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Open AccessArticle

Circa 2010 Land Cover of Canada: Local Optimization Methodology and Product Development

1
Natural Resources Canada, Canadian Centre for Remote Sensing, 560 Rochester, Ottawa, ON K1A 0E4, Canada
2
Environment and Climate Change Canada, Landscape Science and Technology, Ontario, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(11), 1098; https://doi.org/10.3390/rs9111098
Received: 1 September 2017 / Revised: 17 October 2017 / Accepted: 25 October 2017 / Published: 27 October 2017
(This article belongs to the Special Issue GIS and Remote Sensing advances in Land Change Science)
Land cover information is necessary for a large range of environmental applications related to climate impacts and adaption, emergency response, wildlife habitat, etc. In Canada, a 2008 user survey indicated that the most practical land cover data is provided in a nationwide 30 m spatial resolution format, with an update frequency of five years. In response to this need, the Canada Centre for Remote Sensing (CCRS) has generated a 30 m land cover map of Canada for the base year 2010, as the first of a planned series of maps to be updated every five years, or more frequently. This land cover dataset is also the Canadian contribution to the 30 m spatial resolution 2010 Land Cover Map of North America, which is produced by Mexican, American and Canadian government institutions under a collaboration called the North American Land Change Monitoring System (NALCMS). This paper describes the mapping approach used for generating this land cover dataset for Canada from Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) Landsat sensor observations. The innovative part of the mapping approach is the local optimization of the land cover classifier, which has resulted in increased spatial consistency and accuracy. Training and classifying with locally confined reference samples over a large number of partially overlapping areas (i.e., moving windows) ensures the optimization of the classifier to a local land cover distribution, and decreases the negative effect of signature extension. A weighted combination of labels, which is determined by the classifier in overlapping windows, defines the final label for each pixel. Since the approach requires extensive computation, it has been developed and deployed using the Government of Canada’s High-Performance Computing Center (HPC). An accuracy assessment based on 2811 randomly distributed samples shows that land cover data produced with this new approach has achieved 76.60% accuracy with no marked spatial disparities. View Full-Text
Keywords: land cover; land cover change; landsat; Canada; 2010; random forest algorithm; classification land cover; land cover change; landsat; Canada; 2010; random forest algorithm; classification
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MDPI and ACS Style

Latifovic, R.; Pouliot, D.; Olthof, I. Circa 2010 Land Cover of Canada: Local Optimization Methodology and Product Development. Remote Sens. 2017, 9, 1098.

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