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Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles
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The main objective of the GEDI mission is to provide high-accuracy and high global coverage data on canopy structure, which can be used to estimate biomass and canopy height.
“The main objective of the GEDI mission is to provide high-accuracy and high global coverage data on canopy structure, which can be used to estimate biomass [39][40][41] and canopy height [30,38,42].”
From ANADEM: A Digital Terrain Model for South America (2024) · cites this paper as [42]
The Introduction says GEDI is designed to measure ecosystem structure through forest-canopy profiles, and describes this paper’s use of GEDI data to estimate canopy height. But it also says the mission was expected to sample only 4% of land between 51.6° north and south on a sparse grid, so the claim of high global coverage needs qualification. The biomass citations are separate from the canopy-height citations attached to this source. (LLM, conf=0.82)
Experimental — accuracy not yet measuredBesides Sentinel-2, the GEDI LiDAR mission also plays a key role as the source of sparse but uniquely well-distributed reference data at global scale.
“Besides Sentinel-2, the GEDI LiDAR mission also plays a key role as the source of sparse but uniquely well-distributed reference data at global scale.”
From A high-resolution canopy height model of the Earth (2023) · cites this paper as [30]
The procedure of assigning 0 m canopy height to footprints in 'not vegetated' or 'water' categories also addresses the slight positive bias due to slope in the GEDI reference data.
“This procedure also addresses the slight positive bias due to slope in the GEDI reference data30.”
From A high-resolution canopy height model of the Earth (2023) · cites this paper as [30]
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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
Self-citationTolan, J., Yang, H.-I., Nosarzewski, B., Couairon, G., Vo, H. V., Brandt, J., Spore, J., Majumdar, S., Haziza, D., Vamaraju, J., et al. (2024) · Remote Sensing of Environment · doi:10.1016/j.rse.2023.113888 · Cited by 419
ANADEM: A Digital Terrain Model for South America
Laipelt, L., Comini de Andrade, B., Collischonn, W., de Amorim Teixeira, A., Paiva, R. C. D. D., & Ruhoff, A. (2024) · Remote Sensing · doi:10.3390/rs16132321 · Cited by 79
Metadata
- Authors
- Lang, N., Kalischek, N., Armston, J., Schindler, K., Dubayah, R., & Wegner, J. D.
- Venue
- Remote Sensing of Environment
- Publisher
- Elsevier BV
- Published
- 2022-01
- Volume
- 268
- Pages
- 112760
- ISSN
- 0034-4257
- Cited by
- 291
- References
- 10
- Language
- en
- Licence
- Funders
- University of Maryland
- Bournemouth University
- Universiteit Gent
- Tokyo University of Agriculture
- Norges Miljø- og Biovitenskapelige Universitet
- National Aeronautics and Space Administration (15AA03C)
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