The fractional land cover estimates from the Boreal-Arctic Wetland and Lake Dataset (BAWLD), 2021. ...

• Main Authors: Olefeldt, David, Hovemyr, Mikael, Kuhn, McKenzie, Bastviken, David, Bohn, Theodore, Connolly, John, Crill, Patrick, Euskirchen, Eugénie, Finkelstein, Sarah, Genet, Hélène, Grosse, Guido, Harris, Lorna, Heffernan, Liam, Helbig, Manuel, Hugelius, Gustaf, Hutchins, Ryan, Juutinen, Sari, Lara, Mark, Malhotra, Avni, Manies, Kristen, McGuire, David, Natali, Susan, O'Donnell, Jonathan, Parmentier, Frans-Jan, Räsänen, Aleksi, Schädel, Christina, Sonnentag, Oliver, Strack, Maria, Tank, Suzanne, Treat, Claire, Varner, Ryth, Virtanen, Tarmo, Warren, Rebecca, Watts, Jennifer
• Format: Dataset
• Language: English
• Published: NSF Arctic Data Center 2021
• Subjects: Aquatic Ecosystems; Lakes; Wetlands; Terrestrial Ecosystems; Permafrost; Land Use/Land Cover Classification; Arctic; permafrost; Tundra
• Online Access: https://dx.doi.org/10.18739/a2c824f9x; https://arcticdata.io/catalog/view/doi:10.18739/A2C824F9X

The Boreal and Arctic Wetland and Lake Dataset (BAWLD) provides estimates of fractional land cover of 19 land cover classes within 0.5° ×0.5° grid cells. The total area of the BAWLD domain is 25 500 000 kilometers squared (km2), i.e. 17% of the global land surface. The domain includes the boreal and tundra biomes, as well as areas of rocks and glaciers at greater than 50° North (N). The dataset is comprised of 23,469 0.5° ×0.5° grid cells. Each grid cell includes information on the fractional cover of five wetland classes, seven lake classes, three river classes, along with glacier, rockland, tundra, and boreal forest classes. Estimates of land cover fractional extents are based on an expert assessment, and a subsequent extrapolation to the full study region using random forest analysis. The dataset also includes an assessment of the uncertainty of the fractional cover estimates, represented by the 95% high and low estimates for fractional land cover. Each grid cell is further classified as one of fifteen ...