees were acquired at the University of Waterloo (M.A. – 1986, PhD – 1997). From 1989-1995, I was a research scientist in the Earth Observations Laboratory at the University of Waterloo. In 1995 I accepted an academic appointment in the Department of Geography and Faculty of Environmental Studies at York University in Toronto. I moved to Kingston with my wife and two children in 1999 to accept a faculty position with the Department of Geography at Queen’s University. While at Queen’s, I have served the Department as Graduate Chair (2002-2006), Acting Head (2007-2008), Associate Head (2008-2009) and Head of Department (2010-present).
As part of my graduate training, I was a teaching assistant for courses in physical geography and remote sensing. Through this experience, I found that I really enjoyed teaching and working with undergraduate students. This led to my pursuit of a B.Ed. degree. I spent a year (1986-87) in Fenelon Falls, Ontario, teaching high school geography and then two years (1987-89) at the School of Natural Resources, Sir Sandford Fleming College, Lindsay, Ontario teaching remote sensing and photogrammetry. However, the desire to pursue research activities and bring those experiences to the classroom eventually directed me back to school to pursue my PhD. Since arriving at Queen’s, I have developed a suite of remote sensing and digital image processing courses at the undergraduate and graduate levels. My graduate students pursue a range of remote sensing related research interests. These research activities tend to revolve around the application of high spatial and spectral (i.e., hyperspectral, LiDAR) remote sensing data to characterize boreal forest and arctic ecosystems. I have supervised graduate students whose research and field work has extended from the Canadian High Arctic to the equator (Indonesia).
My research focus, and that of my students is on the application of remote sensing data for estimating biophysical variables (e.g., percent cover, aboveground biomass, leaf area index (LAI), fPAR) of arctic and boreal ecosystems. These biophysical variables are linked to many ecosystem processes. For example, biomass information plays a significant role in assessing carbon stocks; is an important element in global change and productivity models; and is a measure of vegetation community structure which influences biodiversity. Current research projects include the examination of tolerant hardwood and boreal forests using LiDAR data to characterize forest stand structure and estimate biophysical/physiological variables. In addition, we are examining satellite remote sensing data and spectral derivatives to classify arctic vegetation communities and estimate biophysical/ecosystem variables with the purpose of linking these to carbon dioxide exchange at study sites on Boothia Peninsula, Melville Island, and Baffin Island, Nunavut. Further, we are also examining the utility of RADARSAT synthetic aperture radar (SAR) data to model soil moisture and permafrost degradation across Arctic watersheds. Central to these studies, and our research, is the influence of spatial resolution on the estimation of these ecosystem/biophysical variables at local and regional scales.
- Remote Sensing of Environmental Change across Northern Terrestrial Ecosystems (NSERC Discovery Grant)
- Precision Planning Inventory Tools for Forest Value Enhancement (GEOIDE Strategic Investment Initiative)
- High Arctic Hydrological, Landscape and Ecosystem Responses to Climate Change: Integrated Watershed Research at the Cape Bounty Arctic Watershed Observatory, Melville Island (ArcticNet)
- Modelling High Arctic Permafrost Landscape Stability and Water Quality for Changing Climate and Resource Development (NSERC Strategic Grant)
- Assessing Forest Biomass as a Bioenergy Feedstock: the Availability and Recovery of biomass in uneven-aged forests (Department of Natural Resources: ecoEnergy Innovation Initiative – Research and Development)
To support this research, I have developed the Laboratory for Remote Sensing
of Earth and Environmental Systems (LARSEES - http://www.geog.queensu.ca/larsees/).
Please visit this website to see more about my research and that of my
Recent Refereed Publications:
Van Ewijk, K., Randin, C., Treitz, P., Scott, N., 2014. Predicting fine-scale species abundance patterns using biotic variables derived from LiDAR and high spatial resolution imagery, Remote Sensing of Environment, (accepted April 2014).
Tamminga, A., N. Scott, P. Treitz and M. Woods, 2014. A biogeochemical examination of Ontario’s Boreal Forest Ecosite Classification System, Forests, 5:325-346. doi:10.3390/f5020325
Collingwood, A., P. Treitz, F. Charbonneau and D. Atkinson, 2014. Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data. Remote Sensing, 6:2134-2153. doi:10.3390/rs6032134.
Collingwood, A., P. Treitz, & F. Charbonneau, 2014. Surface roughness estimation from Radarsat-2 data in a High Arctic environment. International Journal of Applied Earth Observation and Geoinformation, 27:70-80.
Gökkaya, K., V. Thomas, T. Noland, J.H. McCaughey and P. Treitz, 2014. Testing the robustness of predictive models for chlorophyll generated from spaceborne imaging spectroscopy data in mixedwood boreal forest canopy. International Journal of Remote Sensing, 35(1):218-233.
