Carbon storage in vegetation represents an important reservoir within the global carbon cycle, and changes in carbon uptake by and storage within vegetation and their soils can have significant impact on the global carbon balance. Vegetation biomass density (Mg dry weight per hectare) is used to estimate the amount of carbon stored in vegetation and emitted to the atmosphere when ecosystems are disturbed (IPCC, 2006). Emissions from vegetation disturbance and land-use and land-cover change are considered the most uncertain component of the global carbon cycle. The uncertainty is attributed to large errors in the spatial distribution of vegetation biomass as well as discrepancies in estimates of land cover and land use change (Houghton et al., 2009). Apart from its scientific merit in understanding the global carbon cycle, accurate and precise quantification of emissions from land use change has also become a key issue for policy makers in light of recent developments relating to reducing emissions from deforestation and degradation (REDD) in developing nations as a climate mitigation strategy.
NASA’s future DESDynl mission will radically improve the current capability by providing direct measurements of biomass from active sensors (Lidar and SAR). The high precision and accuracy of biomass estimation from DESDynl will quantify carbon stock and changes, improve the geographic distribution of carbon sources and sinks, and reduce the uncertainty in global carbon cycle. However, before the launch of DESDynl, distribution of biomass and carbon storage produced from the existing remote sensing and in situ measurements will provide sub-optimum, but necessary information to develop national and international scale REDD policies and Monitoring, Reporting, and Verification (MRV) frameworks (Goetz et al., 2009; Gibbs et al., 2007) This pilot project is designed to address the urgent need for geospatially explicit, consistent carbon and biomass inventory information to inform national and international policy making by addressing two objectives:
1. To develop prototype data products of national and global biomass (and carbon storage/emissions) that can be assessed with respect to how they meet the nation’s needs for Monitoring, Reporting, and Verification (MRV) of carbon inventories.
2. To demonstrate our readiness to produce a consistent global biomass/carbon stock distribution using the existing in situ and satellite observations to meet the MRV requirements.
Contact Support to request an email list of project participants.
Project URL(s):
None provided.
Data Products:
Product Title: CMS Biomass Pilot Project team presentations
Description: This folder contains
1. County biomass mapping report
2. CMS research on Biomass mapping, ED modeling and error analysis presented at CCE, AGU & Silvilaser 2011
2. Documentation on Ordinary Least Square Regression results for Parker Tract, NC and Maryland.
3. Documentation on Biomass mapping and Change Detection at Garcia Tract.
Status: Preliminary
CMS Science Theme(s):
Keywords:
Spatial Extent:
Spatial Resolution:
Temporal Frequency:
Input Data Products:
Algorithm/Models Used:
Evaluation:
Intercomparison Efforts/Gaps:
Uncertainty Estimates:
Uncertainty Categories:
Application Areas:
Relevant Policies/Programs:
Potential Users: Scientists interested in how the Biomass Carbon Maps were generated
Product Title: CMS- Multi-scale forest biomass maps based on multiple remote sensing inputs
Description: To support both carbon science and carbon management, the CMS-Biomass pilot project is pursuing a multi-scale approach to generate the “best of kind” quantification of above-ground forest biomass for the US using a broad combination of NASA remote sensing, forest inventory, and ancillary data. Traditionally, above-ground forest biomass for the US has been quantified using the US Forest Service Forest Inventory and Analysis (FIA) data. FIA plots, based on a nominal 5km sample spacing, record structural attributes (including DBH and tree height), stand demographics (age, number of stems, growth, removals, and mortality), and composition. Plot-level biomass is calculated by applying species- or type-specific allometric relationships (e.g. Jenkins et al, 2003) to individual tree DBH and/or height measurements. This approach provides an accurate estimate of US forest biomass at broad spatial scales that will be used as the “Golden Standard” to be compared with the CMS-Biomass maps of forest carbon.
The reseach contribution described here was the result of an approach that leveraged multiple remote sensing inputs (e.g., MODIS, PALSAR, GLAS, Landsat) in combination with FIA plot data to map forest biomass across the US at resolutions of 100-500m. Maps were developed for the continental US, California and Maryland using the same approaches for all.
These maps are designed to benchmark the “best possible” estimation of biomass available from current measurements. ICESat GLAS waveforms were linked to FIA-measured biomass via the Lorey’s Height metric. These waveforms (converted to biomass) were then be used as training for statistical models of biomass, with input data consisting of geospatial inputs (e.g., MODIS, PALSAR, climate, topography, Landsat disturbance, Landsat LAI).
