OverviewThis is a global, wall-to-wall map of aboveground biomass (AGB) at approximately 30-meter resolution. This data product expands on the methodology presented in Baccini et al. (2012) to generate a global map of aboveground live woody biomass (AGB) density (megagrams biomass per ha) at 0.00025-degree (approximately 30-meter) resolution for the year 2000. Aboveground biomass was estimated using a multi-step process of calculating AGB at more than seven hundred thousand points with LiDAR with regional allometric equations, then using those to train a wall-to-wall model based on Landsat imagery. Pixels without tree canopy were assigned a biomass density of 0 Mg/ha. Additional information on the creation of this map can be found in Harris et al. 2021.Aboveground biomass is available for download in two different units: 1) megagrams AGB per hectare, and 2) megagrams AGB per pixel. The first is appropriate for visualizing (mapping) AGB and estimating average AGB density in an area of interest because it represents the density of AGB per hectare. The second is appropriate for calculating the total aboveground biomass stock in an area of interest because the values of the pixels in the AOI can be summed to obtain the total AGB stock in that area. The values in the latter were calculated by adjusting the AGB per hectare by the size of each pixel, which varies by latitude

This dataset provides temporally consistent and harmonized global maps of aboveground and belowground biomass carbon density for the year 2010 at a 300-m spatial resolution. The aboveground biomass map integrates land-cover specific, remotely sensed maps of woody, grassland, cropland, and tundra biomass. Input maps were amassed from the published literature and, where necessary, updated to cover the focal extent or time period. The belowground biomass map similarly integrates matching maps derived from each aboveground biomass map and land-cover specific empirical models. Aboveground and belowground maps were then integrated separately using ancillary maps of percent tree cover and landcover and a rule-based decision tree. Maps reporting the accumulated uncertainty of pixel-level estimates are also provided. Provider's note: The UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) carbon biomass dataset represents conditions between 1982 and 2010 depending on land cover type. The relative patterns of carbon stocks are well represented with this dataset. The NASA/ORNL carbon biomass dataset represents biomass conditions for 2010, with uncertainty estimates at the pixel-level. Additional biomass of non-dominant land cover types are represented within each pixel. For more detailed information, please refer to the papers describing each dataset: WCMC (Soto-Navarro et al. 2020) and NASA/ORNL (Spawn et al. 2020).

DATA OVERVIEW

Mapped attributes are

• above ground biomass, AGB;
• downed wood biomass, i.e., the sum of coarse and fine woody debris, DWB;
• canopy bulk density, CBD;
• canopy height,CH;
• canopy base height, CBH;
• and canopy fuel load, CFL.

Models were fit using auxiliary information that included lidar data from 20 acquisitions in Oregon and climate data. Measurements in plots of the Forest Inventory and Analysis program (FIA) were used to obtain plot-level ground observations for predictive modeling. Tree and transect measurements in FIA plots were respectively used to obtain plot-level values of AGB and DWB. To obtain plot-level values of CBD, CH, CBH and CFL, tree measurements in FIA plots were processed with FuelCalc. Plot level auxiliary variables were obtained intersecting the axiliary information layers with the FIA plots. Predictive models were random forest models in which a parametric component was added to model the error variance. The error variance was modeled as a power function of the predictive value and was used to produce uncertainty maps. A different model was fit for each variable and the resulting models were used to obtain maps of synthetic predictions for all areas covered by the 20 lidar acquisitions. The modeled error variance was used to generate uncertainty maps for the predictions of each response variable. Model accuracy was assessed globally (for the entire dataset) and separately for each one of the 20 lidar acquisitions included in the dataset.

Results from the accuracy assessment can be found in Appendix A and Appendix B of Mauro et al. (2021).

