Staff profile

I began my university studies with bachelors degrees in both physics (Université de Sherbrooke, Sherbrooke, Canada) and engineering (Université Laval, Québec, Canada). These degrees gave me the technical skills and the mathematical background that have done much to shape my contributions to physical geography. I eventually came to fluvial geomorphology through the study of turbulence and sediment transport during my master’s degree (INRS-ETE, Québec, Canada). The study of a complex problem such as turbulence prompted an interest in complex phenomena in rivers and thus I undertook a Ph.D. (INRS-ETE, Québec, Canada) on the intergranular voidspaces that constitute the habitat of juvenile atlantic salmon. In addition to gaining an understanding of salmonid habitat, the requirements of my Ph.D. brought me to develop an expertise in the field of remote sensing applied to fluvial environments. Namely, during a Ph.D. internship as a visiting scholar in Fitzwilliam College, Cambridge, I developed skills in digital photogrammetry which were completed with a second internship at the School of geography of the University of Leeds. My post-doctoral work, carried out jointly at the INRS-ETE in Quebec, the School of Geography at the University of Leeds and the department of geography of Durham university, built on my knowledge of remote sensing and salmonid habitat to develop pioneering methods for the catchment-scale characterization of salmonid habitat with high resolution airborne remote sensing methods.

Recently,  my focus has shifted on larger scale studies of fluvial morphology using Big Data and Deep Learning methods.  I have developed algorithms and processing pipelines that can now handle global scale data from Sentinel 1 and 2  and deliver semantic classifications of freshwater where rivers are distinct from lakes and with a high temporal frequency which is independent of clouds.  My current work is using time series of semantic classification rasters to  examine patterns of change in fluvial basins in the Sentinel era.

• Deep Learning and Artificial Intelligence
• Image Time Series
• Fluvial Remote Sensing
• Digital Image Processing
• Fluvial Geomorphology and Ecology

Chapter in book

• The Growing Use of Imagery in Fundamental and Applied River Sciences
  
  Carbonneau, P., & Piégay, H. (2012). The Growing Use of Imagery in Fundamental and Applied River Sciences. In P. Carbonneau & H. Piégay (Eds.), Fluvial Remote Sensing for Science and Management. (pp. 1-18). Wiley. https://doi.org/10.1002/9781119940791
• Future Prospects and challenges for River Scientists and Managers.
  
  Carbonneau, P., & Piégay, H. (2012). Future Prospects and challenges for River Scientists and Managers. In P. Carbonneau & H. Piégay (Eds.), Fluvial Remote Sensing for Science and Management. (pp. 431-434). Wiley. https://doi.org/10.1002/9781119940791
• Hyperspatial Imagery in Riverine Environments.
  
  Carbonneau, P., Piégay, H., Lejôt, J., Dunford, R., & Michel, K. (2012). Hyperspatial Imagery in Riverine Environments. In P. Carbonneau & H. Piégay (Eds.), Fluvial Remote Sensing for River Science and Management. (pp. 163-191). Wiley.
• Geosalar: Innovative Remote Sensing Methods for Spatially Continuous Mapping of Fluvial Habitat at Riverscape Scale.
  
  Bergeron, N., & Carbonneau, P. (2012). Geosalar: Innovative Remote Sensing Methods for Spatially Continuous Mapping of Fluvial Habitat at Riverscape Scale. In P. Carbonneau & H. Piégay (Eds.), Fluvial Remote Sensing for River Science and Management. (pp. 193-213). Wiley.

Conference Paper

• Extending Deep Neural Network Trail Navigation for Unmanned Aerial Vehicle Operation within the Forest Canopy
  
  Maciel-Pearson, B., Carbonneau, P., & Breckon, T. (2018). Extending Deep Neural Network Trail Navigation for Unmanned Aerial Vehicle Operation within the Forest Canopy. In Proc. Towards Autonomous Robotic Systems Conference (pp. 147-158). Springer. https://doi.org/10.1007/978-3-319-96728-8_13

Edited book

• Fluvial Remote Sensing for Science and Management.
  
  Carbonneau, P., & Piégay, H. (Eds.). (2012). Fluvial Remote Sensing for Science and Management. Wiley.

Journal Article

• Enhancing Active Channel Delineation in Alluvial Rivers Using Monthly Aggregation of Sentinel-2 Imagery
  
  Bozzolan, E., Matteligh, E., Brenna, A., Cecchetto, M., Surian, N., Carbonneau, P., & Bizzi, S. (2026). Enhancing Active Channel Delineation in Alluvial Rivers Using Monthly Aggregation of Sentinel-2 Imagery. AGU Earth and Space Science, 13(1), Article e2025EA004642. https://doi.org/10.1029/2025EA004642
• Style Transfer from Sentinel-1 to Sentinel-2 for Fluvial Scenes with Multi-Modal and Multi-Temporal Image Fusion
  
  Carbonneau, P. E. (2025). Style Transfer from Sentinel-1 to Sentinel-2 for Fluvial Scenes with Multi-Modal and Multi-Temporal Image Fusion. Remote Sensing, 17(20), Article 3445. https://doi.org/10.3390/rs17203445
• Automated Mapping of Braided Palaeochannels From Optical Images With Deep Learning Methods
  
  Vanzani, F., Carbonneau, P., & Fontana, A. (2025). Automated Mapping of Braided Palaeochannels From Optical Images With Deep Learning Methods. Journal of Geophysical Research: Earth Surface, 130(2), Article e2024JF008051. https://doi.org/10.1029/2024jf008051
• Style Transfer from Sentinel-1 to Sentinel-2 for Fluvial Scenes with Multi-Modal and Multi-Temporal Image Fusion
  
  Carbonneau, P. E. (2025). Style Transfer from Sentinel-1 to Sentinel-2 for Fluvial Scenes with Multi-Modal and Multi-Temporal Image Fusion. Remote Sensing, 17(20), Article 3445. https://doi.org/10.3390/rs17203445
• Global Semantic Classification of Fluvial Landscapes with Attention-Based Deep Learning
  
  Carbonneau, P. E. (2024). Global Semantic Classification of Fluvial Landscapes with Attention-Based Deep Learning. Remote Sensing, 16(24), Article 4747. https://doi.org/10.3390/rs16244747
• An AI approach to operationalise global daily PlanetScope satellite imagery for river water masking
  
  Valman, S. J., Boyd, D. S., Carbonneau, P. E., Johnson, M. F., & Dugdale, S. J. (2024). An AI approach to operationalise global daily PlanetScope satellite imagery for river water masking. Remote Sensing of Environment, 301, Article 113932. https://doi.org/10.1016/j.rse.2023.113932
• Global mapping of river sediment bars
  
  Carbonneau, P. E., & Bizzi, S. (2024). Global mapping of river sediment bars. Earth Surface Processes and Landforms, 49(1), 15-23. https://doi.org/10.1002/esp.5739
• Quantifying the Impact of Spatiotemporal Resolution on the Interpretation of Fluvial Geomorphic Feature Dynamics From Sentinel 2 Imagery: An Application on a Braided River Reach in Northern Italy
  
  Bozzolan, E., Brenna, A., Surian, N., Carbonneau, P., & Bizzi, S. (2023). Quantifying the Impact of Spatiotemporal Resolution on the Interpretation of Fluvial Geomorphic Feature Dynamics From Sentinel 2 Imagery: An Application on a Braided River Reach in Northern Italy. Water Resources Research, 59(12), Article e2023WR034699. https://doi.org/10.1029/2023wr034699
• Mapping riverbed sediment size from Sentinel‐2 satellite data
  
  Marchetti, G., Bizzi, S., Belletti, B., Lastoria, B., Comiti, F., & Carbonneau, P. E. (2022). Mapping riverbed sediment size from Sentinel‐2 satellite data. Earth Surface Processes and Landforms, 47(10), 2544-2559. https://doi.org/10.1002/esp.5394
• Image classification of marine-terminating outlet glaciers in Greenland using deep learning methods
  
  Marochov, M., Stokes, C., & Carbonneau, P. (2021). Image classification of marine-terminating outlet glaciers in Greenland using deep learning methods. The Cryosphere, 15, 5041-5059. https://doi.org/10.5194/tc-15-5041-2021
• Adopting deep learning methods for airborne RGB fluvial scene classification
  
