Futures Analysis of Urban Land Use and Wetland Change in Saskatoon, Canada: An Application in Strategic Environmental Assessment

• Main Authors: Anton Sizo, Bram Noble, Scott Bell
• Format: Article
• Language: English
• Published: MDPI AG 2015-01-01
• Series: Sustainability
• Subjects: strategic environmental assessment; environmental baseline assessment; scenario analysis; zero alternative; urban planning; wetland conservation; Markov Chain; land use; future analysis
• Online Access: http://www.mdpi.com/2071-1050/7/1/811

This paper presents a scenario-based approach to strategic environmental assessment (SEA) for wetland trend analysis and land use and land cover (LUC) modeling in an urban environment. The application is focused on the Saskatoon urban environment, a rapidly growing urban municipality in Canada’s prairie pothole region. Alternative future LUC was simulated using remote sensing data and city spatial planning documentation using a Markov Chain technique. Two alternatives were developed and compared for LUC change and threats to urban wetland sustainability: a zero alternative that simulated trends in urban development and wetland conservation under a business as usual scenario, in the absence of prescribed planning and zoning actions; and an alternative focused on implementation of current urban development plans, which simulated future LUC to account for prescribed wetland conservation strategies. Results show no improvement in future wetland conditions under the city’s planned growth and wetland conservation scenario versus the business as usual scenario. Results also indicate that a blanket wetland conservation strategy for the city may not be sufficient to overcome the historic trend of urban wetland loss; and that spatially distributed conservation rates, based on individual wetland water catchment LUC peculiarities, may be more effective in terms of wetland conservation. The paper also demonstrates the challenges to applied SEA in a rapidly changing urban planning context, where data are often sparse and inconsistent across the urban region, and provides potential solutions through LUC classification and prediction tools to help overcome data limitations to support land use planning decisions for wetland conservation.