Each variable has two associated maps. These maps are named using the following convention where VARIABLE is the acronym for each variable (AGB, DWB, CBD, CH, CBH or CFL):

Predictions of forest attributes:

VARIABLE.tif

Standard deviation of modeled errors:

SD_VARIABLE.tif

###There are two additional rasters. The first one, year.tif is necessary to obtain the reference year for each lidar acquisition. The second one, forest_mask.tif provides a forest vs non-forest mask. Forested areas are coded as 1s and non-forested areas with no-datas. This mask is a resampled subset of the PALSAR JAXA 2014 ‘New global 25m-resolution PALSAR mosaic and forest/non-forest map (2007-2010) - version 1’ from the Japan Aerospace Exploration Agency Earth Observation Research Center (www.eorc.jaxa.jp/ALOS/en/palsar_fnf/fnf_index.htm). Its reference year is 2009. Models to predict forest attributes were created using ground observations in forested areas. For many applications it is advisable to use the provided mask to excluded non-forested areas from analyses. This can be done, for example, multiplying the desired raster by the forest mask. Exceptions to this may occur in relatively open forested lands where the mask eliminates areas that actually sustain forest. In those areas, the use of an add-hoc forest mask might be more appropriate. ### Reference year: year.tif ### Forest mask: forest_mask.tif ###

UNITS:

For a given variable, both predictions and standard deviation of model errors have the same units. These units are:

• Variable (Abreviation): Units

• Above ground biomass (AGB): Mg/ha

• Downed wood biomass (DWB):Mg/ha

• Canopy bulk density (CBD): Kg/m3 (Kilogram per cubic meter)

• Canopy height (CH): m

• Canopy base height (CBH): m

  Canopy fuel load (CFL):Mg/ha

COORDINATE REFERENCE SYSTEM:

The reference system for all maps is EPSG 5070

USAGE

These data are made freely available to the public and the scientific community in the belief that their wide dissemination will lead to greater understanding and new scientific insights.

Please include the following citation in any publication that uses these data:

Mauro, F., Hudak, A.T., Fekety, P.A., Frank, B., Temesgen, H., Bell, D.M., Gregory, M.J., McCarley, T.R., 2021. Regional Modeling of Forest Fuels and Structural Attributes Using Airborne Laser Scanning Data in Oregon. Remote Sensing 13. https://doi.org/10.3390/rs13020261

This file provides the global biomass map produced with the EU FP7 GEOCARBON project (www.geocarbon.net) and presented by Avitabile et al. (2014) at the Global Vegetation Monitoring and Modeling, 3-7 February 2014, Avignon (France). The map is obtained by combining and harmonizing the pan-tropical biomass map by Avitabile et al. (2016) with the boreal forest biomass map by Santoro et al. (2015). The map covers only forest areas, where forest are defined as areas with dominance of tree cover in the GLC2000 map (Bartholomé and Belward, 2005). For a proper use and description of this dataset, please refer to the mentioned articles.

Source: Avitabile, V., Herold, M., Lewis, S.L., Phillips, O.L., Aguilar-Amuchastegui, N., Asner, G. P., Brienen, R.J.W., DeVries, B., Cazzolla Gatti, R., Feldpausch, T.R., Girardin, C., de Jong, B., Kearsley, E., Klop, E., Lin, X., Lindsell, J., Lopez-Gonzalez, G., Lucas, R., Malhi, Y., Morel, A., Mitchard, E., Pandey, D., Piao, S., Ryan, C., Sales, M., Santoro, M., Vaglio Laurin, G., Valentini, R., Verbeeck, H., Wijaya, A., Willcock, S., 2014. Comparative analysis and fusion for improved global biomass mapping. Global Vegetation Monitoring and Modeling, 3 – 7 February 2014, Avignon (France) (https://colloque.inra.fr/gv2m)

Based on data from: Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O. L., Asner, G. P., Armston, J., Ashton, P. S., Banin, L., Bayol, N., Berry, N. J., Boeckx, P., de Jong, B. H. J., DeVries, B., Girardin, C. A. J., Kearsley, E., Lindsell, J. A., Lopez-Gonzalez, G., Lucas, R., Malhi, Y., Morel, A., Mitchard, E. T. A., Nagy, L., Qie, L., Quinones, M. J., Ryan, C. M., Ferry, S. J. W., Sunderland, T., Laurin, G. V., Gatti, R. C., Valentini, R., Verbeeck, H., Wijaya, A. and Willcock, S. (2016), An integrated pan-tropical biomass map using multiple reference datasets. Glob Change Biol, 22: 1406–1420. doi:10.1111/gcb.13139