Data Citation:
Cite the continental US data set as follows:
CMS US Forest Biomass Map:
Team Members:
NASA/JPL: Saatchi S, Yifan Yu, Fore, Alex, Nuemann, M., Chapman, B., Nguyen,
NASA/ARC: Nemani, R., Ganguly, S., Zhang, G., Votava, P.,
NASA/GSFC: Masek, J., Tucker, C., Hall, F., Nelson, R., Cook, B.
USDA/FS: Birdsey, R., Healey, S., Johnson, K.,
UMD: Dubayah, R.
CSU: Lefsky, M.
Data Characteristics:
Data Layers:
AGB: Aboveground Biomass Density (Mg/ha)
Lorey H: Basal Area Weighted Height (m)
NLCD: Filtered National Land Cover Data
Percent Uncertainty: Biomass Error Map (%)
Projection: Geographic Lat/Lon
Geodetic Datum: WGS-84
Data Type: 32 bits Floating point
Compression Type: Geotiff
Data Application and Derivation:
Data should be used for validation and comparison with other national and local maps. This is a preliminary product with limited validation and uncertainty analysis. We will be using the FIA data to validate and improve the accuracy and the spatial resolution of the data to 3 arcsec (~90 m).
Quality Assessment:
Will be performed by the CMS team to guide the final production of the map.
Cite the California data set as follows:
CMS California Biomass Maps
Team Members:
NASA/JPL: Saatchi S, Yifan Yu, Fore, Alex, Nuemann, M., Chapman, B., Nguyen,
NASA/ARC: Nemani, R., Ganguly, S., Zhang, G., Votava, P.,
NASA/GSFC: Masek, J., Tucker, C., Hall, F., Nelson, R., Cook, B.
USDA/FS: Birdsey, R., Healey, S., Johnson, K.,
UMD: Dubayah, R.
CSU: Lefsky, M.
Data Characteristics:
Data Layers:
AGB: Aboveground Biomass Density (Mg/ha)
Lorey H: Basal Area Weighted Height (m)
NLCD: Filtered National Land Cover Data
Percent Uncertainty: Biomass Error Map (%)
Projection: Geographic Lat/Lon
Geodetic Datum: WGS-84
Data Type: 32 bits Floating point
Compression Type: Geotiff
Product Title: CMS: LiDAR-derived Estimates of Aboveground Biomass at Four Forested Sites, USA
Start Date: 01/2011End Date: 12/2011 (2011)
Description: These data consist of high-resolution maps of aboveground biomass at four forested sites in the US: Garcia River Tract in California, Anne Arundel and Howard Counties in Maryland, Parker Tract in North Carolina, and Hubbard Brook Experimental Forest in New Hampshire. Biomass maps were generated using a combination of field data (forest inventory and Lidar) and modeling approaches. Estimates of uncertainty are also provided for the Maryland site using two different modeling methodologies.These data provide estimates of aboveground biomass for the nominal year of 2011 at 20-50 meter resolution in units of megagrams of carbon per hectare (or acre for the Garcia Tract site).The data are presented as a series of 11 GeoTIFF (.tif) files.
Status: Archived
CMS Science Theme(s): Land Biomass
Keywords: Carbon Stocks (; terrestrial)
Spatial Extent: Site Locations: California: Garcia River; Maryland: Anne Arundel and Howard counties; North Carolina: Parker Tract; New Hampshire: Hubbard Brook
Spatial Resolution: 20-50 m
Temporal Frequency: Once
Input Data Products: Small-footprint LiDAR, ALOS/PALSAR radar data and Landsat time
Algorithm/Models Used: Ordinary least squares regression (OLS), and Bayesian model averaging (BMA)
Evaluation:
Intercomparison Efforts/Gaps:
Uncertainty Estimates:
Uncertainty Categories: Uncertainty in the Maryland biomass estimates derives from multiple factors, including, but not limited to: field sampling errors and uneven spatial distribution, uncertainty in remote-sensing data products, choice of grid cell size, and model error.
