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Merced Vernal Pools and Grassland Reserve sUAS-LiDAR High Resolution 0.25-meter DEM


Kalua, Michael; Viers, Joshua; Anderson, Andreas (2020), Merced Vernal Pools and Grassland Reserve sUAS-LiDAR High Resolution 0.25-meter DEM, Dryad, Dataset,


The Merced Vernal Pools and Grassland Reserve is 6,500 acres of protected habitat adjacent to the University of California Merced containing rare and endangered species and a unique seasonal wetland habitat. These data were gathered to be used for hydrological modelling on the Reserve for potential restoration projects and to be made public for other researchers who may find very high resolution topographical information useful for their work. This dataset contains a Digital Elevation Model created from 8 field survey days of Aerial LiDAR Scanning (ALS) with a small Unmanned Aerial System (sUAS).


Work Completed by Researchers at University of California, Merced under the direction of Dean/Director/Professor Joshua H. Viers | Vicelab and CITRIS Aviation

Spatial Reference: WGS 1984 UTM Zone 10N / WGS84 Geoid

Units: Meters

Equipment: DJI M600 Pro with Phoenix Aerial Systems AL3-32 LiDAR

Software: Phoenix LiDAR Systems SpatialSuite 4.0.3, LasTools, ArcGIS Pro 2.4, Litchi, ArduPilot Mission Planner

Field Crew/Processing: Michael Kalua (sUAS Pilot/Mission Planning/Sensor Operator/Data Processing), Andreas Anderson (sUAS Pilot/Mission Planning/Sensor Operator), Daniel Gomez (Sensor Operator), Hayden Namgostar (Sensor Operator)

Field Methods: An RTK reference station was set up before each field day over a previously-surveyed benchmark near the entrance of the Reserve, which would continuously send RTK corrections to the LiDAR system over an internet connection service. Before flight the LiDAR system was allowed at least 15 minutes to reach thermal equilibrium and for the onboard Intertial Measurement Unit (IMU) to get a fix on the sensor's position and attitude. At the beginning of each set of flights the Pilot in Command (PIC) would perform a manual takeoff and IMU calibration maneuvers (straight-and-level flight and figure-eights) as per Phoenix LiDAR System's recommended procedures. Once the manuevers were completed and the Sensor Operator determined IMU attitude and position uncertainties were below threshold (0.003- typical values ranged an order of magnitude lower) the PIC would begin the automated waypoint mission via Litchi. During flight, the Sensor Operator would ensure the scanner was operational, that the IMU uncertainties were below margin, and address any potential error messages. In the event of errors, the PIC would bring the sUAS back and the section would be re-surveyed after the issues were addressed.

Processing Methods: The raw flightlines were fused using Phoenix SpatialExplorer 4.0.3 to include only the straight-and-level flightlines over the region of interest. The output were individual flightline .las point clouds conforming to LAS 1.4 format. These flightlines were then passed through a noise filter using LasNoise to remove any "birds" or unwanted noise. Using LasTools these noise-removed flightlines were then tiled, classified into ground/non-ground points, and rasterized into 0.25-meter Digital Surface Models (DSM) containing all points and Bare-Earth Digital Elevation Models (DEM) containing only ground-classified points. These tiled raster outputs were then mosiaced together in ArcGIS Pro.    

Please reach out to Michael Kalua ( for any questions about this dataset.    

Usage Notes

This dataset contains a 0.25-meter spatial resolution Digital Elevation Model (DEM) produced by a small Unmanned Aerial System (sUAS) with Airborne Laser Scanning (ALS) capabilities collected over 8 days from November 2019 to March 2020 by CITRIS Aviation and Vicelab at Univerity of California, Merced. These data were collected by a Phoenix LiDAR Systems AL3-32 LiDAR attached to a DJI M600 Pro flown at approximately 60 meters AGL with 60 meter flightline spacing. Points were geolocated in realtime with RTK GPS accuracy with corrections being consistently uploaded to the LiDAR system during aquisition.

To view the raster, please load the 'MVPGR_ALS_DEM.tif' file into your preferred GIS.   

There are two known holes (Avocet Pond & a small area in the NW corner) where no LiDAR returns were obtained due to water. There are also some areas; particularly outside/near borders, where there are visible artifacts from the drone slowing for turns. Please contact Michael Kalua at with any questions or if you find issues; we would love to know!