The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XL-7/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 897–903, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-897-2015
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-7/W3, 897–903, 2015
https://doi.org/10.5194/isprsarchives-XL-7-W3-897-2015

  30 Apr 2015

30 Apr 2015

A comparison of Landsat 8 (OLI) and Landsat 7 (ETM+) in mapping geology and visualising lineaments: A case study of central region Kenya

M. W. Mwaniki1, M. S. Moeller2, and G. Schellmann3 M. W. Mwaniki et al.
  • 1Bamberg University, Storkower Strasse 219.04.21, 10367 Berlin, Germany
  • 2Beuth Hochschule für Technik, Luxembuger Straße 10, 13353 Berlin, Germany
  • 3Otto Friedrich Universität Bamberg, Am Kranen 1 (Kr/111), 96045 Bamberg, Germany

Keywords: Band rationing, False colour Combinations (FCC), Principal Component Analysis (PCA), Intensity Hue Saturation (IHS), Independent Component Analysis (ICA), Knowledge base classification, Enhanced hematic mapper Plus (ETM+), Operational Land Imager (OLI)

Abstract. Availability of multispectral remote sensing data cheaply and its higher spectral resolution compared to remote sensing data with higher spatial resolution has proved valuable for geological mapping exploitation and mineral mapping. This has benefited applications such as landslide quantification, fault pattern mapping, rock and lineament mapping especially with advanced remote sensing techniques and the use of short wave infrared bands. While Landsat and Aster data have been used to map geology in arid areas and band ratios suiting the application established, mapping in geology in highland regions has been challenging due to vegetation land cover. The aim of this study was to map geology and investigate bands suited for geological applications in a study area containing semi arid and highland characteristics. Therefore, Landsat 7 (ETM+, 2000) and Landsat 8 (OLI, 2014) were compared in determining suitable bands suited for geological mapping in the study area. The methodology consist performing principal component and factor loading analysis, IHS transformation and decorrelation stretch of the FCC with the highest contrast, band rationing and examining FCC with highest contrast, and then performing knowledge base classification. PCA factor loading analysis with emphasis on geological information showed band combination (5, 7, 3) for Landsat 7 and (6, 7, 4) for Landsat 8 had the highest contrast and more contrast was enhanced by performing decorrelation stretch. Band ratio combination (3/2, 5/1, 7/3) for Landsat 7 and (4/3, 6/2, 7/4) for Landsat 8 had more contrast on geologic information and formed the input data in knowledge base classification. Lineament visualisazion was achieved by performing IHS transformation of FCC with highest contrast and its saturation band combined as follows: Landsat 7 (IC1, PC2, saturation band), Landsat 8 (IC1, PC4, saturation band). The results were compared against existing geology maps and were superior and could be used to update the existing maps.