TY - GEN
T1 - Estimation of Forest Aboveground Biomass from Derivatives of Vegetation-Structure Profiles
AU - Treuhaft, Robert
AU - Cushman, K. C.
AU - Hensley, Scott
AU - Pinto, Naiara
AU - Stocker, Olivier
AU - Hawkins, Brian
AU - Lavalle, Marco
AU - Chen, Richard
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Several studies have found that the vertical Fourier transform of lidar, interferometric Synthetic Aperture Radar (SAR), and stereo photogrammetric profiles at empirically-determined spatial frequencies enables high-performance forest aboveground biomass (AGB) estimation. Linear combinations of real and imaginary parts of Fourier transforms of Tomographic (multi-baseline) SAR (TomoSAR) profiles, from Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) airborne data, generate ~20%-precision estimates of AGB in the Saskatchewan area of Canada. We found that this 20% precision can be improved to ~15%, a factor of 30% improvement in root mean square error (RMSE) if, in addition to using Fourier transforms of the profile itself, we use Fourier transforms of the spatial, vertical derivative of the profile. The formulation of this "derivative"algorithm is the subject of this paper.
AB - Several studies have found that the vertical Fourier transform of lidar, interferometric Synthetic Aperture Radar (SAR), and stereo photogrammetric profiles at empirically-determined spatial frequencies enables high-performance forest aboveground biomass (AGB) estimation. Linear combinations of real and imaginary parts of Fourier transforms of Tomographic (multi-baseline) SAR (TomoSAR) profiles, from Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) airborne data, generate ~20%-precision estimates of AGB in the Saskatchewan area of Canada. We found that this 20% precision can be improved to ~15%, a factor of 30% improvement in root mean square error (RMSE) if, in addition to using Fourier transforms of the profile itself, we use Fourier transforms of the spatial, vertical derivative of the profile. The formulation of this "derivative"algorithm is the subject of this paper.
KW - Forest vegetation structure
KW - lidar remote sensing
KW - radar remote sensing
UR - http://www.scopus.com/inward/record.url?scp=85204891995&partnerID=8YFLogxK
U2 - 10.1109/IGARSS53475.2024.10641220
DO - 10.1109/IGARSS53475.2024.10641220
M3 - Conference contribution
AN - SCOPUS:85204891995
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 2442
EP - 2445
BT - IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024
Y2 - 7 July 2024 through 12 July 2024
ER -