| NSF Org: |
RISE Integrative and Collaborative Education and Research (ICER) |
| Recipient: |
|
| Initial Amendment Date: | August 29, 2024 |
| Latest Amendment Date: | August 29, 2024 |
| Award Number: | 2425811 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Scott M. White
scwhite@nsf.gov (703)292-8369 RISE Integrative and Collaborative Education and Research (ICER) GEO Directorate for Geosciences |
| Start Date: | January 1, 2025 |
| End Date: | December 31, 2027 (Estimated) |
| Total Intended Award Amount: | $529,636.00 |
| Total Awarded Amount to Date: | $529,636.00 |
| Funds Obligated to Date: |
|
| History of Investigator: |
|
| Recipient Sponsored Research Office: |
104 E UNIVERSITY AVE LAFAYETTE LA US 70503-2014 (337)482-5811 |
| Sponsor Congressional District: |
|
| Primary Place of Performance: |
104 E UNIVERSITY CIR 3RD FL LAFAYETTE LA US 70503-2014 |
| Primary Place of
Performance Congressional District: |
|
| Unique Entity Identifier (UEI): |
|
| Parent UEI: |
|
| NSF Program(s): | GEO CI - GEO Cyberinfrastrctre |
| Primary Program Source: |
|
| Program Reference Code(s): |
|
| Program Element Code(s): |
|
| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.050 |
ABSTRACT
Phytoplankton help regulate climate and provide many important ecosystem services. This project will investigate changes to phytoplankton community composition using space satellite data. Hyperspectral images from space satellites will provide a large-scale view of coasts and estuaries. Phytoplankton absorb and scatter light in distinct ways, which hyperspectral images can capture. Analysis of these images by using artificial intelligence with hyperspectral remote sensing, will reveal many details about the composition of the phytoplankton community. The project will develop an open, large-scale database of phytoplankton observations. The first foundation models for plankton community structure will be built. New software toolkits in artificial intelligence will be developed and shared with the research community. The findings will be integrated into instructional materials. Undergraduate and graduate students, particularly those from under-represented groups, will be engaged in the research.
This project will explore the basic mechanisms and impacts of climatic factors on phytoplankton community composition in order to gain a better understanding of food web structure, higher trophic level production, and biological shifts at regional-to-global levels. This project will address longstanding challenges for ocean color remote sensing applications, through characterizing phytoplankton communities in coastal waters by developing artificial intelligence methods to handle hyperspectral remote sensing data. This project will be organized around three main goals. First, address scarcity of in situ data for estuarine-coastal phytoplankton community compositions by constructing a large-scale database for phytoplankton observations, enabling global data sharing and contribution. Second, build a large foundation model tailored to phytoplankton community composition and artificial intelligence-based methods for predicting change in the phytoplankton community. New software tools for hyperspectral imagery using artificial intelligence will be developed and shared with the plankton and coastal oceanographic research community. Third, use artificial intelligence to address spatial-spectral-temporal variations in phytoplankton community composition.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Please report errors in award information by writing to: awardsearch@nsf.gov.
