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As urbanization continues to increase and extreme climatic events become more prevalent, urban planners and engineers are activelyimplementing adaptive measures to protect urban assets and communities. To support the urban planning adaptation process,mapping of impervious and pervious areas is essential to understanding the hydrodynamic environment within urban areas for floodrisk planning. The application of advance geospatial data and analytical techniques using remote sensing and GIS can improve landsurface characterization to better quantify surface run-off and infiltration. This study presents a method to combine airbornehyperspectral and LiDAR data for classifying pervious (e.g. vegetation, gravel, and soil) and impervious (e.g. asphalt and concrete)areas within road allowance areas for the City of Surrey, British Columbia, Canada. Hyperspectral data was acquired using theCompact Airborne Spectrographic Imager (CASI) at 1 m ground spatial resolution, consisting of 72 spectral bands, and LiDAR dataacquired from Leica Airborne LiDAR system at a density of 20 points/m<sup>2</sup>. A spectral library was established using 10 cmorthophotography and GIS data to identify surface features. In addition to spectral functions such as mean and standard deviation,several spectral indices were developed to discriminate between asphalt, concrete, gravel, vegetation, and shadows respectively. Aspectral analysis of selected endmembers was conducted and an initial classification technique was applied using Spectral AngleMapper (SAM). The classification results (i.e. shadows) were improved by integrating LIDAR data with the hyperspectral data

An Integrated Method for Mapping Impervious and Pervious Areas in Urban Environments Using Hyperspectral and LiDAR Data