Santoro, M., Beaudoin, A., Beer, C., Cartus, O., Fransson, J.E.S., Hall, R.J., Pathe, C., Schmullius, C., Schepaschenko, D., Shvidenko, A., Thurner, M. and Wegmüller, U. (2015). Forest growing stock volume of the northern hemisphere: Spatially explicit estimates for 2010 derived from Envisat ASAR. Remote Sensing of Environment, Vol. 168, pag. 316-334

Source: Avitabile V, Herold M, Heuvelink G, Lewis SL, Phillips OL, Asner GP et al. (2016). An integrated pan-tropical biomass maps using multiple reference datasets. Global Change Biology, 22: 1406–1420. doi:10.1111/gcb.13139.

This Web Map is a subset of Above and below ground biomass carbon (tonnes/ha)This dataset represents above- and below-ground terrestrial carbon storage (tonnes (t) of C per hectare (ha)) for circa 2010.This layer supports analysis but, if needed, a direct download of the data can be accessed here.The dataset was constructed by combining the most reliable publicly available datasets and overlying them with the ESA CCI landcover map for the year 2010 [ESA, 2017], assigning to each grid cell the corresponding above-ground biomass value from the biomass map that was most appropriate for the grid cell’s landcover type.Input carbon datasets were identified through a literature review of existing datasets on biomass carbon in terrestrial ecosystems published in peer-reviewed literature. To determine which datasets to combine to produce the global carbon density map, identified datasets were evaluated based on resolution, accuracy, biomass definition and reference date (see table 1 for further information on datasets selected).DatasetScopeYearResolutionDefinitionSantoro et al. 2018Global2010100 mAbove-ground woody biomass for trees that are >10 cm diameter-at-breast-height, masked to Landsat-derived canopy cover for 2010; biomass is expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots.Xia et al. 2014Global1982-20068 kmAbove-ground grassland biomass.Bouvet et al. 2018Africa201025 mAbove-ground woodland and savannah biomass; low woody biomass areas, which therefore exclude dense forests and deserts.Spawn et al. 2017Global2010300 mSynthetic, global above- and below-ground biomass maps that combine recently released satellite-based data of standing forest biomass with novel estimates for non-forest biomass stocks.After aggregating each selected dataset to a nominal scale of 300 m resolution, forest categories in the CCI ESA 2010 landcover dataset were used to extract above-ground biomass from Santoro et al. 2018 for forest areas. Woodland and savanna biomass were then incorporated for Africa from Bouvet et al. 2018., and from Santoro et al. 2018 for areas outside of Africa and outside of forest. Biomass from croplands, sparse vegetation and grassland landcover classes from CCI ESA, in addition to shrubland areas outside Africa missing from Santoro et al. 2018, were extracted from were extracted from Xia et al. 2014. and Spawn et al. 2017 averaged by ecological zone for each landcover type.Below-ground biomass were added using root-to-shoot ratios from the 2006 IPCC guidelines for National Greenhouse Gas Inventories (IPCC, 2006). No below-ground values were assigned to croplands as ratios were unavailable. Above- and below-ground biomass were then summed together and multiplied by 0.5 to convert to carbon, generating a single above-and-below-ground biomass carbon layer.This dataset has not been validated.

Africa Aboveground Biomass (AGB) map at 100m spatial resolution for the year 2017. AGB and its associated uncertainty were mapped for all woody vegetated areas in the continent. This map was developed with funding from the National Centre for Earth Observation (NCEO) Carbon Cycle and Official Development Assistance (ODA) programmes.

