Tropical forest vertical structure characterization: From GEDI to P-band SAR tomography - Archive ouverte HAL Access content directly
Journal Articles IEEE Geoscience and Remote Sensing Letters Year : 2022

Tropical forest vertical structure characterization: From GEDI to P-band SAR tomography

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Abstract

Estimating tropical forests vertical structure using remote sensing is a challenge. Active sensors such as lowfrequency Synthetic Aperture Radar (SAR) operating at P-band, with a wavelength of ∼ 69 cm wavelength, and Light Detection and Ranging (LiDAR) are able to penetrate thick vegetation layers. While NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne liDAR data, the ESA’s next Earth Explorer BIOMASS mission will acquire multiple acquisitions over the same areas to form three-dimensional images through SAR tomography (TomoSAR) technique. Our study shows the potential value of GEDI and TomoSAR acquisitions in producing accurate estimates of forests vertical structure. By analyzing airborne P-band TomoSAR, airborne LiDAR, and spaceborne GEDI LiDAR at a tropical forest site in Paracou, French Guiana, South America, we show that both GEDI and P-band TomoSAR can directly measure surface, vegetation heights, and vertical profiles with high resolution and precision. Airborne TomoSAR is of higher quality than GEDI due to better penetration properties and precision. However, the GEDI vegetation height root-meansquare error is less than 5 m, for an average forest height value around 30 m at the Paracou site, which is similar to the expected performance of the future spaceborne BIOMASS mission. These results suggest GEDI measurements, i.e. shots with sensitivity greater than 98%, will provide a good reference of forest structure to calibrate the BIOMASS mission algorithms.
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Dates and versions

hal-03793062 , version 1 (23-01-2023)

Licence

Attribution - CC BY 4.0

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Cite

Yen-Nhi Ngo, Yue Huang, Dinh Ho Tong Minh, Laurent Ferro-Famil, Ibrahim Fayad, et al.. Tropical forest vertical structure characterization: From GEDI to P-band SAR tomography. IEEE Geoscience and Remote Sensing Letters, 2022, 19, pp.7004705. ⟨10.1109/LGRS.2022.3208744⟩. ⟨hal-03793062⟩
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