NA24OARX021G0018
Project Grant
Overview
Grant Description
Purpose: During Phase I, we will demonstrate the feasibility of a proposed platform for automated characterization of carbon assets by developing foundational machine learning tools.
This will include identification and validation of useable datasets for areas with known biomass density, and development of machine learning models linking multi-modal geospatial data, both on a pixel and regional level, with mappings of above-ground biomass.
We will apply these mappings to validated historical data in order to develop predictive models based on long-short term neural networks, capable of forecasting biomass accumulation over time for vegetated areas.
These models will be applied to existing predictions of land-use change in a demonstration region, identifying high-priority targets for conservation efforts and planning intervention.
Combining historical data and accurate and automated measurement of carbon sequestration capacity with predictions of land use changes will serve to provide invaluable insight into priority areas that represent a high-risk of sequestered carbon loss on a regional scale.
Additionally, such tools will enable rapid identification of the carbon cost of local actions impacting land use conversion, allowing examination of these impacts in consideration of triple bottom line.
We propose to develop a globally deployable region-focused platform to evaluate the current carbon storage and future sequestration potential of vegetated areas with enhanced spatial and temporal resolution.
The platform will accurately predict the impact of forecasted land-use changes and support planning, mitigation, and conservation efforts.
Policymakers and land-use management entities may employ the tool to directly evaluate the impact of planning decisions in terms of region-wide carbon goals.
Commercial entities which own carbon assets, or which conduct activities impacting land use, may use such characterizations of carbon impact to meet environmental and climate regulations, or participate in carbon offset or carbon credit trading programs.
Finally, as carbon sequestering assets become an insurable commodity, evaluation of their value and any potential risks will provide insurance providers direct value in managing portfolios.
This will include identification and validation of useable datasets for areas with known biomass density, and development of machine learning models linking multi-modal geospatial data, both on a pixel and regional level, with mappings of above-ground biomass.
We will apply these mappings to validated historical data in order to develop predictive models based on long-short term neural networks, capable of forecasting biomass accumulation over time for vegetated areas.
These models will be applied to existing predictions of land-use change in a demonstration region, identifying high-priority targets for conservation efforts and planning intervention.
Combining historical data and accurate and automated measurement of carbon sequestration capacity with predictions of land use changes will serve to provide invaluable insight into priority areas that represent a high-risk of sequestered carbon loss on a regional scale.
Additionally, such tools will enable rapid identification of the carbon cost of local actions impacting land use conversion, allowing examination of these impacts in consideration of triple bottom line.
We propose to develop a globally deployable region-focused platform to evaluate the current carbon storage and future sequestration potential of vegetated areas with enhanced spatial and temporal resolution.
The platform will accurately predict the impact of forecasted land-use changes and support planning, mitigation, and conservation efforts.
Policymakers and land-use management entities may employ the tool to directly evaluate the impact of planning decisions in terms of region-wide carbon goals.
Commercial entities which own carbon assets, or which conduct activities impacting land use, may use such characterizations of carbon impact to meet environmental and climate regulations, or participate in carbon offset or carbon credit trading programs.
Finally, as carbon sequestering assets become an insurable commodity, evaluation of their value and any potential risks will provide insurance providers direct value in managing portfolios.
Awardee
Funding Goals
18 CLIMATE ADAPTATION AND MITIGATION 19 WEATHER-READY NATION 20 HEALTHY OCEANS 21 RESILIENT COASTAL COMMUNITIES AND ECONOMIES
Grant Program (CFDA)
Awarding / Funding Agency
Place of Performance
Seattle,
Washington
981037943
United States
Geographic Scope
Single Zip Code
Related Opportunity
Synthetik Applied Technologies was awarded
Project Grant NA24OARX021G0018
worth $174,689
from National Oceanic and Atmospheric Administration in August 2024 with work to be completed primarily in Seattle Washington United States.
The grant
has a duration of 5 months and
was awarded through assistance program 11.021 NOAA Small Business Innovation Research (SBIR) Program.
The Project Grant was awarded through grant opportunity NOAA SBIR FY 2024 Phase I.
SBIR Details
Research Type
SBIR Phase I
Title
DeepCarbon: Machine learning-based software tool for characterization of carbon assets and impacts of land-use changes for informed planning and decision making.
Abstract
During Phase I, We will demonstrate the feasibility of a proposed platform for automated characterization of carbon assets by developing foundational machine learning tools. This will include identification and validation of useable datasets for areas with known biomass density, and development of machine learning models linking multi-modal geospatial data, both on a pixel and regional level, with mappings of above-ground biomass. We will apply these mappings to validated historical data in order to develop predictive models based on long-short term neural networks, capable of forecasting biomass accumulation over time for vegetated areas. These models will be applied to existing predictions of land-use change in a demonstration region, identifying high-priority targets for conservation efforts and planning intervention. Combining historical data and accurate and automated measurement of carbon sequestration capacity with predictions of land use changes will serve to provide invaluable insight into priority areas that represent a high-risk of sequestered carbon loss on a regional scale. Additionally, such tools will enable rapid identification of the carbon cost of local actions impacting land use conversion, allowing examination of these impacts in consideration of triple bottom line.
Topic Code
9.6
Solicitation Number
NOAA-OAR-TPO-2024-2008184
Status
(Complete)
Last Modified 3/5/25
Period of Performance
8/1/24
Start Date
1/31/25
End Date
Funding Split
$174.7K
Federal Obligation
$0.0
Non-Federal Obligation
$174.7K
Total Obligated
Activity Timeline
Transaction History
Modifications to NA24OARX021G0018
Additional Detail
Award ID FAIN
NA24OARX021G0018
SAI Number
NA24OARX021G0018-003
Award ID URI
None
Awardee Classifications
Small Business
Awarding Office
1305N2 DEPT OF COMMERCE NOAA
Funding Office
1333BR OFC OF PROG.PLANNING&INTEGRATION
Awardee UEI
FKY5GG92KBN8
Awardee CAGE
7VSQ9
Performance District
WA-07
Senators
Maria Cantwell
Patty Murray
Patty Murray
Modified: 3/5/25