ORNL DAAC Data Release - CMS Aboveground Biomass & Geospatial Maps
Posted: Thu Jun 11, 2026 1:30 pm America/New_York
The Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) released new datasets for the NASA Carbon Monitoring System (CMS) program that focus on carbon stocks in soils and aboveground biomass.
The NASA Carbon Monitoring System (CMS) program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. CMS uses NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.
Geospatial Data for Modeling Soil Carbon Stocks across Pacific Northwest Watersheds
This dataset provides predicted soil organic carbon (SOC) for 2021-2022 (nominal) as well as the predictor data for spatial models in four watersheds of the Pacific Northwest (PNW). These data support the study of wetland carbon storage within this landscape. Field sample collection for soil carbon stocks at 114 locations in 2021-2022 provide observations for prediction modeling. The raster and vector predictor layers are sourced from lidar and satellite imagery, which span dates from 2012-2022. The four study watersheds include the Heen Latinee Experimental Forest (HLEF) located in southeast Alaska near Juneau and three watersheds in Washington state: the Hoh River Watershed (HRW) located on the west coast of the Olympic Peninsula, the Mashel River Watershed (MRW) located on the western side of the Cascade Mountain Range near Tahoma (Mt. Rainier), and the Colville Watershed (CVW) located in northeastern Washington. The Wetland Intrinsic Potential (WIP) tool was implemented for each study watershed to model the gridded land surface as a continuous probability of wetland presence; each grid pixel containing a value from 0-100%. Geospatial datasets related to vegetation, climate, lithology and geology, and topography were gathered to determine predictors for SOC stocks. The data are provided in comma separated values (CSV), GeoTIFF, and GeoPackage formats.
Aboveground Biomass Maps for Howland Research Forest, Maine, 2012-2025
This dataset provides aboveground biomass (AGB) maps at 10-m spatial resolution for the Howland Research Forest in central Maine for the years 2012, 2015, 2017, 2021, 2023, and 2025. AGB was estimated from airborne lidar data calibrated from field surveys of vegetation structure. Forest inventory measurements were collected from 38 plots during the summers of 2024 and 2025. Airborne LiDAR data were obtained from multiple sources and acquisition periods. The USGS 3D Elevation Program 3DEP data were acquired in 2015 and 2023 under leaf-off conditions. The NASA G-LiHT project collected data in 2012, 2017, and 2021 under leaf-on conditions. An additional leaf-on lidar survey was conducted on 13 August 2025 using the Phoenix RANGER-U580 system (Riegl VQ-580II-S laser scanner). Area-based Random Forest models were calibrated separately for leaf-off conditions using USGS 3DEP 2023 data and for leaf-on conditions using Phoenix 2025 data. Each calibrated model was applied to LiDAR datasets acquired under the corresponding seasonal conditions to produce wall-to-wall AGB maps for each acquisition year. The data are provided in cloud optimized GeoTIFF format; one for each year.
West African Footprint-Level GEDI Aboveground Biomass Estimates
This dataset holds revised NASA's Global Ecosystem Dynamics Investigation (GEDI) footprint-level Aboveground Biomass Density estimates for West Africa based on training data only from the region. New field data and uninhabited aerial vehicle (UAV) lidar data were collected across a latitudinal gradient in Ghana and used to produce new GEDI West African biomass models, which were applied to on-orbit quality-filtered GEDI data. GEDI mission has been collecting 3-D forest structure data from the International Space Station (ISS) since 2019 and provides global forest aboveground biomass products. One of GEDI's primary science objectives is to produce accurate forest structure and biomass products that are useful for policy applications, including national reporting. In theory, GEDI biomass data are very well suited to fill gaps in National Forest Inventories (NFIs), but only if they are accurate and regionally validated. No West African reference data were included in the generation of GEDI's global biomass products. These refined products should be more accurate in the region and suitable for national reporting and other desired activities in West African countries. The data are provided in comma separated values (CSV) format.
More information on these datasets and others like it can be found on the The NASA Carbon Monitoring System (CMS) program landing page..
Citations:
Stewart, A., Halabisky, M., D'amore, D. V., Spinola, D., Babcock, C., Moskal, L. M., & Butman, D. (2026). Geospatial Data for Modeling Soil Carbon Stocks across Pacific Northwest Watersheds (Version 1). ORNL Distributed Active Archive Center. https://doi.org/10.3334/ORNLDAAC/2449
Wei, X., Hayes, D. J., Sandilands, D., Owen, A., Zhao, J., Ma, L., & Weiskittel, A. (2026). Aboveground Biomass Maps for Howland Research Forest, Maine, 2012-2025 (Version 2). ORNL Distributed Active Archive Center. https://doi.org/10.3334/ORNLDAAC/2490
Duncanson, L., Leitold, V., Minor, D., Adu-Bredu, S., Armston, J., Dannunzio, R., Gamarra, J. G. P., Gutierrez, J. A., Hunka, N., Kellner, J. R., Ruiz Villar, M., Tavani, R., Duah-Gyamfi, A., Kusi, K. K., & Valbuena, R. (2026). West African Footprint-Level GEDI Aboveground Biomass Estimates (Version 1). ORNL Distributed Active Archive Center. https://doi.org/10.3334/ORNLDAAC/2475
The NASA Carbon Monitoring System (CMS) program is designed to make significant contributions in characterizing, quantifying, understanding, and predicting the evolution of global carbon sources and sinks through improved monitoring of carbon stocks and fluxes. CMS uses NASA satellite observations and modeling/analysis capabilities to establish the accuracy, quantitative uncertainties, and utility of products for supporting national and international policy, regulatory, and management activities. CMS data products are designed to inform near-term policy development and planning.
