From need to product: a methodology for completing a land cover map of Canada with Landsat data

Despite its very large territory and the best Landsat archive in the world, Canada has made very limited use of Landsat data for land cover mapping. The primary difficulty has been the prohibitive cost of information extraction and the earlier (and now overcome for Landsat-7 enhanced thematic mapper plus data) high cost of data purchase. The solution to this remaining obstacle lies in decreasing the cost of Landsat data processing and analysis while ensuring the high quality of the extracted information. In this paper, we present an efficient and effective approach to mapping land cover in Canada from Landsat thematic mapper data (single or multiple satellites). The key features of this approach are an increase in the ratio of computer to human analysis and automation for high data volume or large area processing. However, it is essential that the final product quality not suffer because of the greater reliance on computer processing, thus the algorithm performance becomes critical. We describe the overall approach, discuss key challenges, explain the principles behind key algorithms developed to respond to the challenges, present evidence demonstrating the effectiveness of these algorithms in a boreal landscape setting, and consider implementation issues. With a processing system developed to handle large numbers (tens to hundreds) of Landsat scenes, which incorporates most of the algorithms discussed here, the stage is nearly set for large-scale processing leading to a Landsat-based land cover classification product(s) for Canada.