Object based data fusion of landform and ancillary data for upscaling soil-landscape mapping in the western Australian pastoral rangelands

We have investigated methods to derive landform and vegetation data from a Digital Elevation Model and remote sensing, using classification and segmentation. These data are required to upscale existing regional level landsystem mapping to a higher spatial and thematic resolution (subsystems). In tur...

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Main Authors: Wilson, Deanna, Corner, Robert, Schut, Antonius
Other Authors: Irena Hajnsek
Format: Conference Paper
Published: IEEE 2012
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/8017
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author Wilson, Deanna
Corner, Robert
Schut, Antonius
author2 Irena Hajnsek
author_facet Irena Hajnsek
Wilson, Deanna
Corner, Robert
Schut, Antonius
author_sort Wilson, Deanna
building Curtin Institutional Repository
collection Online Access
description We have investigated methods to derive landform and vegetation data from a Digital Elevation Model and remote sensing, using classification and segmentation. These data are required to upscale existing regional level landsystem mapping to a higher spatial and thematic resolution (subsystems). In turn the land subsystem data will be used to support rangeland condition mapping in a large area of leasehold rangeland. The derived landform and vegetation classes have been tested against available landsystem mapping derived by conventional methods and have shown promising relationships. These classifications need to be further revised prior to their use in either an object based or probabilistic classifier.
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institution Curtin University Malaysia
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publishDate 2012
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spelling curtin-20.500.11937-80172023-02-02T07:57:36Z Object based data fusion of landform and ancillary data for upscaling soil-landscape mapping in the western Australian pastoral rangelands Wilson, Deanna Corner, Robert Schut, Antonius Irena Hajnsek Helmut Rott digital elevation models geographic information systems land surface remote sensing We have investigated methods to derive landform and vegetation data from a Digital Elevation Model and remote sensing, using classification and segmentation. These data are required to upscale existing regional level landsystem mapping to a higher spatial and thematic resolution (subsystems). In turn the land subsystem data will be used to support rangeland condition mapping in a large area of leasehold rangeland. The derived landform and vegetation classes have been tested against available landsystem mapping derived by conventional methods and have shown promising relationships. These classifications need to be further revised prior to their use in either an object based or probabilistic classifier. 2012 Conference Paper http://hdl.handle.net/20.500.11937/8017 10.1109/IGARSS.2012.6351910 IEEE restricted
spellingShingle digital elevation models
geographic information systems
land surface
remote sensing
Wilson, Deanna
Corner, Robert
Schut, Antonius
Object based data fusion of landform and ancillary data for upscaling soil-landscape mapping in the western Australian pastoral rangelands
title Object based data fusion of landform and ancillary data for upscaling soil-landscape mapping in the western Australian pastoral rangelands
title_full Object based data fusion of landform and ancillary data for upscaling soil-landscape mapping in the western Australian pastoral rangelands
title_fullStr Object based data fusion of landform and ancillary data for upscaling soil-landscape mapping in the western Australian pastoral rangelands
title_full_unstemmed Object based data fusion of landform and ancillary data for upscaling soil-landscape mapping in the western Australian pastoral rangelands
title_short Object based data fusion of landform and ancillary data for upscaling soil-landscape mapping in the western Australian pastoral rangelands
title_sort object based data fusion of landform and ancillary data for upscaling soil-landscape mapping in the western australian pastoral rangelands
topic digital elevation models
geographic information systems
land surface
remote sensing
url http://hdl.handle.net/20.500.11937/8017