Improved terrain estimation from spaceborne lidar in tropical peatlands using spatial filtering

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Tropical peatlands are estimated to hold carbon stocks of 70 Pg C or more as partly decomposed organic matter, or peat. Peat may accumulate over thousands of years into gently mounded deposits called peat domes with a relief of several meters over distances of kilometers. The mounded shapes of tropical peat domes account for much of the carbon storage in these landscapes, but their subtle topographic relief is difficult to measure. As many of the world's tropical peatlands are remote and inaccessible, spaceborne laser altimetry data from missions such as NASA's Global Ecosystem Dynamics Investigation (GEDI) on the International Space Station (ISS) and the Advanced Topographic Laser Altimeter System (ATLAS) instrument on the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory could help to describe these deposits. We evaluate retrieval of ground elevations derived from GEDI waveform data, as well as single-photon data from ATLAS, with reference to an airborne lidar dataset covering an area of over 300 km2 in the Belait District of Brunei Darussalam on the island of Borneo. Spatial filtering of GEDI L2A version 2, algorithm 1 quality data reduced mean absolute deviations from airborne-lidar-derived ground elevations from 8.35 m to 1.83 m, root-mean-squared error from 15.98 m to 1.97 m, and unbiased root-mean-squared error from 13.62 m to 0.72 m. Similarly, spatial filtering of ATLAS ATL08 version 3 ground photons from strong beams at night reduced mean absolute deviations from 1.51 m to 0.64 m, root-mean-squared error from 3.85 m to 0.77 m, and unbiased root-mean-squared error from 3.54 m to 0.44 m. We conclude that despite sparse ground retrievals, these spaceborne platforms can provide useful data for tropical peatland surface altimetry if postprocessed with a spatial filter. View source
Year

2023

Secondary Title

Science of Remote Sensing

Publisher

Elsevier

Volume

7

Pages

100074

DOI

http://dx.doi.org/10.1016/j.srs.2022.100074

Language

Classification
Form: Journal Article
Geographical Area: Brunei Darussalam

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