  Carbonneau, P., Dugdale, S., Breckon, T., Dietrich, J., Fonstad, M., Miyamoto, H., & Woodget, A. (2020). Adopting deep learning methods for airborne RGB fluvial scene classification. Remote Sensing of Environment, 251, Article 112107. https://doi.org/10.1016/j.rse.2020.112107
• UAV-based training for fully fuzzy classification of Sentinel-2 fluvial scenes
  
  Carbonneau, P., Belletti, B., Micotti, M., Lastoria, B., Casaioli, M., Mariani, S., Marchetti, G., & Bizzi, S. (2020). UAV-based training for fully fuzzy classification of Sentinel-2 fluvial scenes. Earth Surface Processes and Landforms, 45(13), 3120-3140. https://doi.org/10.1002/esp.4955
• Remotely Sensed Rivers in the Anthropocene: State of the Art and Prospects
  
  Piégay, H., Arnaud, F., Belletti, B., Bertrand, M., Bizzi, S., Carbonneau, P., Dufour, S., Liebault, F., Ruiz‐Villanueva, V., & Slater, L. (2020). Remotely Sensed Rivers in the Anthropocene: State of the Art and Prospects. Earth Surface Processes and Landforms, 45(1), 157-188. https://doi.org/10.1002/esp.4787
• The Case Against Vast Glaciation in Valles Marineris, Mars
  
  Kissick, L. E., & Carbonneau, P. E. (2019). The Case Against Vast Glaciation in Valles Marineris, Mars. Icarus, 321, 803-823. https://doi.org/10.1016/j.icarus.2018.12.021
• Robotic photosieving from low-cost multirotor sUAS: A proof-of-concept
  
  Carbonneau, P., Bizzi, S., & Marchetti, G. (2018). Robotic photosieving from low-cost multirotor sUAS: A proof-of-concept. Earth Surface Processes and Landforms, 43(5), 1160-1166. https://doi.org/10.1002/esp.4298
• From manned to unmanned aircraft: Adapting airborne particle size mapping methodologies to the characteristics of sUAS and SfM
  
  Woodget, A., Fyffe, C., & Carbonneau, P. (2018). From manned to unmanned aircraft: Adapting airborne particle size mapping methodologies to the characteristics of sUAS and SfM. Earth Surface Processes and Landforms, 43(4), 857-870. https://doi.org/10.1002/esp.4285
• Population density controls on microbial pollution across the Ganga catchment
  
  Milledge, D., Gurjar, S., Bunce, J., Tare, V., Sinha, R., & Carbonneau, P. (2018). Population density controls on microbial pollution across the Ganga catchment. Water Research, 128, 82-91. https://doi.org/10.1016/j.watres.2017.10.033
• Cost-effective non-metric photogrammetry from consumer-grade sUAS: implications for direct georeferencing of structure from motion photogrammetry
  
  Carbonneau, P., & Dietrich, J. (2017). Cost-effective non-metric photogrammetry from consumer-grade sUAS: implications for direct georeferencing of structure from motion photogrammetry. Earth Surface Processes and Landforms, 42(3), 473-486. https://doi.org/10.1002/esp.4012
• The accuracy and reliability of traditional surface flow type mapping: is it time for a new method of characterizing physical river habitat?
  
  Woodget, A., Visser, F., Maddock, I., & Carbonneau, P. (2016). The accuracy and reliability of traditional surface flow type mapping: is it time for a new method of characterizing physical river habitat? River Research and Applications, 32(9), 1902-1914. https://doi.org/10.1002/rra.3047
• Predicting microbial water quality with models: Over-arching questions for managing risk in agricultural catchments
  
