1. Environment Canada, Meteorological Service of Canada, 1 Meteorological Research Branch 2 Environmental & Emergency Response Div. A.Lemonsu 1, A. Leroux 2, S. Bélair 1, S. Trudel 2 and J. Mailhot 1 CRTI Project # 02-0093RD

2. An urban canopy parameterization was recently implemented in the GEM and MC2 Canadian mesoscale models GEM and MC2 currently use a 1 km global classification including 1 urban class imported from DCW (Danko, 1992) Specific urban cover classifications are required in order to describe the spatial distribution and spatial variability of urban areas Scientific Context Objective Develop a general methodology to provide new urban land- use land-cover classifications for major Canadian cities (extended to North American cities)

3. The methodology is based on a joint analysis of remote sensing data and digital elevation models in order to take both surface characteristics and building height into account Mesoscale urban modeling applications (and possibly future applications to weather forecasting) require: Free of charge or low cost data Data covering large urban areas Data available for major North American cities Possibility of future updates General Approach

4. Data Sources CityDataSpatial Res.Date OKC ASTER L1B15 m2001-07-21 NED (for US)1/3 arc-sec SRTM-DEM (for the world)1 arc-sec MTL Landsat-730 m + 15 m (Pan)2001-06-08 CDED1 (for Canada)1:50,000 SRTM-DEM (for the world)3 arc-sec VAN ASTER L1B15 m2001-08-09 CDED1 (for Canada)1:50,000 SRTM-DEM (for the world)3 arc-sec Classifications were produced for Oklahoma City (OK, US), Montreal (QC, Canada) and Vancouver (BC, Canada) NEDNational Elevation Dataset SRTMShuttle Radar Topography Mission CDEDCanadian Digital Elevation Data

5. 15-m building height database SRTM-DEM minus NED/CDED1 Elevation for built-up pixels General Methodology 15-m unsupervised classification ASTER/Landsat-7 40 built and vegetated simple elements regrouped in 11 simple elements/criteria 1-Excluded covers 2-Water 3-Trees 4-Low vegetation 5-Grass 6-Soil and rocks 7-Roof 8-Concrete 9-Asphalt 10-Veg/built 1 11-Veg/built 2 60-m LULC classification 1-Excluded 2-Water 3-Soils 4-Crops 5-Short grass 6-Mixed forest 7-Mixed shrubs 1-High buildings 2-Mid-heigh buildings 3-Low buildings 4-Very low buildings 5-Industrial areas 6-Sparse buildings 7-Roads and parkings 8-Road mix 9-Dense residential 10-Mid-density residential 11-Low-density residential 12-Mix nature and built 60-m aggregation of classification criteria 1- Excluded covers 2- Water 3- Trees 4- Low vegetation 5- Grass 6- Soil and rocks 7- Roof 8- Concrete 9- Asphalt 10- Veg/built 1 11- Veg/built 2 12-Built 13-Built2 14-Height Decision tree

6. Unsupervised classification Excluded Water Trees Low vegetation Grass Soil and rocks Roof Concrete Asphalt Veg/built 1 Veg/built 2 OKC ASTER image 2001-07-21 N

7. Building heights Bald Earth’s topography provided by NED (US) and CDED1 (Canada) Total elevation provided by SRTM-DEM Height of roughness elements estimated from: SRTM-DEM minus NED SRTM-DEM minus CDED1 Building heights estimated by considering only built-up pixels Offset correction Threshold of 3 m applied to minimum building heights OKC SRTM-DEM minus NED N

8. 15-m building height database SRTM-DEM minus NED/CDED1 Elevation for built-up pixels General Methodology 15-m unsupervised classification ASTER/Landsat-7 40 built and vegetated simple elements regrouped in 11 simple elements/criteria 1-Excluded covers 2-Water 3-Trees 4-Low vegetation 5-Grass 6-Soil and rocks 7-Roof 8-Concrete 9-Asphalt 10-Veg/built 1 11-Veg/built 2 60-m LULC classification 1-Excluded 2-Water 3-Soils 4-Crops 5-Short grass 6-Mixed forest 7-Mixed shrubs 1-High buildings 2-Mid-heigh buildings 3-Low buildings 4-Very low buildings 5-Industrial areas 6-Sparse buildings 7-Roads and parkings 8-Road mix 9-Dense residential 10-Mid-density residential 11-Low-density residential 12-Mix nature and built 60-m aggregation of classification criteria 1- Excluded covers 2- Water 3- Trees 4- Low vegetation 5- Grass 6- Soil and rocks 7- Roof 8- Concrete 9- Asphalt 10- Veg/built 1 11- Veg/built 2 12-Built 13-Built2 14-Height Decision tree

9. DECIDUOUS BROADLEAF TREES SHORT GRASS AND FORBS LONG GRASS CROPS MIXED WOOD FOREST VEGETATION CLASSES LAND/SEA MASK WATER<80% BUILT>10% WATER ROADS >80% VEGETATION BUILT2 >20% ELEVATION >20 m ROADS AND PARKINGS ELEVATION >30 m ELEVATION >10 m HIGH BUILDINGS MID-HIGH BUILDINGS LOW BUILDINGS VERY LOW BUILDINGS RESID + MIX >40% ELEVATION >30 m HIGH BUILDINGS ELEVATION >20 m MID-HIGH BUILDINGS ELEVATION >10 m LOW BUILDINGS BUILT2>20% BUILT>45% VERY LOW BUILDINGS ROAD BORDERS BUILT >70% BUILT >70% BUILT2 >20% BUILT >40% ELEV>10 m BUILT2>20% ELEV>10 m BUILT2>20% SPARSE BUILDINGS LOW VEG+ GRASS>60% DENSE RESIDENTIAL MID-DENSITY RESIDENTIAL ELEVATION >20 m ROAD BORDERS ELEVATION >30 m ELEVATION >10 m HIGH BUILDINGS MID-HIGH BUILDINGS LOW BUILDINGS VERY LOW BUILDINGS SPARSE BUILDINGS ROAD BORDERS LOW-DENSITY RESIDENTIAL BUILT & NATURE MIX ROOF>20%

10. Urban classification OKC 60-m resolution classification N High buildings Mid-high buildings Low buildings Very low buildings Sparse buildings Industrial areas Roads and parkings Road mix Dense residential Mid-density residential Low-density residential Mix of nature and built Soils Crops Short grass Mixed forest Mixed shurbs Water Excluded

11. NN Montreal 60-m resolution classification Vancouver 60-m resolution classification High buildings Mid-high buildings Low buildings Very low buildings Sparse buildings Industrial areas Roads and parkings Road mix Dense residential Mid-density residential Low-density residential Mix of nature and built Zoom

12. Interest of the methodology  Limited number of data sources  Large availability of the databases  Time processing of about 1 week  General and robust approach applicable to any Canadian city (can be extended to North American cities) Urban classifications  Horizontal resolution adapted to meso-gamma-scale modeling  Good identification of the major urban landscapes  Number of urban classes allowing a satisfying representation of urban cover variability Conclusions

13. New approach based on analysis of the vector National Topographic DataBase (NTDB):  Cover of the entire Canada  High resolution: scale of 1:50000 and 1:250000  Large number of urban features  No manual processing/correction  No interpretation Future works Montreal, NTDB Example of urban features Roads Bridges Highways Rails Sparse buildings Buildings Residential areas Vegetation Golf Water