Automated urban land use and land cover classification for mesoscale atmospheric modeling over Canadian cities

An automated geospatial database processing approach has been developed in order to characterize urban areas of major Canadian cities for use in mesoscale atmospheric modeling. High-resolution atmospheric numerical models that include urban canopy models, such as the Town Energy Balance (TEB) model, require detailed surface characteristics to parameterize physical processes occurring at the surface. The developed methodology uses the following pan-Canadian databases: National Topographic Data Base (NTDB) vector data for land use and land cover (LULC) characterization, Shuttle Radar Topography Mission (SRTM-DEM) and Canadian Digital Elevation Data (CDED1) digital elevation models (DEM) for building height assessment, and census data for the characteristics of residential districts. These databases are jointly processed to automatically generate a high-resolution urban LULC classification for Canadian cities.

The processing establishes the NTDB vector layer priority while making use of some of the feature attributes. Additional processing completes the information derived from the NTDB database, such as a population density measurement and an optional building height assessment. The final resulting classes will be associated to parameters used as inputs for urban meteorological modeling. The results over Montreal and Vancouver are discussed. The benefits and limitations of the approach are identified and analyzed. The main benefits of the approach are (a) Canada-wide applicability with available continuous databases, and (b) complete automation, with the exception of some optional post-processing. In spite of limitations, the approach compares advantageously to previously available LULC classifications for mesoscale atmospheric modeling.

Further development of the methodology incorporates additional databases, including 3D buildings datasets of Canadian cities to significantly improve the building height assessment, and the Earth Observation for Sustainable Development of Forests (EOSD) dataset as a source of LULC for vegetated areas.