Abstract.Free and open access to the more than 40 years of data captured in the Landsat archive, combined with improvements in standardized image products and increasing computer processing and storage capabilities, have enabled the production of large-area, cloud-free, surface reflectance pixel-based image composites. Best-available-pixel (BAP) composites represent a new paradigm in remote sensing that is no longer reliant on scene-based analysis. A time series of these BAP image composites affords novel opportunities to generate information products characterizing land cover, land cover change, and forest structural attributes in a manner that is dynamic, transparent, systematic, repeatable, and spatially exhaustive. Herein, we articulate the information needs associated with forest ecosystem science and monitoring in a Canadian context, and indicate how these new image compositing approaches and subsequent derived products can enable us to address these needs. We highlight some of the issues and opportunities associated with an image compositing approach and demonstrate annual composite products at a nationalscale for a single year, with more detailed analyses for two prototype areas using 15 years of Landsat data. Recommendations concerning how to best link compositing decisions to the desired use of the composite (and the information need) are presented, along with future research directions.

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Important and complex spatial decisions, such as allocating land to development or conservationoriented goals, require information and tools to aid in understanding the inherent tradeoffs. They also require mechanisms for incorporating and documenting the value judgements of interest groups and decision makers. Multiple-criteria decision analysis (MCDA) is a family of techniques that aid decision makers in formally structuring multi-faceted decisions and evaluating the alternatives. It has been used for about two decades with geographic information systems (GIS) to analyse spatial problems. However, the variety and complexity of MCDA methods, with their varying terminologies, means that this rich set of tools is not easily accessible to the untrained. This paper provides background for GIS users, analysts and researchers to quickly get up to speed on MCDA, supporting the ultimate goal of making it more accessible to decision makers. A number of factors for describing MCDA problems and selecting methods are outlined then simplified into a decision tree, which organises an introduction of key methods. Approaches range from mathematical programming and heuristic algorithms for simultaneously optimising multiple goals, to more common single-objective techniques based on weighted addition of criteria values, attainment of criteria thresholds, or outranking of alternatives. There is substantial research that demonstrates ways to couple GIS with multi-criteria methods, and to adapt MCDA for use in spatially continuous problems. Increasing the accessibility of GIS-based MCDA provides new opportunities for researchers and practitioners, including web-based participation and advanced visualisation of decision processes.