Alberta Historical Landuse and Landscape Data Library
The Alberta Historical Landuse and Landscape Library represents a non-profit online educational data website where stakeholders can locate, view, graph, and view references for a broad suite of data metrics pertaining to landuses and landscapes in the Province of Alberta. The primary goal is to allow users to observe and discuss provincial trends in landuses and landscapes. This website continues to evolve as new information comes forward from its numerous contributors and users. The foundation of this site is the various datasets assembled by a diverse collection of governmental, industrial, academic, and NGO organizations. This Library is NOT a substitute for the original data sources, but rather a convenient locale to view time series that illustrate key landuse and landscape metrics.
Much of the data contained within this site was collated by Brad Stelfox from original sources during the past 15 years in connection with the attribution of the Alberta Landuse Cumulative Effects Simulator (ALCES) model. Specific thanks are extended to Delinda Ryerson, Heather Gariepy, Percy Rotteveel, and Noah Purves-Smith for data assembly and web site development. The Canada West Foundation, Nature Conservancy of Canada, and the Government of Alberta are thanked for their encouragement and for contributing funds toward updating of data for this website. The efforts of the Miistakis Institute of the Rockies (http://www.rockies.ca) for building the time series maps contained in this website, are greatly appreciated.
Information has been arranged in an hierarchical fashion to facilitate quick access to the information sought by users. The ALCES Group welcomes any new datasets that are relevant to landuses and landscapes and for which references can be provided. Those land use categories for which data are currently missing are being actively research and will be updated as soon as possible.
The Library user is strongly encouraged to use the reference links provided below all tables and figures and review original materials to better understand the methodologies used, and limitations, of each of the datasets.