DOI: 10.5880/gfz.2.1.2016.002
title: Terrestrial laser scanner data covering the summit craters of Láscar Volcano, Chile

description: The datasets included in this data publication are: (1) the TLS combined point cloud (consisting of ∼15 million data points), (2) a Digital Elevation Model (DEM) with 1 m pixel spacing which was generated from (1), and (3) a shaded relief of (2) in kmz format. These datasets are supplement to de Zeeuw-van Dalfsen et al. (2017), who used them to study structural and geomorphological features at the nested summit craters of Láscar Volcano, Chile. However, in the paper the data were used in a local reference frame while we here provide both the TLS point cloud and the DEM product in global coordinates (WGS 1984 UTM Zone 19 South). 
      Light detection and ranging (LiDAR) is a technique where a laser pulse is actively emitted from a LiDAR instrument and the echo that returns from a target object is recorded. The distances between the instrument and the target points are calculated from the round-trip travel time of the laser pulse (Fornaciai et al., 2010). A terrestrial laser scanner (TLS) uses this technique in a scanning mode where the laser beam is deﬂected into different directions by an oscillating mirror while at the same time the scanner’s head is rotating. We used a long-range RIEGL LMS-Z620 instrument with a ﬁeld of view of up to 80° by 360° in the vertical and horizontal plane, respectively. The maximum repeatability of this instrument is 5 mm, but this value increases with increasing distance between the scanner and the target, when viewing geometries or the target reﬂectivity are not optimal or when atmospheric conditions vary and are not ideal. From the acquired 3D point cloud topographic details can be retrieved over a maximum distance of 2 km. However, newer instruments can reach distances of 6 km or more.
description: Georeferencing (local coordinate system)
In total, four TLS scans were acquired on two days in November 2013 (two at each day to overcome shadowing effects). The two point clouds from each view point were combined using tie points, i.e. reﬂectors that were placed in the ﬁeld, and the RiSCAN Pro Software (http://www.riegl.com). For the two point clouds from day 1, we achieved a standard deviation of 0.0023 m using 6 tie points, while for the two point clouds acquired on day 2 we reached a standard deviation of 0.0052 m using 3 tie points. In addition to the TLS measurement, the reﬂectors’ positions were also measured using a total station. This additional data allowed us to 1) orientate each of the two point clouds to a local geodetic reference frame in the XY plane using a 3D afﬁne transformation with a remaining RMSE of ∼1 cm and 2) estimate the orientation about Z and the full translation parameters using hand-held GPS coordinates of a common point and the individual tie points. Following this procedure we produced a combined point cloud of all four TLS scans in a local geodetic reference frame. 
      
      Georeferencing (global coordinate system)
In order to derive the coordinates of the TLS point cloud in a global coordinate system, we used the open-source software Minuit2 5.18/00 which was developed at CERN (James and Winkler, 2004 and references therein). This tool ﬁnds the minimum value of multi-parameter functions and was in our case employed to ﬁnd the minimum root mean square residuals (in elevation) between the TLS point clouds and a reference DEM featuring a 1 m pixel spacing that was calculated from tri-stereo optical Pléiades-1 satellite imagery. When applying this minimization technique, the data are transferred to the same coordinate system as the reference data (WGS 1984 UTM Zone 19 South). In a ﬁrst step, we minimized the two TLS point clouds from the two different acquisition dates separately. We masked out areas from the Pléiades reference DEM that we know are very different when compared to the TLS point data. For instance, areas along the steep crater walls are interpolated to a high degree in the Pléiades DEM, while the scanner-facing crater walls are expected to have comparably precise point values in the TLS dataset. Thereafter, we combined the TLS point clouds and ran another Minuit RMSE minimization onto the masked Pléiades DEM.