  Geolocation error in spaceborne sampling light detection and ranging (LiDAR)
measurements of forest structure can compromise forest attribute estimates and
degrade integration with georeferenced field measurements or other remotely
sensed data. Data integration is especially problematic when geolocation error
is not well quantified. We propose a general model that uses airborne laser
scanning (ALS) data to quantify and correct geolocation error in spaceborne
sampling LiDAR. To illustrate the model, LiDAR data from NASA Goddard's LiDAR
Hyperspectral & Thermal Imager (G-LiHT) was used with a subset of LiDAR data
from NASA's Global Ecosystem Dynamics Investigation (GEDI). The model
accommodates multiple canopy height metrics derived from a simulated GEDI
footprint kernel using spatially coincident G-LiHT, and incorporates both
additive and multiplicative mapping between the canopy height metrics generated
from both datasets. A Bayesian implementation provides probabilistic
uncertainty quantification in both parameter and geolocation error estimates.
Results show a systematic geolocation error of 9.62 m in the southwest
direction. In addition, estimated geolocation errors within GEDI footprints
were highly variable, with results showing a ~0.45 probability the true
footprint center is within 20 m. Estimating and correcting geolocation error
via the model outlined here can help inform subsequent efforts to integrate
spaceborne LiDAR data, like GEDI, with other georeferenced data.