  Oliver, D., Porter, K., Pachepsky, Y., Muirhead, R., Reaney, S., Coffey, R., Kay, D., Milledge, D., Hong, E., Anthony, S., Page, T., Bloodworth, J., Mellander, P., Carbonneau, P., McGrane, S., & Quilliam, R. (2016). Predicting microbial water quality with models: Over-arching questions for managing risk in agricultural catchments. Science of the Total Environment, 544, 39-47. https://doi.org/10.1016/j.scitotenv.2015.11.086
• Surface morphology of fans in the high-Arctic periglacial environment of Svalbard: Controls and processes
  
  de Haas, T., Kleinhans, M., Carbonneau, P., Rubensdotter, L., & Hauber, E. (2015). Surface morphology of fans in the high-Arctic periglacial environment of Svalbard: Controls and processes. Earth-Science Reviews, 146, 163-182. https://doi.org/10.1016/j.earscirev.2015.04.004
• Low-cost inundation modelling at the reach scale with sparse data in the Lower Damodar River basin, India
  
  Sanyal, J., Carbonneau, P., & Densmore, A. (2014). Low-cost inundation modelling at the reach scale with sparse data in the Lower Damodar River basin, India. Hydrological Sciences Journal, 59(12), 2086-2102. https://doi.org/10.1080/02626667.2014.884718
• Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry
  
  Woodget, A., Carbonneau, P., Visser, F., & Maddock, I. (2014). Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry. Earth Surface Processes and Landforms, 40(1), 47-64. https://doi.org/10.1002/esp.3613
• Debris-flow dominance of alluvial fans masked by runoff reworking and weathering
  
  de Haas, T., Ventra, D., Carbonneau, P., & Kleinhans, M. (2014). Debris-flow dominance of alluvial fans masked by runoff reworking and weathering. Geomorphology, 217, 165-181. https://doi.org/10.1016/j.geomorph.2014.04.028
• Analysing the effect of land use/cover changes at sub-catchment levels on downstream flood peaks: a semi-distributed modelling approach with sparse data
  
  Sanyal, J., Densmore, A., & Carbonneau, P. (2014). Analysing the effect of land use/cover changes at sub-catchment levels on downstream flood peaks: a semi-distributed modelling approach with sparse data. CATENA, 118, 28-40. https://doi.org/10.1016/j.catena.2014.01.015
• 2D finite element inundation modelling in anabranching channels with sparse data: examination of uncertainties
  
  Sanyal, J., Densmore, A., & Carbonneau, P. (2014). 2D finite element inundation modelling in anabranching channels with sparse data: examination of uncertainties. Water Resources Management, 28(8), 2351-2366. https://doi.org/10.1007/s11269-014-0619-x
• Mapping sub-pixel fluvial grain sizes with hyperspatial imagery
  
  Black, M., Carbonneau, P., Church, M., & Warburton, J. (2014). Mapping sub-pixel fluvial grain sizes with hyperspatial imagery. Sedimentology, 61(3), 691-711. https://doi.org/10.1111/sed.12072
• Asynchronous formation of Hesperian and Amazonian-aged deltas on Mars and implications for climate
  
  Hauber, E., Platz, T., Reiss, D., Le Deit, L., Kleinhans, M., Marra, W., De Haas, T., & Carbonneau, P. (2013). Asynchronous formation of Hesperian and Amazonian-aged deltas on Mars and implications for climate. Journal of Geophysical Research: Planets, 118(7), 1529-1544. https://doi.org/10.1002/jgre.20107
• Hydraulic routing of extreme floods in a large ungauged river and the estimation of associated uncertainties: a case study of the Damodar River, India
  
  Sanyal, J., Carbonneau, P., & Densmore, A. (2013). Hydraulic routing of extreme floods in a large ungauged river and the estimation of associated uncertainties: a case study of the Damodar River, India. Natural Hazards, 66(2), 1153-1177. https://doi.org/10.1007/s11069-012-0540-7
• Topographic structure from motion: a new development in photogrammetric measurement
  
  Fonstad, M., Dietrich, J., Courville, B., Jensen, J., & Carbonneau, P. (2013). Topographic structure from motion: a new development in photogrammetric measurement. Earth Surface Processes and Landforms, 38(4), 421-430. https://doi.org/10.1002/esp.3366
• Making riverscapes real.
  
  Carbonneau, P., Fonstad, M., Marcus, W., & Dugdale, S. (2012). Making riverscapes real. Geomorphology, 137(1), 74-86. https://doi.org/10.1016/j.geomorph.2010.09.030
• Coherent flow structures in a depth-limited flow over a gravel surface: The influence of surface roughness.
  
