SALPA: Spaceborne LiDAR Point Adjustment for Enhanced GEDI Footprint Geolocation

TL;DR

SALPA is a versatile optimization framework that improves the geolocation accuracy of spaceborne LiDAR data like GEDI using only publicly available elevation data, enhancing forest monitoring accuracy.

Contribution

It introduces a multi-algorithm optimization approach that operates with global elevation data, outperforming existing methods in diverse terrains for LiDAR point correction.

Findings

Achieves 15-16% geolocation accuracy improvement over original GEDI data.

Outperforms state-of-the-art GeoGEDI algorithm by 0.5-2%.

Effective in both complex and flat terrains using various optimization algorithms.

Abstract

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 meter-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 three optimization paradigms with five distance metrics. Operating exclusively with globally available digital elevation models and geoid data, SALPA explores continuous solution spaces through gradient-based, evolutionary, and swarm intelligence approaches. Validation across contrasting sites: topographically complex Nikko, Japan, and flat Landes, France, demonstrates 15-16% improvements over original GEDI positions and 0.5-2% improvements over the state-of-the-art GeoGEDI algorithm. L-BFGS-B with Area-based metrics achieves optimal accuracy-efficiency trade-offs, while population-based algorithms (genetic algorithms, particle swarm optimization) excel in complex terrain. The platform-agnostic framework facilitates straightforward adaptation to emerging spaceborne LiDAR missions, providing a generalizable foundation for universal geolocation correction essential for reliable global forest monitoring and climate policy decisions.