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Publications

2024

  • Treitz, P.M., D.M. Atkinson, A. Blaser, M.T. Bonney, C.A. Braybrook, E.C. Buckley, A. Collingwood, R. Edwards, K. van Ewijk, V. Freemantle, F. Gregory, J. Holloway, J.K.Y. Hung, S.F. Lamoureux, N. Liu, G. Ljubicic, G. Robson, A.C.A Rudy, N.A. Scott, C. Shang, J. Wall, 2024. Remote sensing of biogeophysical variables at the Cape Bounty Arctic Watershed Observatory, Melville Island, Nunavut, Canada. Arctic Science, 10: 281-304 DOI: 10.1139/as-2023-0043

  • Hung, J.K.Y., N.A. Scott, P.M. Treitz, 2024. Drivers of soil nitrogen availability and carbon exchange processes in a High Arctic wetland, Arctic Science.10(1): 22-33 DOI: 10.1139/as-2022-0048

2023

  • Hung, J.K.Y., N.A. Scott, P.M. Treitz, 2023. Investigating ten years of warming and enhanced snow depth on nutrient availability and greenhouse gas fluxes in a High Arctic ecosystem. Arctic, Antarctic, and Alpine Research, 55 (1): 2178428 DOI: 10.1080/15230430.2023.2178428

2021

  • Wright, C.M., A. Blaser, P.M. Treitz. N.A. Scott, 2021. Spatial Variability in carbon exchange processes within wet sedge meadows in the Canadian High Arctic, Advances in Polar Science, 32(1), 1-19. DOI: 10.13679/j.advps.2020.0033

  • Robson, G., P. Treitz, S.F. Lamoureux, K. Murnaghan, B. Brisco, 2021. Seasonal Surface Subsidence and Frost Heave Detected by C-Band DInSAR in a High Arctic Environment, Cape Bounty, Melville Island, Nunavut, Canada. Remote Sensing, 13, 2505. DOI:10.3390/rs13132505

  • Braybrook, C.A., N.A. Scott, P.M. Treitz, E.R. Humphreys, 2021. Inter-annual variability of summer net ecosystem CO2 exchange in High Arctic tundra. Journal of Geophysical Research: Biogeosciences. 126, (e2020JG006) DOI: 10.1029/2020JG006094

  • Coops, N.C., A. Achim, P. Arp, C.W. Bater, J.P. Caspersen, J-F Côte, J.P. Dech, A.R. Dick, D. MacLean, V. Roy, D. Cormier, C. Hennigar, A.R. Dick, K. van Ewijk, R. Fournier, T.R.H. Goodbody, C.R. Hennigar, A. Leboeuf, O.R. van Lier, J.E. Luther, D.A. MacLean, G. McCartney, G. Pelletier, J-F Prieur, P. Tompalski, P.M. Treitz, J.C. White, M. Woods, 2021. Advancing the application of remote sensing for forest information needs in Canada: Lessons learned from a national collaboration of University, Industrial, and Government stakeholders, Forestry Chronicle, 97(2), 109-126.  DOI: 10.5558/tfc2021-014

2020

  • Freemantle, V., J. Freemantle, D. Atkinson, P. Treitz, 2020. A high spatial resolution satellite remote sensing time series analysis of Cape Bounty, Melville Island, Nunavut (2004-2018), Canadian Journal of Remote Sensing, 46(6), 733-752. DOI: 10.1080/07038992.2020.1866979

  • Wai Yeung, Y., K. van Ewijk, P. Treitz, A. Shaker, 2020. Effects of radiometric correction on cover type and spatial resolution for modeling plot level forest attributes using multispectral airborne LiDAR data, ISPRS Journal of Photogrammetry and Remote Sensing, 169, 152-165. DOI: 10.1016/j.isprsjprs.2020.09.001

  • Goodbody, T.R.H., P. Tompalski, N.C.Coops, C. Hopkinson, P. Treitz, K. van Ewijk, 2020. Forest inventory and diversity attribute modelling using structural and intensity metrics from multi-spectral airborne laser scanning data. Remote Sensing, 12, 2109. DOI: 10.3390/rs12132.109

