The Geovation Group

Publications

Refereed Journals (* – with PDFs/RAs, ^ – with Grad Students). Additional publications may be found here:

  1. Griffith^, D.C.; Hay, G. J. 2018. Integrating GEOBIA, Machine Learning, and Volunteered Geographic Information to Map Vegetation over Rooftops. ISPRS Int. J. Geo-Inf. 2018, 7, 462
  2. Chen, G., Weng, Q., Hay, G. J., and He, Y. (2018). Geographic Object-based Image Analysis (GEOBIA): Emerging trends and future opportunities. GIScience & Remote Sensing. Vol 55, Issue 2, pp 159-182. March
  3. Sims, A.W., Robinson, C.E., Smart, C.C., Voogt, J.A., Hay, G. J., Lundholm, J.T., Powers B. and O’Carroll, D.M., 2016. Retention Performance of Green Roofs in Three Different Climate Regions, Journal of Hydrology. Volume 542, November, Pages 115-124. DOI: 10.1016/j.jhydrol.2016.08.055.
  4. Rahman^, M. M., Hay, G. J., Couloigner*, I., Hemachandaran^, B., Bailin, J. 2015. A comparison of four relative radiometric normalization (RRN) techniques for mosaicking H-res multi-temporal thermal infrared (TIR) flight-lines of a complex urban scene (PHOTO-D-14-00266). The ISPRS Journal of Photogrammetry and Remote Sensing Volume 106, August 2015, Pages 82–94. (http://www.sciencedirect.com/science/article/pii/S0924271615001392).
  5. Rahman^, M. M., Hay, G. J., Couloigner*, I., Hemachandaran^, B., Bailin, J. 2014. An assessment of polynomial regression techniques for the relative radiometric normalization (RRN) of high resolution multi-temporal airborne thermal infrared (TIR) imagery. Remote Sensing Special Issue (ISSN 2072-4292): Recent Advances in Thermal Infrared Remote Sensing Remote Sens. 2014, 6(12), 11810-11828; doi:10.3390/rs61211810.
  6. Rahman^, M. M., Hay, G. J., Couloigner* I., Hemachandaran^, B. Transforming image-objects into multiscale fields: A GEOBIA Approach to Mitigate Urban Microclimatic Variability within H-Res Thermal Infrared Airborne Flight-Lines. Remote Sens. 2014, 6, 9435-9457 (http://www.mdpi.com/2072-4292/6/10/9435)
  7. Abdulkarim^, B; Kamberov^, R; Hay, G. J. 2014. “Supporting Urban Energy Efficiency with Volunteered Roof Information and the Google Maps API.” Remote Sens. 6, no. 10: 9691-9711. (http://www.mdpi.com/2072-4292/6/10/9691)
  8. Rahman^, M. M., Hay, G. J., Couloigner*, I., Hemachandaran^, B., Bailin, J. 2014. A comparison of four relative radiometric normalization (RRN) techniques for mosaicking H-res multi-temporal thermal infrared (TIR) flight-lines of a complex urban scene (PHOTO-D-14-00266). The ISPRS Journal of Photogrammetry and Remote Sensing [Accepted with revisions on August 27, 2014].  pp. 41
  9. Blaschke, T., G. J. Hay, K. Maggi, S. Lang, P. Hofmann, E. Addink; R.Q. Feitosa, F. V.D. Meer,  H.V.D. Werff, F.V.Coillie, 2014. Geographic Object-Based Image Analysis, towards a new paradigm. ISPRS Journal of Photogrammetry and Remote Sensing. Volume 87, January 2014, Pages 180-191. DOI:10.1016/j.isprsjprs.2013.09.014
  10. Rahman^, M. M, G. J. Hay, I. Couloigner*, B. Hemachandran^, J. Bailin, Y. Zhang^ and A. Tam. 2012. Geographic Object-Based Mosaicing (OBM) of High-Resolution Thermal Airborne Imagery (TABI-1800) to Improve the Interpretation of Urban Image-Objects. IEEE Geoscience and Remote Sensing Letters – (GEOBIA 2012 Special Issue) Vol 10, NO. 4, July. 918-922.
  11. Chen^, G., Hay, G. J., Carvalho*, L.M.T., and Wulder, M. 2012. Object Based Change Detection. International Journal of Remote Sensing. Vol.33, No.14, 4434-4457. 
  12. Powers^, R., G. J. Hay, G. Chen^. 2012. How wetland type and area differ through scale: A case study of Alberta’s Boreal Plains. Remote Sensing of Environment. volume 117, pp. 135 – 145.
  13. Hay G. J., Kyle^ C., Hemachandran^ B., Chen^ G., Rahman^ M.M., Fung T.S., Arvai J.L. 2011. “Geospatial Technologies to Improve Urban Energy Efficiency.” Remote Sens. 3, no. 7: 1380-1405.
  14. Blaschke, T., Hay, G. J., Weng, Q., and Resch. B. 2011. Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems: An Overview Remote Sens. 3, no. 7. 1743-1776. 
  15. Chen^, G. and G. J. Hay, 2011. An airborne lidar sampling strategy to model forest canopy height from Quickbird imagery and GEOBIA. Remote Sensing of Environment. 115: 1532-1542.
  16. Chen^, G., Hay, G. J., and St-Onge, B. 2011. A GEOBIA framework to estimate forest parameters from lidar transects, Quickbird imagery and machine learning: a case study in Quebec, Canada. International Journal of Applied Earth Observation and Geoinformation. In Press. Corrected Proof Available online 14 June, 2011, DOI:10.1016/j.jag.2011.05.010.
  17. Chen^, G., K. Zhao, G. J. McDermid and G. J. Hay (2011). The influence of sampling density on geographically weighted regression: a case study using forest canopy height and optical data. International Journal of Remote Sensing. Accepted 26 March. TRES-PAP-2010-0686. In Press.
  18. Chen^, G., Hay, G. J., Castilla*, G., St-Onge, B., and Powers, R. 2011. A multiscale geographic object-based image analysis (GEOBIA) to estimate lidar-measured forest canopy height using Quickbird imagery. International Journal of Geographic Information Science, 25:877-893.
  19. Chen^, G. and G. J. Hay. 2011.A support vector regression approach to estimate forest biophysical parameters at the object level using airborne lidar transects and Quickbird data. Photogrammetric Engineering and Remote Sensing, 77: 733-741.
  20. Hay G. J. and Blaschke, T. 2010. Forward: Special Issue on Geographic Object-Based Image Analysis (GEOBIA), Photogrammetric Engineering and Remote Sensing. Vol. 76, No 2, February, pp. 121-122.
  21. Steiniger*, S., and G. J. Hay, 2009. Free and Open Source Geographic Information Tools for Landscape Ecology: A Review. Ecological Informatics. Volume 4, Issue 4, September. pp 183-195.
  22. Castilla*, G., R. Guthrie and G. J. Hay. 2009. The Landcover Change Mapper (LCM) and its applications to timber harvest monitoring in Western Canada. Special Issue on Landcover Change Detection for Photogrammetric Engineering & Remote Sensing, Vol. 75, No 8. pp 941-950.
  23. Ben-Arie^, J.R, G. J. Hay., R.P. Powers^, G. Castilla*, B. St-Onge. 2009. Development of a Pit Filling Algorithm for LiDAR Canopy Height Models. Computers & Geosciences. Volume 35, Issue 9. pp 1940-1949.
  24. Castilla, G*., K. Larkin^, J. Linke and G. J. Hay, 2009. The impact of thematic resolution on the patch-mosaic model of natural landscapes. Landscape Ecology Vol 24: p 15-23
  25. Castilla, G*, G. J., Hay and J. R., Ruiz. 2008. Size-constrained Region Merging (SCRM): An Automated Delineation Tool for Assisted Photointerpretation. Photogrammetric Engineering & Remote Sensing. Vol.74, No.4. April. pp 409-419.
  26. Wulder, M.A., J.C. White, G. J. Hay, and G. Castilla*, 2008. Towards automated segmentation of forest inventory polygons on high spatial resolution satellite imagery , The Forestry Chronicle. Vol. 84, No. 2, pp. 221- 230.
  27. Castilla, G* and G. J. Hay, 2006. Uncertainties in land use data. Hydrology and Earth System Sciences Discussions. Vol 3. pp 3439-3472.
  28. Hay, G. J., 2005. Bridging Scales and Epistemologies: An Introduction. International Journal of Applied Earth Observation and Geoinformation. Vol 7. pp.249-252.
  29. Hay, G. J., G., Castilla*, M. A. Wulder and J. R. Ruiz. 2005. An automated object-based approach for the multiscale image segmentation of forest scenes. International Journal of Applied Earth Observation and Geoinformation. Vol 7, pp. 339-359.
  30. Stewart, S. A., G. J. Hay, P. L. Rosin and T .J. Wynn. 2004. Multiscale Structure in Sedimentary Basins. Journal of Basin Research, Vol 16, 183-197.
  31. Hall, O., G. J. Hay, A. Bouchard, and D. J. Marceau, 2004. Detecting dominant landscape objects through multiple scales: An integration of object-specific methods and watershed segmentation. Landscape Ecology, Vol. 19, No. 1: 59-76.
  32. Hall, O., G. J. Hay, 2003. A Multiscale Object-specific Approach to Digital Change Detection. International Journal of Applied Earth Observation and Geoinformation, Vol. 4/4: 311-327.
  33. Hay, G. J., T. Blaschke, D. J. Marceau, and A. Bouchard, 2003. A comparison of three image-object methods for the multiscale analysis of landscape structure. ISPRS Journal of Photogrammetry and Remote Sensing, Volume 57, Issues 5-6, April 2003, Pages 327-345. Vol 57. 327-345.
  34. Hay, G. J., P. Dube, A. Bouchard, and D. J. Marceau, 2002. A Scale-Space Primer for Exploring and Quantifying Complex Landscapes. Ecological Modelling. Vol. 153, No. 1-2: 27- 49.
  35. Hay, G. J., D. J. Marceau, P. Dube, and A. Bouchard, 2001. A Multiscale Framework for Landscape Analysis: Object-Specific Analysis and Upscaling. Landscape Ecology. Vol.16, No.6: 471 – 490.
  36. D.J. Marceau, and G. J. Hay, 1999. Remote Sensing Contributions to the Scale Issue, Canadian Journal of Remote Sens. Vol 25, No. 4: 357-366.
  37. D.J. Marceau, and G. J. Hay, 1999. Scaling and Modelling in Forestry: Applications in Remote Sensing and GIS. Canadian Journal of Remote Sens. Vol 25, No.4: 342-346.
  38. Hay, G. J., K. O. Niemann, and D. G. Goodenough, 1997. Spatial Thresholds, Image-Objects and Upscaling: A Multi-Scale Evaluation. Remote Sensing of Environment, 62: 1-19.
  39. Hay, G. J., K. O. Niemann, and G. McLean, 1996. An Object-Specific Image-Texture Analysis of H-Resolution Forest Imagery. Remote Sensing of Environment, 55: 108-122.
  40. Hay, G. J., and K. O. Niemann, 1994. Visualizing 3-D Texture: A Three Dimensional Structural Approach to Model Forest Texture. (Cover Article) Canadian Journal of Remote Sens. Vol. 20, No.2, pp. 90-101.

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