SALPA: Spaceborne LiDAR point adjustment for enhanced footprints geolocation

Spaceborne Light Detection and Ranging (LiDAR) systems, such as NASA’s Global Ecosystem Dynamics Investigation (GEDI), provide forest structure for global carbon assessments. However, geolocation uncertainties (typically 5–15 m) propagate systematically through derived products, undermining forest profile estimates, including carbon stock assessments. Existing correction methods face critical limitations: waveform simulation approaches achieve centimeter-level accuracy but require high-resolution LiDAR data unavailable in most regions, while terrain-based methods employ deterministic grid searches that may overlook optimal solutions in continuous solution spaces. We present SALPA (Spaceborne LiDAR Point Adjustment), a multi-algorithm optimization framework integrating four optimization paradigms with six distance metrics. Operating exclusively with digital elevation models and geoid data, SALPA explores continuous solution spaces through gradient-based, evolutionary, swarm intelligence, and bio-inspired approaches. Validation across contrasting sites, topographically complex Nikko, Japan, and flat Landes, France, demonstrates reduced GEDI-DEM elevation discrepancy relative to original GEDI positions (15.6%–16.7% and 5.2%–5.5%, depending on outlier filtering) and, by a small but consistent margin, to the GeoGEDI algorithm (which achieved 15.0%–16.1% and 5.1%–5.3%). GA, PSO, and WOA with the Euclidean, Area, and Manhattan metrics achieved the lowest discrepancy, with GA and PSO requiring less computation time than WOA. A test with the 30 m AW3D30 DEM confirmed that SALPA also operates with a coarse global elevation product. Requiring only footprint coordinates and ground elevations as input, the framework facilitates straightforward adaptation to emerging spaceborne LiDAR missions, requiring only a DEM and a geoid model as reference data.