Volume II-2
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-2, 87-91, 2014
https://doi.org/10.5194/isprsannals-II-2-87-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-2, 87-91, 2014
https://doi.org/10.5194/isprsannals-II-2-87-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

  11 Nov 2014

11 Nov 2014

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

L. Hashemi Beni, S. McArdle, and Y. Khayer L. Hashemi Beni et al.
  • DM-Inc., 671 Danforth Avenue, Suite 305, Toronto, M4J 1L3, Canada

Keywords: Hyperspectral, LiDAR, Urban Area, Integrated Method, SAM Classification, Flood

Abstract. As urbanization continues to increase and extreme climatic events become more prevalent, urban planners and engineers are actively implementing 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 flood risk planning. The application of advance geospatial data and analytical techniques using remote sensing and GIS can improve land surface characterization to better quantify surface run-off and infiltration. This study presents a method to combine airborne hyperspectral 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 the Compact Airborne Spectrographic Imager (CASI) at 1 m ground spatial resolution, consisting of 72 spectral bands, and LiDAR data acquired from Leica Airborne LiDAR system at a density of 20 points/m2. A spectral library was established using 10 cm orthophotography 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. A spectral analysis of selected endmembers was conducted and an initial classification technique was applied using Spectral Angle Mapper (SAM). The classification results (i.e. shadows) were improved by integrating LIDAR data with the hyperspectral data.