Quantifying and correcting geolocation error in sampling LiDAR forest canopy observations using high spatial accuracy ALS: A case study involving GEDI
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Geolocation error in sampling LiDAR measurements of forest structure can compromise forest attribute estimates and degrade integration with georeferenced field measurements or other remotely sensed data. Integrating sampling LiDAR with other georeferenced data is especially problematic when geolocation error is not well quantified. Here, we propose and illustrate a model that uses high spatial accuracy airborne laser scanning (ALS) data from NASA Goddard's LiDAR, Hyperspectral Thermal Imager (G-LiHT) to quantify and correct geolocation error in NASA's Global Ecosystem Dynamics Investigation (GEDI) spaceborne sampling LiDAR. The model accommodates multiple canopy height metrics derived from a simulated GEDI footprint kernel of known functional form using spatially coincident G-LiHT, and incorporates both additive and multiplicative mapping between the canopy height metrics generated from both LiDAR datasets. A Bayesian implementation provides probabilistic uncertainty quantification in both parameter and geolocation error estimates. Results show an estimated maximum a posteriori (MAP) systematic geolocation error (i.e., across all GEDI footprints) of 9.62 m in the southwest direction. In addition, estimated MAP actual geolocation errors (i.e., within GEDI footprints) were highly variable with results showing a 0.15 probability the true GEDI footprint center is within 10 m and a 0.45 probability the true GEDI footprint center is within 20 m. Estimating and correcting geolocation error via the modeling approach outlined here can help inform subsequent efforts to integrate sampling LiDAR, like GEDI, with other georeferenced data.
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