Kirsch, Moritz; Lorenz, Sandra; Zimmermann, Robert; Andreani, Louis; Tusa, Laura; Pospiech, Solveig; Jackisch, Robert; Khodadadzadeh, Mahdi; Ghamisi, Pedram; Unger, Gabriel; Hödl, Philip; Gloaguen, Richard; Middleton, Maarit; Sutinen, Raimo; Ojala, Antti E K; Mattila, Jussi; Nordbäck, Nicklas; Palmu, Jukka-Pekka; Tiljander, Mia; Ruskeeniemi, Timo (2019): Hyperspectral imagery-enhanced virtual outcrop models of two palaeoseismic trenches in northern Finnish Lapland [dataset]. Helmholtz-Zentrum Dresden-Rossendorf, PANGAEA, https://doi.org/10.1594/PANGAEA.904718, Supplement to: Kirsch, M et al. (2019): Hyperspectral outcrop models for palaeoseismic studies. Photogrammetric Record, 34(168), 385-407, https://doi.org/10.1111/phor.12300
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Published: 2019-08-09 • DOI registered: 2020-02-05
Abstract:
The traditional study of palaeoseismic trenches involving logging, stratigraphic and structural interpretation can be time-consuming and affected by biases and inaccuracies. To overcome these limitations, we present a new workflow that integrates infrared hyperspectral and photogrammetric data to support field-based palaeoseismic observations. As a case study, this method is applied on two palaeoseismic trenches excavated across a post-glacial fault scarp in northern Finnish Lapland. The hyperspectral imagery (HSI) is geometrically and radiometrically corrected, processed using established image processing algorithms and machine learning approaches, and co-registered to a Structure-from-Motion point cloud. HSI-enhanced virtual outcrop models are a useful complement to palaeoseismic field studies as they not only provide an intuitive visualisation of the outcrop and a versatile data archive, but also enable an unbiased assessment of the mineralogical composition of lithologic units and a semi-automatic delineation of contacts and deformational structures in a 3D virtual environment.
Uploaded data:
14 individual 3D point clouds (ascii format) from two palaeoseismic trenches, including two structure-from-motion photogrammetric RGB point clouds and 12 hyperspectral-enhanced point clouds.
Data headers contain point coordinates in m (ETRS89/UTM35N), RGB color (0–255), and point normals (only for SfM RGB point clouds) in the following order: X, Y, Z, Red, Green, Blue, Nx, Ny, Nz.
Coverage:
Latitude: 67.225000 * Longitude: 26.148000
Event(s):
Parameter(s):
| # | Name | Short Name | Unit | Principal Investigator | Method/Device | Comment |
|---|---|---|---|---|---|---|
| 1 | File content | Content | Kirsch, Moritz | |||
| 2 | File name | File name | Kirsch, Moritz | |||
| 3 | File format | File format | Kirsch, Moritz | within zip | ||
| 4 | File size | File size | kByte | Kirsch, Moritz | ||
| 5 | Uniform resource locator/link to file | URL file | Kirsch, Moritz |
License:
Creative Commons Attribution 4.0 International (CC-BY-4.0)
Size:
70 data points