Laser Altimeters:  ICESat, and ICESat-2, GEDI, LVIS

ICESat-2 is a laser altimeter launched in 2018 with 6 beams.   Two tracks, 90 m apart, have strong (even numbers) and weak (odd numbers)  beams, and are 3.3 km from the next pair of beams. 

Download ICESat and ICESat-2 data

Demo of data download


ICEsat flew from 2003 to 2009.  The sole instrument on ICESat was the Geoscience Laser Altimeter System (GLAS), a space-based LIDAR. GLAS combined a precision surface LIDAR with a sensitive dual-wavelength cloud and aerosol LIDAR. The GLAS lasers emit infrared and visible laser pulses at 1064 and 532 nm wavelengths. As ICESat orbited, GLAS produces a series of approximately 70 m diameter laser spots that are separated by nearly 170 m along the spacecraft's ground track.  The ICESat mission was designed to provide elevation data needed to determine ice sheet mass balance as well as cloud property information, especially for stratospheric clouds common over polar areas. It provides topography and vegetation data around the globe, in addition to the polar-specific coverage over the Greenland and Antarctic ice sheets. The satellite was found useful in assessing important forest characteristics, including tree density.  ICEsat had three lasers, but they failed prematurely and the satellite did not collect as much data as anticipated.

ICESat data covering a 1 degree by 1 degree region in southern Nevada.

There are about 11000 lidar pulses in the map area.



LVIS was also a prototype for GEDI currently on the ISS.  GEDI data comes in huge HDF5 files (several GB each), which is not particularly friendly for GIS use.  It records the waveforms, a different approach from ICESat-2.  Spatial resolution is 25 m.

As noted in Kokalj and Mast (2021) »the rGEDI package was released by Silva et al. (2020) which provides user-friendly utilities for the processing and visualization of GEDI data in R. For Python users the LP DAAC released the GEDI-subsetter (Krehbiel 2020a), a tool for converting the HDF5 files into geojson objects, which can be more easily used in traditional GIS. The LP DAAC further released a series of jupyter notebooks which showcase the processing of GEDI granules in python (Krehbiel 2020b).«



Last revision 11/1/2021