SCIENCE@home

Sentinel-1 and ALOS data for lineament extraction: a comparative study

J. Cardoso-Fernandes,A. Teodoro,A. Lima,J. Menuge,M. Brönner,Ralf Steiner

Published 2022 in Remote Sensing

ABSTRACT

GREENPEG is a H2020 project developing exploration toolsets for critical materials in pegmatites. Lineament mapping is a powerful tool to identify geological structures at different scales, which is commonly applied in mineral exploration. To extract lineament information, different types of data can be employed, namely: (i) optical remote sensing data, (ii) radar remote sensing data, and (iii) digital elevation models (DEMs). All these data have been used by GREENPEG, where image processing of Sentinel-1 and ALOS (DEM) datasets were evaluated to identify regional-scale tectonic structures, such as faults, that may have controlled pegmatite melt emplacement. Automated geomatics methods to extract lineament information and perform lineament mapping were employed and the mean direction of the extracted lineaments was evaluated through rose diagrams. In general, the results of the radar image processing were affected by noise in the coastal areas and by the topography of the region. ALOS data presented some advantages, due to less influence from topography and human structures (saving time in the visual inspection of the results). Moreover, even though higher thresholds for the minimum length to consider as a lineament were applied to Sentinel-1 images, ALOS-derived lineaments were longer. The main trend of the extracted lineaments is in agreement between the two datasets, although specific trends are clearer in the ALOS-derived lineaments because the shaded reliefs produced already filtered the azimuth of lineaments to be extracted according to light incidence angle. For all three project demonstration sites (Leinster, Ireland; Wolfsberg, Austria; and Tysfjord, Norway) it was possible to identify lineaments that may relate to structures that controlled pegmatite emplacement. This study demonstrates the usefulness of different satellite data in structural mapping and mineral exploration.

PUBLICATION RECORD

  • Publication year

    2022

  • Venue

    Remote Sensing

  • Publication date

    2022-10-26

  • Fields of study

    Geology, Engineering, Environmental Science

  • Identifiers
    DOI 10.1117/12.2636117
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

CITATION MAP

EXTRACTION MAP

CLAIMS

  • No claims are published for this paper.

CONCEPTS

  • No concepts are published for this paper.

REFERENCES

Showing 1-24 of 24 references · Page 1 of 1

CITED BY

Showing 1-10 of 10 citing papers · Page 1 of 1