  • Ewijk, K. van, P. Tompalski, P. Treitz, N.C. Coops, M. Woods and D. Pitt, 2020. Transferability of ALS-derived Forest Resource Inventory Attributes between an Eastern and Western Canadian Boreal Forest Mixedwood Site, Canadian Journal of Remote Sensing, 46(2), 214-236. DOI: 10.1080/07038992.2020.1769470

  • Atkinson, D.M., J.K.Y. Hung, F.M. Gregory, N.A. Scott and P.M. Treitz, 2020. High spatial resolution remote sensing models for landscape-scale CO2 exchange in the Canadian Arctic, Arctic, Antarctic, and Alpine Research, 52(1), 1-16. DOI: 10.1080/15230430.2020.1750805

  • Hung, J. and P. Treitz, 2020. Environmental land-cover classification for integrated watershed studies: Cape Bounty, Melville Island, Nunavut, Arctic Science, DOI: 10.1139/AS-2019-0029

  • Bolton, D.K., P. Tompalski, N.C. Coops, J.C. White, M.A. Wulder, T. Hermosilla, M. Queinnec, J.E. Luther, O.R. van Lier, R.A. Fournier, M. Woods, P.M. Treitz, K.Y. van Ewijk, G. Graham and L. Quist, 2020. Optimizing Landsat time series length for regional mapping of lidar-derived forest structure. Remote Sensing of Environment, 239, 111645. DOI: 10.1016/j.rse.2020.111645

  • Marczak, P.T., K.Y. Van Ewijk, P.M. Treitz, N.A. Scott and D.C.E. Robinson, 2020. Predicting carbon accumulation in temperate forests of Ontario, Canada using a LiDAR-initialized growth-and-yield model. Remote Sensing, 12, 201; doi:10.3390/rs12010201

2019

  • Shang, C., P. Treitz, J. Caspersen and T. Jones, 2019. Estimation of forest structural and compositional variables using ALS data and multi-seasonal satellite imagery. International Journal of Applied Earth Observations and Geoinformation, 78, 360-371.

  • Ewijk, van, K., P. Treitz, M. Woods, T. Jones and J. Caspersen, 2019. Forest site and type variability in ALS-based forest resource inventory attribute predictions over three Ontario forest sites, Forests, 10(3), 226, 26 p. doi:10.3390/f10030226

2018

  • Bonney, M., R. Danby and P. Treitz, 2018. Landscape Variability of Vegetation Change across the Forest to Tundra Transition of Central Canada. Remote Sensing of Environment, 217:18-29.

  • Liu, N. and P. Treitz, 2018. Remote sensing of Arctic percent vegetation cover and fAPAR on Baffin Island, Nunavut, Canada, International Journal of Applied Earth Observations and Geoinformation, 71:159-169 doi.org/10.1016/j.jag.2018.05.011

  • Collingwood, A., F. Charbonneau, C. Shang, and P. Treitz, 2018. Spatiotemporal variability of Arctic soil moisture detected from high resolution RADARSAT-2 SAR data. Advances in Meteorology, doi.org/10.1155/2018/5712046

  • Rudy, A.C.A., S.F. Lamoureux, P. Treitz, N. Short and B. Brisco, 2018. Seasonal and multi-year surface displacements measured by DInSAR in a High Arctic permafrost environment. International Journal of Applied Earth Observation and Geoinformation, 64:51-61.

2017

  • Edwards, R. and P. Treitz, 2017. Vegetation greening trends at two sites in the Canadian Arctic: 1984-2015. Arctic, Antarctic and Alpine Research, 49(4):601-619.

  • Zhang, X., P.M. Treitz, D. Chen, C. Quan, L. Shi,and X. Li, 2017. Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure. International Journal of Applied Earth Observation and Geoinformation, 62:201-214.

  • Holloway, J.E., A.C.A. Rudy, S.F. Lamoureux, and P.M. Treitz, 2017. Determining the terrain characteristics related to the surface expression of subsurface water pressurization in permafrost landscapes using susceptibility modelling. The Cryosphere, 11:1403-1415. doi: 10.5194/tc-11-1403-2017

  • Liu, N., P. Budkewitsch and P. Treitz, 2017. Examining spectral reflectance features related to Arctic percent vegetation cover: Implications for hyperspectral remote sensing of Arctic tundra. Remote Sensing of Environment, 192:58-72.

