Urban Land-Cover Classification for Mesoscale Atmospheric Modeling Alexandre Leroux Aude Lemonsu

Objectives Goal: Provide an urban land-cover database for North-American cities for mesoscale atmospheric modeling Mean: - Approach #1 Satellite imagery and DEM analysis - Approach #2 Vector data processing and DEM analysis

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

Input data processing and analysis Satellite Land-Cover Classification: Mid-resolution unsupervised classification of Landsat-7 and ASTER for urban land-cover –15 m pixel size (L7 pan-sharpened) –30 to 40 “simple elements” such as: asphalt, concrete, mix vege+asphalt, roofs, water, trees, grass & fields, soils & rock, etc.

Input data processing and analysis Building Height Appraisal: SRTM-DEM and CDED1 (Canada) / NED (USA) data for building height appraisal –SRTM-DEM for elevation at top of features (e.g. trees, buildings) –CDED1 and NED for soil elevation –The subtraction evaluates the building height Spatial resolutions –SRTM-DEM: 1 to 3 arc-seconds of pixels size (+/- 30 (USA) to 90m (Canada)) –NED: 1/3 arc-second for Oklahoma City (8 x 10m) –CDED1: 1: (MTL = 16 x 23m)

Statistics and fractions Statistical measurements and fraction calculations by coupling both databases Done at a 60, 105 and 195 m resolution Fractions fed to the decision tree

Decision tree Uses statistics and fractions in order to generate an urban classification –12 new urban classes generated –+/- 5 vegetation classes associated to gengeo

Decision tree - Detail

Oklahoma City, 60 m

Montreal, 60 m

Mean building height (1 km) m 5 km Descriptive parameters of the urban landscape are associated to each urban class and used as input parameters for TEB:  Geometric parameters describing the urban arrangement  Material properties of roofs, roads, and walls

Operational process Results over OKC and MTL are satisfactory Satellite approach: –Method development being finalized over Vancouver –Using free data exclusively Operational processing of cities –The less human intervention possible –Estimation of about a week per city required for analysis and processing under operational conditions

Final words Much more processing and analysis of data has been done –Hyperion hyperspectral data –Aerial photography –etc… Most of the work is thoroughly documented Vector approach under development and quite promising

Aerial photography classification example

Montreal, vectorial approach, proof of concept, 60 m