GI_Forum 2015, Volume 3 Journal for Geographic Information Science
Geospatial Minds for Society
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |
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DATUM, UNTERSCHRIFT / DATE, SIGNATURE
BANK AUSTRIA CREDITANSTALT, WIEN (IBAN AT04 1100 0006 2280 0100, BIC BKAUATWW), DEUTSCHE BANK MÜNCHEN (IBAN DE16 7007 0024 0238 8270 00, BIC DEUTDEDBMUC)
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GI_Forum 2015, Volume 3 Journal for Geographic Information Science
Geospatial Minds for Society ISSN 2308-1708 Online Edition ISBN 978-3-87907-558-4 Print Edition ISBN 978-3-7001-7826-2 Online Edition
doi:10.1553/giscience2015
GI_Forum, 2015Volume 3 2015, 645 pages Print edition is available at Wichmann-Verlag, Berlin
Mercy Mwaniki,
Matthias Möller,
Gerhard Schellmann
S. 209 - 219 doi:10.1553/giscience2015s209 Verlag der Österreichischen Akademie der Wissenschaften
Abstract: Advances in classification using multispectral remote sensing imagery have gained increasing attention in solving environmental problems, and the management of disasters such as floods and landslides, due to their wide coverage and enabling ease of access in times of calamities. Multispectral data has facilitated the mapping of soils, land-cover, and structural geology, all of which are factors affecting landslide occurrence. The main aim of this research was to map landslide-affected areas using remote sensing techniques for the central region of Kenya, where landslide disasters are common occurrences. The study area has a highly rugged terrain, and rainfall has been the main trigger of recent landslide events. False colour composite (FCC), Principal Component Analysis (PCA), Independent Component Analysis (ICA), spectral indices in the form of Normalised Difference Index (NDI), and knowledge-based classification formed the methodology. PCA and ICA were performed on Landsat data sets, and the components with the most geologic information after factor loading analysis were chosen to be used in a colour composite. The blue component of the colour composite was a spectral index involving bands 7 and 3 for Landsat ETM+, or bands 7 and 4 for Landsat OLI. The FCC formed the inputs for knowledge based classification with the following 13 classes: runoff, extreme erosions, other erosions, landslide areas, highly erodible, stable, weathering rocks, agriculture, green, new forest regrowth areas, as well as clear, turbid, and salty water. Validation of the mapped landslide areas with field GPS locations of landslide affected areas showed that 66% and 62% of the points coincided well with landslide areas mapped in the years 2000 and 2014, respectively. The classification maps showed extreme erosions taking place along drainage channels and other erosions in agricultural areas; with highly eroble zones charchaterised by already weathered rocks or deposit area, while fluvial deposits mainly characterised runoff areas. Thus, landuse and rainfall processes play a major role in landslide processes in the study area. Published Online: 2015/06/26 08:48:49 Object Identifier: 0xc1aa5576 0x003249fd Rights:https://creativecommons.org/licenses/by-nd/4.0/
The Journal for Geographic Information Science issue 1-2015 presents peer-reviewed papers
presented at the Geoinformatics
Forum (www.gi-forum.org), held in Salzburg from July 7-10,
2015. The annual GI_Forum symposium provides a platform for dialogue among geospatial minds
in an ongoing effort to support the creation of an informed GISociety.
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |