Opportunity Information: Apply for G19AS00106
This grant opportunity, titled "Cooperative Ecosystem Studies Unit, Colorado Plateau CESU" (Funding Opportunity Number G19AS00106), is a US Geological Survey (USGS) Northern Rocky Mountain Science Center (NOROCK) effort to improve how snow albedo is measured and scaled across mountainous landscapes. The work centers on deploying an uninhabited aerial system (UAS) outfitted with radiation sensors to collect high-resolution observations of snow albedo, then using those observations to evaluate and compare satellite-based albedo estimates from MODIS and Landsat. The core scientific issue being targeted is the mismatch in scale between traditional ground measurements, which are typically point-based (for example, measurements collected at fixed weather stations), and satellite products that represent much larger pixels. By flying a sensor-equipped UAS, the project aims to fill the spatial data gap between these two worlds and make snow albedo validation more reliable, especially in complex terrain where albedo can vary sharply over short distances.
The project is designed to quantify "scale-dependent controls" on snow albedo, meaning it will examine what processes or landscape conditions drive albedo differences at different spatial scales. It will explicitly compare undisturbed montane snow conditions to post-fire environments, where burned vegetation and deposition of dark material can alter the reflectivity of the snowpack and accelerate melt. A key component is testing how well satellite products capture these patterns and whether biases appear when satellite pixels average over mixed land cover, variable slope and aspect, tree canopy, or burned patches. The outcome is intended to strengthen the link between detailed field observations and operational remote sensing products used in hydrology and snowpack modeling.
Field data collection is planned for two winter-spring seasons, with campaigns running from January through May in both 2020 and 2021. Work will be conducted across three headwater study areas in Montana, selected to represent contrasting conditions and management contexts. The first site, referred to as Namaste, is located on private land within the Yellowstone Club in southwest Montana. The other two sites are within Glacier National Park: one along the Going-to-the-Sun Road corridor, and another at Reynolds Creek. Reynolds Creek is highlighted because it experienced severe wildfire during the summer of 2015, making it an important post-fire case study for understanding how disturbances change snow reflectivity and how those changes propagate into water supply timing and magnitude.
Beyond the research questions, the opportunity places emphasis on practical, transferable methods. The project is expected to develop tools, techniques, and protocols that allow albedo to be captured efficiently over snow-covered terrain using UAS platforms. The intent is not only to produce a one-off dataset, but to generate a workflow that resource managers and decision makers can adopt for near-real-time monitoring, water resource forecasting, and improved interpretation of satellite products. In other words, the project is framed as both a scientific investigation and a capacity-building effort that can improve operational snow monitoring in mountain watersheds.
The funding mechanism is a discretionary cooperative agreement through the Department of the Interior, USGS, under CFDA number 15.808, within the Science and Technology and other Research and Development activity category. The opportunity anticipated a single award, with an award ceiling of $50,000. The posting was created on July 10, 2019, with an original closing date of July 26, 2019. Eligibility is listed as "Others" with additional clarification expected in the official eligibility text associated with the announcement.
Anticipated deliverables include spatially explicit geospatial products and publishable scientific results. Specifically, the project expects to produce digital surface models and gridded albedo datasets for each of the study sites, providing a detailed map-based record of snow reflectivity patterns over time. In addition, a peer-reviewed journal article is planned, focusing on how mountain snowpack albedo varies spatially and temporally and on the scaling problems that arise when fixed in situ measurements are used to validate coarser remote sensing products. Taken together, the datasets and methods are meant to improve the accuracy and usefulness of satellite albedo products in mountainous, and especially post-fire, landscapes where snowmelt dynamics are highly sensitive to changes in surface reflectance.Apply for G19AS00106
- The Department of the Interior, U. S. Geological Survey in the science and technology and other research and development sector is offering a public funding opportunity titled "Cooperative Ecosystem Studies Unit, Colorado Plateau CESU" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 15.808.
- This funding opportunity was created on Jul 10, 2019.
- Applicants must submit their applications by Jul 26, 2019. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- Each selected applicant is eligible to receive up to $50,000.00 in funding.
- The number of recipients for this funding is limited to 1 candidate(s).
- Eligible applicants include: Others (see text field entitled Additional Information on Eligibility for clarification).
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FAQs: Cooperative Ecosystem Studies Unit, Colorado Plateau CESU (G19AS00106)
What is the title of this grant opportunity?
The opportunity is titled "Cooperative Ecosystem Studies Unit, Colorado Plateau CESU."
What is the Funding Opportunity Number?
The Funding Opportunity Number is G19AS00106.
Which agency is offering this opportunity?
This is a US Geological Survey (USGS) opportunity led by the Northern Rocky Mountain Science Center (NOROCK) under the Department of the Interior.
What type of funding instrument is expected?
The funding mechanism is a discretionary cooperative agreement.
What is the CFDA number and activity category listed for this opportunity?
The opportunity is listed under CFDA 15.808 and falls within the Science and Technology and other Research and Development activity category.
How many awards were anticipated?
The opportunity anticipated a single award.
What is the maximum (ceiling) award amount?
The award ceiling is $50,000.
When was the opportunity posted and when did it close?
The posting was created on July 10, 2019, and the original closing date was July 26, 2019.
Who is eligible to apply?
Eligibility is listed as "Others," with additional clarification expected in the official eligibility text associated with the announcement.
What problem is the project trying to solve?
The project targets a core scaling problem in snow albedo monitoring: traditional ground measurements are typically point-based (for example, fixed weather stations), while satellite products represent much larger pixels. This mismatch can make satellite validation difficult, especially in complex mountain terrain where albedo can change over short distances.
What is snow albedo in the context of this project?
In this opportunity, snow albedo refers to the reflectivity of the snow surface. The work focuses on measuring albedo in detail across mountainous landscapes and improving how satellite-based albedo products represent that reflectivity.
What is the main scientific approach described?
The work centers on deploying an uninhabited aerial system (UAS) outfitted with radiation sensors to collect high-resolution observations of snow albedo. Those UAS observations are then used to evaluate and compare satellite-based albedo estimates.
Which satellite products are specifically being evaluated?
The opportunity specifically mentions evaluating and comparing albedo estimates from MODIS and Landsat.
Why use a UAS (drone) for this work?
The UAS is intended to fill the spatial data gap between point measurements on the ground and coarse satellite pixels. By collecting high-resolution, spatially continuous observations, the project aims to make snow albedo validation more reliable across variable terrain and land cover.
What are "scale-dependent controls" on snow albedo?
In this project, "scale-dependent controls" refers to processes or landscape conditions that drive albedo differences at different spatial scales. The study aims to quantify how these controls influence observed albedo patterns when viewed at fine scales (UAS) versus coarser scales (satellites).
How does the project address complex terrain and mixed landscapes?
The opportunity highlights potential satellite biases that can appear when pixels average across mixed land cover and terrain conditions, including variable slope and aspect, tree canopy, and burned patches. The UAS-based mapping is intended to reveal fine-scale variability that satellites may smooth over.
Does the project include post-fire snow conditions?
Yes. The project explicitly compares undisturbed montane snow conditions to post-fire environments, where burned vegetation and deposition of dark material can reduce reflectivity and accelerate melt.
Where will the field work take place?
Field work will be conducted across three headwater study areas in Montana. One site (Namaste) is on private land within the Yellowstone Club in southwest Montana. Two additional sites are within Glacier National Park: one along the Going-to-the-Sun Road corridor, and another at Reynolds Creek.
Why is Reynolds Creek an important study site?
Reynolds Creek experienced severe wildfire during the summer of 2015, making it a key post-fire case study for understanding how disturbance changes snow reflectivity and how those changes may affect water supply timing and magnitude.
What is the planned field data collection window?
Field data collection is planned for two winter-spring seasons, with campaigns running from January through May in both 2020 and 2021.
What are the expected outputs or deliverables?
Anticipated deliverables include spatially explicit geospatial products and publishable scientific results. The opportunity calls out digital surface models and gridded albedo datasets for each study site, plus a planned peer-reviewed journal article on spatial/temporal variability in mountain snowpack albedo and scaling issues in validating satellite products with fixed in situ measurements.
What geospatial datasets are specifically mentioned?
The opportunity specifically mentions producing digital surface models and gridded albedo datasets for each of the study sites.
Is a peer-reviewed publication expected?
Yes. A peer-reviewed journal article is planned, focusing on how mountain snowpack albedo varies spatially and temporally and on scaling problems that arise when fixed in situ measurements are used to validate coarser remote sensing products.
What is the intended practical value of the project beyond research?
The opportunity emphasizes developing practical, transferable tools, techniques, and protocols for capturing albedo efficiently over snow-covered terrain using UAS platforms. The goal is to create a workflow that can be adopted for near-real-time monitoring, water resource forecasting, and better interpretation of satellite products.
How will this work improve satellite-based snow monitoring?
By linking fine-scale UAS observations with coarser MODIS and Landsat products, the project aims to identify where satellite biases occur and improve the accuracy and usefulness of satellite albedo products in mountainous landscapes, including post-fire settings where snowmelt is sensitive to changes in reflectance.
What kinds of landscape factors are expected to influence satellite albedo bias in this study?
The opportunity points to factors such as mixed land cover within satellite pixels, variable slope and aspect, tree canopy effects, and burned patches that can cause a satellite pixel to represent an average that differs from conditions at specific ground locations.
What broader modeling or operational uses are mentioned for the results?
The opportunity notes that outputs are intended to strengthen connections between detailed field observations and operational remote sensing products used in hydrology and snowpack modeling, with an emphasis on near-real-time monitoring and water resource forecasting applications.
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