Geospatial Data for Modeling Soil Carbon Stocks across Pacific Northwest Watersheds
This dataset provides predicted soil organic carbon (SOC) for 2021-2022 (nominal) as well as the predictor data for spatial models in four watersheds of the Pacific Northwest (PNW). These data support the study of wetland carbon storage within this landscape. Field sample collection for soil carbon stocks at 114 locations in 2021-2022 provide observations for prediction modeling. The raster and vector predictor layers are sourced from lidar and satellite imagery, which span dates from 2012-2022. The four study watersheds include the Heen Latinee Experimental Forest (HLEF) located in southeast Alaska near Juneau and three watersheds in Washington state: the Hoh River Watershed (HRW) located on the west coast of the Olympic Peninsula, the Mashel River Watershed (MRW) located on the western side of the Cascade Mountain Range near Tahoma (Mt. Rainier), and the Colville Watershed (CVW) located in northeastern Washington. The Wetland Intrinsic Potential (WIP) tool was implemented for each study watershed to model the gridded land surface as a continuous probability of wetland presence; each grid pixel containing a value from 0-100%. Geospatial datasets related to vegetation, climate, lithology and geology, and topography were gathered to determine predictors for SOC stocks. The data are provided in comma separated values (CSV), GeoTIFF, and GeoPackage formats.
Aboveground Biomass Maps for Howland Research Forest, Maine, 2012-2025
This dataset provides aboveground biomass (AGB) maps at 10-m spatial resolution for the Howland Research Forest in central Maine for the years 2012, 2015, 2017, 2021, 2023, and 2025. AGB was estimated from airborne lidar data calibrated from field surveys of vegetation structure. Forest inventory measurements were collected from 38 plots during the summers of 2024 and 2025. Airborne LiDAR data were obtained from multiple sources and acquisition periods. The USGS 3D Elevation Program 3DEP data were acquired in 2015 and 2023 under leaf-off conditions. The NASA G-LiHT project collected data in 2012, 2017, and 2021 under leaf-on conditions. An additional leaf-on lidar survey was conducted on 13 August 2025 using the Phoenix RANGER-U580 system (Riegl VQ-580II-S laser scanner). Area-based Random Forest models were calibrated separately for leaf-off conditions using USGS 3DEP 2023 data and for leaf-on conditions using Phoenix 2025 data. Each calibrated model was applied to LiDAR datasets acquired under the corresponding seasonal conditions to produce wall-to-wall AGB maps for each acquisition year. The data are provided in cloud optimized GeoTIFF format; one for each year.
West African Footprint-Level GEDI Aboveground Biomass Estimates
This dataset holds revised NASA's Global Ecosystem Dynamics Investigation (GEDI) footprint-level Aboveground Biomass Density estimates for West Africa based on training data only from the region. New field data and uninhabited aerial vehicle (UAV) lidar data were collected across a latitudinal gradient in Ghana and used to produce new GEDI West African biomass models, which were applied to on-orbit quality-filtered GEDI data. GEDI mission has been collecting 3-D forest structure data from the International Space Station (ISS) since 2019 and provides global forest aboveground biomass products. One of GEDI's primary science objectives is to produce accurate forest structure and biomass products that are useful for policy applications, including national reporting. In theory, GEDI biomass data are very well suited to fill gaps in National Forest Inventories (NFIs), but only if they are accurate and regionally validated. No West African reference data were included in the generation of GEDI's global biomass products. These refined products should be more accurate in the region and suitable for national reporting and other desired activities in West African countries. The data are provided in comma separated values (CSV) format.
More information on these datasets and others like it can be found on the The NASA Carbon Monitoring System (CMS) program landing page..
Citations:
Stewart, A., Halabisky, M., D'amore, D. V., Spinola, D., Babcock, C., Moskal, L. M., & Butman, D. (2026). Geospatial Data for Modeling Soil Carbon Stocks across Pacific Northwest Watersheds (Version 1). ORNL Distributed Active Archive Center. https://doi.org/10.3334/ORNLDAAC/2449
Wei, X., Hayes, D. J., Sandilands, D., Owen, A., Zhao, J., Ma, L., & Weiskittel, A. (2026). Aboveground Biomass Maps for Howland Research Forest, Maine, 2012-2025 (Version 2). ORNL Distributed Active Archive Center. https://doi.org/10.3334/ORNLDAAC/2490
Duncanson, L., Leitold, V., Minor, D., Adu-Bredu, S., Armston, J., Dannunzio, R., Gamarra, J. G. P., Gutierrez, J. A., Hunka, N., Kellner, J. R., Ruiz Villar, M., Tavani, R., Duah-Gyamfi, A., Kusi, K. K., & Valbuena, R. (2026). West African Footprint-Level GEDI Aboveground Biomass Estimates (Version 1). ORNL Distributed Active Archive Center. https://doi.org/10.3334/ORNLDAAC/2475