Remote Sensing and Machine Learning to Define Wetland Loss and Mississippi Delta

Remote Sensing and Machine Learning to Better Define Wetland Loss and Gain in the Mississippi Delta

Join to apply for the Remote Sensing and Machine Learning to Better Define Wetland Loss and Gain in the Mississippi Delta role at ORAU

Organization
:
National Aeronautics and Space Administration (NASA)

Reference Code
: 0106-NPP-MAR
26-ARC-Earth Sci

How To Apply
:
All applications must be submitted in Zintellect. Please visit the NASA Postdoctoral Program website for application instructions and requirements:  A complete application to the NASA Postdoctoral Program includes:

• Research proposal
• Three letters of recommendation
• Official doctoral transcript documents

Final date to receive applications
: 3/1/2026 6:00:59 PM Eastern Time Zone

Description
:
As part of the State of Louisiana’s 50-year Master Plan for Coastal Restoration and Protection, the state is now on course to spend several billion dollars constructing sediment diversions in the Mississippi River down‑river of New Orleans as a primary tool for building and sustaining coastal wetlands in the Barataria and Breton Sound basins. Hyperspectral remote sensing efforts to date have been unable to adequately map vegetation community structure on basin‑scales, such as will be needed to assess how these coastal wetlands are responding to climate change stressors, sea‑level rise, and to planned coastal restoration activities.

Key science issues linked to extent and health of the Mississippi Delta wetlands and to the usefulness of these new sediment diversions include determining (a) the amount and character of new wetlands that will be gained during diversion operations (b) the newly evolving vegetation community structure and ecosystem functioning benefits. This Post‑doctoral Opportunity will offer the selected candidate a research project focused on three main themes in Mississippi Delta wetlands of Louisiana:

• Define and map changes in wetland‑water separation classes and water level (tidal) corrections in the Barataria and Breton Sound basins using hundreds of spectral bands from drone flights.
• Analysis of marsh vegetation community structure using field surveys and hyperspectral image data collected from drones.
• Detection of submerged aquatic vegetation (SAV) in imagery from subaerial marsh information to identify the extent of SAV.

This will aid in defining its role in the ecosystem in trapping sediment, leading to land gain in areas that are connected to a riverine sediment supply. Skills required include mastery of image processing applications, vegetation field survey techniques for mapping plant community composition, and machine learning for image classification products.

Field of Science
:
Earth Science

Advisors
:

• Christopher Potter - chris.potter

Applications with citizens from Designated Countries will not be accepted at this time, unless they are Legal Permanent Residents of the United States. A complete list of Designated Countries can be found at: https://(Use the "Apply for this Job" box below)..

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.

Eligibility Requirements
:

• Degree:
  Doctoral Degree.

Questions about this opportunity? Please email npp