This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017, 2018, 2019 and 2020. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. This release of the data is version 4. Compared to version 3, version 4 consists of an update of the three maps of AGB for the years 2010, 2017 and 2018 and new AGB maps for 2019 and 2020. New AGB change maps have been created for consecutive years (2018-2017, 2019-2018 and 2020-2019) and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.The data products consist of two (2) global layers that include estimates of:1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots2) per-pixel estimates of above-ground biomass uncertainty expressed as the standard deviation in Mg/ha (raster dataset)In addition, files describing the AGB change between two consecutive years (i.e., 2018-2017, 2019-2018 and 2020-2010) and over a decade (2020-2010) are provided (labelled as 2018_2017, 2019_2018, 2020_2019 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.Data are provided in both netcdf and geotiff format.

This dataset corresponds to the article, 'Towards Prediction and Mapping of Grassland Aboveground Biomass using Handheld LiDAR'.

This dataset consist of: 1. An R script for the Random Forest model. 2. A table containing fifteen metrics with corresponding biomass values for the 30 retrieved samples. 3. An ArcGIS project package with the AGB maps, sample locations, OBIA segments, processed point cloud, and Canopy Height Model. 4. An AGB map. 5. Handheld-LiDAR collection video recordings of area A and B, looped and zigzag trajectory.

This dataset provides annual maps of aboveground biomass (AGB, Mg/ha) for forests in Washington, Oregon, Idaho, and western Montana, USA, for the years 2000-2016, at a spatial resolution of 30 meters. Tree measurements were summarized with the Fire and Fuels Extension of the Forest Vegetation Simulator (FFE-FVS) to estimate AGB in field plots contributed by stakeholders, then lidar was used to predict plot-level AGB using the Random Forests machine learning algorithm. The machine learning outputs were used to predict AGB from Landsat time series imagery processed through LandTrendr, climate metrics generated from 30-year climate normals, and topographic metrics generated from a 30-m Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). The non-forested pixels were masked using the PALSAR 2009 forest/nonforest mask.

Woody biomass across the state of Alaska, from the Alaska Energy Gateway.Source: Alaska Energy AuthorityThis data is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Energy Data Gateway

The U.S. has been providing national-scale estimates of forest carbon stocks and stock change to meet United Nations Framework Convention on Climate Change reporting requirements for years. Through application of a nearest-neighbor imputation approach, mapped estimates of forest biomass density were developed for the contiguous United States using the annual forest inventory conducted by the USDA Forest Service Forest Inventory and Analysis (FIA) program, MODIS satellite imagery, and ancillary geospatial datasets. This data product would contain the following 7 raster maps: Aboveground Forest Biomass, Belowground Forest Biomass, Forest Tree Bole Biomass, Forest Sapling Biomass, Forest Stump Biomass, Forest Top Biomass, Woodland Specias Biomass. All layers have a 250 meter pixel resolution and values represent biomass pounds per acre. The paper on which these maps are based may be found here: https://dx.doi.org/10.2737/RDS-2013-0004 Access to full metadata and other information can be accessed here: https://dx.doi.org/10.2737/RDS-2013-0004

Here we present a new global map of biomass carbon stored in above and belowground living vegetation created using the International Panel on Climate Change (IPCC) Good Practice Guidance for reporting national greenhouse gas inventories. This map provides important benchmarks for climate policy dialogues aiming to reduce carbon emissions from land-use change, and may also advance global terrestrial and climate modeling efforts by providing improved representation of global vegetation carbon stocks.

This dataset provides maps of aboveground forest biomass (AGB) of living trees and standing dead trees in Mg/ha across portions of Northwestern United States, including Washington, Oregon, Idaho, and Montana, at a spatial resolution of 30 m. Forest inventory data were compiled from 29 stakeholders that had overlapping lidar imagery. The collection totaled 3805 field plots with lidar imagery for 176 collections acquired between 2002 and 2016. Plot-level AGB estimates were calculated from tree measurements using the default allometric equations found in the Fire Fuels Extension (FFE) of the Forest Vegetation Simulator (FVS). The random forest algorithm was used to model AGB from lidar height and density metrics that were generated from the lidar returns within fixed-radius field plot footprints, gridded climate metrics obtained from the Climate-FVS Ready Data Server, and topographic estimates extracted from Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global elevation rasters. AGB was then mapped from the same lidar metrics gridded across the extent of the lidar collections at 30-m resolution. The standard deviation of estimated AGB of the terminal nodes from the random forest predictions was also mapped to show pixel-level model uncertainty. Note that the AGB estimates are, for the most part, a single snapshot in time and that the forest conditions are not necessarily representative of the larger study area.

