Based primarily on the most recent release of LANDFIRE v2.0.0, this generalized land cover dataset provides full coverage of California including to the three nautical mile limit offshore. It represents a ground condition of 2016 divided into 30mx30m cells across the entire state. The state is grouped into the following land cover classes: forests, shrublands and chaparral, grasslands, croplands, wetlands, seagrasses and seaweeds, developed lands, and sparsely vegetated lands. The mapped area has been extended offshore to three nautical miles. Lakes, reservoirs, rivers, and oceans that do not overlay seagrasses and seaweeds are identified in as “open water.” LANDFIRE v.2.0.0 provides the source for much of the land cover and is an integrated dataset with many layers. The Existing Vegetation Type (EVT) and Biophysical Settings (BPS) layers provide inputs to this data set. The EVT layer contains data on life form (tree, shrub, herb, developed, agriculture, sparse, barren, snow-ice, or water), a named vegetation type, and notes on recent disturbance. These are used to assign a likely generalized land cover type to each pixel. This result is then refined using the BPS layer to suggest the land cover that might exist in recently disturbed (fire or logging) areas absent that disturbance. These results are then supplemented through the creation of a seagrasses and seaweeds dataset by combining data on the presence of eelgrass and kelp canopy and replacing the water category with seagrasses and seaweeds where it is present.These data result from the integration of remote sensing (satellite imagery analysis), with field data, using computer algorithms under the oversight of the LANDFIRE team or the teams developing the seagrass and kelp maps. Errors are expected in all data and while every attempt is made to minimize and understand them, they cannot be eliminated. As a result, the cells in the data represent an estimate of what is on the ground at that specific location. Validation techniques used in the production of the data help identify and allow for correction of gross errors, but individual pixels, or even small groupings of them may differ from real world conditions. Similarly, while efforts are made to be consistent with the selection of the source satellite data, the difference between seasons or a wet versus dry year do impact the final maps, notably water and wetlands.Data SourcesLANDFIRE: LANDFIRE Existing Vegetation Type layer.(2013 - 2021). U.S. Department of Interior, Geological Survey.[Online]. Available: https://landfire.gov/version_download.php [Accessed: February 3, 2021].LANDFIRE: LANDFIRE Biophysical Setting layer.(2013 - 2021). U.S. Department of Interior, Geological Survey.[Online]. Available: https://landfire.gov/version_download.php [Accessed: February 3, 2021].Bell, T, K. Cavanaugh, D. Siegel. 2020. SBC LTER: Time series of quarterly NetCDF files of kelp biomass in the canopy from Landsat 5, 7 and 8, since 1984 (ongoing) ver 13. Environmental Data Initiative. https://doi.org/10.6073/pasta/5d3fb6fd293bd403a0714d870a4dd7d8. Accessed 2021-04-08. (Data extraction performed by T. Bell April 8, 2021)Eelgrass Survey GIS Data version 2.0 (2017, updated 2020), National Marine Fisheries Service West Coast Region. Available: https://www.sfei.org/data/eelgrass-survey-gis-data#sthash.u94SjLu7.afUwqGJA.dpbs [Accessed: April 6, 2021)
