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.