2007 IEEE International Geoscience and Remote Sensing Symposium 2007
DOI: 10.1109/igarss.2007.4423581
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Evaluation of driving forces of land-use change and urban growth in North Rhine-Westphalia (Germany)

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Cited by 6 publications
(9 citation statements)
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“…Previous studies at the local and regional level have confirmed the potential of these techniques to determine the extent of soil sealing both in Germany (such as Agglomeration Cologne/Bonn: [19]; Stuttgart: [20]; North Rhine-Westphalia: [21]; Bavaria: [22,23] and elsewhere (such as the Columbus Metropolitan Area, Ohio: [24]; large regions in the USA: [25]; and Italy: [26,27]). Furthermore, efforts have been made to predict impervious surface extents based on urban growth models (e.g., [28]).…”
Section: Datasets From Remote Sensingmentioning
confidence: 99%
“…Previous studies at the local and regional level have confirmed the potential of these techniques to determine the extent of soil sealing both in Germany (such as Agglomeration Cologne/Bonn: [19]; Stuttgart: [20]; North Rhine-Westphalia: [21]; Bavaria: [22,23] and elsewhere (such as the Columbus Metropolitan Area, Ohio: [24]; large regions in the USA: [25]; and Italy: [26,27]). Furthermore, efforts have been made to predict impervious surface extents based on urban growth models (e.g., [28]).…”
Section: Datasets From Remote Sensingmentioning
confidence: 99%
“…Finer scale objects, such as houses and farms are then identified at local scale using semantic rules defined by object attributes at both regional and local scale. The decomposition of the land-use classification process in three scale domains has proven to be very useful in similar studies as reported in the literature (Donnay et al, 2000;Goetzke et al, 2006). It consists on a hybrid approach that combines multi-resolutions remote sensing data.…”
Section: Object-based Classification Methodsmentioning
confidence: 93%
“…Distance measures that display accessibility were weighted by the road network. For a detailed description of the driving forces used in this study refer to [8]. We had to reduce our set of LU classes to 6 (urban, agricultural areas, grassland, forest, water, other) and resample the original 30m data to 100m, because of (1) computational reasons and (2) because the intention of the study was to map trends and too much of detail would have concealed the general relations and processes in LU.…”
Section: Geophysical and Socioeconomic Driversmentioning
confidence: 99%
“…The Landsat data have been classified using a combination of knowledge based and supervised methods. For a detailed description of the preprocessing and classification refer to [6,7]. Altogether 12 LU-classes have been created.…”
Section: Remote Sensing Datamentioning
confidence: 99%