Archived Data Citation: Cook, B., A. Swatantran, L. Duncanson, A. Armstrong, N. Pinto, R. Nelson. 2014. CMS: LiDAR-derived Estimates of Aboveground Biomass at Four Forested Sites, USA. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1257
Bounding Coordinates:
West Longitude:
-123.61000
East Longitude:
-76.36000
North Latitude:
43.96000
South Latitude:
35.78000
Product Title: CMS: LiDAR-derived Estimates of Aboveground Biomass at Four Forested Sites, USA
Start Date: 01/2011End Date: 12/2011
Description: These data consist of high-resolution maps of aboveground biomass at four forested sites in the US: Garcia River Tract in California, Anne Arundel and Howard Counties in Maryland, Parker Tract in North Carolina, and Hubbard Brook Experimental Forest in New Hampshire. Biomass maps were generated using a combination of field data (forest inventory and Lidar) and modeling approaches. Estimates of uncertainty are also provided for the Maryland site using two different modeling methodologies.These data provide estimates of aboveground biomass for the nominal year of 2011 at 20-50 meter resolution in units of megagrams of carbon per hectare (or acre for the Garcia Tract site).The data are presented as a series of 11 GeoTIFF (.tif) files.
Status: Archived
CMS Science Theme(s):
Keywords:
Spatial Extent:
Spatial Resolution:
Temporal Frequency:
Input Data Products:
Algorithm/Models Used:
Evaluation:
Intercomparison Efforts/Gaps:
Uncertainty Estimates:
Uncertainty Categories:
Application Areas:
Relevant Policies/Programs:
Potential Users: Scientists interested in terrestrial carbon budgets
Archived Data Citation: Cook, B., A. Swatantran, L. Duncanson, A. Armstrong, N. Pinto, R. Nelson. 2014. CMS: LiDAR-derived Estimates of Aboveground Biomass at Four Forested Sites, USA. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1257
Bounding Coordinates:
West Longitude:
-123.61000
East Longitude:
-76.36000
North Latitude:
43.96000
South Latitude:
35.78000
Publications:
Hall, F. G., Hilker, T., Coops, N. C. 2012. Data assimilation of photosynthetic light-use efficiency using multi-angular satellite data: I. Model formulation. Remote Sensing of Environment. 121, 301-308. DOI: 10.1016/j.rse.2012.02.007
Hilker, T., Hall, F. G., Tucker, C. J., Coops, N. C., Black, T. A., Nichol, C. J., Sellers, P. J., Barr, A., Hollinger, D. Y., Munger, J. W. 2012. Data assimilation of photosynthetic light-use efficiency using multi-angular satellite data: II Model implementation and validation. Remote Sensing of Environment. 121, 287-300. DOI: 10.1016/j.rse.2012.02.008
Archived Data Citations:
Cook, B., A. Swatantran, L. Duncanson, A. Armstrong, N. Pinto, R. Nelson. 2014. CMS: LiDAR-derived Estimates of Aboveground Biomass at Four Forested Sites, USA. ORNL DAAC, Oak Ridge, Tennessee, USA. DOI: 10.3334/ORNLDAAC/1257
2015 NASA Carbon Cycle & Ecosystems Joint Science Workshop Poster(s)
G-LiHT: Multi-Sensor Airborne Image Data from Denali to the Yucatan
-- (Bruce Cook, Lawrence A Corp, Douglas Morton, Joel McCorkel)
[abstract]
[poster]
Terrestrial Ecosystem, Carbon Cycle, Landuse Landcover Change, Biodiversity (TECLUB) Measurement Requirements for the Next Decade
-- (Forrest G Hall, Scott J Goetz)
[abstract]
Mapping Stand Ages of Primary Forests in Northern Hemisphere Using Remote Sensing Data
-- (Gong Zhang, Weile Wang, Ensheng Weng, Sangram Ganguly, Sassan Saatchi, Ramakrishna R. Nemani)
[abstract]
Mapping Stand Age of global primary forests Using Remote Sensing Data -- (Gong Zhang, Weile Wang, Sangram Ganguly, Ensheng Weng, Sassan Saatchi, Yifan Yu, Ramakrishna R. Nemani) [abstract]
Estimation of Aboveground Biomass at a High Spatial Resolution Using an Extensive Data Record of Satellite Derived Metrics: A Case Study with California -- (Sangram Ganguly, Gong Zhang, Ramakrishna R. Nemani, Sassan Saatchi, Cristina Milesi, Michael White, Yifan Yu, Alexander Fore, Weile Wang, Petr Votava, Ranga B. Myneni) [abstract]