The DFG Priority Program 1803 “EarthShape” (www.earthshape.net) investigates Earth surface shaping by biota. As part of this project, we present Light Detection and Ranging (LiDAR) data of land surface areas for the four core research sites of the project. The research sites are located along a latitudinal gradient between ~26 °S and ~38 °S in the Chilean Coastal Cordillera. From north to south, the names of these sites are: National Park Pan de Azúcar; Private Reserve Santa Gracia; National Park La Campana; and National Park Nahuelbuta.
The three datasets contain raw 3D point cloud data captured from an airborne LiDAR system, and the following derivative products: a) digital terrain models (DTM, sometimes also referred to as DEM [digital elevation model]) which are (2.5D) raster datasets created by rendering only the LiDAR returns which are assumed to be ground/bare-earth returns and b) digital surface models (DSM) which are also 2.5D raster datasets produced by rendering all the returns from the top of the Earth’s surface, including all objects and structures (e.g. buildings and vegetation).
The LiDAR data were acquired in 2008 (southernmost Nahuelbuta [NAB] catchment), 2016 (central La Campana [LC] catchment) and 2020 (central Santa Gracia [SGA] catchment). Except for Nahuelbuta (data already was available from the data provider from a previous project), the flights were carried out as part of the "EarthShape" project. The LiDAR raw data (point cloud/ *.las files) were compressed, merged (as *.laz files) and projected using UTM 19 S (UTM 18 S for the southernmost Nahuelbuta catchment, respectively) and WGS84 as coordinate reference system.
A complementary fourth dataset for the northernmost site in the National Park Pan de Azúcar, derived from Uncrewed Aerial Vehicle (UAV) flights and Structure from Motion (SfM) photogrammetry, is expected to be obtained during the first half of 2022 and will be added to the above data set.
This python software (version 1.0) is linked to the publication "Determining the Optimal Grid Resolution for Topographic Analysis on an Airborne Lidar Dataset" by T. Smith, A. Rheinwalt, and B. Bookhagen (2019). Software updates can be found at: https://github.com/UP-RS-ESP/TopoMetricUncertainty,
TopoMetricUncertainty is a set of python codes which can be used to determine the optimal grid resolution of a given lidar dataset which minimizes overall uncertainties in slope and aspect calculations. The software contains examples with both synthetic and real gridded data covering the Santa Cruz Island, California.
The following components are included in this software release:
(1) surfaces.py: code used to create the synthetic surfaces used in Smith et al. (2019)
(2) uncertainty.py: code used to calculate truncation error and propagated elevation uncertainty
(3) the detailed description of several gridding methods for lidar data, including the ones used in this paper, can be found here: https://github.com/BodoBookhagen/Lidar_PC_interpolation
(4) A full example and script for choosing the optimal grid resolution is included in the 'example' directory. This directory contains elevation and elevation standard deviation estimates for a subset of SCI from 2m to 30m resolution. Running the included script will generate a simple figure showing the optimal grid resolution for that region, given that error model.
(5) optimize_grid_spacing.py is one other potential method of finding the optimal grid spacing directly from a lidar dataset. This method was not used in the above linked paper but is included in this software publication and available via github.
This data publication is a high resolution Digital Elevation Model (DEM) generated for the Merapi summit by combining terrestrial laser scanning (TLS) and unmanned aerial vehicles (UAVs) photogrammetry data acquired in 2014 and 2015, respectively. The structures of the data are further analysed in Darmawan et al. 2017 (http://doi.org/10.1016/j.jvolgeores.2017.11.006).
The published datasets consist of combined point clouds with ~65 million data points and a DEM with a resampled resolution of 0.5 m. The DEM data covers the complexity of the Merapi summit with area of 2 km2. The coordinate of the datasets is projected to global coordinates (WGS 1984 UTM Zone 49 South).
TLS is a topography mapping technique which exploits the travel time of a laser beam to measure the range between the ground-based scanning instrument and the earth’s surface. TLS provides high accuracy, precision, and resolution for topography mapping, however, it requires different scan position to obtain accurate topography model in a complex topography. The TLS dataset was acquired by using a long-range RIEGL VZ-6000 instrument with a Pulse Repetition Rate (PRR) of 30 kHz.
The Merapi data includes an observation range of 0.129 – 4393.75 m, a theta range (vertical) of 73 – 120° with a sampling angle of 0.041°, a phi range (horizontal) of 33° - 233° with a sampling angle of 0.05°, and 12 reflectors for each scan. The used TLS dataset was achieved by combining two scan positions, both realized in September 2014. In order to reduce still eminent shadowing, we conducted additionally a UAV photogrammetry survey. The UAV data allows to fill data gaps and generate a complete 3D point cloud.
The UAV photogrammetry was conducted by using DJI Phantom 2 quadcopter drone in October 2015. The drone carried GoPro HERO 3+ camera and a H3-3D gimbal to reduce image shaking. We obtained over 300 images which cover the summit area of Merapi. By applying the Structure from Motion algorithm, we are able to generate a 3D point cloud model of Merapi summit. Further details on this procedure are provided in Darmawan et al. (2017).
Structure from Motion is a technique to generate a 3D model based on 2D overlapped images. The algorithm detects and matches the same ground features of 2D images, reconstructs a 3D scene, and calculates a depth map for each camera frame. The algorithm used is implemented in Agisoft Photoscan Professional software. After importing the images in Agisoft, we used the ‘align image’ function with high accuracy setting to generate 3D sparse point cloud and ‘build dense cloud’ function with high quality to generate 3D dense point cloud.
The 3D point clouds of TLS and UAV photogrammetry were then georeferenced to our georeferenced 3D point cloud which acquired in 2012. The RMS of TLS and UAV photogrammetry during georeferenced is 0.60 and 0.44 m, respectively, as described in Further details on this procedure are provided in Darmawan et al. (2017). After georeferencing, both 3D point clouds were merged and interpolated to a raster format in the ArcMap software.
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.