Paper
13 February 2004 Mapping imperviousness using NDVI and linear spectral unmixing of ASTER data in the Cologne-Bonn region (Germany)
Matthias Braun, Martin Herold
Author Affiliations +
Abstract
Information about imperviousness surface distributions is essential for several environmental applications and the planning and management of sustainable development of urban areas. Satellite remote sensing based mapping of imperviousness has shown important potentials to acquire such information in great spatial detail but the actual mapping process has been challenged by the heterogeneity of urban environment and limited spatial and spectral sensor capabilities. This study explores and compares two methods based on the vegetation fraction from linear spectral unmixing and the NDVI to map the degree of imperviousness in the urban agglomeration of Cologne/Bonn in Western Germany. The study employed data from the ASTER satellite sensor with improved spatial and spectral resolution. Fieldwork was carried out in the area of Bonn to obtain a comprehensive set of reference data with estimated degrees of imperviousness for different types of urban areas. Rural areas were excluded using data from the governmental land information system (ATKIS). The applied simple linear spectral unmixing approach revealed less suitable results for the built area fraction due to the heterogeneity of the spectral response from urban targets. The vegetation fraction and the NDVI provided sufficient results in estimating the impervious surface fraction that were used to derive related maps for the study areas.
© (2004) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Matthias Braun and Martin Herold "Mapping imperviousness using NDVI and linear spectral unmixing of ASTER data in the Cologne-Bonn region (Germany)", Proc. SPIE 5239, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology III, (13 February 2004); https://doi.org/10.1117/12.510978
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Cited by 35 scholarly publications.
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KEYWORDS
Vegetation

Remote sensing

Agriculture

Associative arrays

Satellites

Soil science

Sensors

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