| Title | Circa 2010 land cover of Canada: local optimization methodology and product development |
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| Author | Latifovic, R; Pouliot, D; Olthof, I |
| Source | Remote Sensing vol. 9, no. 11, 1098, 2017 p. 1-18, https://doi.org/10.3390/rs9111098  Open Access |
| Image |  |
| Year | 2017 |
| Alt Series | Natural Resources Canada, Contribution Series 20190015 |
| Publisher | MDPI AG |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf; html |
| Subjects | geophysics; remote sensing |
| Program | Remote Sensing Science |
| Released | 2017 10 27 |
| Abstract | 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. © 2017 by the authors. |
| Summary | (Plain Language Summary, not published)
National scale land cover and land cover change information are required for studying land-surface processes that characterize environmental, social and
economic aspects of sustainability. In Canada, a user survey revealed that the most practical format for provision of land cover data is 30 m, nationwide, with an update frequency of every five years. In response to this need, the Canada Centre for
Remote Sensing has generated a 30 m Land Cover Map of Canada for the base year 2010 as part of planned series of maps at 5 years or more frequent update. In this article we describe improved land cover mapping methodology used to generate Land Cover
Map of Canada from medium resolution (30m mapping unit) satellite optical data. In addition, article provides land cover dataset characteristics such as spatial consistency and accuracy. |
| GEOSCAN ID | 311405 |
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