4.7 Article

Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944

Journal

LANDSCAPE AND URBAN PLANNING
Volume 192, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.landurbplan.2019.103652

Keywords

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Funding

  1. Natural Sciences and Engineering Research Council of Canada (NSERC) [RGPIN-386183]
  2. Ontario Graduate Scholarship
  3. Department of Geography at UTM through the Graduate Expansion Funds

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Urban expansion, especially suburbanization, represents a major social, economic and environmental shift that has escalated since the mid-1900s in North America. Suburban development leads to corresponding changes in the treed environment of urban-rural fringes. It is important to understand where, when and why trees change in response to development over many decades, but this is difficult since long-term data are scarce. We used 70+ years (1944-2017) of leaf-off aerial photographs, often representing the only long-term landscape record, to quantify and map spatlo-temporal changes in tree density through the entirety of the agricultural-suburban transitional period. Photo-interpretation of individual tree locations, along with recording observable drivers of change, was completed across six different modern landscapes in Mississauga, Ontario, Canada. Results indicate that tree density often recovers, or even increases, post-development. There are differences between landscapes, but most tree density gains are associated with forest expansion and tree planting, while most losses are associated with building and road construction. The influence of these drivers, along with the temporal trajectory of tree density changes, are shaped by a landscape's socioecological legacy and the length, scope and intensity of development (as decided by decision makers). Processes include initial tree losses followed by recovery from tree planting, and forest succession in abandoned fields after land purchase and nearby development. We assert that the spatio-temporal changes in tree density and related drivers quantified here can be generalized to gain knowledge on how tree density and distribution across agricultural landscapes will change under different development scenarios.

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