Estimation of Forest Aboveground Biomass from Derivatives of Vegetation-Structure Profiles

Robert Treuhaft, K. C. Cushman, Scott Hensley, Naiara Pinto, Olivier Stocker, Brian Hawkins, Marco Lavalle, Richard Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationIGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2442-2445
Number of pages4
ISBN (Electronic)9798350360325
DOIs
StatePublished - 2024
Event2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024 - Athens, Greece
Duration: Jul 7 2024Jul 12 2024

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024
Country/TerritoryGreece
CityAthens
Period07/7/2407/12/24

Keywords

  • Forest vegetation structure
  • lidar remote sensing
  • radar remote sensing

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