Simulation-Based Correction of Geolocation Errors in GEDI Footprint Positions Using Monte Carlo Approach DOI Open Access

Xiaoyan Wang,

Ruirui Wang, Banghui Yang

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(5), P. 768 - 768

Published: April 30, 2025

Traditional remote sensing techniques face notable limitations in accurately estimating forest canopy height. Optical data often suffer from vegetation occlusion, while radar systems, though capable of penetrating foliage, show reduced accuracy complex terrains. The Global Ecosystem Dynamics Investigation (GEDI), a spaceborne LiDAR mission, offers high-resolution measurements that address these challenges. However, the complexity waveform processing and influence geolocation uncertainty demand rigorous assessment. This study employs GEDI Version 2.0 data, which demonstrates substantial improvement compared to 1.0, integrates airborne laser scanning (ALS) Changbai Mountain region simulate waveforms. A Monte Carlo-based approach was used quantify correct offsets, resulting reduction average relative error (defined as mean absolute differences between estimated reference heights divided by values) height estimates 11.92% 8.55%. Compared traditional correction strategies, this method stronger robustness heterogeneous conditions. findings emphasize effectiveness simulation-based optimization enhancing retrieval reliability especially terrain environments. contributes more precise global structure assessments provides methodological foundation for future improvements applications.

Language: Английский

Simulation-Based Correction of Geolocation Errors in GEDI Footprint Positions Using Monte Carlo Approach DOI Open Access

Xiaoyan Wang,

Ruirui Wang, Banghui Yang

et al.

Forests, Journal Year: 2025, Volume and Issue: 16(5), P. 768 - 768

Published: April 30, 2025

Traditional remote sensing techniques face notable limitations in accurately estimating forest canopy height. Optical data often suffer from vegetation occlusion, while radar systems, though capable of penetrating foliage, show reduced accuracy complex terrains. The Global Ecosystem Dynamics Investigation (GEDI), a spaceborne LiDAR mission, offers high-resolution measurements that address these challenges. However, the complexity waveform processing and influence geolocation uncertainty demand rigorous assessment. This study employs GEDI Version 2.0 data, which demonstrates substantial improvement compared to 1.0, integrates airborne laser scanning (ALS) Changbai Mountain region simulate waveforms. A Monte Carlo-based approach was used quantify correct offsets, resulting reduction average relative error (defined as mean absolute differences between estimated reference heights divided by values) height estimates 11.92% 8.55%. Compared traditional correction strategies, this method stronger robustness heterogeneous conditions. findings emphasize effectiveness simulation-based optimization enhancing retrieval reliability especially terrain environments. contributes more precise global structure assessments provides methodological foundation for future improvements applications.

Language: Английский

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