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Urban Land-Cover Classification for Mesoscale Atmospheric Modeling

Urban Land-Cover Classification for Mesoscale Atmospheric Modeling. Alexandre Leroux. Objectives. Goal: Provide an urban land-cover database for North-American cities for mesoscale atmospheric modeling, specifically, for the Town Energy Balance model (TEB).

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Urban Land-Cover Classification for Mesoscale Atmospheric Modeling

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  1. Urban Land-Cover Classification for Mesoscale Atmospheric Modeling Alexandre Leroux

  2. Objectives • Goal: Provide an urban land-cover database for North-American cities for mesoscale atmospheric modeling, specifically, for the Town Energy Balance model (TEB). • Mean: - Approach #1 (presented last year) Satellite imagery and DEM analysis - Approach #2 Vector data processing and DEM analysis

  3. Satellite approach - Workflow Statistics and fractions at a lower scale Decision tree Results readied for atmospheric modeling Satellite imagery unsupervised classification Processing and analysis Building height assessment through SRTM-DEM minus CDED1 or NED

  4. Satellite approach results • 30 to 40 “simple elements” identified on satellite imagery at a 15-m spatial resolution • e.g. asphalt, concrete, roofs, water, trees, grass & fields • Results from the decision tree: • 12 new urban classes generated at 60m • +/- 5 vegetation classes associated to gengeo • Processing and analysis: ~ 1 week / urban area

  5. Oklahoma City, 60 m

  6. Montreal, 60 m (detail, zoom 2x)

  7. Vancouver, 60 m (detail, zoom 4x)

  8. Vector approach - Workflow

  9. National Topographic Data Base • Vector data with 110 thematic layers • e.g. water, vegetation, golf course, built-up areas, buildings (points and polygons), roads, bridges, railway, etc • Most layers with attributes • e.g. a road feature can be ‘highway’, ‘paved’, ‘underground’. • A total of 2474 1:50,000 sheets covering Canada • Available internally within the federal government

  10. Statistics Canada - 2001 Census Data • Canada-wide coverage • Used to distinguish residential districts • Population density calculated using this dataset • Includes the number of residences • Available internally (license purchased by EC)

  11. Statistics Canada – Population density

  12. Topography and Height data • SRTM-DEM • Top of features (e.g. buildings, vegetation) • Worldwide coverage and free • “Poor” spatial resolution (3 arc-second, ~90m) • CDED1 • Ground elevation • Canada-wide coverage and free • 1:50,000 (mtl: 16 x 23m) • Subtraction to evaluated building height

  13. “AutoTEB” Spatial Data Processing • Automated dataset identification • Read/write multiple formats, including ‘.fstd’ • On-the-fly reprojection and datum management • Different spatial resolution / scale management • Spatial data cropping, subtraction (cookie cutting), buffering, rasterizing, SQL queries, multiple layer flattening (merge down), basic spatial queries, LUT value attribution and much more…

  14. Results • Some results for Montreal and Vancouver • Raster output at 5m spatial resolution, generates rater data up to 10,000 x 12,000 pixels (Toronto) • Other processed cities • Calgary, Edmonton, Halifax, Ottawa, Quebec, Regina, Toronto, Victoria, Winnipeg (SRTM-DEM - CDED1 not yet processed for those cities) • The methodology, processing, analysis and results are well documented

  15. TEB classes • 46 ‘final’ aggregated classes • Buildings (18 classes) • 1D & 2D, height, use (i.e. 24/7, industrial-commercial) • Residential areas, divided by population density • Roads and transportation network • Industrial and other constructions • e.g. tanks, towers, chimneys • Mixed covers • Natural covers

  16. Population density classes, Montreal 1 km

  17. 1 km Population density classes, Vancouver

  18. 1 km

  19. 1 km

  20. 1 km Transportation network, Vancouver

  21. Detail of Montreal, Scaled-down, 46 classes 1 km

  22. 1 km

  23. Detail of Vancouver, Scaled-down, 46 classes 1 km

  24. 1 km

  25. Main benefits • Canada-wide applicability • Full data coverage • Approach directly applied anywhere over Canada • Complete automation • Single command with only one input parameter • One optional exception: SRTM-DEM minus CDED1 • Fast! From 3 min to 40 min for the whole processing • Numerous other advantages identified… • No interpretation and reduced human intervention • Flexible approach, code developed reusable • Spatial resolution of the results

  26. Main limitations • Up-to-date data • BNDT data based on “old” aerial imagery: missing some downtown buildings and suburbs • Thematic representation • No layer corresponding to rural areas and parking lots • Almost no distinction in vegetation types • Various other minor limitations identified…

  27. The future of the vector approach • Adaptation to CanVect and other datasets, potentially including US territory datasets • Use of 3D building models required for CFD modeling within the vector approach • Various other improvements envisioned… • TEB sensibility analysis to urban LULC databases • Scientific article to be written • much more…

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