HR: 16:15h
AN: B24B-02    [Abstracts]
TI: Estimating and Mapping Urban Impervious Surfaces: Reflection on Spectral, Spatial, and Temporal Resolutions
AU: * Weng, Q
EM: qweng@indstate.edu
AF: Indiana State University, Center for Urban and Environmental Change, Department of Geography, 200 N. 7th St., Terre Haute, IN 47809, United States
AB: Impervious surface is a key indicator of urban environmental quality and urbanization degree. Therefore, estimation and mapping of impervious surfaces in urban areas has attracted more and more attention recently by using remote sensing digital images. In this paper, satellite images with various spectral, spatial, and temporal resolutions are employed to examine the effects of these remote sensing data characteristics on mapping accuracy of urban impervious surfaces. The study area was the city proper of Indianapolis (Marion County), Indiana, United States. Linear spectral mixture analysis was applied to generate high albedo, low albedo, vegetation, and soil fraction images (endmembers) from the satellite images, and impervious surfaces were then estimated by adding high albedo and low albedo fraction images. A comparison of EO-1 ALI (multispectral) and Hyperion (hyperspectral) images indicates that the Hyperion image was more effective in discerning low albedo surface materials, especially the spectral bands in the mid-infrared region. Linear spectral mixing modeling was found more useful for medium spatial resolution images, such as Landsat TM/ETM+ and ASTER images, due to the existence of a large amount of mixed pixels in the urban areas. The model, however, may not be suitable for high spatial resolution images, such as IKONOS images, because of less influence from the mixing pixel. The shadow problem in the high spatial resolution images, caused by tall buildings and large tree crowns, is a challenge in impervious surface extraction. Alternative image processing algorithms such as decision tree classifier may be more appropriate to achieve high mapping accuracy. For mid-latitude cities, seasonal vegetation phenology has a significant effect on the spectral response of terrestrial features, and therefore, image analysis must take into account of this environmental characteristic. Three ASTER images, acquired on April 5, 2004, June 16, 2001, and October 3, 2000, respectively, were used to test the seasonal sensitivity of impervious surface estimation. Our results indicated that the summer (June) image was better than the spring (April) and the fall (October) ones. The summer image was most appropriate because there was full growth of vegetation, and mapping of impervious surfaces was more effective with contrasting spectral response from green vegetation. The mixing space, based on the four endmembers was perfectly three-dimensional. In contrast, there was significant amount of bare soils/grounds and non-photosynthesis vegetation in the spring and fall images. Plant phenology caused changes in the variance partitioning and impacted the mixing space characterization, leading to the difficulty in the estimation of impervious surfaces.
UR: http://www.gis.indstate.edu/
DE: 0426 Biosphere/atmosphere interactions (0315)
DE: 0480 Remote sensing
DE: 1655 Water cycles (1836)
DE: 1813 Eco-hydrology
DE: 1855 Remote sensing (1640)
SC: Biogeosciences [B]
MN: 2007 Fall Meeting