Lithological mapping of the Troodos ophiolite, Cyprus, using airborne LiDAR topographic data

REMOTE SENSING OF ENVIRONMENT

卷:114

Article

Grebby, Stephen¹  Cunningham, Dickson¹  Naden, Jonathan²  Tansey, Kevin³ 

[1] Univ Leicester, Dept Geol, Leicester LE1 7RH, Leics, England

[2] British Geol Survey, Keyworth NG12 5GG, Notts, England

[3] Univ Leicester, Dept Geog, Leicester LE1 7RH, Leics, England

关键词: Airborne LiDAR;    Lithological mapping;    Morphometry;    Troodos ophiolite;    Self-Organizing Map;   

DOI  :  10.1016/j.rse.2009.11.006

来源: Elsevier

【 摘 要 】

Traditional field-based lithological mapping can be a time-consuming, costly and challenging endeavour when large areas need to be investigated, where terrain is remote and difficult to access and where the geology is highly variable over short distances. Consequently, rock units are often mapped at coarse-scales, resulting in lithological maps that have generalised contacts which in many cases are inaccurately located. Remote sensing data, such as aerial photographs and satellite imagery are commonly incorporated into geological mapping programmes to obtain geological information that is best revealed by overhead perspectives. However, spatial and spectral limitations of the imagery and dense vegetation cover can limit the utility of traditional remote sensing products. The advent of Airborne Light Detection And Ranging (LiDAR) as a remote sensing tool offers the potential to provide a novel solution to these problems because accurate and high-resolution topographic data can be acquired in either forested or non-forested terrain, allowing discrimination of individual rock types that typically have distinct topographic characteristics. This study assesses the efficacy of airborne LiDAR as a tool for detailed lithological mapping in the upper section of the Troodos ophiolite, Cyprus. Morphometric variables (including slope, curvature and surface roughness) were derived from a 4 m digital terrain model in order to quantify the topographic characteristics of four principal lithologies found in the area. An artificial neural network (the Kohonen Self-Organizing Map) was then employed to classify the lithological units based upon these variables. The algorithm presented here was used to generate a detailed lithological map which defines lithological contacts much more accurately than the best existing geological map. In addition, a separate map of classification uncertainty highlights potential follow-up targets for ground-based verification. The results of this study demonstrate the significant potential of airborne LiDAR for lithological discrimination and rapid generation of detailed lithological maps, as a contribution to conventional geological mapping programmes. (C) 2009 Elsevier Inc. All rights reserved.

【 授权许可】

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