# SALPA: Spaceborne LiDAR Point Adjustment for Enhanced GEDI Footprint Geolocation

arXiv: https://arxiv.org/abs/2511.17600 · HTML: https://mlsanity.com/paper/2511.17600/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 47 references that are in the map.
- First posted on arXiv 18 Nov 2025.

## Builds on (most cited first, 2 of 2)

- [Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles](https://mlsanity.com/paper/2103.03975/) (2021)
- [Nature-Inspired Optimization Algorithms: Challenges and Open Problems](https://mlsanity.com/paper/2003.03776/) (2020)

## Abstract (arXiv)

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.