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The impact of geolocation uncertainty on GEDI tropical forest canopy height estimation and change monitoring
Abstract
The Global Ecosystem Dynamics Investigation (GEDI) LiDAR provides new spaceborne vegetation canopy structural information including relative canopy height products defined with respect to 25 m diameter footprints. The GEDI geolocation requirement is that each 25 m footprint center is horizontally georeferenced to within 10 m (1σ), assuming normally distributed geolocation errors with a 0 m mean and a 10 m standard deviation. The impact of this geolocation uncertainty on the reliability of forest canopy height estimation is examined considering Airborne Laser scanner (ALS) and GEDI data acquired in 2014 and 2019 respectively. A total of 445 GEDI footprints acquired over 2000 ha of unforested and tropical secondary forest in the western Democratic Republic of the Congo with vegetation heights ranging from 1 m to 42 m are considered. Airborne true color 10 cm imagery and an ALS derived canopy height model are examined to contextualize the results. GEDI waveforms are simulated from the ALS data at the reported locations of the GEDI footprints and used to derive h ˆ 95 , h ˆ 85 , h ˆ 75 relative heights that define the canopy height relative to the ground below which 95%, 85% and 75% of the simulated cumulative waveform energy is returned. A Monte Carlo simulation is undertaken, moving the centers of each GEDI footprint with 300 randomly generated position errors modelled using the GEDI geolocation uncertainty (0 m mean, 10 m standard deviation), and each time simulating the GEDI…
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“the sparse supervision means that the model never explicitly sees high-frequency variations of the canopy height between adjacent pixels, and misalignments caused by the geolocation uncertainty (15–20 m) of the GEDI version 1 data12,40,41 introduce further noise.”
From A high-resolution canopy height model of the Earth (2023) · cites this paper as [40]
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Sorted by citations
A high-resolution canopy height model of the Earth
Lang, N., Jetz, W., Schindler, K., & Wegner, J. D. (2023) · Nature Ecology & Evolution · doi:10.1038/s41559-023-02206-6 · Cited by 810
Moudrý, V., Gábor, L., Marselis, S., Pracná, P., Barták, V., Prošek, J., Navrátilová, B., Novotný, J., Potůčková, M., Gdulová, K., et al. (2024) · Ecosphere · doi:10.1002/ecs2.70026 · Cited by 43
Metadata
- Authors
- Roy, D. P., Kashongwe, H. B., & Armston, J.
- ORCID
- Roy, David P.
- Venue
- Science of Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2021-12
- Volume
- 4
- Pages
- 100024
- ISSN
- 2666-0172
- Cited by
- 112
- References
- 0
- Language
- en
- Licence
- Funders
- University of Maryland
- Michigan State University
- National Aeronautics and Space Administration (15AA03C)
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