Photo of Associate Professor Scott Mitchell

Associate Professor Scott Mitchell

Spatial pattern & environmental processes; Global change impacts on agriculture; Biodiversity and ecosystem services

Degrees:B.Sc. (Queen's), M.Sc., Ph.D. (Toronto)
Office:B350A Loeb Building
Website:Scott's research lab web site


Before coming to Carleton, I studied geography and biology at Queen’s University at Kingston, and geography and environmental studies at the University of Toronto. I was a faculty member at the University of Toronto for one year, and worked on several research projects there in addition to my doctoral studies. In 2003 I joined Carleton University and the Department of Geography and Environmental Studies.

I am a co-director of Carleton’s Geomatics and Landscape Ecology Laboratory, and most of the graduate students I work with are based in that facility.  We work on a range of projects analyzing and developing analysis tools for impacts of spatial patterns on environmental processes. Some more specific research projects include:

  • the role of spatial and temporal heterogeneity in farmland on regional biodiversity; we first studied this in eastern Ontario, and our approach has been replicated in various locations in Europe.  See Our official (funded) projects are now over but the resulting database still fuels fascinating projects;
  • primary productivity patterns in agricultural areas, including natural grasslands, under contemporary conditions and climate change or alternative management scenarios;
  • impacts of alternative climate scenarios, especially with respect to changes to extreme weather, on Ontario’s agricultural sector.  A lot of my current research effort is to further develop and demonstrate the potential of crop-specific extreme weather indices, and a spatial scenario modelling framework to study this.

2022 – 2023

  • On Sabbatical

General Research Interests

  • Uncertainty in environmental modelling and monitoring
  • Geographic Information Systems, decision support, and model interfaces
  • Primary productivity / crop yield, carbon cycling and landscape productivity patterns, especially in semi-arid or agricultural areas

Selected Publications

Remmel, Tarmo K. and Scott W. Mitchell.  2021.  Landscape Pattern Analysis.  Chapter 15 in Francis et al., ed., The Routledge Handbook of Landscape Ecology.  Abingdon, Routledge.

Hosseini, M., H. McNairn, S. Mitchell, L. Dingle Robertson, A. Davidson, et al. 2021. A Comparison between Support Vector Machine and Water Cloud Model for Estimating Crop Leaf Area Index. Remote Sensing. 13(7): 1348. DOI: 10.3390/rs13071348.

Dingle Robertson, L. A.M. Davidson, H. McNairn, M. Hosseini, S. Mitchell, D. de Abelleyra, S. Verón, G. Le Maire, M. Plannells, S. Valero, N. Ahmadian, A. Coffin, D. Bosch, M. H. Cosh, B. Basso, N. Saliendra. 2020. C-band synthetic aperture radar (SAR) imagery for the classification of diverse cropping systems. International J Remote Sensing. 41(24): 9638-9649. DOI: 10.1080/01431161.2020.1805136.

Dingle Robertson, L., A. Davidson, H. McNairn, M. Hosseini, S. Mitchell, D. De Abelleyra, S. Verón, M.H. Cosh. 2020. Synthetic Aperture Radar (SAR) image processing for operational space-based agriculture mapping. Int. J. Remote Sensing. 41(18): 7112-7144. DOI: 10.1080/01431161.2020.1754494 [PDF].

Hosseini, M., H. McNairn, S. Mitchell, L. Dingle Robertson, A. Davidson, S. Homayouni. 2020. Integration of synthetic aperture radar and optical satellite data for corn biomass estimation. MethodsX. 7:100857. DOI: 10.1016/j.mex.2020.100857.

Jacobs, K.T., and S. Mitchell. 2020. OpenStreetMap quality assessment using unsupervised machine learning methods. Transactions in GIS. 24(5): 1280-1298. DOI: 10.1111/tgis.12680.. Recognized in 2022 by Wiley as one of their top cited articles.

Martin, A.E., Sara J Collins, Susie Crowe, Judith Girard, Ilona Naujokaitis-Lewis, A. C. Smith, K. Lindsay, S. Mitchell, and L. Fahrig. 2020. Effects of farmland heterogeneity are similar to – or even larger than – the effects of farming practices. Agriculture, Ecosystems & Environment. 288: 106698. DOI:10.1016/j.agee.2019.106698 (open access) [PDF]

Hosseini, M., H. McNairn, S. Mitchell, L. Dingle Robertson, A. Davidson, S. Homayouni. 2019. Synthetic Aperture Radar and optical satellite data for estimating the biomass of corn. Int. J. Appl. Earth Observation and Geoinformation. 83:101933. DOI: 10.1016/j.jag.2019.101933..

Teixeira, Fernanda Zimmerman, A Kindel, SM Hartz, S Mitchell, and L Fahrig. 2017. When road-kill hotspots do not indicate the best sites for road-kill mitigation. Journal of Applied Ecology. 54(5):1544–1551. [DOI]

Virk, Ravinder, and S.W. Mitchell. 2015. Effect of Different Grazing Intensities on the Spatial-temporal Variability in Above-ground live plant biomass in North American Mixed Grasslands. Canadian J of Remote Sensing. online. [DOI] [PDF]

Czerwinski, Christopher, D. J. King and S. W. Mitchell . 2014. Mapping forest growth and decline in a temperate mixed forest using temporal trend analysis of Landsat imagery, 1987–2010. Remote Sensing of Environment. 141: 188-200. [DOI]

Duro, Dennis, J Girard, D J King, L Fahrig, S Mitchell, K Lindsay, and L Tischendorf . 2014. Predicting species diversity in agricultural environments using Landsat TM imagery. Remote Sensing of Environment. 144(C): 214-225. [DOI]

Eberhardt, Ewen, S Mitchell and L Fahrig. 2013. Road kill hotspots do not effectively indicate mitigation locations when past road kill has depressed populations. The Journal of Wildlife Management. 77(7): 1353-1359. [DOI] [PDF]

Pasher, Jon, S W Mitchell, D J King, L Fahrig, A C Smith, and K E Lindsay. 2013. Optimizing landscape selection for estimating relative effects of landscape variables on ecological responses. Landscape Ecology. 28(3): 371-383. [DOI] [PDF]

Remmel, T K, and S W Mitchell. 2013. The importance of accurate visibility parameterization during atmospheric correction: impact on boreal forest classification. Int J Remote Sensing. 34(14): 5213-5227. [DOI]

Graduate Supervisions We are conducting a wide range of projects in spatial analysis and environmental processes; please consult our lab web pages for more details.

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