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

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

11. 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)

12. Statistics Canada – Population density

13. 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

14. “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…

15. 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

16. 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

17. Population density classes, Montreal 1 km

18. Population density classes, Vancouver 1 km

21. Transportation network, Vancouver 1 km

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

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

26. 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

27. 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…

28. 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…