Mass of all aboveground organic matter per unit area at particular time; estimate of biomass derived from correlation with NDVI and LAI parameters; mosaicked from the total biomass level 2 product onto a spatially uniform grid at 1 m spatial resolution and provided as 1 km by 1 km tiles. Please note this product is currently suspended pending improvements to the underlying algorithm.

The GIS Biomass Atlas of Pakistan is the final output from the biomass resource mapping component of the activity “ Renewable Energy Resource Mapping and Geospatial Planning – Pakistan” [Project ID: P146140]. You can find more information about the project here: https://www.esmap.org/re_mapping_pakistan To visualize the geospatial data generated in this study, please access: https://irena.masdar.ac.ae/GIS/?map=2636 Please cite as: [Data/information/map obtained from the] “World Bank via ENERGYDATA.info, under a project funded by the Energy Sector Management Assistance Program (ESMAP). For more information: Pakistan Biomass GIS Atlas, 2016, https://energydata.info/dataset/pakistan-biomass-gis-atlas"

Ground data from the National Forest and Soil Inventory of Mexico (INFyS) were used to calibrate a maximum entropy (MaxEnt) algorithm to generate forest biomass (AGB), its associated uncertainty, and forest probability maps. The input predictor layers for the MaxEnt algorithm were extracted from the moderate resolution imaging spectrometer (MODIS) vegetation index (VI) products, ALOS PALSAR L-band dual-polarization backscatter coefficient images, and the Shuttle Radar Topography Mission (SRTM) digital elevation model. A Jackknife analysis of the model accuracy indicated that the ALOS PALSAR layers have the highest relative contribution (50.9%) to the estimation of AGB, followed by MODIS-VI (32.9%) and SRTM (16.2%). The forest cover mask derived from the forest probability map showed higher accuracy (κ = 0.83) than alternative masks derived from ALOS PALSAR (κ = 0.72–0.78) or MODIS vegetation continuous fields (VCF) with a 10% tree cover threshold (κ = 0.66). The use of different forest cover masks yielded differences of about 30 million ha in forest cover extent and 0.45 Gt C in total carbon stocks. The AGB map showed a root mean square error (RMSE) of 17.3 t C ha− 1 and R2 = 0.31 when validated at the 250 m pixel scale with inventory plots. The error and accuracy at municipality and state levels were RMSE = ± 4.4 t C ha− 1, R2 = 0.75 and RMSE = ± 2.1 t C ha− 1, R2 = 0.94 respectively. We estimate the total carbon stored in the aboveground live biomass of forests of Mexico to be 1.69 Gt C ± 1% (mean carbon density of 21.8 t C ha− 1), which agrees with the total carbon estimated by FAO for the FRA 2010 (1.68 Gt C). The new map, derived directly from the biomass estimates of the national inventory, proved to have similar accuracy as existing forest biomass maps of Mexico, but is more representative of the shape of the probability distribution function of AGB in the national forest inventory data. Our results suggest that the use of a non-parametric maximum entropy model trained with forest inventory plots, even at the sub-pixel size, can provide accurate spatial maps for national or regional REDD + applications and MRV systems.

This file provides the pan-tropical biomass map published by Avitabile et al. (2016) "An integrated pan-tropical biomass map using multiple reference datasets". The data shows the aboveground biomass in Mg per ha in the tropic region at approximately 1 km resolution. For a proper use and description of this dataset, please refer to the mentioned article.