Atkinson, D., and P. Treitz, 2013. Modeling biophysical variables across an arctic latitudinal gradient using high spatial resolution remote sensing data. Arctic, Antarctic and Alpine Research, 45(2):161-178.
Pope, G., and P. Treitz, 2013. Leaf area index (LAI) estimation in boreal mixedwood forest of Ontario, Canada using light detection and ranging (LiDAR) and WorldView-2 Imagery, Remote Sensing, 5:5040-5063. doi:10.3390/rs5105040
Rudy, A.C.A., S.F. Lamoureux, P. Treitz, and A. Collingwood, 2013. Identifying permafrost slope disturbance using multi-temporal optical satellite images and change detection techniques. Cold Regions Science and Technology, 88:37-49.
Atkinson, D.M., and P.M. Treitz, 2012. Arctic Ecological Classifications Derived from Vegetation Community and Satellite Spectral Data, Remote Sensing, 4:3948-3971.
Southee, F.M., P. Treitz and N. Scott, 2012. Application of LIDAR terrain surfaces for soil moisture modeling. Photogrammetric Engineering & Remote Sensing, 78(12):1241-1251.
Middleton, M., P. Närhi, H. Arkimaa, E. Hyvönen, V. Kuosmanen, P. Treitz and R. Sutinen, 2012. Ordination and hyperspectral remote sensing approach to classify peatland biotopes along soil moisture and fertility gradients, Remote Sensing of Environment, 124: 596-609.
Treitz, P., K. Lim, M. Woods, D. Pitt, D. Nesbitt and D. Etheridge, 2012. LiDAR Sampling Intensity for Forest Resource Inventories in Ontario, Canada, Remote Sensing, 4(4):830-848.
Maher, A., P. Treitz and M. Ferguson, 2012. Can Landsat data detect variations in snow cover within habitats of arctic ungulates? Wildlife Biology, 18:1-13.
Woods, M., D. Pitt, K. Lim, D. Nesbitt, D. Etheridge, M. Penner and P. Treitz, 2011. Operational implementation of a LiDAR inventory in Boreal Ontario, Forestry Chronicle, 87(4):512-528.
Thomas, V., T. Noland, P. Treitz, and H. McCaughey, 2011. Leaf area and clumping indices for a boreal mixedwood forest: lidar, hyperspectral, and Landsat models, International Journal of Remote Sensing, 32 (23): 8271–8297.
Van Ewijk, K.Y., P.M. Treitz, and N.A. Scott, 2011. Characterizing Forest Succession in Central Ontario using LiDAR derived Indices, Photogrammetric Engineering and Remote Sensing, 77 (3): 261-269.
Wall, J., A. Collingwood and P. Treitz, 2010. Monitoring surface moisture state in the Canadian High Arctic using synthetic aperture radar (SAR), Canadian Journal of Remote Sensing, Vol. 36, Supplement 1: S124-S134.
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.
Chasmer, L., A. Barr, C. Hopkinson, H. McCaughey, P. Treitz, A. Black, and A. Shashkov, 2009. Scaling and assessment of GPP from MODIS using a combination of airborne lidar and eddy covariance measurements over jack pine forests. Remote Sensing of Environment,113: 82-93.
Chasmer, L., C. Hopkinson, P. Treitz, H. McCaughey, A. Barr, and A. Black. 2008. A lidar-based hierarchical approach for assessing MODIS fPAR. Remote Sensing of Environment, 112:4344-4357.
Woods, M., K. Lim, and P. Treitz. 2008. Predicting forest stand variables from LiDAR data in the Great Lakes St. Lawrence Forest of Ontario, Forestry Chronicle, 84(6): 827-839.
Chasmer, L., N. Kljun, A. Barr, A. Black, C. Hopkinson, H. McCaughey, and P. Treitz. 2008. Influences of vegetation structure and elevation on CO2 uptake in a mature jack pine forest in Saskatchewan, Canada, Canadian Journal of Forest Research, 38:2746-2761.
Lim, K., C. Hopkinson, and P. Treitz. 2008. Examining the effects of sampling point densities on laser canopy height and density metrics for forest studies at the plot level, Forestry Chronicle, 84(6): 876-885.
Chasmer, L., A. Barr, A. Black, H. McCaughey, A. Shashkov, and P. Treitz, 2008. Investigating light use efficiency (LUE) across a jack pine chronosequence during dry and wet years. Tree Physiology, 28(9):1395-1406.
Laidler, G., P. Treitz, and D. Atkinson, 2008. Estimating Percent-Vegetation Cover in the Canadian Arctic: The utility of multi-resolution remote sensing data and vegetation indices, Arctic, 61(1):1-13.
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.
* Names in bold indicate past and present graduate students and Post Doctoral Fellows.