Improving high resolution (m scale) mapping of snow covered areas in complex forested terrain is crucial to understanding changes and related responses of species and water systems to climate…
Ended Grant For Research Awarded Last action
- Contract number
- 80NSSC21K1151
- Awardee
-
University Of Washington UEI HD1WMN6945W6 Part of University Of Washington
- Awarding agency
-
Shared Services Center
National Aeronautics and Space Administration
- Obligated
- $232,263
Award facts
- Contract value 100% obligated
- $232,262.92 Current and potential, with all options
- Completion date 100% complete
- Current and potential, with all options
- NAICS code
- Not on record
- PSC
- AJ11 General Science And Technology R&D Services; General Science And Technology; Basic Research
- Set-aside
- No set-aside
- Competition
- Full and Open Competition Basic Research
- Pricing
- Other
- Place of performance
- Seattle, WA 98195, USA
Award details come from SAM.gov/FPDS.
Transactions
| Action | Obligated | Date |
|---|---|---|
| Funding Only Action | -$25 | |
| Other Administrative Action | $0 | |
| Funding Only Action | $100,000 | |
| Original award | $132,288 |
About this award
This is a $232,262.92 grant awarded by the National Aeronautics and Space Administration (NASA) Shared Services Center to the University of Washington to improve high-resolution mapping of snow cover in complex forested terrain using Planet Labs satellite imagery. The University of Washington will couple ground and airborne snow observations with Planet imagery in mountain systems in Washington, California, and Colorado. This work aims to enhance understanding of climate change impacts on species and water systems. The grant does not have a set-aside designation. The availability of high-resolution snow maps can improve water, forest, and ecosystem management, and inform corrections for coarser resolution snow products and albedo estimates across forested regions.
Description
Improving high resolution (m scale) mapping of snow covered areas in complex forested terrain is crucial to understanding changes and related responses of species and water systems to climate change. Planet labs, INC. (planet) is a promising new source of commercial high-resolution imagery that can be used in environmental science, as IT has both high spatial (0.7-3.0 m) and temporal (1-2 day) resolution. However, its immediate utility with respect to inferring snow cover is more restrictive due to the limitations in the number and bandwidth of the near-infrared (NIR) band, which makes distinguishing snow pixels from other land cover pixels challenging using a traditional radiometric index (such as the normalized difference snow index, ndsi), and therefore requires an alternative approach. We will extend a machine learning framework based on convolutional neural networks to improve snow cover mapping in complex terrain and forested regions in forest gaps and between sparse trees. We will couple ground and airborne-derived snow observations with planet imagery in different mountain systems in washington, california, and colorado, USA. These sites are ideal candidates for our analysis as they differ in climate and topographic features and are sites where ground and airborne snow observations at high resolution (3M) have been collected by the NASA airborne snow observatory (ASO) and snowex missions. While basic workflows will be developed using planet data, this proposal will be considering data from other smallsat providers. The availability of high resolution snow maps can improve water, forest and ecosystems management, inform corrections for coarser resolution snow products and albedo estimates across forested regions.
Quick answers
Who was awarded contract 80NSSC21K1151?
80NSSC21K1151 was awarded to University Of Washington by Shared Services Center (National Aeronautics and Space Administration) on July 1, 2021.
How much is contract 80NSSC21K1151 worth?
The government has obligated $232,263 on 80NSSC21K1151.
Was contract 80NSSC21K1151 competed?
The extent of competition for 80NSSC21K1151 was full and open competition.
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