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 web site|
I studied geography and biology at Queen’s University at Kingston, and geography and environmental studies at the University of Toronto. Before coming to Carleton, I was a faculty member at the University of Toronto for a year, and worked on several research projects there in addition to my doctoral studies. This included work on soil erosion and environmental decision support systems in the Loess Plateau region of China, evaluation of potential forest nitrogen saturation and lake acidification in the Adirondack region of New York State, and a project looking at new methods for measuring and classifying Canada’s forest inventory. In 2003, while working on that last project, I joined Carleton University, and have been working here ever since, teaching in all the programs offered by the Department of Geography and Environmental Studies.
I am a co-director of Carleton’s Geomatics and Landscape Ecology Laboratory, and most of my graduate students work out of 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 http://www.farmland-biodiversity.org/index.php?sujet=1&lang=en. Our official (funded) projects are now over but we still have lots of data analysis to get through;
- 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 a spatial scenario modelling framework to study this.
2019 – 2020 Courses
- GEOM 4008 Advanced Topics in Geographic Information Systems
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
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.