SALPA: Spaceborne LiDAR Point Adjustment for Enhanced GEDI Footprint Geolocation

TL;DR

Geolocation uncertainties in spaceborne LiDAR like GEDI propagate into forest products, necessitating robust post-processing corrections. SALPA introduces a platform-agnostic, DEM- and geoid-based optimization framework that jointly leverages three optimization paradigms and five distance metrics to correct footprints in a continuous 2D displacement space, within a ±25 m search window, using only reference DEMs. Across two contrasting study sites (Nikko, Japan and Landes, France), SALPA achieves substantial improvements over original GEDI positions (≈15-16%) and modest gains over the current GeoGEDI approach (≈0.5-2%), with L-BFGS-B plus Area metrics often providing the best balance of accuracy and efficiency; GA and PSO offer advantages in more complex terrain. These findings provide actionable guidance for operational geolocation correction in diverse spaceborne LiDAR missions, enabling more reliable global forest monitoring and climate policy supporting biomass and carbon stock assessments, and the SALPA framework is released as open-source for broad adoption and extension. In mathematical terms, the optimal displacement is $\boldsymbol{\delta}^* = \arg\min_{\boldsymbol{\delta}} D(\mathbf{E}_g, \mathbf{R}_g(\mathbf{P}_g + \boldsymbol{\delta}))$ with $|\boldsymbol{\delta}| \leq 25$ m, where $D$ is chosen from five metrics including $D_{\text{Area}}$ and $D_{\text{Correlation}}$.

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.

Figures (2)

• Figure 1: Overview of the SALPA algorithm.
• Figure 2: Study areas used for SALPA algorithm evaluation showing contrasting topographic conditions: (a) complex mountainous terrain in Nikko, Japan, and (b) low-relief managed forest landscape in Landes, France.