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Open AccessArticle

Comparison and Assessment of Regional and Global Land Cover Datasets for Use in CLASS over Canada

1
Climate Research Division, Environment and Climate Change Canada, 4905 Dufferin Street, Toronto, ON M3H 5T4, Canada
2
Landscape Science and Technology, Environment and Climate Change Canada, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
3
Earth and Life Institute, Environmental Sciences, Université catholique de Louvain, Croix du Sud, 2-L7.05.16, 1348 Louvain-la-Neuve, Belgium
4
Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Road, Victoria, BC V8Z 1M5, Canada
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2286; https://doi.org/10.3390/rs11192286
Received: 13 August 2019 / Revised: 23 September 2019 / Accepted: 25 September 2019 / Published: 30 September 2019
Global land cover information is required to initialize land surface and Earth system models. In recent years, new land cover (LC) datasets at finer spatial resolutions have become available while those currently implemented in most models are outdated. This study assesses the applicability of the Climate Change Initiative (CCI) LC product for use in the Canadian Land Surface Scheme (CLASS) through comparison with finer resolution datasets over Canada, assisted with reference sample data and a vegetation continuous field tree cover fraction dataset. The results show that in comparison with the finer resolution maps over Canada, the 300 m CCI product provides much improved LC distribution over that from the 1 km GLC2000 dataset currently used to provide initial surface conditions in CLASS. However, the CCI dataset appears to overestimate needleleaf forest cover especially in the taiga-tundra transition zone of northwestern Canada. This may have partly resulted from limited availability of clear sky MEdium Resolution Imaging Spectrometer (MERIS) images used to generate the CCI classification maps due to the long snow cover season in Canada. In addition, changes based on the CCI time series are not always consistent with those from the MODIS or a Landsat-based forest cover change dataset, especially prior to 2003 when only coarse spatial resolution satellite data were available for change detection in the CCI product. It will be helpful for application in global simulations to determine whether these results also apply to other regions with similar landscapes, such as Eurasia. Nevertheless, the detailed LC classes and finer spatial resolution in the CCI dataset provide an improved reference map for use in land surface models in Canada. The results also suggest that uncertainties in the current cross-walking tables are a major source of the often large differences in the plant functional types (PFT) maps, and should be an area of focus in future work. View Full-Text
Keywords: land cover; forest cover; plant functional type; land surface model; CLASS; Canada land cover; forest cover; plant functional type; land surface model; CLASS; Canada
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Wang, L.; Bartlett, P.; Pouliot, D.; Chan, E.; Lamarche, C.; Wulder, M.A.; Defourny, P.; Brady, M. Comparison and Assessment of Regional and Global Land Cover Datasets for Use in CLASS over Canada. Remote Sens. 2019, 11, 2286.

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