Application of airborne LiDAR data and airborne multispectral imagery to structural mapping of the upper section of the Troodos ophiolite, Cyprus

Abstract

(2012) Application of airborne LiDAR data and airborne multispectral imagery to structural mapping of the upper section of the Troodos ophiolite, Cyprus. International Journal of Earth Sciences, 101 (6). pp. 1645 -1660 . ISSN 1437 Access from the University of Nottingham repository: http://eprints.nottingham.ac.uk/33862/1/Grebby_et_al_2012.pdf Copyright and reuse:The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions. This article is made available under the University of Nottingham End User licence and may be reused according to the conditions of the licence. For more details see: http://eprints.nottingham.ac.uk/end_user_agreement.pdf A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. history. In addition, structural maps may inform seismic and landslide hazard 58 assessments, and provide useful information for major engineering projects and 59 the exploration of groundwater, petroleum and mineral resources (Moore and 60 Waltz 1983; Kresic 1995;Karnieli et al. 1996;Wladis 1999; Harris et al. 2001; 61 Peña and Abdelsalam 2006;Corgne et al. 2010). 62Traditionally, structural maps are produced by mapping features such as 63 faults, folds, fabrics, fractures and joints in the field. Although arguably the most 64 reliable and accurate maps are those produced using this approach, large map 65 areas, time constraints and the limited ground perspective of the field geologist 66 has the potential to increase the possibility that not all structural features will be 67 identified (Süzen and Toprak 1998 area is responsible for a lack of completely exposed outcrops in the study area. 178Vegetation cover type generally varies from moderate-to-dense lichen cover, to 179 crops (e.g., cereals, olive groves) as well as both green and dry grasses, to what 180 can be broadly described as garrigue or maquis, predominantly comprising 181 scrubby short dry grasses, short-to-medium height shrubs and scattered small 182 trees. Other types of mostly sporadic vegetation cover occurring throughout the 183 study area include trees -ranging from isolated trees (e.g., pines and oaks) to 184 dense thickets and copses -and areas covered by tall, dry grasses and scrubland. Ltd., Finland). Points corresponding to non-ground returns were subsequently 203 discarded, whilst those classified as ground returns were interpolated using a 204 block kriging algorithm in order to generate a 4 m digital terrain model (DTM) or 205 "bare-earth" DEM (Fig. 1b). A more detailed description of the airborne LiDAR 206 data processing steps is provided by Grebby et al. (2010). 207The ATM imagery initially comprised 11 spectral bands located in the 208 visible/near-infrared (VNIR; Bands 1-8), ...

Improving Lithological Mapping by SVM Classification of Spectral and Morphological Features: The Discovery of a New Chromite Body in the Mawat Ophiolite Complex (Kurdistan, NE Iraq)

“…The diorite and valley fill sediments follows a different trend from the other signatures, with enhanced absorption in band 8 ( Figure 4A). We classified the vegetation into five classes as follows (1) vegetation that grew on the valley fill sediments, has a higher reflectance in the NIR band (band 3) than the others; (2) Vegetation that grew on conglomerate; (3) vegetation that grew on clastics rocks; (4) vegetation that grew on carbonate and (5) vegetation on gabbro; which is less dense compared to the other types ( Figure 4B).…”

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“…With the rapid advancement of remote sensing technique, multispectral imagery has gained popularity with a variety of applications [Gevaert et al, 2015;Grebby et al, 2012;Johnson et al, 2003;Whited et al, 2002]. Inspired by the capability of multispectral imagery to characterize specific land cover distinctive to its spectral signature, i.e., how a certain land cover appears (measured by its reflectance magnitude) under different band or wavelength [Smith, 2012], the present study further explores the correlation between the SRTM DEM error and the spectral signatures of different land cover.…”

Challenges to global mineral resource security and options for future supply

“…Scientists are also developing novel methodologies to improve baseline data capture with applications to mineral exploration. For example, airborne Light Detection and Ranging (LiDAR) and airborne multispectral imaging have been applied to geological and structural mapping in areas of dense vegetation cover (Grebby et al 2010(Grebby et al , 2012.…”