  Hardy, R., Best, J., Lane, S., & Carbonneau, P. (2010). Coherent flow structures in a depth-limited flow over a gravel surface: The influence of surface roughness. Journal of Geophysical Research: Earth Surface, 115(F3), Article F03006. https://doi.org/10.1029/2009jf001416
• Aerial photosieving of exposed gravel bars for the rapid calibration of airborne grain size maps.
  
  Dugdale, S., Carbonneau, P., & Campbell, D. (2010). Aerial photosieving of exposed gravel bars for the rapid calibration of airborne grain size maps. Earth Surface Processes and Landforms, 35(6), 627-639. https://doi.org/10.1002/esp.1936
• Coherent flow structures in a depth-limited flow over a gravel surface : the role of near-bed turbulance and influence of Reynolds number
  
  Hardy, R., Best, J., Lane, S., & Carbonneau, P. (2009). Coherent flow structures in a depth-limited flow over a gravel surface : the role of near-bed turbulance and influence of Reynolds number. Journal of Geophysical Research. Earth Surface., 114, Article F01003. https://doi.org/10.1029/2007jf000970
• An Automated Georeferencing Tool for Watershed Scale Fluvial Remote Sensing.
  
  Carbonneau, P., Dugdale, S., & Clough, S. (2009). An Automated Georeferencing Tool for Watershed Scale Fluvial Remote Sensing. River Research and Applications, 26(5), 650-658. https://doi.org/10.1002/rra.1263
• Investigating the effects of DEM error in scaling analysis
  
  James, T., Carbonneau, P., & Lane, S. (2007). Investigating the effects of DEM error in scaling analysis. Photogrammetric Engineering and Remote Sensing., 73(1), 67-78. https://doi.org/10.14358/pers.73.1.67
• Feature based image processing methods applied to bathymetric measurements from airborne remote sensing in fluvial environments
  
  Carbonneau, P., Lane, S., & Bergeron, N. (2006). Feature based image processing methods applied to bathymetric measurements from airborne remote sensing in fluvial environments. Earth Surface Processes and Landforms, 31(11), 1413-1423. https://doi.org/10.1002/esp.1341
• Automated grain size measurements from airborne remote sensing for long profile measurements of fluvial grain sizes
  
  Carbonneau, P., Bergeron, N., & Lane, S. (2005). Automated grain size measurements from airborne remote sensing for long profile measurements of fluvial grain sizes. Water Resources Research, 41(11), Article W11426. https://doi.org/10.1029/2005wr003994
• The threshold effect of image resolution on image-based automated grain size mapping in fluvial environments.
  
  Carbonneau, P. (2005). The threshold effect of image resolution on image-based automated grain size mapping in fluvial environments. Earth Surface Processes and Landforms, 30, 1687-1693. https://doi.org/10.1002/esp.1288
• Texture-based image segmentation applied to the quantification of superficial sand in salmonid river gravels.
  
  Carbonneau, P., Bergeron, N., & Lane, S. (2005). Texture-based image segmentation applied to the quantification of superficial sand in salmonid river gravels. Earth Surface Processes and Landforms, 30(1), 121-127. https://doi.org/10.1002/esp.1140
• Catchment-scale mapping of surface grain size in gravel bed rivers using airborne digital imagery
  
  Carbonneau, P., Lane, S., & Bergeron, N. (2004). Catchment-scale mapping of surface grain size in gravel bed rivers using airborne digital imagery. Water Resources Research, 40(7), Article W07202. https://doi.org/10.1029/2003wr002759
• Cost-effective non-metric close-range digital photogrammetry and its application to a study of coarse gravel river beds
  
  Carbonneau, P., Lane, S., & Bergeron, N. (2003). Cost-effective non-metric close-range digital photogrammetry and its application to a study of coarse gravel river beds. International Journal of Remote Sensing, 24(14), 2837-2854. https://doi.org/10.1080/01431160110108364
• The effect of bedload transport on mean and turbulent flow properties.
  
  Carbonneau, P., & Bergeron, N. (2000). The effect of bedload transport on mean and turbulent flow properties. Geomorphology, 35(3-4), 267-278.