Avitabile, V., Herold, M., Heuvelink, G. B. M., Lewis, S. L., Phillips, O. L., Asner, G. P., Armston, J., Ashton, P. S., Banin, L., Bayol, N., Berry, N. J., Boeckx, P., de Jong, B. H. J., DeVries, B., Girardin, C. A. J., Kearsley, E., Lindsell, J. A., Lopez-Gonzalez, G., Lucas, R., Malhi, Y., Morel, A., Mitchard, E. T. A., Nagy, L., Qie, L., Quinones, M. J., Ryan, C. M., Ferry, S. J. W., Sunderland, T., Laurin, G. V., Gatti, R. C., Valentini, R., Verbeeck, H., Wijaya, A. and Willcock, S. (2016), An integrated pan-tropical biomass map using multiple reference datasets. Glob Change Biol, 22: 1406–1420. doi:10.1111/gcb.13139

The NCEO Kenya forest aboveground biomass map shows aboveground woody biomass (AGB) in Kenyan forests. Forest areas include vegetated wetlands and wooded grassland for the year 2015. The map was generated by combining field inventory plots (KFS) with Advanced Land Observing Satellite (ALOS-2), Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) and multispectral optical data (NASA Landsat 8), by means of a Random Forests algorithm within a k-Fold calibration/validation framework.

The characterization of carbon stocks and dynamics at the national level is critical for countries engaging in climate change mitigation and adaptation strategies. However, several tropical countries, including Kenya, lack the essential information typically provided by a complete national forest inventory. These data were produced by the National Centre for Earth Observation (NCEO), University of Leicester, in collaboration with the Kenya Forest Service (KFS) with funding from the NCEO ODA Programme.

Known Issues: Residual scan line corrector (SLC) effects due to the use of the SLEEK land cover product as a retrieval mask (derived from Landsat imagery) are visible in some areas

This dataset provides 30 m gridded estimates of aboveground biomass density (AGBD), forest canopy height, and tree canopy coverage for the New England Region of the U.S., including the state of Maine, Vermont, New Hampshire, Massachusetts, Connecticut, and Rhode Island, for the nominal year 2015. It is based on inputs from 1 m resolution Leaf-off LiDAR data collected from 2010 through 2015, high-resolution leaf-on agricultural imagery, and FIA plot-level measurements. Canopy height and tree cover were derived directly from LiDAR data while AGBD was estimated by statistical models that link remote sensing data and FIA plots at the pixel level. Error in AGBD was calculated at the 90% confidence interval. This approach can directly contribute to the formation of a cohesive forest carbon accounting system at national and even international levels, especially via future integrations with NASA's spaceborne LiDAR missions.

Remote sensing based maps of tidal marshes, both of their extents and carbon stocks, have the potential to play a key role in conducting greenhouse gas inventories and implementing climate mitigation policies. Our objective was to generate a single remote sensing model of tidal marsh aboveground biomass and carbon that represents nationally diverse tidal marshes within the conterminous United States (CONUS). To meet this objective we developed the first national-scale dataset of aboveground tidal marsh biomass, species composition, and aboveground plant carbon content (%C) from six CONUS regions: Cape Cod, MA, Chesapeake Bay, MD, Everglades, FL, Mississippi Delta, LA, San Francisco Bay, CA, and Puget Sound, WA. We tested how plant community composition and vegetation structure differences across estuaries influence model development, and whether data from multiple sensors, in particular Sentinel-1 C-band synthetic aperture radar and Landsat, can improve model performance. The final model, driven by six Landsat vegetation indices and with the soil adjusted vegetation index as the most important (n=409, RMSE=464 g/m2, 12.2% normalized RMSE), successfully predicted biomass and carbon for a range of marsh plant functional types defined by height, leaf angle and growth form. Model error was reduced by scaling field measured biomass by Landsat fraction green vegetation derived from object-based classification of National Agriculture Imagery Program imagery. We generated 30m resolution biomass maps for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program map for each region. With a mean plant %C of 44.1% (n=1384, 95% C.I.=43.99% - 44.37%) we estimated mean aboveground carbon densities (Mg/ha) and total carbon stocks for each wetland type for each region. We applied a multivariate delta method to calculate uncertainties in regional carbon estimates that considered standard error in map area, mean biomass and mean %C. The original version 1.0 of the dataset can be obtained by contacting kbyrd@usgs.gov.