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 national-scale 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. Résumé L’accès libre et gratuit à plus de 40 ans de données dans l’archive Landsat combiné à l’amélioration des produits d’imagerie standardisés et l’augmentation des capacités de traitement et de stockage informatiques ont permis la production d’images composites basées sur les pixels de réflectance de surface de grande superficie sans nuages. Les composites du « meilleur pixel disponible » (best-available-pixel; BAP) représentent un nouveau paradigme en matière de télédétection qui ne dépend plus de l’analyse par scène. Une série chronologique de ces images composites BAP offre de nouvelles occasions de générer des produits d’information qui caractérisent la couverture terrestre, le changement de la couverture terrestre et les attributs structurels de la forêt d’une manière dynamique, transparente, systématique, répétable et spatialement exhaustive. Ici, nous articulons les besoins d’information liés à la science et à la surveillance des écosystèmes forestiers dans un contexte canadien, et nous indiquons comment ces nouvelles approches de composition d’image et les produits qui en découlent peuvent nous permettre de répondre à ces besoins. Nous soulignons quelques-uns des problèmes et des possibilités associés à une approche de composition d’image et nous démontrons des produits composites annuels à l’échelle nationale pour une année, avec des analyses plus détaillées pour deux zones prototypes utilisant 15 ans de données Landsat. Des recommandations concernant la meilleure façon de lier des décisions de composition d’images à l’utilisation souhaitée du composite (et le besoin d’information) ainsi que les orientations futures de la recherche sont présentées.

Capture of land cover information is a key requirement for supporting forest monitoring and management. In Canada, provincial and territorial forest stewards use land cover information to aid in management and planning activities. At the federal level, land cover information is required to aid in meeting national and international reporting obligations. To support monitoring of Canada’s forests, the Earth Observation for Sustainable Developments of Forests (EOSD) project was initiated as a partnership between the Canadian Forest Service (CFS) and the Canadian Space Agency (CSA), with provincial and territorial participation and support. The EOSD project produced a 23 class land cover map of the forested area of Canada representing circa year 2000 conditions (EOSD LC 2000). Including image overlap outside of the forested area of Canada, over 480 Landsat-7 Enhanced Thematic Mapper Plus (ETM+) images were classified and more than 80% of Canada was mapped, culminating in the production of 630 1:250 000 map sh... L’acquisition d’information sur le couvert est un élément essentiel pour le suivi et la gestion de la forêt. Au Canada, les gestionnaires de la forêt tant au niveau provincial que territorial utilisent l’information sur le couvert en soutien aux activités de gestion et de planification. Au plan fédéral, l’information sur le couvert est nécessaire pour remplir les obligations nationales et internationales du Canada en matière de préparation de rapports. Le projet OTDD (Observation de la Terre pour le développement durable des forets), projet de partenariat entre le Service canadien des forêts (SCF) et l’Agence spatiale canadienne (ASC) avec la participation et le soutien des autorités provinciales et territoriales, a été mis sur pied pour aider au suivi des forêts canadiennes. Le projet OTDD a produit une carte du couvert comportant vingt-trois classes de la zone forestière du Canada représentant les conditions de l’année 2000 (EOSD LC 2000). Si l’on inclut la zone de superposition des images située en deh...

A priority of the Canadian Forest Service and Canadian Space Agency joint project, Earth Observation for Sustainable Development of Forests (EOSD), is the production of a land cover map of the forested area of Canada based upon Landsat data. The land cover will be produced through a partnership of federal, provincial and territorial governments, universities, and industry. The short-term goal of EOSD is to complete a land cover map representing year 2000 forested area conditions by early 2006. Over the longer term, EOSD will aim to produce land cover products to capture changes in forest conditions over time to support national and international reporting requirements. The forested area of Canada represents approximately half of Canada's landmass, requiring over 450 scenes for complete coverage (with overlap minimized). EOSD is working with provincial and territorial mapping agencies that have on-going land cover mapping programs to optimize production capacity. It is envisioned that the combined output of EOSD and provincial and territorial land cover mapping programs will be integrated with maps developed by other sectors and agencies (such as agriculture) to produce a complete representation of the land cover of Canada. Large-area land cover mapping using remote sensing is a relatively new phenomenon. Advances in data storage capabilities, computing power, and increases in the affordability of data have allowed for large-area projects to be undertaken in ways previously not possible. The manner in which a large-area mapping project is approached is related to a number of factors including the spatial extent of the area of interest, the spatial resolution of the selected sensor, and the products which are to be generated. In this communication we report on the strategy, methods, and status of the EOSD land cover mapping program of the forested area of Canada. Key words: Canada, land cover, forest inventory, EOSD, Landsat, unsupervised classification, NFI

Forest inventory data often provide the required base data to enable the largearea mapping of biomass over a range of scales. However, spatially explicit estimates ofabove-ground biomass (AGB) over large areas may be limited by the spatial extent of theforest inventory relative to the area of interest (i.e., inventories not spatially exhaustive), orby the omission of inventory attributes required for biomass estimation. These spatial andattributional gaps in the forest inventory may result in an underestimation of large areaAGB. The continuous nature and synoptic coverage of remotely sensed data have led totheir increased application for AGB estimation over large areas, although the use of thesedata remains challenging in complex forest environments. In this paper, we present anapproach to generating spatially explicit estimates of large area AGB by integrating AGBestimates from multiple data sources; 1. using a lookup table of conversion factors appliedto a non-spatially exhaustive forest inventory dataset (R² = 0.64; RMSE = 16.95 t/ha), 2.applying a lookup table to unique combinations of land cover and vegetation densityoutputs derived from remotely sensed data (R² = 0.52; RMSE = 19.97 t/ha), and 3. hybridmapping by augmenting forest inventory AGB estimates with remotely sensed AGB estimates where there are spatial or attributional gaps in the forest inventory data. Over our714,852 ha study area in central Saskatchewan, Canada, the AGB estimate generated fromthe forest inventory was approximately 40 Mega tonnes (Mt); however, the inventoryestimate represents only 51% of the total study area. The AGB estimate generated from theremotely sensed outputs that overlap those made from the forest inventory based approachdiffer by only 2 %; however in total, the remotely sensed estimate is 30 % greater (58 Mt)than the estimate generated from the forest inventory when the entire study area isaccounted for. Finally, using the hybrid approach, whereby the remotely sensed inputswere used to fill spatial gaps in the forest inventory, the total AGB for the study area wasestimated at 62 Mt. In the example presented, data integration facilitates comprehensiveand spatially explicit estimation of AGB for the entire study area.