Forested Aboveground Biomass from Mechanistic and Statistical Analyses of Structural Remote-Sensing Data

About the NASA Postdoctoral Program

The NASA Postdoctoral Program (NPP) offers unique research opportunities to highly-talented scientists to engage in ongoing NASA research projects at a NASA Center, NASA Headquarters, or at a NASA-affiliated research institute. These one- to three-year fellowships are competitive and are designed to advance NASA’s missions in space science, Earth science, aeronautics, space operations, exploration systems, and astrobiology.

Description

Remotely sensed estimates of aboveground biomass (AGB) are important inputs to ecological and climate modeling. They have evolved over the last 30 years. AGB measurements used to center around the correlation between remote-sensing signal strength (radar or optical) and AGB. With the advent of interferometric SAR (InSAR) and lidar, remote measurements of tree height have been combined with average-power measurements to improve accuracy and resolution of AGB estimates. In this project, radar profiles from multi-baseline InSAR, called "tomoSAR", and/or lidar profiles from waveforms will be used to estimate AGB. Properties of the vertical profiles of radar (tomograms) and lidar (waveforms) bearing on AGB include InSAR phase and amplitude, averages of InSAR phase or waveform height, and/or Fourier transforms of both tomograms and waveforms. These properties are principal observables for NASA’s Surface Topography and Vegetation (STV) observation program.

Field of Science

- Technology Development

Advisors

• robert.n.treuhaft@jpl.nasa.gov
• (818) 354-6216

Eligibility

• U.S. Citizens;
• U.S. Lawful Permanent Residents (LPR);
• Foreign Nationals eligible for an Exchange Visitor J-1 visa status;
• Applicants for LPR, asylees, or refugees in the U.S. at the time of application with 1) a valid EAD card and 2) I-485 or I-589 forms in pending status

Preferred qualifications

• PhD in applied math, physics, electrical engineering or computer science.
• Experimental experience preferred, with hands-on data acquisition and analysis.
• Interested in a hybrid of mechanistic, science based models and statistical approaches to model forest structure and its relationship to biomass estimation.
• Familiarity with machine language would be helpful.
• Good communication skills are of paramount importance so that colleagues and mentors of the Postdoc can learn from her/his ongoing discoveries.
• Ability to work independently, as most PhD's do, would be appreciated.
• Experience in coding physical, and statistical models in Python or other computer language would be helpful.

Questions

Please email npp@orau.org if you have questions about this opportunity.