1 / 30

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).

phuong
Download Presentation

Urban Land-Cover Classification for Mesoscale Atmospheric Modeling

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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…

More Related