1. Urban Land-Cover Classification for Mesoscale Atmospheric Modeling Alexandre Leroux, M.Sc., Ing.

2. Canadian Meteorological Centre Environment Canada’s National Center for data assimilation and numerical weather prediction, climate and air quality modeling Environmental Emergency Response Division Provides highly specialized support to environmental emergencies including atmospheric dispersion and trajectory modeling

3. Context High resolution atmospheric numerical models require detailed characterisation of the Earth’s surface to drive sophisticated surface parametrisation schemes. This requirement is even more important for complex urban environments

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

5. 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

6. Input data processing and analysis Satellite urban land-cover classification: Mid-resolution unsupervised classification of Landsat-7 and ASTER data Building height appraisal: –SRTM-DEM for elevation at top of features (e.g. trees, buildings) –CDED1 (Canada) and NED (USA) for soil elevation –The subtraction evaluates the building height

7. Satellite approach results Computed statistics and fractions are feed to the decision tree Main results: –12 new urban classes generated at 60m –+/- 5 vegetation classes Processing and analysis: ~ 1 week / urban area Results over OkC, Mtl and Van are satisfactory

8. Oklahoma City, 60 m

9. Montreal, 60 m (detail)

10. Vancouver, 60 m (detail)

11. Vector approach - Workflow

12. 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

14. 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)

15. Statistics Canada – Population density

16. 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

17. Scripted Spatial Data Processing Complete automation: Automated dataset identification Read/write multiple formats, including CMC custom formats On-the-fly reprojection and datum management Different spatial resolution / scale management Spatial data cropping, subtraction (cookie cutting), buffering, rasterizing, SQL queries on attributes, multiple layer flattening (merge down), basic spatial queries, LUT value attribution and much more… Makes use of GDAL and OGR open C libraries

18. Results Results for Montreal and Vancouver –Raster output at 5m spatial resolution, generates rater data with 10,000 x 12,000 pixels (50 x 60 km, Toronto) Other processed cities –Calgary, Edmonton, Halifax, Ottawa, Quebec, Regina, Toronto, Victoria, Winnipeg The methodology, processing, analysis and results are well documented

19. TEB classes 44 ‘final’ aggregated classes –Buildings (18 classes) 1D & 2D, height, use (i.e. 24/7, industrial-commercial) –Residential areas, divided by population density (5 classes) –Roads and transportation network (6 classes) –Industrial and other constructions (5 classes) e.g. tanks, towers, chimneys –Mixed covers (3 classes) –Natural covers (7 classes)

20. Population density classes, Montreal 1 km

21. Population density classes, Vancouver 1 km

22. 18 building classes, Downtown Montreal

23. 1 km 18 building classes, Downtown Vancouver

24. Transportation network, Vancouver 1 km

25. Detail of Montreal, Scaled-down, 44 classes 1 km

27. Detail of Vancouver, Scaled-down, 44 classes 1 km

29. 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

30. Main limitations Up-to-date data –NTDB 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…

31. 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…

34. Urban canyon modeling: linking mesoscale models to CFD models at the urban scale