  • Shang, C., P. Treitz, J. Casperson and T. Jones, 2017. Estimating stem diameter distributions in a management context for a tolerant hardwood forest using ALS height and intensity data. Canadian Journal of Remote Sensing, 43(1):79-94.

  • Rudy, A.C.A., S.F. Lamoureux, P. Treitz, K. Van Ewijk, P.P. Bonnaventure and P. Budkewitsch, 2017. Terrain controls and landscape-scale modelling of active-layer detachments, Sabine Peninsula, Melville Island, Nunavut. Permafrost and Periglacial Processes, 28:79-91.

2016

  • Liu, N. and P. Treitz, 2016. Modelling High Arctic percent vegetation cover using field digital images and high resolution satellite data, International Journal of Applied Earth Observation and Geoinformation, 52:445-456.

  • Rudy, A.C.A., S.F. Lamoureux, P. Treitz and K. Van Ewijk, 2016. Transferability of regional permafrost disturbance susceptibility modelling using generalized linear and generalized additive models. Geomorphology, 264:95-108.

2015

  • Gökkaya, K., V. Thomas, T. Noland, J.H. McCaughey, P. Treitz and I. Morrison, 2015. Prediction of macronutrients at the canopy level using spaceborne imaging spectroscopy and LiDAR Data in a mixedwood boreal forest. Remote Sensing, 7:9045-9069.

  • Gökkaya, K., V. Thomas, T. Noland, J.H. McCaughey, I. Morrison and P. Treitz, 2015. Mapping continuous forest type variation by means of correlating remotely sensed metrics to canopy N:P ratio in a boreal mixedwood forest. Applied Vegetation Science, 18(1):143-157.

2014

  • Ewijk, K.Y. van, C. Randin, P. Treitz and N. Scott, 2014. Predicting fine-scale species abundance patterns using biotic variables derived from LiDAR and high spatial resolution imagery. Remote Sensing of Environment, 150:120-131.

  • 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.

  • 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.

  • Collingwood, A., P. Treitz, and F. Charbonneau, 2014. Surface roughness estimation from RADARSAT-2 SAR 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.

2013

  • 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.

  • 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.

2012

  • Southee, M., P.M. Treitz and N. Scott, 2012. Applications of Lidar Terrain Surfaces to Soil Moisture Modeling, Photogrammetric Engineering and Remote Sensing, Vol. 78 (12):1241-1251.

  • Atkinson, D.M., and P.M. Treitz, 2012. Arctic ecological classifications derived from vegetation community and satellite spectral data. Remote Sensing, 4, 3948-3971.

  • 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.

2011

  • 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.

  • Ewijk, K.Y., van, P.M. Treitz, and N.A. Scott, 2011. Characterizing Forest Succession in Central Ontario using LiDAR derived Indices, Photogrammetric Engineering and Remote Sensing, Vol. 77 (3):261-269.

2010

  • Treitz, P., V. Thomas, P. Zarco-Tejada, P. Gong, and P. Curran, 2010. Hyperspectral Remote Sensing for Forestry, Monograph Series, American Society for Photogrammetry and Remote Sensing, Bethesda, Maryland, 107 p.

  • 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.

2009

  • 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, A. Black, H. McCaughey, A. Shashkov,  P. Treitz, and T. Zha. 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.

2008

  • 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.

  • 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., 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, 28:1395-1406.

  • 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.

  • 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.

  • Chasmer, L., A. Barr, A. Black, H. McCaughey, A. Shashkov, P. Treitz, and T. Zha. 2008. 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, 133 (1):82-93.

  • Laidler, G., P. Treitz and Atkinson, D. 2008. Remote Sensing of Arctic Vegetation: Relations between the NDVI, Spatial Resolution, and Vegetation Cover on Boothia Peninsula, Nunavut. Arctic, 61(1):1-13.

  • Thomas, V., Treitz, P., McCaughey, J.H., Noland, T., and Rich, L., 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.